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Joint time-frequency and kernel principal component based SOM for machine maintenance
Guo QJ(郭前进); Yu HB(于海斌); Nie YY(聂义勇); Xu AD(徐皑冬)
Department工业控制系统研究室
Conference Name3rd International Symposium on Neural Networks (ISNN 2006)
Conference DateMay 28-31, 2006
Conference PlaceChengdu, China
Author of SourceUniv Electr Sci & Technol China, Chinese Univ Hong Kong, Asia Pacific Neural Network Assembly, European Neural Network Soc, IEEE Circuits & Syst Soc, IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Nat Sci Fdn China, KC Wong Educ Fdn Hong Kong
Source PublicationADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS
PublisherSPRINGER-VERLAG
Publication PlaceBERLIN
2006
Pages1144-1154
Indexed BySCI ; EI ; CPCI(ISTP)
EI Accession number20062910011122
WOS IDWOS:000239485300167
Contribution Rank1
ISSN0302-9743
ISBN3-540-34482-9
Abstract

Conventional vibration signals processing techniques are most suitable for stationary processes. However, most mechanical faults in machinery reveal themselves through transient events in vibration signals. That is, the vibration generated by industrial machines always contains nonlinear and nonstationary signals. It is expected that a desired time-frequency analysis method should have good computation efficiency, and have good resolution in both time domain and frequency domain. In this paper, the auto-regressive model based pseudo-Wigner-Ville distribution for an integrated time-frequency signature extraction of the machine vibration is designed, the method offers the advantage of good localization of the vibration signal energy in the time-frequency domain. Kernel principal component analysis (KPCA) is used for the redundancy reduction and feature extraction in the time-frequency domain, and the self-organizing map (SOM) was employed to identify the faults of the rotating machinery. Experimental results show that the proposed method is very effective.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/8097
Collection工业信息学研究室_工业控制系统研究室
Corresponding AuthorGuo QJ(郭前进)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Liaoning 110016, China
2.Graduate School, Chinese Academy of Sciences, Beijing 100039, China
Recommended Citation
GB/T 7714
Guo QJ,Yu HB,Nie YY,et al. Joint time-frequency and kernel principal component based SOM for machine maintenance[C]//Univ Electr Sci & Technol China, Chinese Univ Hong Kong, Asia Pacific Neural Network Assembly, European Neural Network Soc, IEEE Circuits & Syst Soc, IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Nat Sci Fdn China, KC Wong Educ Fdn Hong Kong. BERLIN:SPRINGER-VERLAG,2006:1144-1154.
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