Fault feature analysis of high-speed train bogie based on empirical mode decomposition entropy

QIN Na; WANG Kai-yun; JIN Wei-dong; HUANG Jin; SUN Yong-kui

Volume 14 Issue 1

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QIN Na, WANG Kai-yun, JIN Wei-dong, HUANG Jin, SUN Yong-kui. Fault feature analysis of high-speed train bogie based on empirical mode decomposition entropy[J]. Journal of Traffic and Transportation Engineering, 2014, 14(1): 57-64.

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QIN Na, WANG Kai-yun, JIN Wei-dong, HUANG Jin, SUN Yong-kui. Fault feature analysis of high-speed train bogie based on empirical mode decomposition entropy[J]. Journal of Traffic and Transportation Engineering, 2014, 14(1): 57-64.

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Fault feature analysis of high-speed train bogie based on empirical mode decomposition entropy

1.
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, China
2.
State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

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Author Bio:
QIN Na(1978-), female, doctoral student, +86-28-87601579, qinna@home.swjtu.edu.cn

JIN Wei-dong(1959-), male, professor, PhD, +86-28-87601579, wdjin@home.swjtu.edu.cn
Received Date: 2013-07-21
Publish Date: 2014-02-25

Abstract

Abstract

A novel method of feature extraction was proposed by combining ensemble empirical mode decomposition (EEMD) and five entropies based on the characteristics of vibration signal for high-speed train bogie in failure station. Firstly, vibration signal was decomposed by EEMD to avoid mode mixing effectively. Secondly, EEMD entropy feature was calculated for describing the complexity of intrinsic mode functions (IMFs). Vibration signals were obtained under four typical working conditions including normal condition, air spring fault, lateral damper fault and yaw damper fault. There were 280 sample data including 60% training samples and 40% test samples. Analysis result shows that the method is good adaptivity for unselecting basis functions and decomposition levels. The recognition rate is above 95% at the running speed of 200 km·h^-1. Therefore, the feature extraction method is effective to analyze the vibration signal of high-speed train bogie in fault station.
- high-speed train,
- fault diagnosis,
- feature extraction,
- ensemble empirical mode decomposition,
- entropy,
- empirical mode entropy

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References(15)

References

[1]	DING Jian-ming, LIN Jian-hui, ZHAO Jie, et al. Comparison method of energy transfer characteristics for fault detection of vehicle suspension spring[J]. Journal of Traffic and Transportation Engineering, 2013, 13 (4): 51-55, 62. (in Chinese). doi: 10.3969/j.issn.1671-1637.2013.04.008
[2]	YAN Qiu, LIU Yong-ming. The analysis of vehicle model establishment and malfunction based on MATLAB/Simulink[J]. Journal of East China Jiaotong University, 2012, 29 (5): 13-17. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HDJT201205004.htm
[3]	HAN Qing-peng. Evaluation of human mental stress states based on wavelet package transformation and nonlinear analysis of EEG signals[J]. Journal of Vibration and Shock, 2013, 32 (2): 182-188. (in Chinese). doi: 10.3969/j.issn.1000-3835.2013.02.035
[4]	HUANG Juan, HUANG Chun, JIANG Ya-qun, et al. Identification method of fault characteristics in transmission lines based on wavelet packet and approximate entropy[J]. Chinese Journal of Scientific Instrument, 2012, 33 (9): 2009-2015. (in Chinese). doi: 10.3969/j.issn.0254-3087.2012.09.013
[5]	SESHADRINATH J, SINGH B, PARNIGRAHI B K. Vibration analysis based interturn fault diagnosis in induction machines[J]. IEEE Transactions on Industrial Informatics. 2014, 10 (1): 340-350.
[6]	WU Z H, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[R]. Calverton: Center for Ocean-Land-Atmosphere Studies, 2009.
[7]	WU Z H, HUANG N E. A study of the characteristics of white noise using the empirical mode decomposition method[C]∥The Royal Society. Proceedings of the Royal Society, Series A: Mathematical, Physical and Engineering Sciences. London: The Royal Society, 2004: 1597-1611.
[8]	HU Ai-jun, MA Wan-li, TANG Gui-ji. Rolling bearing fault feature extraction method based on ensemble empirical mode decomposition and kurtosis criterion[J]. Proceedings of the CSEE, 2012, 32 (11): 106-111. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201211016.htm
[9]	LEI Ya-guo, HE Zheng-jia, ZI Yan-yang. EEMD method and WNN for fault diagnosis of locomotive roller bearings[J]. Expert Systems with Applications, 2011, 38 (6): 7334-7341.
[10]	ZVOKELJ M, ZUPAN S, PREBIL I. Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method[J]. Mechanical Systems and Signal Processing, 2011, 25 (7): 2631-2653.
[11]	ZHANG Xue-qing, LIANG Jun. Chaotic time series prediction model of wind power based on ensemble empirical mode decomposition-approximate entropy and reservoir[J]. Acta Physica Sinica, 2013, 62 (5): 76-85. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201305009.htm
[12]	HUANG Jian, HU Xiao-guang, GENG Xin. An intelligent fault diagnosis method of high voltage circuit breaker based on improved EMD energy entropy and multi-class support vector machine[J]. Electric Power Systems Research, 2011, 81 (2): 400-407.
[13]	LABATE D, FORESTA F L, MORABITO G, et al. Entropic measures of EEG complexity in alzheimer's disease through a multivariate multiscale approach[J]. IEEE Sensors Journal, 2013, 13 (9): 3284-3292.
[14]	HE Zheng-you, CHEN Xiao-qing, LUO Guo-ming. Wavelet entropy measure definition and its application for transmission line fault detection and identification, partⅠ: definition and methodology[C]∥IEEE. 2006International Conference on Power System Technology. Chongqing: IEEE, 2006: 1-6.
[15]	AN Xue-li, JIANG Dong-xiang, LI Shao-hua, et al. Application of the ensemble empirical mode decomposition and Hilbert transform to pedestal looseness study of direct-drive wind turbine[J]. Energy, 2011, 36 (9): 5508-5520.