Fault Diagnosis of High Speed Train Bogie Based on Multi-domain Fusion CNN

Authors

Yunpu Wu, School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China;
Weidong Jin, School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China;
Yingkun Huang, School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China;

Abstract

Abstract: The performance degradation and failures of high-speed train bogie components directly threaten the operation security of train. A fault detection method based on multi-domain fusion convolutional neural network is proposed to address the high complexity, high coupling and strong nonlinearity of vibration signals. Noise injection for time domain signal is used to enhance noise robustness and generalization of the model. Signal time-frequency representation information is obtained through embedded time-frequency transformation layer. Adaptive weight-based fusion is implemented through intrinsic characteristics of the convolutional neural network to handle the multi-domain multi-channel information. The experimental results show that the proposed method improves the accuracy of fault diagnosis of high-speed train bogies with good noise robustness and adaptability to work condition.

Recommended Citation

Wu, Yunpu; Jin, Weidong; and Huang, Yingkun (2019) "Fault Diagnosis of High Speed Train Bogie Based on Multi-domain Fusion CNN," Journal of System Simulation: Vol. 30: Iss. 11, Article 53.
DOI: 10.16182/j.issn1004731x.joss.201811053
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss11/53

First Page

4492

Revised Date

2018-07-12

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201811053

Last Page

4497

CLC

TP277

Recommended Citation

Wu Yunpu, Jin Weidong, Huang Yingkun. Fault Diagnosis of High Speed Train Bogie Based on Multi-domain Fusion CNN[J]. Journal of System Simulation, 2018, 30(11): 4492-4497.

DOI

10.16182/j.issn1004731x.joss.201811053