Sequential-Fault Diagnosis Strategy for High-Speed Train Traction Systems Based on Unreliable Tests
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Published:2023-07-15
Issue:14
Volume:13
Page:8226
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Li Mengwei123, Zhou Ying123, Jia Limin123, Qin Yong123, Wang Zhipeng123ORCID
Affiliation:
1. State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044, China 2. Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China 3. Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University, Beijing 100044, China
Abstract
A train traction system is an important part of an urban rail transit system. However, a train traction system has many components and a high risk of internal faults. How to systematically evaluate the fault coverage and diagnosis ability of testing equipment is a fundamental problem in the technical field of train operation. In response to this problem, this study attempts to apply testability technology to the test capability analysis of train traction systems for rail transit. In view of the uncertainty in actual tests, a method for constructing a fault diagnosis strategy for a traction system under unreliable testing is proposed. The concept of test credibility is introduced for the first time, and the quantitative evaluation of test credibility is realized using a cloud model, so as to construct a new “fault-test” credibility correlation matrix. On this basis, a single-fault diagnosis strategy of the traction system is constructed and compared based on information theory. The results show that a using a fault diagnosis strategy under the condition of unreliable testing is more similar to actual maintenance work, proving the significance of the diagnosis strategy constructed using this method for the practical application of the project.
Funder
Fundamental Research Funds for the Central Universities National Key R&D Program of China National Natural Science Foundation of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference31 articles.
1. Geng, Y., Wang, Z., Jia, L., Qin, Y., Chai, Y., Liu, K., and Tong, L. (2023). 3DGraphSeg: A Unified Graph Representation-Based Point Cloud Segmentation Framework for Full-Range Highspeed Railway Environments. IEEE Trans. Ind. Inform., 1–13. 2. Tong, L., Jia, L., Geng, Y., Liu, K., Qin, Y., and Wang, Z. (2023). Anchor-adaptive railway track detection from unmanned aerial vehicle images. Comput.-Aided Civ. Infrastruct. Eng. 3. Aizenberg, I., Belardi, R., Bindi, M., Grasso, F., Manetti, S., Luchetta, A., and Piccirilli, M.C. (2021). A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis. Electronics, 10. 4. Aizenberg, I., Belardi, R., Bindi, M., Grasso, F., Manetti, S., Luchetta, A., and Piccirilli, M.C. (2021). Failure Prevention and Malfunction Localization in Underground Medium Voltage Cables. Energies, 14. 5. Farkas, G., Sarkany, Z., and Rencz, M. (2019). Structural Analysis of Power Devices and Assemblies by Thermal Transient Measurements. Energies, 12.
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