Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Yassaie, Negar
Master of Applied Science - MASc
University of British Columbia
The significant increase in industrial data generation and the development of machine learning and data mining tools and techniques, has led to a surge of interest in data-driven approaches for addressing industrial challenges. Specifically, this thesis aims to propose novel data-driven approaches for fault classification in industrial systems using the data from a reduced number of process variables, and designing a recommender system for human operators to help them take corrective action when such anomalies happen. First, to reduce computational expenses and make the procedure capable of classifying faults even when data of some process variables are/become unavailable, a novel three-stage computational procedure is proposed which uses collected data of process variables (i.e., measurements), and achieves the following: (i) time series data clustering using k-means clustering and soft dynamic time warping, (ii) variable selection using a modified LASSO-based or Elastic Net-based regression methods, (iii) data-to-image conversion and fault classification using convolutional neural networks. The proposed procedure can be applicable in industrial systems such as chemical production plants and power generation systems. Second, a decision-support tool for human operators in industrial automation and control systems is proposed to help them take proper actions when anomalies such as faults occur, which is believed to be the first in the literature. The proposed recommender system uses the historical data and is based on a three-stage computational procedure, which achieves the following: (i) clustering historical process data segments containing timestamped events using a k-medoids-based algorithm, where instead of distance metrics, structural similarity of the segments is used and obtained using a Smith- Waterman-based algorithm, (ii) predicting the missing action ratings using a modified collaborative filtering technique applicable for time sequences, (iii) presenting proper and improper actions to the human operators. The recommender system can effectively be used in industrial systems where a human operator is responsible for making decisions and taking corrective actions.
Data-Driven Fault Classification andOperator Action Recommendation inIndustrial Control SystemsbyNegar YassaieA THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)May 2023© Negar Yassaie, 2023The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis entitled:Data-Driven Fault Classification and Operator Action Rec-ommendation in Industrial Control Systemssubmitted by Negar Yassaie in partial fulfilment of the requirements ofthe degree of Master of Applied ScienceProf. Ahmad W. Al-Dabbagh, School of EngineeringSupervisorProf. Anas Chaaban, School of EngineeringSupervisory Committee MemberProf. Chen Feng, School of EngineeringSupervisory Committee MemberProf. Zheng Liu, School of EngineeringUniversity ExamineriiAbstractThe significant increase in industrial data generation and the develop-ment of machine learning and data mining tools and techniques, has led toa surge of interest in data-driven approaches for addressing industrial chal-lenges. Specifically, this thesis aims to propose novel data-driven approachesfor fault classification in industrial systems using the data from a reducednumber of process variables, and designing a recommender system for humanoperators to help them take corrective action when such anomalies happen.First, to reduce computational expenses and make the procedure capableof classifying faults even when data of some process variables are/becomeunavailable, a novel three-stage computational procedure is proposed whichuses collected data of process variables (i.e., measurements), and achieves thefollowing: (i) time series data clustering using k-means clustering and softdynamic time warping, (ii) variable selection using a modified LASSO-basedor Elastic Net-based regression methods, (iii) data-to-image conversion andfault classification using convolutional neural networks. The proposed pro-cedure can be applicable in industrial systems such as chemical productionplants and power generation systems.Second, a decision-support tool for human operators in industrial au-tomation and control systems is proposed to help them take proper actionswhen anomalies such as faults occur, which is believed to be the first in theliterature. The proposed recommender system uses the historical data and isbased on a three-stage computational procedure, which achieves the follow-ing: (i) clustering historical process data segments containing timestampedevents using a k-medoids-based algorithm, where instead of distance metrics,structural similarity of the segments is used and obtained using a Smith-Waterman-based algorithm, (ii) predicting the missing action ratings usinga modified collaborative filtering technique applicable for time sequences,(iii) presenting proper and improper actions to the human operators. Therecommender system can effectively be used in industrial systems where ahuman operator is responsible for making decisions and taking correctiveactions.iiiLay SummaryAnomalies, such as faults, can occur at any time in industrial systems.To maintain the system’s desired performance, it is crucial to detect theseanomalies and gather as much information about them as possible. Thisinformation can assist the human operator, who is responsible for monitor-ing and controlling the system, in taking corrective actions to prevent anydamage to the system. However, due to various factors, such as the opera-tor’s skill level or stress, they may not be able to make timely and accuratedecisions. Therefore, having a decision-support system for human operatorsin industrial systems can be highly beneficial.This thesis proposes a novel data-driven computational procedure toclassify faults in industrial systems. Furthermore, a recommender systemis developed for human operators capable of assisting them in the decision-making process, which can provide them with proper actions to take whenanomalies happen, such as the detected faults.ivPrefaceA version of Chapter 2 has been submitted to a journal as N. Yas-saie, S. Gargoum, and A. W. Al-Dabbagh, “Data-Driven Fault Classifica-tion in Large-Scale Industrial Processes Using Reduced Number of ProcessVariables,” 2022. I was the lead author and researcher, responsible for allmajor areas of problem formulation, methodology and analysis, as well asmanuscript composition. Sara Gargoum, a M.A.Sc. student in Dr. Al-Dabbagh’s group, was involved in the early stages of problem formulationand contributed to manuscript revisions and introduction writing. Dr. Ah-mad Al-Dabbagh was the supervisor of this work and was involved through-out the project in problem formulation and manuscript revision.A version of Chapter 3 has been submitted to a journal as N. Yassaieand A. W. Al-Dabbagh, “A Recommender System for Human Operators inIndustrial Automation,” 2023. I was the lead author and researcher, respon-sible for all major areas of problem formulation, methodology and analysis,as well as manuscript composition. Dr. Ahmad Al-Dabbagh was the su-pervisor of this work and was involved throughout the project in problemformulation and manuscript revision.vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . xDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiChapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.4 Thesis Outline and Contributions . . . . . . . . . . . . . . . . 6Chapter 2: Data-Driven Fault Classification in Large-ScaleIndustrial Processes Using Reduced Number ofProcess Variables . . . . . . . . . . . . . . . . . . . . 82.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.1 Stage I: Clustering of Process Variables . . . . . . . . 112.2.2 Stage II: Selection of Representative Variables . . . . 132.2.3 Stage III: Data-to-Image Conversion and Image Clas-sification . . . . . . . . . . . . . . . . . . . . . . . . . 17viTABLE OF CONTENTS2.3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.3.1 Stage I: Clustering of Process Variables . . . . . . . . 242.3.2 Stage II: Selection of Representative Variables . . . . 252.3.3 Stage III: Data-to-Image Conversion and Image Clas-sification . . . . . . . . . . . . . . . . . . . . . . . . . 292.3.4 Other Design Considerations . . . . . . . . . . . . . . 332.3.5 Comparison Between Using LASSO-based and ElasticNet-based Regression Methods . . . . . . . . . . . . . 342.3.6 Ablation Studies . . . . . . . . . . . . . . . . . . . . . 392.3.7 Fault Classification in Distillation Column . . . . . . . 392.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Chapter 3: Recommender System for Human Operator Ac-tions in Response to Anomalies such as Faults . . 433.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433.2 Proposed Recommender System . . . . . . . . . . . . . . . . . 453.2.1 Stage I: Clustering Process Data Segments Using K-medoids-based and Smith-Waterman-based Algorithms 463.2.2 Stage II: Prediction of Missing Ratings for OperatorActions Using a Collaborative Filtering-Based Algo-rithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 563.2.3 Stage III: Recommending Actions to the Human Op-erator . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70Chapter 4: Conclusion and Future Works . . . . . . . . . . . . 714.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 72Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74viiList of TablesTable 2.1 Process variables of the Tennessee Eastman process . . 24Table 2.2 Observable faults in the Tennessee Eastman process . 25Table 2.3 Types and outputs of the layers of the CNN model . . 30Table 2.4 The test accuracy for different faults . . . . . . . . . . 32Table 2.5 Fault classification accuracy using different procedures 32Table 2.6 Training time (seconds) using different methods . . . . 32Table 2.7 Types and outputs of the layers of the CNN modeldesigned for the distillation column case study . . . . . 42Table 3.1 Definition of the events/actions . . . . . . . . . . . . . 63Table 3.2 The effect of changing ζ on accuracy . . . . . . . . . . 69viiiList of FiguresFigure 2.1 The framework of the proposed fault classificationprocedure . . . . . . . . . . . . . . . . . . . . . . . . . 22Figure 2.2 Simplified P&ID of the Tennessee Eastman process . 23Figure 2.3 Time series data of process variables . . . . . . . . . . 26Figure 2.4 Normalized sensitivity using Lasso-based regression . 26Figure 2.5 Obtained results LASSO-based regression . . . . . . . 28Figure 2.6 Generated images using fault-affected data . . . . . . 31Figure 2.7 Normalized sensitivity using Elastic Net-based regres-sion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Figure 2.8 Obtained results using Elastic Net-based regression . 36Figure 2.9 Process variables’ correlation . . . . . . . . . . . . . . 37Figure 2.10 Measurements for v19 . . . . . . . . . . . . . . . . . . 40Figure 2.11 Simplified P&ID of the distillation column . . . . . . 41Figure 3.1 Recommender system framework . . . . . . . . . . . . 62Figure 3.2 The l2rpn–case14–sandbox power grid . . . . . . . . . 64Figure 3.3 The l2rpn–case14–sandbox power grid after the oc-currence of Ein . . . . . . . . . . . . . . . . . . . . . . 65Figure 3.4 The l2rpn–case14–sandbox power grid after takingai = B23 . . . . . . . . . . . . . . . . . . . . . . . . . . 65Figure 3.5 Blackout in the l2rpn–case14–sandbox power grid . . 66Figure 3.6 π − k plot . . . . . . . . . . . . . . . . . . . . . . . . 67ixAcknowledgementsI would like to express my heartfelt gratitude to my supervisor, Prof.Ahmad Al-Dabbagh, who has guided me through this journey, encouragedme, and believed in me. I cannot express my thankfulness adequately inwords.I would like to express my appreciation for my supervisory/examinationcommittee members, Prof. Anas Chaaban, Prof. Chen Feng, and Prof.Zheng Liu, for their time and invaluable suggestions.I would also like to acknowledge the UBC Graduate Blockchain TrainingPathway for providing me with an exceptional experience during my grad-uate studies. Specifically, I would like to thank Prof. Victoria Lemieux forher kind support.I am grateful to everyone I have had the pleasure of working with, espe-cially my colleagues in Prof. Al-Dabbagh’s research group.I could not have completed this work without the help, support, andlove of my friends, who stood by me during difficult times and always liftedmy spirits. I would like to give a special thanks to Sanam, Mersad, Hanieh,Maryam M., Maryam F., Amir Mehdi, Pooya, Shayan, Amir, Mahshid,Yousef, Mojdeh, and Mehrnaz. I would also like to extend my gratitude toDr. Mehran Shirazi for his generous support.Most importantly, I would like to express my deepest gratitude to myparents, Fatemeh (Azita) and Reza, who have always been there for me withtheir loving hearts and unwavering support, even though they are more thanten thousand kilometers away from me. I could never have embarked on thisjourney without their encouragement. They taught me to believe in myselfand pursue my dreams. My brother Mani, with his kind heart, has alwaysbeen my best friend and biggest cheerleader, reminding me that life is worthliving. Words cannot fully express my gratefulness to him.Lastly, but foremost, I would like to express my immeasurable gratitudeto my partner, Ahmadreza, for his support and appreciation during ourseven years of romantic relationship, including the past two years of long-distance love. He has always encouraged and motivated me to accomplishmy goals, and I am truly grateful for his presence in my life.xDedicationThis thesis is dedicated to the brave Iranians, particularly Iranian women,who have fought for their rights, their freedom, and their lives. It is a greathonor for me to dedicate this thesis with deep respect and admiration to allthe fighters of Woman–Life–Freedom.xiChapter 1Introduction1.1 BackgroundThe abundance of measuring devices and instruments, and the develop-ment of data acquisition systems have provided an opportunity for data-driven methods to emerge. This can be beneficial for industrial systemssuch as chemical production systems, power generation systems, manufac-turing systems, and oil & gas refineries. As obtaining the exact model ofthe system, especially large-scale or complex systems, is hard or infeasible,data-driven methods, which do not require neither the model of the systemnor prior information about the processes in the system, have attracted theattention from both academia and industry. Data-driven methods are heav-ily dependent on the available data, and consequently, the accuracy of suchmethods is dependent on the quality and size of the data.With the development of technology and tools such as artificial intelli-gence, automation of the tasks in industrial systems/processes has becomesmoother and faster. Methods and algorithms developed using artificialintelligence, lead to better integration of information, data analysis, andimproved insights for decision-making process by extracting useful infor-mation and patterns. Machine learning and data mining techniques, withthe capability of extracting patterns (i.e., knowledge) and useful informa-tion from large amount of data, have become favorable tools for developingdata-driven methods. With abundance of data, deep learning techniques, asubfield of machine learning techniques, have also become a surge of interest.In recent years, data-driven methods have been widely investigated totackle various challenges and problems in the field of industrial automationand control systems. For example, various studies and works focused ondeploying data-driven methods to address such challenges in the field ofalarm management and alarm floods, quality control, attack detection, faultdiagnosis, and human operator actions. The main focus of this thesis is onproblems and gaps related to fault detection and classification and humanoperator actions.11.2. LITERATURE REVIEW1.2 Literature ReviewResearchers have developed data-driven methods to address quality con-trol challenges. For example, the authors of [1], proposed a quality controlmethod for ensuring the quality of hydrological data, using various learningtools such as Adaboost classifier, long short-term memory (LSTM) networks,and support vector machine (SVM). In [2], to detect rare quality events, anl1-regularized linear regression technique was followed. In [3], the use of im-age classification for increasing the knowledge of the quality control systemabout successful products was studied.The development and use of data-driven approaches can be observed inthe field of industrial alarm management as well. Industrial alarms are de-signed and used to indicate process deviations from normal state, to informoperators, so that they can take proper actions [4]. When the alarms inan industrial system/process function properly, and the operator, plant ex-pert, or controller, properly responds in a timely manner, unwanted eventsor catastrophic events can be prevented [5]. However, when the count ofalarms is more than the capability of an operator to respond, an alarmflood occurs, and the performance of the system may highly be deteriorated[6]. According to [7], a few dozens of root causes account for as high as 90%of alarms. Detecting the root causes leads to better responses in case of theannunciation of large number of alarms. Data-driven and machine learn-ing techniques have been widely employed to tackle the problems regardingalarm floods and root cause analysis of alarms. The authors of [8] developeda multiblock transfer entropy based Bayesian network model with the aimof root cause analysis of alarms in industrial systems. In [9], an itemsetmining approach was proposed to extract all meaningful alarm patterns. In[10], with using the Gaussian mixture model, a semisupervised approach forearly classification of alarm floods was proposed. An agglomerative hier-archical clustering approach was suggested in [11] for the ulimiate goal ofdetecting the causal alarm of an alarm flood and grouping alarms. In [12], anew clustering approach, called exponentially attenuated component analy-sis, was proposed with the purpose of alarm flood classification. In [13], anattention-based long short-term memory method was proposed to improveGragner causality models, with the aim of alarm root-cause diagnosis.The combination of the physical components, including measurementinstruments and actuators, and cyber components, including computationand communication units, results in formation of cyber physical systems(CPS) [14]. Industrial control systems are a subset of CPSs, and the at-tacks and cyber threats, threaten their performance considerably. Attack21.2. LITERATURE REVIEWdetection has also been an interesting objective to deploy data-driven ap-proaches. In [15], a multi-layer data-driven cyber-attack detection systemwas utilized, where the use of various supervised classification models andbig data analytics models were studied as defense layers. In [16], attackdetection was done based on an ensemble deep learning algorithm on thebasis of deep neural networks and decision trees. In [17], a method usingautoencoders was proposed capable of detecting false data injection attacks,and by deploying a denoising autoencoder approach, uncorrupted data wasrecovered from corrupted data. In [18], data integrity attacks, such as falsedata injection, denial-of-service, and replay attacks were detected using amodified denoising autoencoder approach. In [19], a neural network-basedreplay attack detection model was proposed, where both spectral and spa-tial information were taken into consideration. In the mentioned approach,convolutional layers, LSTM, and deep neural networks helped in extractingthe patterns useful for replay attack detection.Fault diagnosis, which according to [20], includes determining the faulttype, size, location, time, etc., has also been an interesting topic for re-searchers to apply various data-driven methods. In [21], a data-driven ap-proach was followed with the purpose of bearing fault diagnosis in inductionmotors, where statistical features, genetic algorithm, and machine learningmodels (e.g., k-nearest neighbors, decision tree, and random forest) werecombined. In [22], sparse filtering method, which is beneficial for learn-ing the features of unlabeled data, and softmax regression, for multi-classclassification, were modified and combined for fault diagnosis purposes. In[23], convolutional neural networks for feature extraction, and deep autoen-coders for classification, were used to produce a CNN–DAE model for faultdiagnosis. In [24], fault classification was performed using a stacked sparseautoencoder. In [25], preprocessing the sensor data was done through ashort-time Fourier transform, and then the resulted matrices were fed to alarge memory storage and retrieval neural network, for fault diagnosis. In[26], sensor data was converted to images, using a wavelet transformation,and transfer learning was used for fault classification. In [27], a domain-adversarial neural network architecture was used for the purpose of super-vised fault diagnosis. Machine learning has also been investigated in thefields of maintenance management, production planning, and supply chain,in industrial and manufacturing systems [28].Blockchain technology by providing a secure and tamper-resistant wayof storing large-scale data and a decentralized infrastructure with the aimof secure access control, can be integrated with machine learning techniquesto generate more secure and efficient solutions [29]. The combination of31.3. GAPSblockchain technology and machine learning methods has resulted in inter-esting works which address the industrial challenges. For instance in [30], asecure data sharing framework was designed using permissioned blockchainand deep learning. In [31], the integration of deep learning and blockchainprovided a secure learning operation and eliminated the centralized author-ity control. In [32], the use of blockchain for data safety, and machine learn-ing and big data techniques was studied with the purpose of fault predictionin manufacturing systems.Even though the level of autonomy is getting higher, human operatorsare still an inseparable part of industrial systems. Various studies proposeddifferent approaches to improve the decision making process of human op-erators. For example, in [33], reinforcement learning was used to develop alearning-based agent capable of making decisions automatically, which canbe provided to robotic operators. Also, in [34], a decision support techniquebased on both the knowledge and data of the system, was developed withthe goal of zero defect manufacturing.1.3 GapsAnomalies, such as faults and attacks, can always happen in an industrialsystem/process. Most of the time, their correct detection, and accordingly,promptly providing a proper response to the detected abnormality, avoidsperformance deterioration. With further development of technology, espe-cially, artificial intelligence, data analytics, and automation and control,such anomalies can sometimes automatically be detected, diagnosed, andmitigated. However, still, the need for human operators is not eliminated,as they can sometimes choose responses/actions that better suits the statusof an industrial system, compared to automated approaches.The main motivation of this thesis is to improve the performance ofindustrial automation and control systems, by employing data-driven toolsand developing new algorithms to tackle some of the current challenges andgaps.As discussed before, fault diagnosis, is one of the main focuses of controlengineers as faults may affect the reliability, security, and performance ofan industrial system. Hence, it is of paramount importance to monitor andanalyze the system all the time to be able detect if any faults occur. The aimis to find information about the faults that have occurred, such as the placeof the occurred fault and its magnitude. With this information, the faultcan usually be isolated, to avoid performance deterioration in the system.41.3. GAPSLarge-scale industrial systems usually have large datasets, generated usingrecorded data from various measuring instruments that measure variousquantities such as level, pressure, temperature, voltage, current, and more,in different process runs. By extracting the required information from suchdatasets, faults can be detected and classified so that corrective actions orproper control strategies can be followed promptly.Even though abundance of data is preferable as it provides more completeinformation, it usually causes more computational burden and expenses(e.g., computational time and cost). Hence, in data-driven techniques, one ofthe purposes is to obtain desirable results with as less computational burdenas possible. Also, in case of employing data-driven techniques for real-timeand online applications, not only the historical data is important, but alsothe information and data obtained from the process in real-time becomes ofhigh importance. However, due to various reasons, such as device and mea-suring instrument failures, part of the data may not be recorded. Thus, itis crucial to develop data-driven techniques that do not rely on all the data,especially when they are deployed in physical systems, where the chanceof device failure is relatively high. Consequently, being able to classify thefaults in a system with the data of a smaller number process variables, wouldbe beneficial. This has not been investigated in the literature to the bestknowledge of the author, while it is very crucial in real-world applications.To tackle the fault classification problem in industrial systems, this thesisproposes a data-driven technique comprised of a three-stage computationalprocedure, capable of classifying the faults in both large and small-scale in-dustrial systems, with the data from a reduced number of process variables(i.e., the recorded data from a few measuring instruments). Since the pro-posed procedure is completely data driven, there is no need to have any priorinformation about the model of the system/process. This is especially ad-vantageous in large-scale industrial systems where obtaining the exact modelis challenging. Besides, by using only a few number of process variables, notonly the computational burden of fault classification is reduced considerably,but also the procedure can be applicable even when the real-time data ispartly available.After detecting anomalies, such as faults, a human operator may decideto take an action to keep the performance of the system/process as desired.In critical situations, where an action should be decided to be taken from avariety of available actions, in a short duration of time, a human operatormay become overwhelmed, preventing them from taking proper correctiveactions, leading to performance deterioration or even catastrophic events.The process of decision-making for humans always relies on their psycho-51.4. THESIS OUTLINE AND CONTRIBUTIONSlogical and emotional state as well as their level of skills and knowledge.Similarly in industrial systems, fatigue, lack of required skills, being inexpe-rienced, stress level, etc. may affect the ability of a human operator to makeproper decisions and take corrective actions. On the other hand, human op-erators are still an inseparable part of industrial systems, since they are ableto make time-sensitive decisions in critical and rare situations, where data-driven algorithms are not as capable. Consequently, a framework which bothuses the expertise and knowledge of the human operators, to assist them inthe decision-making process in real-time, is preferable. To the best knowl-edge of the author, none of the previous works in the literature addressedthis challenge.In this thesis, a recommender system for human operators is proposed,which can be used as a decision-support tool, and helps the operators takeprompt corrective actions in critical situations. The human operator in thesystem is provided with the proper actions to take and improper ones toavoid. This reduces the workload and stress on human operators, leadingthem to focus on more critical tasks, and consequently, better performanceof the system.To put it in a nutshell, in this thesis, first, a novel computational pro-cedure for fault classification in large-scale industrial systems with reducednumber of process variables is proposed, where an anomaly (namely a faultin this case) can be detected and classified, so that a human operator cantake a corrective action accordingly. Second, to assist human operators intheir decision-making process, a recommender system is proposed which canprovide a human operator with the proper and improper actions to take oravoid, to retain the desired performance of the system.1.4 Thesis Outline and ContributionsChapter 2 describes the proposed data-driven computational procedurewhich enables fault classification in large-scale industrial systems using re-duced number of process variables. Specifically, in Section 2.1, an intro-duction to fault diagnosis using data-driven methods is provided, followedby Section 2.2, where the proposed three-stage computational procedure,comprised of clustering of process variables, selection of representative vari-ables, and data-to-image conversion and image classification, is described indetails. The Tennessee Eastman process, as a large-scale industrial system,and a distillation column, as a small-scale one, are considered in Section2.3 to illustrate the applicability of the proposed computational procedure61.4. THESIS OUTLINE AND CONTRIBUTIONSand its effectiveness for fault classification using reduced number of processvariables. Finally, the summary of the chapter is presented in Section 2.4.The main contribution of this chapter is modifying and combining threedifferent computational procedure to allow for fault classification using re-duced number of process variables. More specifically, the process variablesare first clustered based on their time series pattern. Then, by proposing aprocedure based on PCA and LASSO/Elastic Net-based regression, and in-troducing a novel sensitivity metric, the fault-sensitive process variables arechosen as the representative process variables. Afterwards, by convertingthe data of the representative process variables to 3D images, fault classifi-cation is done using a deep learning architecture. The proposed three-stagecomputational procedure does not require all the process data, providing afaster procedure for fault classification with less computations, capable ofclassifying the faults even when the data of some of the process variablesbecome/are inaccessible.Chapter 3 is dedicated to the proposed recommender system for humanoperators in industrial automation and control systems. Specifically, in Sec-tion 3.1, an introduction to the human operators in industrial automationand control systems, and their importance, as well as relevant data-drivenmethods for this matter are discussed. Section 3.2 describes the proposedrecommender system which is on the basis of a three-stage computationalprocedure, followed by Section 3.3 where the effectiveness of the recom-mender system is studied using a power grid model. Section 3.4 providesthe summary of the chapter.Proposing a recommender system suitable for human operators in in-dustrial systems, as a decision-support tool, is the major contribution ofthis chapter. To design such system, a three-stage computational proce-dure is designed which is on the basis of a k-medoids-based algorithm, aSmith-Waterman-based similarity index, and a modified collaborative filter-ing technique. The historical timestamped events are first put into differ-ent clusters using the proposed k-medoids-based algorithm which uses theSmith-Waterman-based similarity index. Then, the missing action ratingsare predicted using the modified collaborative filtering technique which isdesigned to be applicable for time series data and is based on the Pearson cor-relation and the Smith-Waterman-based algorithm. Also, additional stepsand procedures are proposed to address the cold start problem in industrialsystems. Finally, a framework is designed to provide human operators withthe proper and improper actions to take and avoid, respectively.Finally, the concluding remarks and suggested future works are presentedin Chapter 4.7Chapter 2Data-Driven FaultClassification in Large-ScaleIndustrial Processes UsingReduced Number of ProcessVariables2.1 IntroductionLarge-scale industrial processes have multiple interconnected processesand systems, such in oil & gas refineries, manufacturing and chemical plants,and power and water distribution networks. As industrial applications areexpanding in size and distribution, and also increasing in complexity, faultsbecome more probable and fault diagnosis becomes more challenging. Sim-ply stated, a fault is the deviation from the desired or expected output ofthe system [35]. The occurrence of a fault may raise operational and safetyconcerns, and may possibly lead to disastrous consequences. For this reason,fault diagnosis for large-scale industrial processes is of crucial importance.Diagnosis steps consist of: (i) fault detection, where it should be decided ifa fault has occurred or not, (ii) fault isolation (i.e., classification), in whichthe exact fault location is determined, and (iii) fault identification and es-timation, that results in determining the kind and severity of the occurredfault [36]. Fault diagnosis methods can be categorized into model-based,signal-based, and knowledge-based [37]. The latter can also be referred toas data-driven fault diagnosis, where fault modes are determined by onlyusing historical operational data, and therefore, deemed more feasible whencompared to the former methods. In addition, data-driven methods maybetter cope with complexity caused by process nonlinearity and disturbance[38].In industrial facilities, operational data, including process measurements,82.1. INTRODUCTIONcontrol commands, and alarms displayed to human operators, can be read-ily recorded/historized as well as accessed to extract information. Variousresearch works have recently tackled different challenges using operationaldata. For example, the authors in [39] developed visualization plots and analarm reordering technique during alarm floods, based on alarm data thatdescribes faults. In [40], further visualization plots were developed to betterextract and display alarm-related information, and statistical alarms wereused to improve robustness of fault detection and identification in [41]. Forfurther details on fault related alarms, refer to [42]. In addition, there hasbeen an increasing focus from researchers on developing data-driven faultdiagnosis methods that mainly utilize machine learning techniques.For example, some deep learning architectures were explored, such asdeep belief network [43] and sparse auto-encoder [44]. As another example,in [45], an orthogonal self-attentive variational autoencoder was proposedto detect the faults in a system based on the correlation of variables andtemporal dependency. Another popular architecture is convolutional neuralnetwork (CNN), where its use in fault diagnosis has been investigated inseveral studies [46–48]. For instance, in [47], the authors converted the rawuni-variate time-domain signals of process variables to gray-scale images.Then, the images were given as inputs to a CNN for fault classification. Theauthors in [48] proposed a fault diagnosis method using CNN, for featureextraction, combined with a Long Short-Term Memory Network (LSTM),for time delay consideration. More specifically, 2D images were obtainedfrom samples of multi-variate time series data by sliding window processing.However, the use of all process variables along with LSTM creates computa-tional complexities. To address such data dimensionality limitations, deeplearning techniques are often used with statistical analysis methods, such asprincipal component analysis (PCA) and partial least squares (PLS) [49, 50].Both PCA and PLS can efficiently be used to extract variable correlationfrom large datasets, making them favorable tools in fault diagnosis.As large-scale industrial processes are prone to many faults, and due tothe existence of many correlated process variables, data-driven fault diagno-sis can become a complex task, which may necessitate high computationalrequirements. To overcome this limitation, in this thesis, data-driven faultclassification is tackled by using a reduced number of process variables, in-stead of relying on all process variables. This also allows for achieving faultdiagnosis even when some measurements are not available in the collectedoperational data. Specifically, a three-stage computational procedure is pro-posed, where in each of the cascaded stages, a computational algorithm isdesigned, as follows:92.1. INTRODUCTION(i) In the first stage, time series process measurement data is clusteredusing k-means clustering with soft dynamic time warping approach.The proposed approach builds on existing works in the literature forclustering process variables for fault diagnosis, such as [51–53], andit specifically allows for unsupervised clustering of process variableswithout having any prior knowledge about the process or its variables.(ii) In the second stage, in order to reduce the number of required processvariables for fault classification, a new sensitivity-based metric is pro-posed to characterize the sensitivity of process variables to faults. Thismetric is calculated based on a new method which combines PCA andLASSO-based or Elastic Net-based regression approaches. It is usedto determine representative variables that are deemed more sensitiveto faults, in each cluster.(iii) In the third stage, the raw process measurement data of the selected/representative process variables is used to form multi-dimensional im-ages (i.e., time series data-to-image conversion), which are fed to adesigned CNN to classify faults, which provides an automated processfor the extraction of features that highly contribute to the occurrenceof faults. Thus, instead of directly using raw time series data of processvariables, they are converted to 2D images. In order to be able to usedata from all representative process variables, by concatenating the 2Dimages, 3D images with a depth equal to the number of clusters arebuilt. This provides more complete feature extraction and accurateresults, in contrast to other approaches in the literature which rely ona single process variable or single-channel images for fault classification(e.g., [47, 54]).A detailed simulation case study is presented for implementing the pro-posed three-stage computational procedure, using the Tennessee Eastmanprocess benchmark simulation system/model. Detailed comparisons for dif-ferent design and operational scenarios are addressed, namely, the use ofLASSO vs. Elastic Net in the second stage, the use of single and multiple-channel images in the third stage, and the introduction of different types offaults that occur in the process. To illustrate the necessity of the stages andtheir associated steps, ablation studies were conducted to show the impacton the proposed procedure. In addition, to illustrate the applicability of theproposed procedure in any size industrial process, a small-scale distillationcolumn is also used as a case study.102.2. METHODOLOGYThe chapter is organized as follows. Section 2.2 describes the proposedthree-stage computational procedure for fault classification and presents itscorresponding algorithms. In Section 2.3, the proposed procedure is evalu-ated using the two case studies, and finally, Section 2.4 provides the sum-mary of the chapter.2.2 MethodologyThis section describes the proposed fault classification method, wherecollected process measurement data is used without any prior knowledge ofthe process model.2.2.1 Stage I: Clustering of Process VariablesSince correlated process variables may have a similar time-domain pat-tern, in this thesis, such process variables are determined and accordinglythey are assigned with different groups/clusters. As prior knowledge of theprocess model is assumed unavailable, the optimal number of clusters is notknown, and hence, the grouping of process variables is considered as unsu-pervised clustering. The proposed approach for clustering time series dataof process variables in large-scale industrial facilities is based on combiningk-means clustering and soft dynamic time warping. k-means clustering is acommonly used method for clustering data points, as it offers guaranteedconvergence as well as scalability when having large datasets. However, dueto having time series data for process variables, k-means clustering is com-bined with dynamic time warping (DTW), which can compute the distancebetween such data. For further details on k-means clustering and DTW,refer to [55–58]. Further, in the proposed method, soft-DTW is used sinceit provides a smoothed formulation with respect to the standard DTW, byusing a softmin function instead of a min function [59]. As soft-DTW pro-poses a differentiable loss function, it can outperform DTW in clusteringproblems. As the standard DTW and soft-DTW are applied to the datasequences in which equidistant points in time are used for sampling [58],fixed sampling time is required in collecting process measurement data.Considering a large-scale industrial process with K number of processvariables, a process variable i is denoted by vi, i ∈ {1, . . . ,K}. Consideringcollecting Ni measurement samples of vi, the process measurement data isdenoted as a column vector Dvi ∈ RNi . When collecting the measurementswith a fixed sampling time and with an equal number of samples, the processmeasurement data of all process variables can be denoted as matrix Dv =112.2. METHODOLOGY[Dv1 . . .DvK ] ∈ RN×K . Further, data collected under normal (i.e., fault-free)operating scenario is denoted with the superscript 0 (e.g., D0v).The purpose of this stage is to put the process variables into k numberof clusters using k-means clustering, which assigns each process variable toa cluster with the closest centroid. For measuring the distance, soft-DTWis used. As soft-DTW measures the distance in time series data, the lessthe distance, the more similar the time series data of two process variables(i.e., similar patterns). Considering the time series data of any two processvariables denoted as D0va ∈ RNa and D0vb ∈ RNb , a, b ∈ {1, . . . ,K}, with anequal number of samples (i.e., Na = Nb = N), a distance matrix is definedasD(D0va ,D0vb ) =d1,1 . . . d1,N... . . . ...dN,1 . . . dN,N (2.1)where each entry di,j is calculated asdi,j =∣∣∣D0vai −D0vbj ∣∣∣ (2.2)and where Dvai and Dvbj represent the collected measurements of processvariables va and vb in the ith and jth samples. Then, a transformationmatrix T(Dv0a,Dv0b) is constructed as follows:T(D0va ,D0vb ) =r1,1 . . . r1,N... . . . ...rN,1 . . . rN,N (2.3)where r1,1 = d1,1 , and each of the other entries, denoted as ri,j , is calculatedasri,j = di,j +min(T [i− 1, j − 1], T [i− 1, j], T [i, j − 1]) (2.4)Further, a set P containing the indices of contiguous entries of T(D0va ,D0vb ),which minimize the distance between D0va and D0vb is determined. The lengthof P , denoted by L, is restricted by N , such that N ≤ L ≤ 2N . The lthentry of P is denoted by pl, l ∈ {1, . . . , L}. Using the softmin operator, thesoft-DTW distance is obtained as follows [60]:Dγ(D0va ,D0vb) = −γ logL∑l=1e(dplL · γ)(2.5)122.2. METHODOLOGYwhere γ is a smoothing factor set by the user, which is usually chosen to bea positive number close to zero.For employing k-means clustering, at the end of each iteration, the av-erage of the time series data in a cluster should be calculated so that thecentroids could update accordingly. However, in case of clustering time se-ries data, the normal averaging method is not applicable. A global averagingmethod for DTW, called DTW barycenter averaging (DBA) was proposedin [61], which calculates the average of data in an iterative manner untilconsensus is met. This approach is therefore employed in this thesis, whichuses barycentric coordinates, such that for each cluster, arbitrarily chosentime series data is chosen as the initial average sequence. Then, Euclideandistances between each coordinate of the average sequence and the coordi-nates of the other associated ones are computed. The barycenter functionis denoted and defined as follows:B(A1, . . . , Ah) =∑hn=1Anh(2.6)where An is the coordinate of the data sequence that associates with thecoordinate of the average sequence, and h is the number of associated co-ordinates on the data sequence. Afterwards, the coordinates of the averagesequences update according to the calculated barycenters.At the end of this stage, each process variable i, i ∈ {1, . . . ,K} belongs toone of the k clusters, denoted by Cc, c ∈ {1, . . . , k} such that the intra-clustersoft-DTW distance is minimized. Given the described steps for clusteringprocess variables using k-means clustering with soft-DTW, the first stage ofthe proposed computational procedure is summarized in Algorithm 1.Remark: It should be noted that the proposed procedure assumes that allprocess variables are sampled equally (i.e., fixed sampling rate) and the samenumber of samples are collected for each process variable. If this assumptionis not satisfied in practice, then an additional data preprocessing step willbe needed.2.2.2 Stage II: Selection of Representative VariablesIn this stage of the computational procedure, a subset of process vari-ables are selected for fault classification. Based on Algorithm 1, as eachcluster contains process variables with similar patterns under normal oper-ating scenario, one variable is selected from each cluster (i.e., representativevariable), which is deemed to be most sensitive to faults. To select theseprocess variables, a LASSO-based or Elastic Net-based regression method is132.2. METHODOLOGYAlgorithm 1: Clustering of process variables using k-means clus-tering with soft-DTWInput: D0vOutput: Cc, c ∈ {1, . . . , k}1: Define k as the number of clusters, k random centroids from theprocess measurement data, and γ2: for (1 ≤ i ≤ K) do3: for (1 ≤ j ≤ k) do4: Calculate D(D0va ,D0vb ) using (2.1) and (2.2)5: Find T(D0va ,D0vb ) using (2.3) and (2.4)6: Calculate Dγ(D0va ,D0vb) using (2.5)7: end for8: Assign vi to the cluster with the least soft-DTW distance to itscentroid9: end for10: Update the centroids using DBA, by calculating the barycenterfunction in (2.6) for each sample11: If centroids change, go to 2used. LASSO, first introduced in [62], is a regression analysis method thatcan be used for predictor (i.e., variable) selection [63], and has been appliedin various applications, such as sensing and control [64, 65].LASSO-based regression can be solved using Least Angle Regression(LARS) instead of the common forward selection methods [66], when hav-ing a high dimensional dataset. LARS chooses an equiangular directionbetween the most highly correlated predictors [67], and unlike other meth-ods that produce a vector, LARS produces a curve between predictors. Thecombination of LASSO and LARS has been widely used in research. Forexample, the authors in [68] applied this approach for isolating faulty vari-ables in the Tennessee Eastman process, while addressing discriminant androot cause analyses, where data from all the process variables are needed tofind the fault. In the second stage of the proposed computational procedure,a similar approach is used, however, the developed method uses a reducednumber of process variables.It should be noted that when using LASSO and LARS in a fault-affectedprocess, such as in [68], usually the steady-state data of the fault-affectedprocess variables are used. However, in real industrial processes, identify-ing the occurrence time of a fault as well as the time that distinguishes142.2. METHODOLOGYsteady-state data may not be feasible, as it can require significant efforts.Therefore, this thesis eliminates the need for such requirement by using theentire dataset as an input, where data for normal and fault-affect operatingscenarios are used.A predictor matrix for the LASSO-based regression for each fault sce-nario denoted by f is defined asXf = Dfv (2.7)To produce a response vector, first PCA is applied to the differencebetween the normal and fault-affected data, using only one principal com-ponent (PC). The involved steps are as follows: (i) a mean vector denotedby µ ∈ RN is built by calculating the mean of the values, (ii) µ is sub-tracted from all the values of Dfvi , i ∈ {1, . . . ,K} (i.e., standardization), (iii)the covariance matrix is computed, (iv) its eigenvalues and eigenvectors arecalculated, (v) the eigenvectors based on the order of their correspondingeigenvalues are sorted in a descending order, and (vi) then the principalcomponents are found. The first PC is only considered, since the PCs areorthonormal and uncorrelated, and the maximum variance of the data isrepresented by the first PC. For further details on PCA, refer to [69]. Asa result, a response vector that uses the data collected under normal andfault-affected operating scenarios is constructed asyf =(D0v −Dfv)·Wf =D0v11 −Dfv11. . . D0vK1 −DfvK1.... . ....D0v1N −Dfv1N. . . D0vKN −DfvKN·Wf (2.8)where Wf ∈ RK is a weight vector and is built from the entries of the firstPC. Then, the following optimization problem is defined for the LASSO-based regression method:minβ(12N||yf −Xfβ||22 + α||β||1)(2.9)where β is a coefficient vector, ||β||1 denotes its l1 norm, and α is a tuningparameter, which is a positive scalar alterable by the user. If α = 0, (2.9)reduces to a least squares optimization problem for linear regression. Asα increases, some variables may be eliminated/removed, as they will notaffect the response. In the proposed method, the value of α can be gradually152.2. METHODOLOGYincreased to remove variables by shrinking the corresponding entry of β tozero, where their order of removal and the corresponding α value are noted,which will allow for selecting only one process variable from each of the kclusters Cc, c ∈ {1, . . . , k} produced using Algorithm 1.When applying the proposed LASSO-based regression method, eachprocess variable is assigned an index for the order in which it is elimi-nated/removed with increasing the value of α. As a result, for each faultf, f ∈ {1, . . . , F}, each process variable i, i ∈ {1, . . . ,K} has an associatedorder of removal index denoted by of,i with a corresponding α value denotedby αf,i, which can be combined and presented in the following matrices:o1,1 . . . o1,K... . . . ...oF,1 . . . oF,K (2.10)α1,1 . . . α1,K... . . . ...αF,1 . . . αF,K (2.11)Then, a new sensitivity-based metric that characterizes the sensitivity ofprocess variables to faults is defined. The metric is used in the proposedmethod to select the representative process variables from each cluster Cc, c ∈{1, . . . , k}. It is defined for process variable i with respect to fault f assf,i = of,i · αf,i (2.12)Considering all process variables and all faults, a fault sensitivity matrixdenoted by S ∈ RF×K is constructed to include the sensitivity of all theprocess variables to all the faults using (12). In order to compare the sensi-tivity of different process variables to different faults, S is normalized. Anaverage sensitivity vector of size K is obtained by computing the averageof each of the columns of the normalized sensitivity matrix S, such that forprocess variable i, i ∈ {1, . . . ,K},S¯i =∑Ff=1 sf,iF(2.13)defines the ith entry of the average sensitivity vector.The final step of the proposed method is to examine the average sensi-tivity of each process variable to all faults, and accordingly sort the processvariables in each cluster Cc, c ∈ {1, . . . , k} in ascending order. These mod-ified clusters are denoted with superscript s, namely, Csc , c ∈ {1, . . . , k}.162.2. METHODOLOGYFurther, the last process variable of each modified cluster is selected as therepresentative process variable of the cluster, as it has the highest averagesensitivity to all faults.If some process variables are highly correlated, very close coefficient val-ues are assigned to them, and LASSO-based regression randomly selectsone of them, and disregards the others. This is called the grouping effect,which in case of predictor/variable selection, is not ideal, as usually the aimis to find all the important predictors/variables. In [70], to overcome thislimitation, the authors proposed using Elastic Net-based regression, whichcombines LASSO and Ridge regression, and produces a convex penalty func-tion that guarantees the grouping effect. Then, using the Elastic Net-basedregression method, the following optimization problem is formulated, whichcan be used instead of (2.9) for selecting the representative process variables:minβ(12N||yf −Xfβ||22 + µα||β||1 +12(1− µ)α||β||22)(2.14)where µ is an l1-ratio, which can be set to 0.5, and the changes of the coeffi-cients with respect to α are monitored. Similar to LASSO-based regression,Elastic Net-based regression in the proposed method is used with LARS.Given the described steps for selecting representative process variablesfrom each cluster Cc, c ∈ {1, . . . , k} using PCA and either LASSO-based orElastic Net-based regression, the second stage of the proposed computationalprocedure is summarized in Algorithm 2.Remark: Faults that affect the process, but cannot be observed throughsensor measurement data are referred to as unobservable faults. The pro-posed LASSO-based and Elastic Net-based approaches cannot find fault-sensitive process variables to unobservable faults. Consequently, the pro-posed procedure is not applicable for unobservable faults.2.2.3 Stage III: Data-to-Image Conversion and ImageClassificationIn this stage of the proposed computational procedure, the time seriesdata of the selected process variables in Algorithm 2 is converted to images,and then fed to a designed convolutional neural network (CNN), which re-sults in fault classification, indicating which fault has occurred in the large-scale industrial process. Given that process variables were clustered into knumber of clusters in Algorithm 1, and those deemed most sensitive on aver-age in each cluster are found using Algorithm 2, 3D images with a depth of172.2. METHODOLOGYAlgorithm 2: Selection of representative variablesInput: Cc, c ∈ {1, . . . , k}, Dfv, f ∈ {1, . . . , F}, and D0vOutput: Csc , c ∈ {1, . . . , k} and representative process variablesfor 1 ≤ f ≤ F doFind the predictor matrix and response vector using (2.7) and (2.8)By altering α and using either (2.9) for LASSO-based or (2.14) forElastic Net-based regression, find the corresponding βfor 1 ≤ i ≤ K doαf,i = α which set β[i] to zeroend forSort all αf,i valuesFind all of,j indicesend forUsing (2.10), (2.11), and (2.12), find SNormalize Sfor 1 ≤ i ≤ K doFind the average sensitivity using (2.13)end forSort process variables in Cc based on S¯i in an ascending order, toproduce the sorted clusters Csc , c ∈ {1, . . . , k}Define representative process variables as the last entry in Csck (i.e., k number of channels) can now be constructed, where each channelcorresponds to the time series data of one of the k selected/representativeprocess variables, and where all k channels have the same height and width.Unlike most studies that produce images from just one process variable,the proposed method in this stage produces multi-dimensional images basedon multiple process variables. For example, in [47], time series data for asingle variable was converted to single-channel (i.e, gray-scale) images, andwere fed to a CNN for fault classification. In [54] multi-channel images, suchas RGB images, rather than gray-scale ones, were used for improving theaccuracy. However, the authors only focused on a single variable. In [71],the data extracted from vibration signals from multiple sensors were fusedand converted to single channel images.In the proposed method, for producing the images, a randomized seg-ment with η2 (1 ≤ η ≤ N) number of data points in the time series data ofeach of the representative process variables affected by fault f is extractedfirst. The extracted segment, denoted by Rfj , j ∈ {1, . . . , k}, is then con-182.2. METHODOLOGYverted to a matrix, denoted by W fj ∈ Rη×η and whose entries are definedaswfj [x, y] = ⌈Rfj ((x− 1)× η + y)−min(Rfj )max(Rfj )−min(Rfj )× 255⌉ (2.15)where ⌈·⌉ denotes the ceil operator, and x and y start from 1 and continue toη (1 ≤ x, y ≤ η). The operators min (·) and max (·) describe the minimumand maximum values, respectively.As a 3D image with depth k has k number of channels, it can be seen asa kth order tensor, where the channels are W fj , j ∈ {1, . . . , k}. For example,with k = 3, W f1 produces the first channel, Wf2 produces the second one,andW f3 produces the final channel. This concatenation procedure along thechannel dimension is defined byIf =W f1 ⌢Wf2 ⌢ · · ·⌢W fk (2.16)where ⌢ denotes the concatenation operator. In addition, when the imageis produced using data collected under normal operating conditions, super-script 0 is used instead of f (i.e., I0).It should be noted that various images can be built based on the proposedmethod. Two considerations are worth discussing. The first considerationis the advantage of using randomized segments, rather than the entire timeseries data. This results in building various images rather than a singleimage, which leads to better feature extraction and overfitting avoidancewhen provided to the CNN. Also, as the extracted segments from the timeseries data are picked randomly, the built images will have well dispersion,which leads to improvement in classification. The second consideration isthe specification of η. To reduce the computational burden and producingmore number of images, without loss of important data, η2 should be smallerthan the number of samples. However, if η2 is chosen to be very small, thenimportant features may be lost, and the model could either be overfittedor underfitted. Therefore, the specification of η strongly relies on the useddataset.The output layer of the designed CNN architecture is specified withf + 1 number of neurons to be able to successfully classify the occurrenceof f faults, alongside the fault-free case associated with the normal operat-ing scenario. The Rectified Linear Unit (ReLu) is chosen as the activationfunction for all layers except the output layer. ReLu function speeds upthe training, and can also provide the benefits of other activation functions,192.2. METHODOLOGYsuch as Sigmoid, while being calculated much easier. The last layer usesSoftmax function as its activation function. The merit of the Softmax func-tion is that it can be used in cases with multiple classes, e.g., when havingmultiple operating scenarios. As the outputs of the Softmax function areinterrelated, the probabilities will always add up to one. Also, in the con-volutional layer, maximum pooling is used for further downsampling andparameter reduction. Zero padding is utilized to better control the size ofthe output volumes and avoid the loss of edge features. For 3D images, 2DCNN is employed. Having images with depth, sometimes, 2D CNN has someproblems while detecting objects in the images, especially in a case wherethe scale of the objects changes. This is because of the scale-variant fea-ture of 2D CNNs. To address this shortcoming, some research works haveproposed using 3D CNNs. However, employing 3D CNNs has a memoryusage problem [72]. This is why for image classification problems in whichthe scale-invariant feature is important, hybrid solutions are proposed suchas in [73]. However, the proposed method in this stage does not requirea scale-invariant feature, as there is no need to identify any objects withdifferent scales in the images. As a result, the design and deployment of a2D CNN is sufficient.Converting the time series data to images can be beneficial as it helpsin detection of the faults that do not have continuous effect on processvariables, as it leads to better pattern extraction.Given the described steps for data-to-image conversion, and feeding theimages to the designed CNN for fault classification, the third stage of theproposed computational procedure is summarized in Algorithm 3.Remark: To be able to extract sufficient information from time seriesdata of process variables, when a very large number of samples are collected,the value of η increases. This increase causes more computations as thenumber of entries of W fi also increases, and (2.15) is used more number oftimes. However, producing images even with high η value is usually lesscomputationally expensive, when compared to other methods. In addition,when the number of clusters increases, the number of image channels alsoincreases and leads to higher dimensional images, requiring more complexCNN architectures, as well as longer computational time to classify faults.The framework of the proposed three-stage computational procedure issummarized and depicted in Fig. 2.1.202.3. CASE STUDYAlgorithm 3: Data-to-image conversion using representative pro-cess variables and training the CNN for fault classificationInput: Representative process variables and number of faults FOutput: Trained CNN for fault classification1: Assign each representative variable an index j, j ∈ {1, . . . , k}2: Define η3: for 0 ≤ l ≤ F do4: for 1 ≤ j ≤ k do5: Rlj ← randomized η × η segment extracted from thejth representative variable6: Find W lj by calculating its entries using (2.15)7: end for8: Build the image I l using (2.16)9: end for10: Repeat until desired number of images are produced11: Feed the produced images and their corresponding l, as their labels,to the CNN12: Train the CNN2.3 Case StudyTo examine and demonstrate the effectiveness of the proposed three-stage computational procedure for data-driven fault classification in large-scale industrial processes, first, the Tennessee Eastman process benchmarksimulation system/model is used [74–76]. Subsequently, a distillation col-umn is used to demonstrate that the procedure is also applicable for small-scale industrial processes.The Tennessee Eastman process has widely been used as a case study forfault classification methods. The work in [77] took use of unlabeled data incomplex industrial processes with the aim of fault classification based on anapproach using semi-supervised deep generative models, and the TennesseeEastman process was used as a case study, where all of its 52 process vari-ables were used, and 6 kinds of faults in the system were classified. In [78],different algorithms were applied to the Tennessee Eastman dataset withthe aim of fault detection, and a new Temporal CNN1D2D was proposed.In [79], a new approach using 1D-CNN was suggested for fault detection,which was applied to the Tennessee Eastman process to group faults intoeasy, medium, and hard-detectable faults. In [80], a new approach using212.3. CASE STUDY Fault-freeFault-affectedStage I: Clustering of Process VariablesPCA andLASSO-based/Elastic Net-basedregressionStage II: Selection of RepresentativeVariablesDatasetStage III: Data-to-Image Conversionand Image Classification Generating the images:Designed CNNFigure 2.1: The framework of the proposed three-stage computational pro-cedurea CNN with multi-channel and multi-head along the time dimension (mm-CNN) was suggested to extract the independent features of the process vari-able, which can be beneficial in multivariate fault classification problems. In[81], a methodology based on change point detection and an immune/neuralapproach was used to classify the faults in the Tennessee Eastman process.In [82], distance and spatial angle relationship were used by representingspatial geometric features which can help in the process of fault diagnosis,and can be derived based on heat-kernel and cosine weights. The Shapleyvalue framework-based method was used in [83] to identify the variableswhich enable a deep neural network to classify a fault, and was applied tothe Tennessee Eastman process. Additionally, the work in [48] was appliedto the Tennessee Eastman process for evaluation purposes, where the pro-222.3. CASE STUDYABECCWRPurgeProductCondCondenserStripperVap/LiqSeparatorCompressorCWSFIFIFIFIFISteamFIFIFIFI FITITITITILILILIPIPITIPIJIReactorCWSCWRFigure 2.2: Simplified Piping and Instrument Diagram (P&ID) of the Ten-nessee Eastman processposed sliding window was applied to all process variables to produce 2Dsamples which were fed to CNN-LSTM models. In none of those studies, anapproach for fault classification where only a few number of process vari-ables are used, was suggested, and neither of them constructs 3D imagesfrom the time series data, to be able to extract the important features fromfault-sensitive process variables.The Tennessee Eastman process is depicted in Fig. 2.2, which consistsof 5 units, with 52 variables; 41 of which are process variables, and 11are manipulated variables. From the 41 process variables, the first 22 areused for continuous measurement and the remaining 19 are regarded ascompositions. In addition, 20 fault states can be introduced, where the first7 faults result from a step change in one or more variables, the 8th to 11thfaults are caused by random changes, and the remaining faults are othertypes of faults, some of them being unknown. The 3rd, 9th, and 15th faultsare unobservable, making their classification challenging and complex, sincetheir effects in the recorded data cannot be detected and observed easily.More specifically, in this thesis, the dataset from [84] is used, wheredata from the first 22 process variables is utilized (i.e., K = 22), which arelisted in Table 2.1. The dataset has resulted from recording 500 samples ineach simulation run (i.e., N = 500) and running the simulation 500 times.The assumption is that no knowledge about (i) the model of the process,232.3. CASE STUDY(ii) the relation between neither the variables with each other, and (iii) therelation between the variables and the faults is available. However, basedon the dataset, the number of faults is known. The proposed computationalprocedure is applied to the first group of observable faults, which are listed inTable 2.2. Prior to using the dataset, all data were zero-centered and scaledto unit variance. Further, the programming was done using Python version3.7, on a system with an 11th generation Intel core i5-1145G7 processor.Table 2.1: Process variables of the Tennessee Eastman processProcess Variable i Description1 A feed2 D feed3 E feed4 Total feed5 Recycle flow6 Reactor feed rate7 Reactor pressure8 Reactor level9 Reactor temperature10 Purge rate11 Product separator temperature12 Product separator level13 Product separator pressure14 Product separator underflow15 Stripper level16 Stripper pressure17 Stripper underflow18 Stripper temperature19 Stripper steam flow20 Compressor work21 Reactor cooling water outlet temperature22 Separator cooling water outlet temperature2.3.1 Stage I: Clustering of Process VariablesTo cluster the 22 process variables listed in Table I, based on their time-domain patterns in the normal operating scenario, k-means clustering with242.3. CASE STUDYTable 2.2: Observable faults in the Tennessee Eastman processFault f Description1 A/C feed ratio, B composition constant2 B composition, A/C feed ratio constant3 Reactor cooling water inlet temperature4 Condenser cooling water inlet temperature5 A feed loss6 C header pressure loss - Reduced availabilitysoft-DTW is deployed according to Algorithm 1. The number of clusterswas chosen based on the elbow method, such that k = 3. Also, γ = 0.01was chosen. The resulting 3 clusters were found to be:C1 = {v7, v13, v16, v18, v19, v20}C2 = {v5, v6, v9, v12, v14, v15, v17} (2.17)C3 = {v1, v2, v3, v4, v8, v10, v11, v21, v22}The similarity of the time-domain patterns of the process variables withinthe same cluster is depicted in Fig. 2.3. As can be observed in Figs. 2.3(a)to 2.3(c), process variables v16, v19, and v20 from cluster C1 have similar pat-terns. Similarly, it can be observed in Figs. 2.3(d) to 2.3(f) and Figs. 2.3(g)to 2.3(i) that the shown process variables from clusters C2 and C3, respec-tively, have similar patterns. These similarities in patterns may signal thatthe process variables are affected by similar root causes and may thereforebe correlated/dependent in the process.2.3.2 Stage II: Selection of Representative VariablesAfter determining the 3 clusters in (2.17), the next step is to selectone representative process variable from each cluster, which is deemed mostsensitive to faults. To achieve this, Algorithm 2 was implemented given thefaults listed in Table 2.2. In addition, the normalized sensitivity based on(2.13) is computed for the process variables for each fault scenario, as shownin Fig. 2.4.Using (2.7) and (2.8), the predictor matrix and response vector for eachfault are produced, and then using (2.9), the LASSO-based regression isapplied. The calculated coefficients are plotted with respect to α for all the252.3. CASE STUDY(a) v16 (b) v19 (c) v20(d) v5 (e) v9 (f) v14(g) v1 (h) v11 (i) v21Figure 2.3: Time series data of process variables from different clustersFigure 2.4: Normalized sensitivity of the 22 process variables for each faultscenario f, f ∈ {1, . . . , 6} using LASSO-based regression262.3. CASE STUDYprocess variables under each of the fault-affected conditions. As examples,two cases are discussed in detail.Case I (Fault 1)According to Table 2.2, the first observable fault f = 1 results from astep change in the A/C feed ratio. With using the LASSO-based regressionmethod in Algorithm 2, the value of α was varied to examine the eliminationof process variables (i.e., when the corresponding β coefficient becomes zero).The result of this analysis is depicted in Fig. 2.5(a). It can be observed thatthe last variable which is eliminated is v16, which is the stripper pressure.It can also be observed that the next two variables that are eliminatedare v8 and v1. According to [85], the stripper is the nearest equipmentto where this fault occurs. Because of this fault, the amount of A in therecycle stream decreases. To maintain the A composition, process variablev1 changes, which results in a change in the level of the reactor, defined asprocess variable v8. Thus, the three process variables v16, v8, and v1 can beconsidered most sensitive to this fault, when compared to the other processvariables, which is also confirmed by the observations from Fig. 2.5(a). Inaddition, this conclusion is also confirmed through Fig. 2.4, by noting thehigh normalized sensitivity values of the three process variables, comparedto the others.Case II (Fault 4)According to Table 2.2, the third observable fault (fourth fault in thedataset [84]) f = 3 results from a step change in the reactor cooling wa-ter inlet temperature. With using the LASSO-based regression method inAlgorithm 2, the value of α was varied to examine the elimination of pro-cess variables. The result of this analysis is depicted in Fig. 2.5(b), andaccording to which, the last eliminated variable is v9, which is the reactortemperature. Other process variables are not as affected as v9, however,those corresponding to the reactor cooling water outlet temperature (v21),stripper steam flow (v19), and product separator underflow (v14), are elim-inated next. The controllers trying to maintain the performance of theprocess are the reason of negligible changes in other process variables in theoccurrence of f = 3. Therefore, based on Fig. 2.5(b), v9 is considered themost sensitive process variable to this fault. This conclusion is also con-firmed through Fig. 2.4, by noting the high normalized sensitivity value ofthis process variable compared to the others.272.3. CASE STUDY(a) Case I (f = 1)(b) Case II (f = 3)Figure 2.5: Results obtained when using Algorithm 2 with LASSO-basedregression, where the effect of varying the α tuning parameter value on theβ coefficient value of each process variable is shown282.3. CASE STUDYFor selecting the representative process variables, the sensitivity of eachprocess variable to faults needs to be investigated. For example, for Case II,with using (2.10), its 3rd row provides the order index of each process vari-able, which was found to be: [10, 18, 7, 13, 11, 13, 4, 17, 22, 7, 16, 5, 1, 20, 9, 15,11, 1, 20, 6, 19, 1]. This indicates that v1 is the 10th variable whose β coef-ficient shrinks to zero (i.e., eliminated), and v9 is the 22nd process vari-able. Similarly, matrix (2.11) can also be found, which provides the cor-responding α value for each process variable. Then by computing thesensitivity of the process variables to each fault, and the average sensi-tivity, using (2.12) and (2.13), respectively, the sorted process variablesbased on their average sensitivity, in an ascending order was found to be:v22, v18, v15, v10, v12, v6, v17, v2, v5, v4, v21, v3, v14, v8, v13, v1, v7, v9, v20, v11, v16,v19.Then, the clustered process variables in (2.17) are used to create sortedclusters, based on average sensitivity using LASSO-based regression, suchthatCs1 = {v18, v13, v7, v20, v16, v19}Cs2 = {v15, v12, v6, v17, v5, v14, v9} (2.18)Cs3 = {v22, v10, v2, v4, v21, v3, v8, v1, v11}where the last entries of each set is the process variable that is deemed mostsensitive to all faults (i.e., on average). As a result, the subset {v19, v9, v11}was specified to contain the selected process variables that will be used inthe next stage.2.3.3 Stage III: Data-to-Image Conversion and ImageClassificationGiven the representative process variables v19, v9, and v11, the objectivein this stage is to implement time series data-to-image conversion, in orderto achieve fault classification, based on Algorithm 3. As mentioned before,in the dataset, the length of each of the time series data is 500. Because ofthe rather small number of samples in each simulation run, 10 segments oflength η2 = 162 were randomly extracted from the data and converted to2D images (i.e., images with no depth) using (2.15), and then, 3D imageswith k number of channels were produced using (2.16). As in this case studyk = 3, 3D images with 3 channels, known as RGB images, were produced.Hence, there would be 500 × 10 = 5000 images to be given as input to thedesigned CNN, the layers of which are summarized in Table 2.3.292.3. CASE STUDYTable 2.3: Types and outputs of the layers of the CNN modelLayer type Output sizeConv-2D (16, 16, 8)Max-Pooling-2D (8, 8, 8)Conv-2D (8, 8, 16)Max-Pooling-2D (4, 4, 16)Dropout (4, 4, 16)Flatten (256)Dense (32)Dense (7)Some samples of produced images using the data of the representativeprocess variables are shown Figs. 2.6(a) to 2.6(c), which were producedwhen having the third observable fault f = 3, whereas Figs. 2.6(d) to 2.6(f)and Figs. 2.6(g) to 2.6(i) were produced when having the fifth and sixthobservable faults (i.e, f = 5 and f = 6), respectively. It can be observedthat the images produced by the data affected by a similar fault, have indeedsimilarities in their appearance.In verifying the results, from the 5000 produced images, 20% were used asthe test set, where from the remaining 80%, 30% were used as the validationset and 70% as the training set. When using the data with 20 epochs,accuracies of 95.28% for the training set and 88.91% for the test set wereobtained, which are considerably high accuracies. The test accuracy forthe fault-free data was found to be 73%, and for each fault scenario wasfound to be as listed in Table 2.4. It can be noted that using the proposedcomputational procedure, high accuracy for fault classification was obtainedfor each of the observable fault scenarios.Given the proposed computational procedure, it was possible to clas-sify faults with only using 3 of the process variables. This demonstratesits usefulness for fault diagnosis, especially for scenarios when some processvariable data are/become unavailable as well as when it is required to re-duce computational complexity. To further illustrate these merits, Table 2.5and Table 2.6 are provided for comparisons with other methods in the litera-ture, where accuracies and required training times are listed, respectively. Itshould be noted that while in this thesis only 3 process variables were used,the other methods in Table 2.5 used data from all process variables in theTennessee Eastman process. Even with such small number of process vari-302.3. CASE STUDY(a) (b) (c)(d) (e) (f)(g) (h) (i)Figure 2.6: Samples of 16 × 16 RGB images produced by using (2.15) and(2.16), for different faults312.3. CASE STUDYTable 2.4: The test accuracy for different faultsf Accuracy using Accuracy using{v19, v9, v11} {v16, v14, v1}1 91% 79%2 91% 79%3 92% 55%4 81% 80%5 100% 100%6 95% 61%ables, the proposed procedure of this thesis provided very high accuracies.According to Table 2.6, the proposed procedure also required considerableshorter time for training the system responsible for classifying the faults.Table 2.5: Fault classification accuracy using different proceduresf This thesis Using [78] Using [80] Using [81] Using [83]1 91% 100% 100% 100% 99.25%2 91% 100% 100% 100% 98.25%3 92% 100% 99% 68% 99.87%4 81% 100% 96% 100% 99.87%5 100% 100% 100% 98% –6 95% 99% 100% 97% 100%Table 2.6: Training time (seconds) using different methodsThis thesis LSTM Random Forest XGBoost65.69 4134.19 19232.23 23855.27322.3. CASE STUDY2.3.4 Other Design ConsiderationsLower or higher number of channelsIf only data from a single process variable was considered, and imageswere generated accordingly, it would be impossible to obtain a comprehen-sive knowledge about all types of changes that a fault may cause. This iswhy the use of multi-channel images is suggested. On the other hand, ifthe number of channels was high, the computational burden would increasesignificantly, while the new/additional obtained knowledge would not be ashelpful for fault classification, as it would have redundant information. Thisis the reason that producing 3D images with k number of channels wasproposed, where k is the number of clusters. However, if one is required toreduce the computational burden of classifying 3D images, the average of allchannels can be taken and the images can be converted to 2D or gray-scaleimages. It should be noted that even though the obtained images have theinformation from all the k variables, some important information/featuresmay be lost during conversion from 3D to 2D images (i.e., reducing k numberof channels to 1), resulting in decrease in accuracy.For the Tennessee Eastman process studied above, when the RGB imageswere converted to gray-scale images, a test accuracy of 69.69% was obtained,which is less than the accuracy of 88.91% that was obtained with RGBimages. On the other hand, when the process variables were assigned to alarger number of groups/clusters (e.g. leading to 4 representative variablesv16, v19, v9, and v11), higher-dimension inputs for the CNN were used, and asa result, a test accuracy of 91.01% was obtained. Although a small increasein the test accuracy was obtained, much more additional computationaleffort for producing the images was needed.Choosing other process variablesIn large-scale industrial processes, it is possible that sensors or commu-nication channels fail to operate properly. In the case when some processmeasurement data may not be available, the proposed method can be ex-tended such that (i) the set containing the selected representative processvariables should be such that they have an acceptable sensitivity to faults,and (ii) the selected variables be uncorrelated in the normal operating sce-nario, namely, do not have similar time-domain patterns. The first conditionrequires that each fault should affect at least one of the selected process vari-ables. The second condition requires that process variables be selected fromdifferent clusters, so that all the pattern changes due to occurrence of a fault332.3. CASE STUDYFigure 2.7: Normalized sensitivity of the 22 process variables for each faultscenario f, f ∈ {1, . . . , 6} using Elastic Net-based regressioncan be extracted.For example, in the Tennessee Eastman process studied above, if none ofthe representative process variables are available, the process variables thatare deemed second most sensitive on average to faults can be selected fromeach cluster. Given (2.18), the other process variables to be used for faultclassification are v16, v14, and v1. As shown in Fig. 2.4, for each of the faults,at least one of these process variables is sensitive. The test accuracy for eachfault scenario is listed in Table 2.4. Compared to the original representativevariables, it can be noted that the accuracy is reduced, as the new selectedprocess variables are not as sensitive to the faults. However, they have betteraccuracy when compared to using the remaining process variables.2.3.5 Comparison Between Using LASSO-based and ElasticNet-based Regression MethodsAs mentioned before, in the second stage of the proposed computationalprocedure summarized in Algorithm 2, Elastic Net-based regression can beused instead of LASSO-based regression. For the Tennessee Eastman processstudied above, the normalized sensitivity calculated using (2.12) was com-puted for the process variables for each fault scenario, as shown in Fig. 2.7.Similar to before, the two fault cases were considered.Case I (Fault 1)When the process was affected by the first observable fault (f = 1)in Table 2.2 and with using the Elastic Net-based regression method in342.3. CASE STUDYAlgorithm 2, the value of α was varied to examine the elimination of processvariables. The result of this analysis is depicted in Fig. 2.8(a). It canbe observed that the last variable which is eliminated is v16. Also, thecorresponding coefficients of v7, v13, v8, and v11 become zero with largevalues of α. This conclusion is also confirmed by observing Fig. 2.7, bynoting the high normalized sensitivity of these process variables comparedto the others.The correlation matrix of the data of the process variables (i.e., D1v) isprovided in Fig. 2.9(a), and according to which, it can be observed thatthe data of the mentioned process variables have high correlation. It canbe seen that some of these variables did not appear when using LASSO-based regression, as it randomly eliminates some of the variables that arehighly correlated. This confirms that Elastic Net-based regression has betterperformance on data with high correlation.Case II (Fault 4)When the process was affected by the third observable fault (f = 3) inTable 2.2, with using the Elastic Net-based regression method in Algorithm2, the value of α was varied to examine the elimination of process variables.The result of this analysis is depicted in Fig. 2.8(b). It can be observed thatthe last variable which is eliminated is v9. Also, the coefficients of otherprocess variables become zero with much lower values of α. This conclusionis confirmed by observing Fig. 2.7, by noting the high normalized sensitivityof this process variable compared to the others.The corresponding correlation matrix of the data of the process vari-ables (i.e., D3v) is shown in Fig. 2.9(b). It can be observed that the onlyprocess variables that are highly correlated are v7, v13, and v16. Accord-ing to Fig. 2.8(b), these variables are eliminated with a small α value, andtherefore, are not considered as sensitive process variables to this fault. Con-sequently, the results using LASSO-based regression and Elastic Net-basedregression are similar, which is confirmed by comparing 2.5(b) and 2.8(b),and also the normalized sensitivity of the process variables to this fault inFig. 2.4 and Fig. 2.7.The sorted clusters based on average sensitivity using LASSO-based re-gression were shown in (2.18), while using Elastic Net-based regression weredetermined as352.3. CASE STUDY(a) Case I (f = 1)(b) Case II (f = 3)Figure 2.8: Results obtained when using Algorithm 2 with Elastic Net-basedregression, where the effect of varying the α tuning parameter value on theβ coefficient value of each process variable is shown362.3. CASE STUDY(a) D1v(b) D3vFigure 2.9: The correlation matrices of the fault-affected process variables372.3. CASE STUDYCs1 = {v18, v20, v19, v16, v13, v7}Cs2 = {v15, v12, v6, v5, v17, v14, v9} (2.19)Cs3 = {v4, v21, v2, v8, v10, v1, v3, v22, v11}Comparing Fig. 2.4 with Fig. 2.7, it can observed that some of the processvariables that were not deemed sensitive to some faults, were found to besensitive when Elastic Net-based regression was used. For instance, v7,v13, and v22 were among the first entries of the sorted clusters in (2.18),while they appear to be among the last entries in (2.19), showing their highaverage sensitivity to the studied faults. Using LASSO-based regression, v19,v9, and v11 were chosen as the representative process variables. However,using Elastic Net-based regression resulted in v7, v9, and v11 to be therepresentative variables. When using the representative variables in thethird stage of the computational procedure, a test accuracy of 77.51% wasobtained, which is significantly less than the accuracy of 88.91% obtainedwhen using the LASSO-based regression.As previously mentioned, the classification should be implemented usingprocess variables that have different time-domain patterns. Further, whenoperating under normal condition, v7 and v11 were not found to be corre-lated. However, for all the studied faults, except the third observable fault,correlation between these two process variables was noted. Therefore, an-other process variable should be used instead of one of them, in order toreveal new information about the changes in the time-domain patterns ofother process variables. To do so, suitable replacements should be obtainedfrom their own sorted clusters (i.e., Cs1 and Cs3). In Cs1, the second processvariables with the highest average sensitivity are v13 and v16, which alsoshow correlation to v11 in most of the fault-affected scenarios. Therefore,they are not suitable candidates. The next process variable is v19 with anaverage sensitivity of 0.39. The other alternative is to replace v11 instead ofv7. In Cs3, the second process variable with the highest average sensitivity isv22 with an average sensitivity of 0.26. Since S¯19 is higher than S¯22, v19 wasused as a replacement for v7, while v11 remains as a representative processvariable. Consequently, the representative variables are v19, v9, and v11,which are exactly the ones chosen using LASSO-based regression.Even though Elastic Net-based regression has better performance whenthe process variables are correlated, the corresponding representative processvariables should be checked to avoid choosing ones that are correlated insome fault-affected scenarios. On the contrary, the representative process382.3. CASE STUDYvariables resulting from using LASSO-based regression are never correlated.However, as it randomly disregards some of the correlated variables, duringthe process of calculating the sensitivities using the results from LASSO-based regression, some variables that are sensitive in reality may be deemedto be not much sensitive, and hence, may not be chosen as representativevariables. This may therefore reduce the accuracy of fault classification.2.3.6 Ablation StudiesTo study the influence of the stages and also the steps in each algorithmon the fault classification performance, some ablation experiments are con-ducted.− Removing stage I: If clustering is not performed, then instead of choos-ing the most fault-sensitive process variable in each cluster as a rep-resentative variable, without knowing the clusters and k, the processvariable with the highest average sensitivity, calculated using (2.13),should be used as the representative one. In the discussed case study,having v19 as the representative variable, the fault classification accu-racy is reduced to 71.5%. As can be seen in Fig. 2.10, fault 4 doesnot affect v19 much, as a consequence, the CNN cannot classify theno fault and fault 4 scenarios. Hence, it is beneficial to use k numberof representative process variables, each from a cluster, to extract asmuch possible information for fault classification.− Removing the step associated with using (2.15): In this way, the vec-tors containing the time series data of the representative variables areconcatenated rather than building 2D matrices using (2.15). Eventhough this is computationally less expensive, the faults that do notaffect most of the representative process variables, and their effectcan be observed in small number of consecutive samples, could not beclassified with high accuracies. In this case study, the effect of fault4 is only considerable in a small portion of v9, and if the mentionedstep is eliminated, the fault classification accuracy associated with thisfault would be 0% as all of them are classified as a fault-free/normalscenario.2.3.7 Fault Classification in Distillation ColumnTo evaluate the effectiveness of the proposed three-stage computationalprocedure in small-scale industrial processes, it is applied to a distillation392.3. CASE STUDY0 100 200 300 400 500Samples (every 3 minutes)100150200250300350400450 NormalFault1Fault2Fault4Fault5Fault6Fault7Figure 2.10: Different measurement scenarios (normal and fault-affected)for v19column with the aim of classifying its faults.Distillation columns are commonly used in chemical and process systemsengineering [86]. The model of the studied distillation column is accessiblethrough [87], and is also used and discussed in [86]. Its simplified schematic isdepicted in Fig. 2.11. To produce faults, a step change was applied to refluxflow (L), boilup flow (B), feed rate (F ), feed composition (zF ), and fractionof liquid in feed, respectively. Due to higher number of samples, imagesshould be built with higher dimensions. Using the first stage, the timeseries data obtained from distillate and bottom product composition (xDand xB), and condenser and reboiler holdup, were assigned to k = 2 numberof clusters. Afterwards, using LASSO-based regression and the sensitivitymetric in (2.12), the first two were detected as the sensitive process variablesto the generated faults. The mentioned process variables (i.e., xD and xB)with 2154 samples, are converted to 32 × 32 matrices using (2.15). Then,using (2.16), the matrices are concatenated and 32 × 32 × 2 images aregenerated. Generating 300 images using the data of the process variablesin each of the 5 fault-affected conditions as well as the fault-free condition,300 × 6 = 1800 images are fed to a CNN described in Table 2.7. Similar402.4. SUMMARYLCLCPCFigure 2.11: Simplified P&ID of the distillation columnto the previous case study, 20% of the images are used as the test set, andfrom the remaining 80%, 30% are used as the validation set and 70% as thetraining set.Applying the proposed three-stage computational procedure using onlytwo process variables, 5 fault-affected conditions and the normal conditionwere successfully classified with 99.44% accuracy for the test set. It canbe concluded that the proposed computational procedure is also effectivein classifying faults with reduced number of process variables in small-scaleindustrial processes, in addition to large-scale applications as previouslydemonstrated.2.4 SummaryThis chapter addressed the problem of fault classification in large-scaleindustrial processes with a reduced number of process variables. It proposeda computational procedure that uses process measurement data as inputand assumes no prior knowledge about the process, which consists of threecascaded stages. These stages are clustering of process variables using k-means clustering with soft-DTW, variable selection using LASSO-based orElastic Net-based regression methods as well as a sensitivity metric used412.4. SUMMARYTable 2.7: Types and outputs of the layers of the CNN model designed forthe distillation column case studyLayer type Output sizeConv-2D (32, 32, 64)Max-Pooling-2D (16, 16, 64)Conv-2D (16, 16, 128)Max-Pooling-2D (8, 8, 128)Dropout (8, 8, 128)Flatten (8192)Dense (64)Dense (6)in selecting the representative variables, and finally, converting the datafrom the selected variables to images and feeding them to a designed CNNto achieve fault classification. The Tennessee Eastman process was usedto demonstrate the effectiveness of the proposed computational procedure,where it was shown that it is possible that only 3 out of 22 process variablesbe used in fault classification, while obtaining high accuracy. Interestingfuture research directions include fault classification using data obtainedfrom sensors with different sampling times and finding an optimal numberof clusters (and consequently number of representative process variables).42Chapter 3Recommender System forHuman Operator Actions inResponse to Anomalies suchas Faults3.1 IntroductionHuman machine interface (HMI) platforms provide the means for humanoperators to observe the operation of industrial processes, and to accord-ingly make operational changes (e.g., opening a valve, running a pump, orchanging a setpoint/threshold) in response to process events. The operatoractions and process events data can be historized/logged through industrialautomation/control systems (e.g., in historians), and can therefore be read-ily accessed for data analytics. However, in the literature, there are only afew pieces of works that try to utilize this type of data to identify relationsbetween operator actions and process events.For example, the authors of [88] aimed to capture the knowledge of theoperators by creating industrial workflow models of logged events. In [89]and [90], operator actions with respect to single and multiple alarms werestudied, where data mining and visualization approaches were used (formore details on alarms, refer to [91] and [39]). The work in [92] suggesteda machine learning method for analyzing operator-machine interaction thatis capable of detecting the pattern of changes in interactions when thereare repetitive and similar sequences. In [93], an adaptive industrial HMIwas designed to adapt to skills and capabilities of an operator. In [94], theuse of deep learning and sequence mining for predicting future events wasproposed, with the aim of facilitating the work of operators. However, suchworks do not identify which actions to take during process operation (i.e.,when certain events occur), and when new consecutive events are detected.Identifying relations between operator actions and process events can be433.1. INTRODUCTIONbeneficial in enhancing process operation, for example, by creating a recom-mender system for human operators. Generally speaking and according to[95], a recommender (or recommendation) system is an information filteringsystem that tries to predict a user’s preference, and accordingly recommendsitems to the user. It utilizes actual and predicted ratings of the users in mak-ing the recommendations. Many organizations use recommender systems.For example, the Netflix recommender system uses a large amount of datafrom its users and deploys various algorithms in order to suggest content tothe users [96]. Amazon and YouTube also use recommender systems to rec-ommend merchandises and videos to their users, respectively [97]. However,it is believed no recommender system has been proposed in the literaturefor human operators in industrial automation applications.Thus, this thesis proposes a recommender system which uses historicalprocess data available through industrial control systems, including opera-tor actions and process events. It is based on a three-stage computationalprocedure, where in each stage an algorithm is designed as follows:(i) In the first stage, process data segments containing timestamped eventsare put into different clusters based on a k-medoids-based clusteringalgorithm, which uses a Smith-Waterman-based similarity index as op-posed to standard distance metrics. The k-medoids-based clusteringapproach is designed to be applicable for clustering time sequencesbased on their similarity, and the Smith-Waterman algorithm is mod-ified to be able to find the similarity indices between the process datasegments based on their timestamped events. Using the similarities be-tween the segments, computational steps are designed for finding theoptimal number of clusters and initializing the medoids. In addition,rather than hard-labeling, soft-labeling is used; instead of assigningthe segments to only one cluster, a score is defined so the segmentscan be assigned to different clusters. This clustering stage is designedto find similar segments for predicting the ratings in the recommendersystem, instead of using dissimilar/weakly similar ones, as well as toreduce the size of the dataset for the next stage, in order to lower thecomputational complexity and time.(ii) In the second stage, to predict the ratings of unrated operator ac-tions for the purpose of action recommendation, a modified collabora-tive filtering technique for time sequences is proposed, which not onlytakes into account the correlation between the ratings associated withthe segments, but also considers their structural similarity using the443.2. PROPOSED RECOMMENDER SYSTEMmodified Smith-Waterman algorithm and a defined score of belong-ing to each cluster. Each predicted rating is found by calculating theweighted average of the similarity and correlation indices, obtained us-ing the Smith-Waterman-based algorithm and the Pearson correlation,respectively. Additionally, thresholds are defined and used to preventthe effect of dissimilar segments on predicted ratings. Also, to predictratings associated with a new incoming segment during online imple-mentation, an approach is proposed which uses the weighted average ofthe ratings associated with similar segments. This addresses the coldstart problem of memory-based collaborative filtering techniques.(iii) In the third stage, a framework is proposed to generate two sets ofactions, containing proper actions to take and improper actions toavoid. To improve the accuracy of the recommender system, a relia-bility threshold is defined and used.This chapter is organized as follows: In Section 3.2, the proposed recom-mender system is discussed, followed by Section 3.3, where the effectivenessof the recommender system is examined for the operation of a power grid.Finally, concluding remarks are given in Section 3.4.3.2 Proposed Recommender SystemHistorized process data consist of consecutive events with correspondingtiming information, namely, in the form (‘event name’, ‘event occurrencetime’). Such data sequences can be split into smaller segments that capturespecific timestamped events requiring human operator actions (e.g., in re-sponse to consecutive events during a process startup or consecutive alarmsin an alarm flood).More specifically, extracting L ≥ 1 number of consecutive timestampedevents results in a segment, denoted as Ei, whose lth timestamped event isdenoted by the pairεil = (eil , til), l ∈ {1, · · · , L} (3.1)where eil and til are the event name and event occurrence time, respectively,and therefore Ei = {εi1, · · · , εiL}.Suppose there are κ number of segments and ι number of unique seg-ments. Then, the two following sets of sequences are constructed:E = {E1, · · · , Eκ} (3.2)Eu = {Eu1 , · · · , Euι } (3.3)453.2. PROPOSED RECOMMENDER SYSTEM3.2.1 Stage I: Clustering Process Data Segments UsingK-medoids-based and Smith-Waterman-basedAlgorithmsIn the first stage, the segments in Eu are assigned to k number of clustersdenoted as Cc, c ∈ {1, · · · , k}, based on their similarity. The reason to per-form clustering is twofold. First, it is desired to predict the missing ratingsassociated with a segment, based on structurally similar segments in thedataset. Second, since industrial automation and control system datasetscan contain a vast amount of data (e.g., for several years), performing com-putation on a portion of the dataset reduces computational burden, thusthe clustering will help in reducing the computational needs in the secondstage of the proposed procedure.Two well-known clustering methods are k-means and k-medoids, whichare easy to implement. Based on [98–100], k-means clustering works betterwhen having less number of data points, while k-medoids clustering performsmore efficiently when the number of data points is increased, and addition-ally it is more robust to outliers. In this thesis, a k-medoids-based clusteringis used for two reasons. First, the datasets are usually large, and second,the medoids are chosen from the segments in dataset without the need formaking artificial medoids or centroids. Further, in this thesis, instead ofusing data points, sequences with timestamped events are used.Both k-means and k-medoids clustering approaches use distance met-rics to assign data points to different clusters, by minimizing the sum ofintra-cluster distances. However, for sequences containing words or bio-logical data, where calculating the distance between sequences does notconvey an actual meaning, new concepts for distance/similarity betweensequences/segments were proposed. For instance, in [101], the edit distancewas proposed, which is defined by counting the minimum number of op-erations to transform a string to another one. The proposed method is aglobal alignment method, where the whole sequence/string is considered.However, in matching sequences, usually the interest is in a specific partof the string, or sequential parts of a sequence. Local alignment methodswere proposed to serve this goal. The authors in [102] tried to overcomethe shortcoming of edit distance by proposing a string block edit distance,however, this method was computationally expensive. In [103–105], meth-ods were proposed to find the distance between segments to be later usedfor clustering, however, timing information was not taken into account. Theuse of Smith-Waterman similarity index as a distance/similarity measurewas studied in [106], however, the authors used OPTICS for clustering, and463.2. PROPOSED RECOMMENDER SYSTEMalso did not consider timing information. The authors of [107] proposedmodifications to the Smith-Waterman algorithm to consider timing infor-mation, for the application of alarm management in industrial automation.In this thesis, the modified Smith-Waterman algorithm in [107], with someadditional modifications, is used to find the similarity between segments andthe medoids of each cluster.Smith-Waterman-based algorithmThe Smith-Waterman algorithm, first introduced in [108], is a local align-ment algorithm that matches contiguous subsequences/segments of two se-quences [109]. The length of any two segments can be equalized by insertinggaps in one of the segments or both. After this operation, each element inthe first segment has a corresponding element at the same position in thesecond segment. This procedure is known as local alignment of two seg-ments. The Smith-Waterman algorithm is used to find similarity betweenthe segments and best alignment of their elements.For instance, for two segments that contain consecutive elements Ea ={5, 2, 8, 1, 5, 4} and Eb = {3, 2, 1, 4}, there are many different possible align-ments, as gap insertion can be achieved in different ways. Two examplesare: (i) E˜a = {5, 2, 8, 1, 5, 4} and E˜b = {3, 2,−, 1,−, 4}, and (ii) E˜a ={5,−, 2, 8,−, 1, 5, 4} and E˜b = {3, 2,−,−, 1,−, 4,−}, where ‘−’ denotes aninserted gap. In the first example, (2, 2), (1, 1), and (4, 4) are matchingpairs, while in the second example, no matching pairs exists, which leads toprefering the first alignment. To better distinguish between possible align-ments, a scoring system is required, which can be based on defining differentscores with opposite signs for matches and mismatches, where a match in-creases the score while a mismatch decreases it. Denoting eai and ebj as twoelements, belonging to Ea and Eb, respectively, if neither eai and ebj is agap, then their similarity score function is defined ass(eai , ebj ) ={1, if eai = ebjµ, if eai ̸= ebj(3.4)where µ is the mismatch penalty. If either eai or ebj is a gap, the scorebecomes a gap insertion penalty, denoted by δ. The penalties µ and δ aredefined by the user, to reduce the final similarity index when mismatches orgaps happen, which can be chosen between 0 and −1.The shortcoming of the Smith-Waterman algorithm is its intolerance tothe ambiguity of the order of elements. To overcome this limitation, the473.2. PROPOSED RECOMMENDER SYSTEMmodified Smith-Waterman algorithm in [107] was proposed and used foralarm flood pattern matching. It takes into consideration the time thateach alarm happens, in addition to their order. Since timestamped processdata segments are considered in this thesis, the approach proposed in [107]is beneficial and is therefore applied in developing the first stage of thecomputational procedure.Remark: In [107], pattern matching for alarm floods was the objective,where segments containing alarm occurrence data were used as input data,and the best alignment between the segments was determined. However,in this thesis, the objective is calculating the similarity between segmentsfor clustering. In [107], timestamped events (i.e., alarms) were aligned, andnot all of them were important, as the objective was to find the alignedones. However, in this thesis, all the timestamped events are important asthey may motivate an operator to take an action. Therefore, it is crucialto modify the proposed algorithm in [107], and therefore, the procedure toobtain the similarity index, in order to make the algorithm applicable forclustering the segments rather than pattern matching.Consider two segments Ea and Eb as follows:Ea = {εa1 , · · · , εaLa}Eb = {εb1 , · · · , εbLb}which are of lengths La and Lb, respectively. E¯a and E¯b can be built byremoving uncommon events in Ea and Eb, as follows:E¯a = {ε¯a1 , · · · , ε¯aL¯a}E¯b = {ε¯b1 , · · · , ε¯bL¯b}which are of lengths L¯a and L¯b, respectively. Having η number of uniqueevents in common between E¯a and E¯b, a unique integer in {1, · · · , η} isassigned to each of the common events. Only the common events are con-sidered in this step, while the effect of uncommon ones are considered later.To find the similarity of Ea to Eb, a similarity table H(E¯a, E¯b) with size(L¯a+1)× (L¯b+1) is built, where the entries of its first row and column areset to zero, and the other entries, denoted by hi+1,j+1, i ∈ {1, · · · , L¯a} andj ∈ {1, · · · , L¯b}, are determined as follows:max{hi,j + s(ε¯ai , ε¯bj ), hi,j+1 + δ, hi+1,j + δ, 0}(3.5)Using the original Smith-Waterman algorithm, s(ε¯ai , ε¯bj)can be computedusing (3.4) if timing information is not considered. However, to consider483.2. PROPOSED RECOMMENDER SYSTEMthe event occurrence time in the segments, the similarity score functionis redefined using time distance and time weight vectors [107]. The timedistance vector indicates how far or close two events happen in terms oftime. It is modified and redefined for the lth event in segments E¯a and E¯b,respectively, asdal =[d1al , d2al, · · · , dηal]⊤, l ∈ {1, · · · , L¯a}dbl =[d1bl , d2bl, · · · , dηbl]⊤, l ∈ {1, · · · , L¯b}(3.6)where each entry dzal , z ∈ {1, · · · , η}, is defined asdzal = min1≤i≤L¯a{|tl − ti| : e¯ai = z} (3.7)Similarly, dzbl , z ∈ {1, · · · , η}, can be defined. The authors in [107] appliedtheir approach to the original segments (e.g., Ea and Eb), leading them touse a piecewise function to find the distance vector. This is while in thisthesis, the common events are extracted first, and the proposed algorithmis then applied to the new segments (e.g., E¯a and E¯b); hence, there is noneed to use a piecewise function anymore. The time weight vector appliesa time weighting function to the time distances, and for E¯a and E¯b, it isrespectively defined aswal =[w1al , w2al, · · · , wηal]⊤=[f(d1al), f(d2al), · · · , f(dηal)]⊤(3.8)wbl =[w1bl , w2bl, · · · , wηbl]⊤=[f(d1bl), f(d2bl), · · · , f(dηbl)]⊤(3.9)where f(·) : R 7→ R is the time weighting function, which according to [107]is a positive function, and is monotonically decreasing, with f(0) = 1 andf(∞) = 0. Two different time weighting functions were chosen for the twosequences. The first function is defined asf(d) = exp(−d22σ2)(3.10)where exp(·) is the exponential function, σ2 is the variance, and d is an entryof the distance vectors (e.g., dzal calculated in (3.7)). The second function isdefined asf(d) ={1, if d = 00, if d ̸= 0 (3.11)Aiming to calculate the similarity index between two segments, each func-tion is applied to the calculated time distances obtained using one of the493.2. PROPOSED RECOMMENDER SYSTEMsegments. For instance, to find the similarity table H(E¯a, E¯b), after obtain-ing the distance vectors using (3.6), the function in (3.10) is used with (3.8)when calculating the weight vector for E¯a, and the function in (3.11) is usedwith (3.9) to obtain the weight vector for E¯b. Then, the similarity score ofa pair of timestamped events (ε¯ai , ε¯bj ), as a linear combination of the matchscore and mismatch penalty, according to [107], is redefined ass(ε¯ai , ε¯bj ) = max1≤z≤η[wzai × wzbj](1− µ) + µ (3.12)The similarity index of E¯a to E¯b is equal to the highest value in H(E¯a, E¯b).Also, the optimal alignment can be found by the path resulting from movingbackward from the highest value until a value equal to 0 is found. Thehighest value in matrix H(E¯a, E¯b) is the similarity value of E¯a to E¯b, and isdenoted by S(E¯a, E¯b). Similarly, H(E¯b, E¯a) can be calculated using (3.10)with (3.9) and (3.11) with (3.8), and following the same procedure, to findthe similarity of E¯b to E¯a, denoted by S(E¯b, E¯a).Further, the effect of uncommon events is captured by the coefficientθ =L¯a + L¯bLa + Lb(3.13)which is 1 if all the events in Ea and Eb are common, and zero if Ea and Ebhave no events in common. The new similarity index between segments Eaand Eb, named as the structural similarity index, is therefore defined asS(Ea, Eb) = θ × max(S(E¯a, E¯b), S(E¯b, E¯a))min(L¯a, L¯b)(3.14)where its minimum and maximum values are 0 and 1. This is while in [107],the similarity index was obtained using:S(Ea, Eb) = max(S(Ea, Eb), S(Eb, Ea))min(La, Lb)(3.15)The following example is provided to better illustrate the use of theSmith-Waterman algorithm, and justifies the proposed modifications in thisthesis. Consider the following two segments:Ea = {(1, 10), (2, 15), (1, 27), (3, 45), (4, 60)}Eb = {(1, 52), (3, 59), (2, 70), (1, 76)}503.2. PROPOSED RECOMMENDER SYSTEMThen, E¯a and E¯b are obtained asE¯a = {(1, 10), (2, 15), (1, 27), (3, 45)}E¯b = {(1, 52), (3, 59), (2, 70), (1, 76)}where L¯a = 4 and L¯b = 4, and the number of unique common events betweenthese segments is η = 3.Using (3.6) and (3.7), the distance vectors, dal , l ∈ {1, · · · , 4}, and dbl ,l ∈ {1, · · · , 4}, are produced. For producing H(E¯a, E¯b), the function in(3.10) is used with (3.8), while the function in (3.11) is used with (3.9).Then, using (3.12) and (3.5), the entries of H(E¯a, E¯b) are found. Similarly,H(E¯b, E¯a) can be found. Then,H(E¯a, E¯b) =0 0 0 0 00 1 0.6 0.2 10 0.6 0.2 1.6 1.20 1 0.6 1.2 2.60 0.6 2 1.6 2.2H(E¯b, E¯a) =0 0 0 0 00 1 0.6 1 0.60 0.6 0.2 0.6 20 0.2 1.6 1.2 1.60 1 1.2 2.6 2.2where S(E¯a, E¯b) = 2.6 and S(E¯b, E¯a) = 2.6, and the minimum length ismin(L¯a, L¯b) = min(4, 4) = 4. Using (3.13), θ =4 + 45 + 4= 0.89, and using(3.14), the new similarity index (i.e., structural similarity index) is found asS(Ea, Eb) = θ × 2.64 = 0.57.If the modified Smith-Waterman algorithm proposed in [107] was used,and the modifications proposed in this thesis were disregarded, the similaritytables would beH(Ea, Eb) =0 0 0 0 00 1 0.6 0.2 10 0.6 0.4 1.6 1.20 1 0.6 1.2 2.60 0.6 2 1.6 2.20 0.2 1.6 1.4 1.8513.2. PROPOSED RECOMMENDER SYSTEMH(Eb, Ea) =0 0 0 0 0 00 1 0.6 1 0.6 0.20 0.6 0.4 0.6 2 1.60 0.2 1.6 1.2 1.6 1.40 1 1.2 2.6 2.2 1.8and therefore, the similarity index using (3.15) would be2.64= 0.65.Now, consider the aim is to find the similarity index between the follow-ing new segment and Eb:Ec = {(1, 10), (2, 15), (1, 27), (3, 45)}which is similar to Ea, except it does not have the last timestamped eventof Ea. As in this case, L¯c = Lc and L¯b = Lb, similar results are obtainedwhether using the approach in this thesis or that in [107], and the similarityindex is 0.65.When compared with the results for the similarity between Ea and Eb,it can be noted that the approach in [107] is not capable of distinguishingbetween Ea and Ec. However, the approach in this thesis provides differentsimilarity results for the two cases, and hence distinguishes between thesequences.K-medoids-based clustering algorithmThe structural similarity index obtained using the Smith-Waterman-based algorithm is utilized for both initializing the medoids and assigningthe segments to clusters Cc, c ∈ {1, · · · , k}.For initializing the medoids, k is first defined as the number of clus-ters. Then k number of medoids, denoted as Emc , c ∈ {1, · · · , k}, are se-lected from E in (3.2). Converging to local minimum/maximum is one ofthe shortcomings of initializing the medoids randomly. Various initializa-tion algorithms were proposed to overcome this shortcoming, such as [110].However, using an algorithm rather than randomized initialization causesmore computations. It is worth mentioning that as in this thesis the aim isto maximize the similarity between the segments within a cluster, reachinglocal maximum is our concern rather than local minimum. In addition, asin this thesis segments containing timestamped events are used rather thandata points, and as finding the similarity index is computationally moreexpensive compared to calculating distance metrics, randomized choice ofinitialized medoids and the algorithm in [110] are combined in this stage523.2. PROPOSED RECOMMENDER SYSTEMof the computational procedure, in order to perform clustering as fast aspossible while avoiding local maximum convergence. The steps to choosethe initial medoids are as follows. The first medoid Em1 is initialized byrandomly choosing one of the segments in E. The similarity index betweenEm1 and each of the non-medoid segments is calculated using (3.14). Thenon-medoid segment Ei with the least similarity index to the first medoid(i.e., minimum S(Ei, Em1 )) is chosen as the second medoid denoted as Em2 ,as the aim for the medoids is to be as dissimilar as possible. For initializingthe next medoids (i.e., Emc , c ∈ {3, · · · , k}), the similarity indices betweenall segments and each of the initialized medoids is calculated. Then, thenon-medoid segment which has the least average similarity index to thepreviously initialized medoids is chosen. To find the initial medoids, thefollowing equation is used:Emc = argminEi∑c−1n=1 S(Ei, Emn )c− 1 , c ∈ {2, · · · , k} (3.16)The initialization of the medoids are summarized in Algorithm 4 (steps 1–7).After the medoids Em1 , · · · , Emk are initialized, every segment from E in(3.2) is assigned to a cluster whose medoid results in the maximum similarityindex with the segment, when compared to all other medoids. To analyzewhether proper or improper medoids have been chosen, an incentive denotedby π, which stores the sum of intra-cluster similarity indices is defined asπ =k∑c=1∑Ei∈CcS(Ei, Emc ) (3.17)A larger π means that the medoids are chosen in a way that clusters con-tain more similar segments. Accordingly, clusters are best selected when thevalue of π is maximized. Therefore, in the next steps of the algorithm, one ofthe medoids swaps with a non-medoid segment, and all the aforementionedsteps are repeated, including assigning segments to different clusters andcalculating π. The new obtained π, denoted by πnew, is compared to theoriginal π calculated in the previous iteration. If πnew is greater, the medoidis updated, until a better medoid is found; else, the swap action is disre-garded and swap is made for another medoid or another segment. Thesecontinue in an iterative manner until no better π is found for k number ofmedoids. These steps are summarized in Algorithm 4 (steps 8–25).Remark: Instead of clustering segments from Eu in (3.3), all segmentsfrom E in (3.2) are used, to form better and more accurate medoids, since533.2. PROPOSED RECOMMENDER SYSTEMall segments in the dataset are used. In the final clusters, however, onlyunique segments are kept, to avoid repetitive and redundant calculationsin the next stage of the computational procedure. So, when π reaches itsmaximum value, and the medoids are chosen, all the segments from Eu areassigned to the clusters Cc, c ∈ {1, · · · , k}. As soft labeling is used insteadof hard labeling, a unique segment Eui , i ∈ {1, · · · , ι}, can be assigned tomore than one cluster, for which a labeling score is defined. The probabilityof Eui belonging to Cc is defined asP CcEui =S(Eui , Emc )k∑n=1S(Eui , Emn )(3.18)A threshold γP is then defined and used to determine the cluster(s) thata unique segment belongs to, given an acceptable probability. A row-normalized matrix denoted by P ∈ Rι×k is built using the belonging proba-bilities of unique segments from Eu to each cluster (i.e., P CcEui > γP ), whereeach entry pi,c is defined aspi,c =P CcEuik∑n=1P CnEui, P CcEui , PCnEui> γP , i ∈ {1, · · · , ι} (3.19)For each entry pi,c, where PCnEui≤ γP , 0 is placed, meaning that Eui is notconsidered as a segment belonging to Cc. To find the clusters a uniquesegment Eui belongs to, the non-zero entries of the corresponding row in Pcan be extracted. As in the second stage the segments in the clusters are usedfor predicting the missing ratings, by using γP , weakly similar/dissimilarsegments are prevented from affecting the predicted ratings, and thereforethe recommendations. Assigning unique segments to k clusters as well asproducing matrix P are summarized in Algorithm 4 (steps 26–34).It should be noted that to choose the optimal number of clusters k, var-ious methods and algorithms were proposed in the literature, one of whichis the well-known elbow method. The optimal number of clusters can bedetermined by considering the increase k causes to intra-cluster similarityindices, and by aiming to choose a k so that adding clusters does not con-tribute much to forming better clusters [111]. In this thesis, the optimalnumber of clusters is determined by choosing the cutoff point in a generatedπ–k plot, as will be presented in Section 3.3.543.2. PROPOSED RECOMMENDER SYSTEMAlgorithm 4: Stage I: Clustering process data segments using k-medoids-based and Smith-Waterman-based algorithmsInput: E and EuγP , k, δ, and µ (defined by user)Output: P and Cc, c ∈ {1, · · · , k}1: Choose Em1 randomly from E in (3.2)2: for (2 ≤ c ≤ k) do3: for (1 ≤ i ≤ κ) do4: Calculate S(Ei, Emc ) using (3.14)5: end for6: Find Ei with the minimum average similarity index using (3.16)7: end for8: Define Flag = True9: while Flag == True do10: for (1 ≤ i ≤ κ) do11: Calculate S(Ei, Emc ), c ∈ {1, · · · , k}12: Assign Ei to cth cluster with maximum S(Ei, Emc )13: end for14: Calculate π using (3.17)15: Swap one of the medoids16: Repeat steps 10− 1317: Calculate the new π (πnew)18: if πnew > π then19: π = πnew20: Update the medoids21: end if22: if π reached its maximum then23: Flag = False24: end if25: end while26: for (1 ≤ i ≤ ι) do27: for (1 ≤ c ≤ k) do28: Calculate P CcEui using (3.18)29: if P CcEui > γP then30: Assign Eui to Cc31: end if32: end for33: end for34: Produce P using (3.19)553.2. PROPOSED RECOMMENDER SYSTEM3.2.2 Stage II: Prediction of Missing Ratings for OperatorActions Using a Collaborative Filtering-BasedAlgorithmConsecutive events can motivate human operators to take correspondingactions through HMI screens. Accordingly, the operators can rate the ef-fectiveness of their actions in response to the events. Rating actions can beeither done manually, when the operators enter their rating using the HMI,or automatically, by defining a measurable parameter such as the cost thatevents/actions cause to the system. The latter not only leads to more pre-cise ratings, as human factors and taste differences do not affect the ratings,but also reduces the workload on human operators as it eliminates the needfor them to manually rate the actions they take.Thus, segments of timestamped events can be associated with oper-ator actions and given ratings. Consider a unique segment Eui ∈ Eu,i ∈ {1, · · · , ι}, one of α possible actions denoted by aj , j ∈ {1, . . . , α},and a corresponding rating denoted by ri,j . Then, a rating matrix denotedby R ∈ Rι×α is built to include all available ratings ri,j .Considering a new incoming segment in real-time denoted by Ein, thegoal of this stage is to examine ratings for actions associated with Ein, sothat recommendations can be made for what actions to take and avoid. Ifthe rating to any action aj , j ∈ {1, · · · , α}, is missing (e.g., rin,j = NaN),a predicted rating denoted by rˆin,j should be determined. To achieve this,collaborative filtering is used. Generally speaking, having different usersand items, and assuming that two users who have rated some number ofitems similarly would also rate other items similarly, collaborative filteringtechniques aim to predict ratings that the target users would give to unrateditems, and based on the predictions, recommend items to the target users[112–114].Memory-based collaborative filtering techniques include algorithms thatrely on correlation between items/users to find recommendations that can beproposed [115, 116]. Such techniques can be easily implemented, as addingnew data is not problematic, and there is no need to study content of theitems. In case of using collaborative filtering for recommending actions tohuman operators in industrial automation, instead of users, segments con-taining timestamped events are used, and instead of items, actions are used.It should be noted that unlike the common approaches for using collabora-tive filtering for data points (namely, items and users), time sequences areused in this thesis. Therefore, some modifications are necessary, and thecommon recommender systems cannot be used.563.2. PROPOSED RECOMMENDER SYSTEMCombining the Smith-Waterman-based structural similarity index andthe Pearson correlation, structural similarity of segments as well as corre-lation based on action ratings can be extracted, resulting in the modifiedcollaborative filtering technique, which is the basis of this stage. Three casesmay happen for an incoming segment Ein, as follows:− Case (i): Ein is one of the segments in Eu (i.e., Ein ∈ Eu).− Case (ii): Ein is not a segment in Eu, however, its similarity indexto at least one of the segments is greater than a predefined structuralsimilarity threshold γS (i.e., Ein /∈ Eu but ∃i|S(Ein, Eui ) > γS , i ∈{1, · · · , ι}).− Case (iii): Both cases (i) and (ii) do not hold, and Ein shows insuf-ficient structural similarity to historical segments. In this case, noactions can be recommended.Consider two unique segments Eua and Eub , and the set A˜ which containsthe action numbers that followed both segments (i.e., A˜ ⊆ {1, · · · , α}). Also,consider r¯a and r¯b as the average ratings of actions aj , j ∈ A˜ associated withEua and Eub , respectively. The Pearson correlation between Eua and Eub iscomputed as follows [113]:P(Eua , Eub ) =∑j∈A˜(ra,j − r¯a) (rb,j − r¯b)√∑j∈A˜(ra,j − r¯a)2√∑j∈A˜(rb,j − r¯b)2(3.20)where ra,j and rb,j are the ratings for action aj associated with Eua and Eub ,respectively.Since the structural similarity index using the Smith-Waterman-basedalgorithm is in the range [0, 1], while the Pearson correlation is in the range[−1, 1], the former is scaled to the range [−1, 1], and is named the scaledstructural similarity index, defined asS¯ = (1− (−1))× (S)− 1 = 2S − 1 (3.21)Then a merged similarity index between the segments Eua and Eub is definedasM(Eua , Eub ) = ζ × S¯(Eua , Eub ) + (1− ζ)× P(Eua , Eub ) (3.22)where 0 ≤ ζ ≤ 1 weighs the effect of the two similarity measures. By using(3.22), both structural similarities between the segments and their similarity573.2. PROPOSED RECOMMENDER SYSTEMbased on the action ratings are considered when determining the predictedratings. If ζ = 0.5, equal weights are given to the Smith-Waterman-basedsimilarity index and Pearson correlation, while ζ = 0 and ζ = 1 remove theireffect, respectively.Remark: It should be noted that structural similarity index (S), Pearsoncorrelation index (P), and merged similarity index (M) calculations arecommutative.Case (i)Any missing ratings for Eui should be predicted. To do so, the ith row ofrating matrix R is examined. For each action aj , j ∈ {1, · · · , α}, associatedwith Eui which is missing a rating, rˆi,j is computed. By extracting the non-zero entries of the ith row of P, the clusters that Eui belongs to are found.Then, for each of these clusters Cc, all the unique segments Eun are foundso that pn,c > 0, and the merged similarity M(Eui , Eun) is calculated using(3.22).To avoid the effect of weakly similar segments on the final recommenda-tions, only merged similarity values greater than a merged similarity thresh-old γM are considered (i.e.,M(Eui , Eun) > γM). The predicted rating for ajderived by the segments in cth cluster (i.e., Cc), is defined as follows:rˆci,j =∑Eun∈Cc|rn,j ̸=NaN,M(Eun ,Eui )>γMM(Eun, Eui )× rn,jT c(3.23)where T c is the number of segments Eun ∈ Cc, such that rn,j ̸= NaN andM(Eun, Eui ) > γM. These steps are summarized in Algorithm 5 (steps 1–17). If T c = 0, then that cluster is not considered in the next step.To combine the predicted ratings in (3.23) over different clusters, theprobability of Eui belonging to each cluster is used as a weight, and theweighted average of the predicted ratings rˆci,j is calculated. Hence, the pre-dicted rating of aj associated with Eui is determined byrˆi,j =k∑c=1pi,c × rˆci,j (3.24)where in this case, rˆi,j = rˆin,j . These steps are summarized in Algorithm 5(steps 18–22). Considering the unrated action aj , if Tc for all the clustersthat Ein belongs to is 0, then, the rating for aj cannot be predicted. In alarger dataset, with more ratings and more similar segments to Ein, this is583.2. PROPOSED RECOMMENDER SYSTEMless probable to happen. Hence, it can be stated that lack of sufficient datacan prevent predicting the rating for some of the unrated actions.Algorithm 5: Stage II: Prediction of missing action ratings for anincoming segment Ein = EuiInput: R and Euζ and γM (defined by user)Cc, c ∈ {1, · · · , k}), and P (from Algorithm 4)Output: Predicted ratings of actions associated with Eui1: for 1 ≤ c ≤ k do2: if pi,c > 0 then3: for each unrated action aj associated with Eui do4: T c = 05: s = 06: for Eun|pn,c > 0 do7: Find P(Eun, Eui ) using (3.20), and S¯(Eun, Eui ) using (3.14)and (3.21)8: Find M(Eun, Eui ) using (3.22)9: if M(Eun, Eui ) > γM then10: s+ =M(Eun, Eui )× rn,j11: T c+ = 112: end if13: end for14: rˆci,j =sT c15: end for16: end if17: end for18: for each unrated action aj associated with Eui do19: for 1 ≤ c ≤ k do20: rˆi,j+ = pi,c × rˆci,j21: end for22: end forCase (ii)It is possible that a new incoming segment may not be found in thehistorical dataset, and therefore, none of the actions aj , j ∈ {1, · · · , α},are rated. However, the recommender system should still be able to makerecommendations. This is similar to a situation that the aim is to suggest593.2. PROPOSED RECOMMENDER SYSTEMan item to a user who has not rated any items, known as the cold startproblem. In this thesis, to tackle this problem and predict the ratings forall actions aj , j ∈ {1, · · · , α}, associated with the segments that are notstored in the dataset, modifications to the collaborative filtering techniqueare proposed, while adding new steps to it. To only allow segments whichare structurally highly similar to the incoming segment Ein contribute to thepredicted ratings, a structural similarity threshold γS is defined. For all thesegments in Eu which satisfy this condition (i.e., ∀Eun ∈ Eu|S(Eun, Ein)>γS),missing ratings are predicted using Algorithm 5. Then, the ratings for Einare predicted usingrˆin,j =∑Eun∈Eu|S(Eun ,Ein)>γSS(Eun, Ein)× rn,j∑Eun∈Eu|S(Eun ,Ein)>γSS(Eun, Ein)(3.25)3.2.3 Stage III: Recommending Actions to the HumanOperatorHaving the actual and predicted ratings, two complementary sets R andR′ are constructed, which contain proper and improper actions (namely,those to take and those to avoid), respectively.To distinguish between the contents of R and R′, a threshold γo is de-fined. If a rating is above γo, it is deemed beneficial, while a rating belowγo indicates the action should be avoided. Since predicted ratings are notexact, a range [−γreliable + γo, γreliable + γo] is defined as a range of relia-bility. For an action with predicted rating that falls within this range, norecommendations should be made. Choosing γreliable very close to γo mayreduce the reliability of the recommended actions, and choosing a very largeγreliable may cause the recommender system to not recommend actions thatshould have been recommended. Consequently, for recommending actionaj when incoming segment Ein is detected, if rin,j exists in the datasetand rin,j > γo, the set R appends aj (i.e., R = {R, aj}). Otherwise,the set R′ appends aj (i.e., R′ = {R′, aj}). If rin,j does not exist in thedataset (i.e., rin,j = NaN), the same procedure is followed for rˆin,j , unlessrˆin,j ∈ [−γreliable + γo, γreliable + γo].To verify the outcome of the proposed recommender system, the obtainedresults are put into 6 categories as follows:− True Positive (TP ): Number of actions in R that are proper actions603.3. CASE STUDYin reality− True Negative (TN): Number of actions in R′ that are improper ac-tions in reality− False Positive (FP ): Number of actions inR that are improper actionsin reality− False Negative (FN): Number of actions in R′ that are proper actionsin reality− No Decision (ND): Number of predicted ratings within the reliabilityrange− No Rating (NR): Number of missing ratings which could not be pre-dicted due insufficient dataThe accuracy of the recommendations is therefore calculated accordingtoTP + TNTP + TN + FP + FN(3.26)and the accuracy of the recommendation system, resulting from consideringNR as well, is calculated according toTP + TNTP + TN + FP + FN +NR(3.27)As wthe thresholds γP , γM, and γS are increased, the first accuracy increaseswhile the second one decreases. This is because an increase in the thresh-olds causes more restrictions. Hence, the accuracy of the recommendationsincreases, which can be observed by an increase in (3.26). In contrast, assmaller number of segments can satisfy the threshold conditions, more num-ber of missing ratings remain unpredicted, resulting in not being able torecommend some actions, which causes an increase in NR, and therefore adecrease in the accuracy in (3.27).The framework of the proposed recommender system is depicted in Fig. 3.1.3.3 Case StudyIn power grids, various reasons cause powerline disconnections, for exam-ple, unpredictable disturbances, such as lightening, or maintenance that re-quires a powerline to be disconnected from the grid [117]. Human operators613.3. CASE STUDYIII) Recommending Actions to the Human OperatorFindFind similarityusing Smith-Waterman-basedsimilarity indexand Pearson correlationNaNNaN NaNIncoming SegmentDatasetI) Clustering Process Data Segments Using K-medoids-based and Smith-Waterman-based AlgorithmsCase (ii): FindYesNoCase (i): FindII) Prediction of Missing Ratings for Operator Actions Using aCollaborative Filtering-Based AlgorithmFindmissinge.g.:YesNo No recommendation can be madeFigure 3.1: Framework of the proposed recommender system, with the three-stage computational procedure623.3. CASE STUDYare a critical part of power grid operation, and they keep the desired perfor-mance by making time-sensitive decisions and taking proper actions [118].In this application, a mistake can cause or fail to prevent cascading failures,which is one of the main causes of large blackouts [119]. Hence, developinga decision support tool to assist human operators can be of paramount im-portance, and the use of proposed recommender system in this thesis canbe advantageous for operators in power grid applications.Re´seau de Transport d’E´lectricite´ (RTE), France’s electricity transmis-sion system operator, initiated “Learning to Run a Power Network (L2PRN)”competition with the aim of determining the potential of artificial intel-ligence and machine learning in support of control rooms of power grids[120]. Those who are interested in this area are provided with an easy-to-use framework known as Grid to Operate (Grid2Op), which according to itstechnical documentation in [121], is suitable for implementing any kind ofcontroller to avoid undesired performances or blackouts in power grids. Inthis thesis, the framework is used for evaluating the proposed recommendersystem.In this case study, l2rpn–case14–sandbox is used, which is an availableenvironment in the Grid2Op framework, and is very similar to the IEEE 14bus power grid, with one more generator. Schematic of the grid is depictedin Fig. 3.2, which consists of 14 substations, 20 powerlines, 6 generators,and 11 loads. One of the important parameters for the power grid operatorsis the observed current flow in each powerline divided by its thermal limit.If this exceeds a limit, the powerline may become disconnected. Further, 6types of events/actions are defined, which are described in Table 3.1.Table 3.1: Definition of the events/actionsDescription NotationForce connection of powerline i L1iForce disconnection of powerline i L0iToggle the status of powerline i LtiAssign bus 1 to elements on substation i B1iAssign bus 2 to elements on substation i B2iToggle the bus of elements on substation i BtiThe produced dataset contains consecutive powerline disconnections (i.e.,L0i ) as the events in E, but actions are chosen from other types, such as forceconnection of a powerline and assigning a bus to the elements (e.g., loads633.3. CASE STUDYdisconnectedpowerlinesubstationloadgeneratorbus 1bus 2no bus16 1545718819322 11591310611 10 10712 12914 118 13 58 9 7 32 44350 0117 1 0 4 6 3 2 0628.61%21.76%17.05%39.32%42.85%45.10%35.84%28.64%12.67%38.63%48.14%50.91%78.51%66.14%50.96%41.32%28.31%30.04%61.66%40.54%Figure 3.2: The l2rpn–case14–sandbox power grid when all the powerlinesare connected. The powerline numbers are in black, while their ratio ofcurrent flow to thermal limit are in purple. A powerline arrow is orange orblue when the ratio is relatively high or low, respectively.and generators) on a substation as well. All the events are historized/loggedwith the time of their occurrence.Assume the human operator detects an incoming segment Ein whichconsists of consecutive pairs of event name and event occurrence time, suchthatEin = {(L014, 5), (L012, 10), (L00, 15)} (3.28)The ratio of current flow to thermal limit for each of the powerlines afteroccurrence of Ein can be observed in Fig. 3.3. It can be seen that powerlines14, 12, and 0 are no longer connected. As more powerlines, such 1, 6, 11, 15,16, 18, and 19, have higher current flow, the human operator may want totake an action to avoid overflows. For instance, the operator may decide toset bus 2 to the elements on substation 3 (i.e., ai = B23). The capacities andthe status of the lines after taking this action are depicted in Fig. 3.4. If theoperator decides to take action aj = L07, which causes force disconnectionof powerline 7, then a blackout occurs due to overflows, as can be seen inFig. 3.5. This illustrates the importance of knowing proper and improperactions, which can be displayed to the human operator.Remark: In this case study, the ratings are defined by a reward. TheGrid2Op framework defines various types of rewards to evaluate the effect643.3. CASE STUDY4578322 11913106107129118532 44350 010622.5%15.43%20.30%64.74%60.45%59.75%64.43%79.65%66.88%0%0%8.67%46.68%16.74%37.43%14.60%62.80%0%21.63%84.75% disconnectedpowerlinesubstationloadgeneratorbus 1bus 2no busFigure 3.3: The l2rpn–case14–sandbox power grid after the occurrence ofEin defined in (3.28), with disconnection of powerlines 14, 12, and 0.4578322 11913106107129118532 44350 010622.25%15.51%20.22%64.99%60.69%59.60%64.51%80.50%67.39%0%0%7.80%46.68%16.74%36.99%14.36%62.80%0%21.86%84.50% disconnectedpowerlinesubstationloadgeneratorbus 1bus 2no busFigure 3.4: The l2rpn–case14–sandbox power grid after taking ai = B23following Ein. This slightly decreased ratio of current flow to thermal limitfor some powerlines, such as powerline 5, 6, and 8.653.3. CASE STUDY4578322 11913106107129118532 44350 01060%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%disconnectedpowerlinesubstationloadgeneratorbus 1bus 2no busFigure 3.5: The l2rpn–case14–sandbox power grid after taking aj = L07following Ein. This causes a blackout.of each event/action on the studied power grid, as a consequence, eachevent/action is associated with a reward. In this thesis, a reward has beenused which is inversely proportional to the cost of an event/action for thegrid. The difference between the obtained reward when an action is taken,and the reward associated with the last event of a segment, is used as therating for the action. For example, in the mentioned example with ai, thedifference between the reward associated with taking action ai after theoccurrence of timestamped segment in (3.28), is 2.0037, and accordingly,rin,i = 2.0037. Similarly, the rating associated with taking action aj afterthe occurrence of Ein in (3.28), is −806.7485. Alternatively, an operatorcan manually enter a rating as previously discussed. Since the rating forai associated with Ein is positive, it can be concluded that it causes a bitof improvement to the system. However, aj deteriorates the performanceof the grid after the occurrence of Ein in (3.28), as the reward change is anegative number with a high magnitude, as demonstrated by the blackoutshown in Fig. 3.5.The aim of the proposed recommender system in this case study is torecommend actions to the operator, that boost the reward of the grid, andwarn them about the actions that cause reward reduction. Hence, γo = 0 canbe specified for this case study, to distinguish between positive and negativechanges of rewards.663.3. CASE STUDY2 3 4 5 6 7 8 9Number of clusters90100110120130140Sum of intra-cluster similarity indicesFigure 3.6: By plotting π with respect to k, k = 3 is chosen as the optimalnumber of clusters. It can be observed that after k = 3, the slope of theconnecting line decreases considerably, and it is the knee/elbow shaped pointof the plot.Various sequences can be produced using the Grid2Op framework forthe l2rpn–case14–sandbox environment. Consequently, numerous segmentsof timestamped events can be extracted. Also, a vast number of actionscan be taken and studied for each of the segments. As extracting all thepossible events and actions is cumbersome, in this thesis, example segmentsand actions are considered, where a dataset is prepared that contains 12unique types of actions, 17 unique segments, and 166 segments followedby an action (i.e., α = 12, ι = 17, and κ = 166, respectively). Hence,12 × 17 − 166 = 38 segments in the dataset are not followed by an action(i.e., there are 38 missing ratings), whose respective entries in the ratingmatrix R are NaN.To implement the three-stage computational procedure, first the seg-ments are clustered. The optimal number of clusters k is chosen to be 3,by finding the cutoff point in the π − k plot, as can be observed in Fig. 3.6.166 segments in E and 12 segments in Eu are given as inputs to Algorithm1. Using the segments in E, the medoids are found, with µ = −0.6 andδ = −0.4 defined in [107]. Then, the segments in Eu are assigned to theclusters, by calculating the scores using (3.19) and by considering γP = 0.2.For case (i) in Section 3.2, assume the incoming segment is (3.28), and is673.3. CASE STUDYamong the segments in the dataset, such that Ein ∈ Eu. The correspondingrow of matrix P is [0, 0.2, 0.8]. Hence, Ein belongs to C2 and C3 with aprobability score equal to 0.2 and 0.8, respectively. Also, according to thedataset, the corresponding row of the rating matrix R is as follows:a1 a2 a3 a4Ein NaN NaN −2.5524 −0.2687a5 a6 a7 a8Ein 2.0037 NaN −0.2687 2.0037a9 a10 a11 a12Ein −2.6032 NaN 1.9520 −2.5306To predict the missing ratings for a1, a2, a6, and a10, ζ = 0.5 andγM = 0.2 are used, and the steps in Algorithm 5 are followed. The obtainedpredicted ratings are as follows: rˆin,1 = −2.9833, rˆin,2 = −573.9124, rˆin,6 =−1.2821, and rˆin,10 = −1.6716.In the third stage, where the recommendations should be made, consid-ering the actual ratings for Ein found in the dataset as well as the predictedones, and defining γreliability = 0.1, the sets of actions R and R′ are gener-ated asR = {a5, a8, a11}R′ = {a1, a2, a3, a4, a6, a7, a9, a10, a12}The actual ratings for the unrated actions associated with Ein are −1.5971,−806.7485, −2.5306, and−0.2687, respectively. These numbers are obtainedby simulation. For example, according to the dataset, action a2 correspondsto force disconnection of powerline 7, and it can be observed that boththe actual and predicted ratings (found using the simulation and Algorithm5, respectively), produce a negative rating with a large magnitude to thisaction, indicating that the action should be avoided. This is because asshown in Fig. 3.5, a blackout happens if powerline 7 is disconnected afterthe occurrence of Ein in (3.28). Further, comparing the actual and predictedratings, it can be observed see that all the recommendations are correct.Considering all 38 missing ratings in the dataset, by using the proposedthree-stage computational procedure, the predictions are categorized, andit is found that TP = 12, TN = 15, FP = 1, FN = 4, ND = 3, andNR = 3. Based on the obtained values and using (3.26), an accuracy of84.37% is obtained, and based on (3.27), an accuracy of 77.14% is obtained,which are relatively high. By changing ζ, the effect of the Smith-Waterman-based similarity index and the Pearson correlation measure change. The683.3. CASE STUDYTable 3.2: The effect of changing ζ on accuracyζ Accuracy using (3.26) Accuracy using (3.27)0.5 84.37% 77.14%0.1 72.97% 72.97%0.8 88.88% 63.15%calculated accuracies with different ζ values are provided in Table 3.2. Ifonly the similarity index is used, many ratings remain unpredicted. If onlyPearson correlation is used, same actions to some segments which are notsimilar in structure, but are similar in some of the ratings are suggested,which causes accuracy reduction. Hence, as suggested before, with usingζ = 0.5, the proposed recommender system would perform better.For case (ii) in Section 3.2, consider an incoming segment asEin = {(L−12, 0:05), (L−14, 0:10), (L−13, 0:15), (L−0 , 0:20)}which does not appear in the dataset. Using Algorithm 5, and the ad-ditional step discussed for case (ii), by defining γS = 0.4, the proposedrecommender system is able to predict all ratings for this segment. Theproposed recommender system results in generating sets R = {a5, a8, a11}and R′ = {a1, a2, a3, a4, a5, a6, a7, a9, a10, a12}. Also, based on simulation,the obtained ratings area1 a2 a3 a4Ein −6.1436 −808.7521 0.8948 −4.7228a5 a6 a7 a8Ein 4.0339 0.9145 −4.7228 4.0339a9 a10 a11 a12Ein 0.8426 −4.7225 3.9822 0.9145which indicate that a3, a5, a6, a8, a9, and a11 are proper actions to takewhile the others should be avoided. Based on the results obtained from theproposed recommender system and the ones above, it can be concluded that66.66% of the final recommendations are correct. a3, a6, a9, and a12, areamong the actions in R′, meaning that the recommender system suggeststhe operator to avoid taking these actions, while in reality, they do not harmthe system, which results in FN = 3. As can be observed, the improvementin the reward in the presence of these actions is not significant, as the mag-nitude of the reward is small. Hence, they do not cause a considerable boost693.4. SUMMARYin the performance of the system. However, the recommender system didnot recommend any actions that would harm the system (namely, FP = 0),which is critical in industrial automation and control systems.3.4 SummaryIn this chapter, a recommender system was proposed which is capable ofproposing actions to human operators in industrial automation and controlsystems. Such recommender system has never been investigated or proposed,to the best knowledge of the author. Utilizing k-medoids-based clusteringand a Smith-Waterman-based similarity approach, a modified collaborativefiltering technique, and a recommendation framework, a three-stage compu-tational procedure was designed. Based on data generated using a powergrid simulation environment, the applicability and effectiveness of the pro-posed recommender system was examined. Interesting future research di-rections include comparisons of possible actions based on their effect onprocess operation and finding ways to optimize the values of the designparameters/thresholds.70Chapter 4Conclusion and FutureWorks4.1 ConclusionThe thesis was dedicated to the potential usages of machine learning anddata mining tools and algorithms in industrial control systems. The focusof the work was on (i) fault classification with reduced number of processvariables to better manage the performance of the system, and (ii) proposinga novel recommender system capable of proposing proper actions to thehuman operator with the aim of reducing the workload on the operators,and avoid undesired situations which happen due to operator’s mistakes.Specifically, in the second chapter, the fault classification procedure,comprised of three stages, was discussed, where in the first stage, the processvariables were put into clusters using k-means algorithm with soft-DTW;then, the fault sensitive variables (i.e., representative process variables) werefound using PCA and LASSO/Elastic Net-based regression, based on a newsensitivity metric; and finally, the data of the representative process vari-ables were converted to images and fed to a CNN with the purpose of faultclassification. In the third chapter, a recommender systems was proposedwhich is suitable for human operators in industrial automation and controlsystems, and is comprised of a three stage computational procedure. Inthe first stage the historized timestamped sequences were put into differentclusters using a k-medoid-based algorithm based on a Smith-Waterman-based similarity index; the missing action ratings were predicted using amodified collaborative filtering on the basis of Pearson correlation and theSmith-Waterman-based algorithm; and finally, a framework for suggestingthe actions was discussed.With using the proposed methods in this thesis, a fault can be detectedand classified in an industrial system where the data of some of the processvariables is not available, or reducing computational expenses is preferred.Furthermore, it is possible to focus on ensuring the health of only the sensors714.2. FUTURE WORKassociated with the most fault-sensitive process variables in a large-scaleindustrial system rather than all the sensors, thus reducing costs.In the case of occurence of an anomaly, such as a fault, various alarmsmay arise, which leads to a timestamped segment which may require at-tention from a human operator. Using the proposed recommender system ahuman operator can decide which actions are proper to take, and which onesshould be avoided. Also, in case of occurrence of faults or other anomalies,the effects of which are not observable in the output of the sensors, stillsequences of events such as alarms may be generated, and a recommendersystem can be helpful in the process of a human operator’s decision-making.4.2 Future WorkEven though this thesis was successfully able achieve the goal of clas-sifying the faults in an industrial system with reduced number of processvariables and proposing a recommender system to suggest corrective actionsto human operators accordingly, there are still several interesting topicswhich are worth being investigated and studied. Some interesting futureresearch work are as follows:− The current fault classification methodology is applicabale in indus-trial processes where the number of recorded samples and the samplingtime of the measurement units are the same. Hence, proposing modifi-cations to the methodology to make it capable of classifying the faultsin industrial systems where the sampling time of the measurementunits is different, can be beneficial.− The recommender system generates two sets including proper actionand improper ones, without any preferences. Proposing a procedureto sort the recommended actions in these two sets based on their effecton the process operation, can be an interesting future work.− In this thesis, the Tennessee Eastman process was used as a case studyfor fault classification and the effectiveness of the recommender systemwas studied on a power grid. Applying both the methodologies forfault classification and action recommender system as a single piece ofwork, to same industrial systems can better show their link, which isworth being investigated in the future.− Using the proposed fault classification methodology, the faults theeffects of which cannot be observed in the time series data of the724.2. FUTURE WORKprocess variables, known as unobservable faults, cannot be classified.The effectiveness of the action recommender system in case such faultshappen, is an interesting future work.− The deep learning architecture for classifying the faults was trainedon data with correct labeling. Studying the effect of data with incor-rect labels on the final fault classification accuracy can be studied tobetter investigate the robustness of the deep learning architecture tomislabeled data.− Currently, the value of the design parameters/thresholds for the pro-posed recommender system are defined by the user. Automating thisprocess can be valuable when employing the action recommender sys-tem into real-world industrial systems.73Bibliography[1] Q. Zhao, Y. Zhu, D. Wan, Y. Yu, and X. Cheng, “Research on thedata-driven quality control method of hydrological time series data,”Water, vol. 10, no. 12, 2018. → pages 2[2] C. A. Escobar and R. 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Thesis/Dissertation
eng
Electrical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Al-Dabbagh, Ahmad
Data-driven fault classification and operator action recommendation in industrial control systems
Text