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G. Q. Cai and J. W. Yang, “Fault Diagnosis of Railway Rolling Bearing Based on Wavelet Packet and Elman Neural Network,” IASTED International Conference of Advances in Computer Science and Engineering, 16-18 March 2009, pp. 268-274.

has been cited by the following article:

• TITLE: Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network

  AUTHORS: Guangwu Liu, Jing Long, Lingzhi Yang, Zhaoyi Su, Dechen Yao, Xiangli Zhong

  KEYWORDS: Fault Diagnosis; Urban Rail Vehicle Auxiliary Inverter; Wavelet Packet; RBF Neural Network

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.5 No.4, November 12, 2013

  ABSTRACT: This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signal, impulsive transient signal and frequency variation signal. In this article, the original signals are decomposed into different frequency subbands by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train RBF neural network. The result shows that the RBF neural network is effective in the detection and diagnosis of various urban rail vehicle auxiliary inverter faults.