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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2003, Vol. 4 Issue (5): 595-601    DOI: 10.1631/jzus.2003.0595
Mechanics & Control Technology     
Extracting invariable fault features of rotating machines with multi-ICA networks
JIAO Wei-dong, YANG Shi-xi, Wu Zhao-tong
Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
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Abstract  This paper proposes novel multi-layer neural networks based on Independent Component Analysis for feature extraction of fault modes. By the use of ICA, invariable features embedded in multi-channel vibration measurements under different operating conditions (rotating speed and/or load) can be captured together. Thus, stable MLP classifiers insensitive to the variation of operation conditions are constructed. The successful results achieved by selected experiments indicate great potential of ICA in health condition monitoring of rotating machines.

Key words： Independent Component Analysis (ICA)      Mutual Information (MI)      Principal Component Analysis (PCA)      Multi-Layer Perceptron (MLP)      Residual Total Correlation (RTC)     
Received: 20 November 2002     
CLC:  TN912.3  
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JIAO Wei-dong
YANG Shi-xi
Wu Zhao-tong
Cite this article:

JIAO Wei-dong, YANG Shi-xi, Wu Zhao-tong. Extracting invariable fault features of rotating machines with multi-ICA networks. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2003, 4(5): 595-601.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.2003.0595     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2003/V4/I5/595

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