Data-driven fault classification and operator action recommendation in industrial control systems
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Yassaie, Negar
issued: 2024-05-31T07:00:00Z
issued: 2023
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: 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) presentin…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/84718
note: 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 clas…
has type: Thesis/Dissertation
date issued: 2023-09
is shown at: 10.14288/1.0432740
language: eng
degree discipline: Electrical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Al-Dabbagh, Ahmad
type: Text
identifier URI: http://hdl.handle.net/2429/84718