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An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis
Zhong J.-H.2; Liang J.1; Yang Z.-X.2; Wong P.K.2; Wang X.-B.2
2016
Source PublicationShock and Vibration
ISSN10709622
Volume2016
Abstract

Fault diagnosis is very important to maintain the operation of a gas turbine generator system (GTGS) in power plants, where any abnormal situations will interrupt the electricity supply. The fault diagnosis of the GTGS faces the main challenge that the acquired data, vibration or sound signals, contain a great deal of redundant information which extends the fault identification time and degrades the diagnostic accuracy. To improve the diagnostic performance in the GTGS, an effective fault feature extraction framework is proposed to solve the problem of the signal disorder and redundant information in the acquired signal. The proposed framework combines feature extraction with a general machine learning method, support vector machine (SVM), to implement an intelligent fault diagnosis. The feature extraction method adopts wavelet packet transform and time-domain statistical features to extract the features of faults from the vibration signal. To further reduce the redundant information in extracted features, kernel principal component analysis is applied in this study. Experimental results indicate that the proposed feature extracted technique is an effective method to extract the useful features of faults, resulting in improvement of the performance of fault diagnosis for the GTGS.

DOI10.1155/2016/9359426
URLView the original
Indexed BySCI
Language英语
WOS Research AreaAcoustics ; Engineering ; Mechanics
WOS SubjectAcoustics ; Engineering, Mechanical ; Mechanics
WOS IDWOS:000375699100001
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被引频次[WOS]:4   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Corresponding AuthorYang Z.-X.
Affiliation1.University of Technology Sydney
2.Universidade de Macau
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GB/T 7714
Zhong J.-H.,Liang J.,Yang Z.-X.,et al. An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis[J]. Shock and Vibration,2016,2016.
APA Zhong J.-H.,Liang J.,Yang Z.-X.,Wong P.K.,&Wang X.-B..(2016).An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis.Shock and Vibration,2016.
MLA Zhong J.-H.,et al."An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis".Shock and Vibration 2016(2016).
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