Fault diagnosis for pantograph cracks based on time-frequency decomposition and sample entropy of vibration signals

被引：0

作者：

Shi Y. ^{[1
]}

Lin J. ^{[1
]}

Zhuang Z. ^{[1
]}

Liu Z. ^{[1
]}

机构：

[1] State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2019年 / 38卷 / 08期

关键词：

Fault diagnosis; Pantograph; Sample entropy; Time-frequency decomposition;

D O I：

10.13465/j.cnki.jvs.2019.08.027

中图分类号：

学科分类号：

摘要：

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. © 2019, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：180 / 187

页数：7

共 16 条

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