Application of machine health monitoring in design optimization of mechatronic systems
department or school: Applied Science, Faculty of
department or school: Mechanical Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Xia, Min
issued: 2017-09-29T15:59:48Z
issued: 2017
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Mechatronic systems are widely used in modern manufacturing. The key machinery of a manufacturing system should be reliable, flexible, intelligent, less complex, and cost effective, which indeed are distinguishing features of a mechatronic system. To achieve these goals, continuous or on-demand design improvements should be incorporated rapidly and effectively, which will address new design requirements or resolve existing weaknesses of the original design. With the advances in sensor technologies, wireless communication, data storage, and data mining, machine health monitoring (MHM) has achieved significant capabilities to monitor the performance of an operating machine. The extensive data from the MHM system can be employed in design improvement of the monitored system. In that context, the present dissertation addresses several challenges in applying MHM in design optimization of a mechatronic system. First, this dissertation develops a systematic framework for continuous design evolution of a mechatronic system with MHM. Possible design weaknesses of the monitored system are detected using the information from MHM. The proposed method incorporates an index to identify a possible design weakness by evaluating the performance, detecting failures and estimating the health status of the system. Second, improved approaches of intelligent machine fault diagnosis (IMFD) that can be applied to more general machinery and faults, are presented. This dissertation develops an IMFD approach based on deep neural networks (DNN). It uses the massive unlabeled MHM data to learn representative features. Using very few items of labeled data, this approach can achieve superior diagnosis performance. The dissertation presents another IMFD approach, which uses the convolutional neural networks (CNN) and sensor fusion and has increased diagnosis accuracy and reliability. The end-to-end learning capability of the two approaches enables diagnosis of fault types or machines for which lim…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/63133
note: APPLICATION OF MACHINE HEALTHMONITORING IN DESIGN OPTIMIZATION OFMECHATRONIC SYSTEMSbyMin XiaB.Eng., Southeast University, 2009M.Eng., University of Science and Technology of China, 2012A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDoctor of PhilosophyinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Mechanical Engineering)The University of British Columbia(Vancouver)September 2017c©Min Xia, 2017AbstractMechatronic systems are widely used in modern manufacturing. The key machin-ery of a manufacturing system should be reliable, flexible, intelligent, less com-plex, and cost effective, which indeed are distinguishing features of a mechatronicsystem. To achieve these goals, continuous or on-demand design improvementsshould be incorporated rapidly and effectively, which will address new design re-quirements or resolve existing weaknesses of the original design.With the advances in sensor technologies, wireless communication, data stor-age, and data mining, machine health monitoring (MHM) has achieved significantcapabilities to monitor the performance of an operating machine. The extensivedata from the MHM system can be employed in design improvement of the moni-tored system. In that context, the present dissertation addresses several challengesin applying MHM in design optimization of a mechatronic system.First, this dissertation develops a systematic framework for continuous designevolution of a mechatronic system with MHM. Possible design weaknesses of themonitored system are detected using the information from MHM. The proposedmethod incorporates an index to identify a possible design weakness by evaluatingthe performance, detecting failures and estimating the health status of the system.Second, improved approaches of intelligent machine fault diagnosis (IMFD)that can be applied to more general machinery and faults, are presented. Thisdissertation develops an IMFD approach based on deep neural networks (DNN).It uses the massive unlabel…
has type: Thesis/Dissertation
date issued: 2017-11
is shown at: 10.14288/1.0355835
language: eng
degree discipline: Mechanical 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
type: Text
identifier URI: http://hdl.handle.net/2429/63133