Constrained independent component analysis and its application to machine fault diagnosis

被引：76

作者：

Wang, Zhiyang ^{[1
]}

Chen, Jin ^{[1
]}

Dong, Guangming ^{[1
]}

Zhou, Yu ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2011年 / 25卷 / 07期

关键词：

Independent component analysis (ICA); Constrained independent component analysis (cICA); Blind source extraction (BSE); Machine fault diagnosis; Machine diagnostics; ICA; SEPARATION;

D O I：

10.1016/j.ymssp.2011.03.006

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

For machine fault diagnosis the signals from working machine are always numerous, even uncountable, but there contains only a little useful information. Hence how to find Out the useful signal from numerous signals, including noises, that is, how to only extract the desired fault signal is very attractive. This paper shows that the constrained independent component analysis (cICA) can solely extract desired faulty signal using some prior mechanical information. The methods of creating reference of cICA for machine diagnostics are discussed, and the effectiveness of the method is successfully verified by simulations and experiments. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

引用

页码：2501 / 2512

页数：12

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