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Received: 2002-11-20

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Journal of Zhejiang University SCIENCE A 2003 Vol.4 No.5 P.595~601

http://doi.org/10.1631/jzus.2003.0595


Extracting invariable fault features of rotating machines with multi-ICA networks


Author(s):  JIAO Wei-dong, YANG Shi-xi, Wu Zhao-tong

Affiliation(s):  Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   lzn_jwd@cmee.zju.edu.cn

Key Words:  Independent Component Analysis (ICA), Mutual Information (MI), Principal Component Analysis (PCA), Multi-Layer Perceptron (MLP), Residual Total Correlation (RTC)


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JIAO Wei-dong, YANG Shi-xi, Wu Zhao-tong. Extracting invariable fault features of rotating machines with multi-ICA networks[J]. Journal of Zhejiang University Science A, 2003, 4(5): 595~601.

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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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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