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JDCTA
2010

Improved Wavelet Neural Network Based on Hybrid Genetic Algorithm Applicationin on Fault Diagnosis of Railway Rolling Bearing

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Improved Wavelet Neural Network Based on Hybrid Genetic Algorithm Applicationin on Fault Diagnosis of Railway Rolling Bearing
The method of improved wavelet transform neural network based on hybrid GA(genetic algorithm) is presented to diagnose rolling bearings faults in this paper. Genetic Artificial Neural Networks(GA-ANN) overcomes BP neural network's fault of slow convergence, long hours of training, and falling into the local minimum point. And First, the signal is processed through the wavelet deoising, Then, three-layer wavelet packet is adopted to decompose the denoising signal of rolling beatings, and constructs the wavelet packet energy eigenvector, then takes those wavelet packet energy eigenvectors as fault samples to train BP neural network. Genetic algorithm is used to optimize the training process of BP network. The experimental results show that the optimized BP network by genetic algorithm can diagnose bearing faults, and it is superior to the BP network without optimization, the method has fair prospects of application for the rotary machine fault diagnosis. Keywords Railway Rrolling B...
Guoqiang Cai, Limin Jia, Jianwei Yang, Haibo Liu
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JDCTA
Authors Guoqiang Cai, Limin Jia, Jianwei Yang, Haibo Liu
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