UM  > 科技學院  > 機電工程系
An Efficient Fault Diagnostic Method for Three-Phase Induction Motors Based on Incremental Broad Learning and Non-Negative Matrix Factorization
Jiang S.B.1; Wong P.K.2; Guan R.1; Liang Y.1; Li J.1
2019
Source PublicationIEEE Access
ISSN21693536
Volume7Pages:17780-17790
Abstract

Three-phase induction motors (TPIMs) are prone to numerous faults due to their complicated stator and rotor conditions and require a fast response, accurate, and intelligent diagnostic system. Recently developed fault diagnostic systems for induction motors are based on machine learning approaches, but their complex structure typically results in long training time. Moreover, they need to be retrained from scratch if the system is not accurate. We apply incremental broad learning (IBL) method to the diagnosis of TPIM faults. The IBL can train and retrain the network efficiently due to its flexible structure. The new diagnostic framework also consists of feature extraction techniques (empirical mode decomposition and sample entropy) and a non-negative matrix factorization (NMF) IBL approach. The experimental results demonstrate that the IBL system is superior to some algorithms, such as deep belief networks, convolutional neural networks, and extreme learning machine. Moreover, the IBL simplified by NMF is more accurate than the IBL without NMF.

KeywordFault Diagnosis Feature Extraction Incremental Board Learning Non-negative Matrix Factorization Three-phase Induction Motor
DOI10.1109/ACCESS.2019.2895909
URLView the original
Indexed BySCI
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000459344100001
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Affiliation1.Jilin University
2.Universidade de Macau
Recommended Citation
GB/T 7714
Jiang S.B.,Wong P.K.,Guan R.,et al. An Efficient Fault Diagnostic Method for Three-Phase Induction Motors Based on Incremental Broad Learning and Non-Negative Matrix Factorization[J]. IEEE Access,2019,7:17780-17790.
APA Jiang S.B.,Wong P.K.,Guan R.,Liang Y.,&Li J..(2019).An Efficient Fault Diagnostic Method for Three-Phase Induction Motors Based on Incremental Broad Learning and Non-Negative Matrix Factorization.IEEE Access,7,17780-17790.
MLA Jiang S.B.,et al."An Efficient Fault Diagnostic Method for Three-Phase Induction Motors Based on Incremental Broad Learning and Non-Negative Matrix Factorization".IEEE Access 7(2019):17780-17790.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Jiang S.B.]'s Articles
[Wong P.K.]'s Articles
[Guan R.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Jiang S.B.]'s Articles
[Wong P.K.]'s Articles
[Guan R.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Jiang S.B.]'s Articles
[Wong P.K.]'s Articles
[Guan R.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.