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

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DOI: 10.4236/jilsa.2013.54023    3,093 Downloads   4,447 Views   Citations

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

G. Liu, J. Long, L. Yang, Z. Su, D. Yao and X. Zhong, "Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network," Journal of Intelligent Learning Systems and Applications, Vol. 5 No. 4, 2013, pp. 211-215. doi: 10.4236/jilsa.2013.54023.

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