Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN

CHEN Xiaohan; HU Xiaoxi; WEN Tao; CAO Yuan

doi:10.23919/cje.2022.00.229

Volume 32 Issue 5

Sep. 2023

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Article Navigation > Chinese Journal of Electronics > 2023 > 32(5): 972-981

CHEN Xiaohan, HU Xiaoxi, WEN Tao, et al., “Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 972-981, 2023, doi: 10.23919/cje.2022.00.229

Citation:

CHEN Xiaohan, HU Xiaoxi, WEN Tao, et al., “Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 972-981, 2023, doi: 10.23919/cje.2022.00.229

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Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN

doi: 10.23919/cje.2022.00.229

CHEN Xiaohan^1
,,
HU Xiaoxi^{1, 2
,},
WEN Tao^1
,,
CAO Yuan^{1, 3
,}

1.
The School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2.
The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
3.
The National Engineering Research Center of Rail Transportation Operation Control System, Beijing Jiaotong University, Beijing 100044, China

Funds: This work was supported by the National Natural Science Foundation of China (62120106011, 52172323, U22A2046)

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Author Bio:
Xiaohan CHEN received the B.E. degree in communication engineering in 2022 from Beijing Jiaotong University, where he is now pursuing the M.E. degree in traffic information engineering and control. His research interests include federated learning and deep learning in railway fault diagnosis. (Email: 18291258@bjtu.edu.cn)

Xiaoxi HU received the B.E. degree in railway transportation signaling and control from Lanzhou Jiaotong University, Lanzhou, China. He is currently pursuing the Ph.D. degree in traffic information engineering and control with Beijing Jiaotong University, Beijing, China. His research interest includes fault diagnosis & prognosis and health management in the railway signaling systems. (Email: xiaoxhu@bjtu.edu.cn)

Tao WEN (corresponding author) received the Ph.D. degree from the Birmingham Centre for Railway Research and Education at the University of Birmingham, Birmingham, UK, in 2018. He is now a Professor with the Beijing Jiaotong University, Beijing, China. His research interests include federated learning, Internet of things, train control system optimization, railway conditional monitoring, machine learning, and digital filter research. (Email: wentao@bjtu.edu.cn)

Yuan CAO received the Ph.D. degree in traffic information engineering and control from Beijing Jiaotong University, Beijing, China, in 2011. He is currently a Professor with the National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University. His research interest includes health management in high-speed railway systems. (Email: ycao@bjtu.edu.cn)
Received Date: 2022-07-25
Accepted Date: 2023-04-04

Available Online: 2023-04-24

Publish Date: 2023-09-05

Abstract

Abstract

In the railway transportation industry, fault diagnosis of railway point machines (RPMs) is vital. Because operational vibration signals can reflect the condition of various faults in mechanical devices, vibration sensing and monitoring and more importantly, vibration signal-based fault diagnosis for RPMs have attracted the attention of scholars and engineers. Most vibration signal-based fault-diagnosis methods for RPMs rely on data collected using high-sampling-rate sensors and manual feature extraction, hence are costly and insufficiently robust. To overcome these shortcomings, we propose a double-scale wide first-layer kernel convolutional neural network (DS-WCNN) for RPMs fault diagnosis using inexpensive and low-sampling-rate vibration sensors. The proposed wide first-layer kernels, which extract features from vibration observations, are particularly suitable for low-sampling-rate signals. Meanwhile, the proposed double-scale structure improves accuracy and noise suppression by combining two types of timescale features. Sufficient experiments, including noise addition and comparison, were conducted to demonstrate the robustness and accuracy of the proposed algorithm.
- Railway point machines,
- Fault diagnosis,
- Condition monitoring,
- Vibration signals,
- Convolutional neural networks

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References(28)

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