NDLI: Fault Diagnosis of Hydroturbine Generating Units Based on Least Squares Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Zou Min Zhou Jianzhong Zhang Yongchuan Liu Zhong
Copyright Year	2007
Description	Author affiliation: Huazhong Univ. of Sci. & Technol., Wuhan (Zou Min; Zhou Jianzhong; Zhang Yongchuan; Liu Zhong)
Abstract	The algorithm of support vector machines (SVM), a novel machine learning method based on statistical learning theory, has been successfully used in pattern recognition and function estimation. The theory of least squares support vector machines (LS-SVM) is a least squares version of standard SVM, which involves equality instead of inequality constraints and works with a least squares object function. A systematic approach based on LS-SVM and wavelet decomposition for fault diagnosis of hydroturbine generating units (HGU) is proposed in this paper. The vibration signals under abnormal conditions are collected and preprocessed with the wavelet decomposition and feature information of signals is extracted as the feature vectors for training and testing the LS-SVM. To classify multiple fault modes of HGU, a multiclass classifier based on LS-SVM with minimum output codes (MOC) is constructed and used in the fault diagnosis for HGU. It's showed in the simulation result that the fault types can be identified and diagnosed by the above method. Compared with the result of a RBF neural network, more excellent identification accuracy indicates the feasibility and effectiveness of LS-SVM in the fault diagnosis of HGU.
Starting Page	152
Ending Page	156
File Size	517436
Page Count	5
File Format	PDF
ISBN	9781424408177
DOI	10.1109/ICCA.2007.4376337
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-05-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Least squares methods Support vector machines Machine learning algorithms Learning systems Statistical learning Pattern recognition Constraint theory Data mining Feature extraction hydroturbine generating units (HGU) fault diagnosistic least squares support vector machines (LS-SVM) wavelet decomposition
Content Type	Text
Resource Type	Article

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