Research ArticleOpen AccessGoogle Scholar indexed
Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network
Guangzhou Metro, Guangzhou, China
Guangzhou Metro, Guangzhou, China
Guangzhou Metro, Guangzhou, China
Guangzhou Metro, Guangzhou, China
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
Jinan Qingqi Mototcycle CO., LTD., Jinan, China.
- 1 Guangzhou Metro, Guangzhou, China
- 2 Guangzhou Metro, Guangzhou, China
- 3 Guangzhou Metro, Guangzhou, China
- 4 Guangzhou Metro, Guangzhou, China
- 5 State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
- 6 Jinan Qingqi Mototcycle CO., LTD., Jinan, China.
Journal of Intelligent Learning Systems and Applications·Volume 05 (2013)·Pages 211–215·Published 12 November 2013·DOI10.4236/jilsa.2013.54023
Copy link · social · email
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.
KeywordsFault DiagnosisUrban Rail Vehicle Auxiliary InverterWavelet PacketRBF Neural Network
- Y. F. Yin and J. W. Yang, “Fault Diagnosis of Rolling Bearing Based on Wavelet Packet and Fourier Analysis,” 2010 International Conference on Computational Aspects of Social Networks (CASoN), Taiyuan, 26-28 September 2010, pp. 703-706.
- F. Jurado and J. R. Saenz, “Comparison between Discrete STFT and Wavelets for the Analysis of Power Quality Events,” Electric Power Systems Research, Vol. 62, No. 3, 2002, pp. 183-190. http://dx.doi.org/10.1016/S0378-7796(02)00035-4
- X. S. Lou and K. A. Loparo, “Bearing Fault Diagnosis Based on Wavelet Transform and Fuzzy Inference,” Mechanical Systems and Signal Processing, Vol. 18, No. 5, 2004, pp. 1077-1095. http://dx.doi.org/10.1016/S0888-3270(03)00077-3
- H. Q. Wang and P. Chen, “Intelligent Diagnosis Method for Rolling Element Bearing Faults Using Possibility Theory and Neural Network,” Computers & Industrial Engineering, Vol. 60, No. 4, 2011, pp. 511-518. http://dx.doi.org/10.1016/j.cie.2010.12.004
- C. van den B. Lambrecht and M. Karrakchou, “Wavelet Packets-Based High-Resolution Spectral Estimation,” Signal Processing, Vol. 47, No. 2, 1995, pp. 135-144. http://dx.doi.org/10.1016/0165-1684(95)00102-6
- 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.
- X. L. Wen and H. T. Wang, “Prediction Model of Flow Boiling Heat Transfer for R407C inside Horizontal Smooth Tubes Based on RBF Neural Network,” Procedia Engineering, Vol. 31, 2012, pp. 233-239. http://dx.doi.org/10.1016/j.proeng.2012.01.1017
- J. D. Wu and J. C. Chen, “Continuous Wavelet Transform Technique for Fault Signal Diagnosis of Internal Combustion Engines,” NDT&E International, Vol. 39, No. 4, 2006, pp. 304-311. http://dx.doi.org/10.1016/j.ndteint.2005.09.002
- D. C. Yao and J. W. Yang, “Fault Diagnosis of Railway Bearing Based on Muti-Method Fusion Techniques,” Machine Design and Research, Vol. 26, No. 3, 2010, pp. 70-73.
- G. Scalabrin, M. Condosta and P. Marchi, “Modeling Flow Boiling Heat Transfer of Pure Fluids through Artificial Neural Networks,” International Journal of Thermal Sciences, Vol. 45, No. 7, 2006, pp. 643-663. http://dx.doi.org/10.1016/j.ijthermalsci.2005.09.009
- G. Scalabrin, M. Condosta and P. Marchi, “Mixtures Flow Boiling: Modeling Heat Transfer through Artificial Neural Networks,” International Journal of Thermal Sciences, Vol. 45, No. 7, 2006, pp. 664-680. http://dx.doi.org/10.1016/j.ijthermalsci.2005.09.011