FastICA Genetic Neural Networks Method

doi:10.13190/j.jbupt.2014.04.006

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2014, Vol. 37 ›› Issue (4): 25-28.doi: 10.13190/j.jbupt.2014.04.006

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FastICA Genetic Neural Networks Method

XU Tong-le, HOU Meng-meng, CAI Dao-yong, XUE Lei-jiang

Mechanical Engineering School, Shandong University of Technology, Zibo Shandong 255049, China

Received:2013-11-07 Online:2014-08-28 Published:2014-08-09

Abstract

Abstract:

Depending on the intrinsic weakness and advantages of back propagation(BP) neural network and Fast Independent Component Analysis(FastICA), a Fast Independent Component Analysis(FastICA) Genetic Neural Networks Method was proposed for fault characteristic signal recognition. The FastICA is used to decompose signals to obtain the independent components successively, each of Independent components corresponding to an energy band, and feature vector of each energy band is used as input sample to optimize neural network. Secondly, the genetic algorithm is used to optimize the weights and thresholds of BP neural network to obtain the Genetic Neural Network. Thirdly,the feature vector is used as input sample of the genetic neural network to identify the fault. Using this method can analysis and identify many kinds of rolling bearings fault signal, and through this method the ability of fault identification isimproved.

Key words: fast independent component analysis, fault diagnosis, bearing fault, genetic algorithm

CLC Number:

TN911.22

XU Tong-le, HOU Meng-meng, CAI Dao-yong, XUE Lei-jiang. FastICA Genetic Neural Networks Method[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2014, 37(4): 25-28.

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FastICA Genetic Neural Networks Method

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