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Real-time fault diagnosis for gas turbine generator systems using extreme learning machine
Wong P.K.1; Yang Z.1; Vong C.M.2; Zhong J.1
2014
Source PublicationNeurocomputing
ISSN9252312
Volume128Pages:249
Abstract

Real-time fault diagnostic system is very important to maintain the operation of the gas turbine generator system (GTGS) in power plants, where any abnormal situation will interrupt the electricity supply. The GTGS is complicated and has many types of component faults. To prevent from interruption of electricity supply, a reliable and quick response framework for real-time fault diagnosis of the GTGS is necessary. As the architecture and the learning algorithm of extreme learning machine (ELM) are simple and effective respectively, ELM can identify faults quickly and precisely as compared with traditional identification techniques such as support vector machines (SVM). This paper therefore proposes a new application of ELM for building a real-time fault diagnostic system in which data pre-processing techniques are integrated. In terms of data pre-processing, wavelet packet transform and time-domain statistical features are proposed for extraction of vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features in order to shorten the fault identification time and improve accuracy. To evaluate the system performance, a comparison between ELM and the prevailing SVM on the fault detection was conducted. Experimental results show that the proposed diagnostic framework can detect component faults much faster than SVM, while ELM is competitive with SVM in accuracy. This paper is also the first in the literature that explores the superiority of the fault identification time of ELM. © 2013 Elsevier B.V.

KeywordExtreme Learning Machine Gas Turbine Generator System Kernel Principal Component Analysis Real-time Fault Diagnosis Time-domain Statistical Features Wavelet Packet Transform
DOI10.1016/j.neucom.2013.03.059
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000331851700030
The Source to ArticleScopus
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被引频次[WOS]:59   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorWong P.K.
Affiliation1.Department of Electromechanical Engineering, University of Macau, Macau, China
2.Department of Computer and Information Science, University of Macau, Macau, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
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Wong P.K.,Yang Z.,Vong C.M.,et al. Real-time fault diagnosis for gas turbine generator systems using extreme learning machine[J]. Neurocomputing,2014,128:249.
APA Wong P.K.,Yang Z.,Vong C.M.,&Zhong J..(2014).Real-time fault diagnosis for gas turbine generator systems using extreme learning machine.Neurocomputing,128,249.
MLA Wong P.K.,et al."Real-time fault diagnosis for gas turbine generator systems using extreme learning machine".Neurocomputing 128(2014):249.
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