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

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 captur...

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Veröffentlicht in:	Journal of Zhejiang University. Science 2003-09, Vol.4 (5), p.595-601
1. Verfasser:	焦卫东杨世锡吴昭同
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Sprache:	eng
Schlagworte:	Algorithms Computer Simulation Neural Networks (Computer) Principal Component Analysis Time Factors 多层神经网络振动测量故障诊断旋转电机特征提取独立组分分析
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container_title	Journal of Zhejiang University. Science
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creator	焦卫东杨世锡吴昭同
description	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.
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subjects	Algorithms Computer Simulation Neural Networks (Computer) Principal Component Analysis Time Factors 多层神经网络振动测量故障诊断旋转电机特征提取独立组分分析
title	Extracting invariable fault features of rotating machines with multi-ICA networks
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