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A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine
Wong, Pak Kin1; Zhong, Jian-Hua1; Yang, Zhi-Xin1; Vong, Chi Man2
2017-03
Source PublicationPROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
ISSN0954-4062
Volume231Issue:6Pages:1146-1161
Abstract

This paper proposes a new diagnostic framework, namely, probabilistic committee machine, to diagnose simultaneous-fault in the rotating machinery. The new framework combines a feature extraction method with ensemble empirical mode decomposition and singular value decomposition, multiple pairwise-coupled sparse Bayesian extreme learning machines (PCSBELM), and a parameter optimization algorithm to create an intelligent diagnostic framework. The feature extraction method is employed to find the features of single faults in a simultaneous-fault pattern. Multiple PCSBELM networks are built as different signal committee members, and each member is trained using vibration or sound signals respectively. The individual diagnostic result from each fault detection member is then combined by a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable fault as compared to individual classifier acting alone. The effectiveness of the proposed framework is verified by a case study on a gearbox fault detection. Experimental results show the proposed framework is superior to the existing single probabilistic classifier. Moreover, the proposed system can diagnose both single- and simultaneous-faults for the rotating machinery while the framework is trained by single-fault patterns only.

KeywordRotating Machinery Simultaneous-fault Diagnosis Pairwise-coupled Sparse Bayesian Extreme Learning Machine Probabilistic Committee Machine
DOI10.1177/0954406216632022
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Mechanical
WOS IDWOS:000394785400010
PublisherSAGE PUBLICATIONS LTD
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:7   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorYang, Zhi-Xin
Affiliation1.Department of Electromechanical Engineering, University of Macau, Macao
2.Department of Computer and Information Science, University of Macau, Macao
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wong, Pak Kin,Zhong, Jian-Hua,Yang, Zhi-Xin,et al. A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine[J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE,2017,231(6):1146-1161.
APA Wong, Pak Kin,Zhong, Jian-Hua,Yang, Zhi-Xin,&Vong, Chi Man.(2017).A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine.PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE,231(6),1146-1161.
MLA Wong, Pak Kin,et al."A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine".PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 231.6(2017):1146-1161.
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