Real-time fault diagnosis for gas turbine generator systems using extreme learning machine

UM > Faculty of Science and Technology > DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE

Status	已發表Published
	Real-time fault diagnosis for gas turbine generator systems using extreme learning machine
	Wong, P.K.; Yang, Z. X.; Vong, C. M.; Zhong, J. H.
	2014-03-01
Source Publication	Neurocomputing (SCI-E)
ISSN	0925-2312
Pages	249-257
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.
Keyword	Real-time fault diagnosis gas turbine generator system extreme learning machine support vector machines wavelet packet transform time-domain statistical features kernel principal component analysis
Language	英語English
The Source to Article	PB_Publication
PUB ID	9748
Document Type	Journal article
Collection	DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding Author	Wong, P.K.
Recommended Citation GB/T 7714	Wong, P.K.,Yang, Z. X.,Vong, C. M.,et al. Real-time fault diagnosis for gas turbine generator systems using extreme learning machine[J]. Neurocomputing (SCI-E), 2014, 249-257.
APA	Wong, P.K.., Yang, Z. X.., Vong, C. M.., & Zhong, J. H. (2014). Real-time fault diagnosis for gas turbine generator systems using extreme learning machine. Neurocomputing (SCI-E), 249-257.
MLA	Wong, P.K.,et al."Real-time fault diagnosis for gas turbine generator systems using extreme learning machine".Neurocomputing (SCI-E) (2014):249-257.

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