Turnout Fault Diagnosis Based on CNNs with Self-Generated Samples
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 9
Abstract
China’s rapid development of high-speed railways has imposed increasing requirements for safety and reliability of signal systems, especially the critical part: turnouts. In this paper, we propose an intelligent fault diagnosis approach that can effectively detect turnout faults based on self-generated fault samples. First, the action mechanism of a switch machine is analyzed and we establish a turnout action model to simulate the turnout operation current curves, thus considerable samples for a following diagnosis can be obtained. Second, we develop a turnout fault diagnosis model based on convolutional neural networks (CNNs). The networks can be trained by those simulated samples. Our experiments verify that the turnout action model can accurately simulate turnout fault curves and the diagnosis model can effectively identify faults through various formats of curve pictures.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Data includes current curves of turnout images, code includes some .py files written in Python, and the models include .m and .slxc files.
Acknowledgments
This research is supported by the National Key R&D Program of China (2016YFB1200402), the National Natural Science Foundation of China (Grant No. 61703308), and Sichuan science and technology program (2019YFG0040). The authors gratefully acknowledge the invaluable contribution of the reviewers.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 9 • September 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Feb 25, 2020
Accepted: May 14, 2020
Published online: Jul 11, 2020
Published in print: Sep 1, 2020
Discussion open until: Dec 11, 2020
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