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	Fault diagnostics based on particle swarm optimisation and support vector machines
	Yuan, Sheng-Fa ; Chu, Fu-Lei
	2010-05-10 ; 2010-05-10
关键词	ALGORITHM Engineering, Mechanical
中文摘要	In the fault diagnosis based on support vector machines (SVM), irrelevant variables in the fault samples spoil the performance of the SVM classifier and reduce the recognition accuracy. On the other hand, some SVM parameters are usually selected artificially, which hampers the efficiency of the SVM algorithm in practical applications. A new method that jointly optimises the feature selection and the SVM parameters with a modified discrete particle swarm optimisation is presented in this paper. A correct ratio based on a new evaluation method is used to estimate the performance of the SVM, and serves as the target function in the optimisation problem. A hybrid vector that describes both the fault features and the SVM parameters is taken as the constraint condition. This new method can select the best fault features in a shorter time, and improves the performance of the SVM classifier, and has fewer errors and a better real-time capacity than the method based on principal component analysis (PCA) and SVM, or the method based on Genetic Algorithm (GA) and SVM, as shown in the application of fault diagnosis of the turbo pump rotor. (c) 2006 Elsevier Ltd. All rights reserved.
语种	英语 ; 英语
出版者	ACADEMIC PRESS LTD ELSEVIER SCIENCE LTD ; LONDON ; 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/24400]
专题	清华大学
推荐引用方式 GB/T 7714	Yuan, Sheng-Fa,Chu, Fu-Lei. Fault diagnostics based on particle swarm optimisation and support vector machines[J],2010, 2010.
APA	Yuan, Sheng-Fa,&Chu, Fu-Lei.(2010).Fault diagnostics based on particle swarm optimisation and support vector machines..
MLA	Yuan, Sheng-Fa,et al."Fault diagnostics based on particle swarm optimisation and support vector machines".(2010).

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