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 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.
Jiao
Wei-dong
WD
Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China. lzn_jwd@cmee.zju.edu.cn
Yang
Shi-xi
SX
Wu
Zhao-tong
ZT
D000465:Algorithms; D003198:Computer Simulation; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D013997:Time Factors