基于振动信号时频分解-样本熵的受电弓裂纹故障诊断

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Abstract A fault feature extraction model of pantograph vibration signal based on time-frequency decomposition and sample entropy was constructed.Firstly, ensemble empirical mode decomposition was conducted for vibration signal, then sample entropy was calculated after optimizing parameters for the intrinsic modal function obtained by EEMD.The sample entropy features were input to the support vector machine (PSO-SVM) based on particle swarm optimization (PSO) to identify the pantograph fault identification.The results show that the EEMD sample entropy fault diagnosis based on the pantograph panhead top pipe vibration signal has good accuracy, and the carbon contact strip vibration signal has poor diagnosis effect.According to this, the second-generation wavelet sample entropy was used to optimize and further improve the fault diagnosis results of carbon contact strip vibration signal.It verified the feasibility and effectiveness of the combination of the modern time-frequency analysis algorithm and the information entropy in the feature extraction and fault diagnosis of pantograph fault vibration signals.

Key words： pantograph fault diagnosis time-frequency decomposition sample entropy

Received: 13 June 2018 Published: 15 April 2019

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	SHI Ying
	LIN Jianhui
	ZHUANG Zhe
	LIU Zechao

Cite this article:

SHI Ying,LIN Jianhui,ZHUANG Zhe, et al. Fault diagnosis for pantograph cracks based on time-frequency decomposition and sample entropy of vibration signals[J]. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(8): 180-187.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2019/V38/I8/180

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