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Fault diagnosis of rotating machinery based on multiple probabilistic classifiers
Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin
2018-08
Source PublicationMECHANICAL SYSTEMS AND SIGNAL PROCESSING
ISSN0888-3270
Volume108Pages:99-114
Abstract

Intelligent fault diagnosis of rotating machinery is vital for industries to improve fault prediction performance and reduce the maintenance cost. The new fault diagnostic framework is proposed which consists of three stages: (1) signal processing and feature extraction, (2) fault diagnosis by combining the classification results through a probabilistic ensemble method, and (3) parameter optimization and performance evaluation. In the first stage, ensemble empirical mode decomposition (EEMD) decomposes the acquired signal into a suite of intrinsic mode functions (IMF) which encounters redundant components and large data problems. To eliminate the redundant IMF and select fault feature from residual IMF, correlation coefficient (CC) and singular value decomposition (SVD) method are applied respectively. In the second stage, to improve the performance of fault diagnosis based on single classifier and increase the number of detectable fault, a new probabilistic committee machine (PCM) method is proposed, in which multiple pairwise-coupled sparse Bayesian extreme learning machines (PCSBELM) are individually trained using air ration, ignition pattern and engine sound signal. In addition, each classifier is assigned with an optimal weight in accordance with their reliability and accuracy so that a reliable and widely covered fault diagnostic result can be obtained from the weighted combination of the members. To verify the effectiveness of the proposed fault diagnostic framework, it is applied to automotive engine fault detection. The evaluation results show the proposed framework is superior to the existing single classifier in terms of both single- and simultaneous-faults in automotive engine. (C) 2018 Elsevier Ltd. All rights reserved.

KeywordRotating Machinery Automotive Engine Simultaneous-fault Diagnosis Sparse Bayesian Extreme Learning Machine Probabilistic Committee Machine
DOI10.1016/j.ymssp.2018.02.009
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Mechanical
WOS IDWOS:000428481900008
PublisherACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:10   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Corresponding AuthorZhong, Jian-Hua
AffiliationDepartment of Electromechanical Engineering, Faculty of Science and TechnologyUniversity of MacauMacau SARChina
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Zhong, Jian-Hua,Wong, Pak Kin,Yang, Zhi-Xin. Fault diagnosis of rotating machinery based on multiple probabilistic classifiers[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING,2018,108:99-114.
APA Zhong, Jian-Hua,Wong, Pak Kin,&Yang, Zhi-Xin.(2018).Fault diagnosis of rotating machinery based on multiple probabilistic classifiers.MECHANICAL SYSTEMS AND SIGNAL PROCESSING,108,99-114.
MLA Zhong, Jian-Hua,et al."Fault diagnosis of rotating machinery based on multiple probabilistic classifiers".MECHANICAL SYSTEMS AND SIGNAL PROCESSING 108(2018):99-114.
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