Keywords
TCMS data
Remote diagnosis
Data processing
Fault prediction
Submitted : 2025-09-17
Accepted : 2025-10-02
Published : 2025-10-17
Abstract
This article focuses on the remote diagnosis and analysis of rail vehicle status based on the data of the Train Control Management System (TCMS). It first expounds on the importance of train diagnostic analysis and designs a unified TCMS data frame transmission format. Subsequently, a remote data transmission link using 4G signals and data processing methods is introduced. The advantages of remote diagnosis are analyzed, and common methods such as correlation analysis, fault diagnosis, and fault prediction are explained in detail. Then, challenges such as data security and the balance between diagnostic accuracy and real-time performance are discussed, along with development prospects in technological innovation, algorithm optimization, and application promotion. This research provides ideas for remote analysis and diagnosis based on TCMS data, contributing to the safe and efficient operation of rail vehicles.
References
Zhu H, Yu H, 2024, Analysis and Troubleshooting of Urban Rail Vehicle Electrical Faults Based on TCMS Terminal Simulation Device. Internal Combustion Engine and Accessories, (4): 78–80.
Lin B, Zhang J, Zheng D, et al., 2023, Fully Automatic Unmanned Train Air Conditioning System Control Scheme Based on TCMS. Electric Locomotive and the Urban Rail Vehicles, 46–48(6): 78–82.
Zhang S, Zheng B, Pan Y, et al., 2019, Analysis on TCMS Data Organization and Transmission Mechanism of “Fuxing” EMU. Railway Rolling Stock, 39(06): 1–4.
Sun J, Yang X, Liu B, et al., 2018, Remote Fault Diagnosis and Recovery of Fully Automatic Train Control System. Urban Rail Transit Research, 21(08): 149–151.
Feng Q, 2017, Design and Implementation of Remote Monitoring and Diagnosis System for Urban Rail Vehicles, dissertation, Dalian University of Technology.
Wang H, 2015, Research on Subway Vehicle Remote Diagnosis Ground System, dissertation, Beijing Jiaotong University.
Song J, 2016, Metro Vehicle Monitoring and Control System (TCMS) research, dissertation, Nanjing University of Science and Technology.
Xu J, Zhang T, 2015, Design of TCMS State Monitoring Simulation System for Tram. Railway Locomotive and EMU, (04): 5–7.
Nie D, 2016, Application Analysis of WTB Process Data Messages Based on Time Division Multiplexing. Technology Outlook, 26(36): 120.
Li R, 2021, Optimization Method for TCMS Signal Transmission Delay of Light Rail Trains Exported to Israel. Smart Rail Transit, 58(05): 19–24.
Shen R, Gong L, 2017, Research on the Overall Plan of China Locomotive Remote Monitoring and Diagnostic System (CMD System). China Railway, (03): 9–15.
Goyal D, Sharma A, Houran AM, 2024, Intelligent Machinery Fault Diagnostics and Prognostics: The Future of Smart Manufacturing, CRC Press.
Rigatos G, Abbaszadeh M, Hamida M, et al., 2024, Fault Diagnosis for Electric Power Systems and Electric Vehicles, CRC Press.
Yuan X, Liu H, Yang F, et al., 2024, Symmetric Circulant Matrix Decomposition-Based Multivariable Group Sparse Coding for Rolling Bearing Fault Diagnosis. Measurement Science and Technology, 35(6).
Ding X, Deng A, Deng M, et al., 2024, A Dual-Weighted Adversarial Network for Partial Domain Fault Diagnosis of Machinery. Measurement Science and Technology, 35(6).
Lin Y, Wang Y, Zhang M, et al., 2024, Information-Guided Signal Multi-Granularity Contrastive Feature Learning for Fault Diagnosis with Few Labeled Data. Advanced Engineering Informatics, 61102471.
Wen T, Gao S, Wang J, et al., 2024, Signed-Data Reinforced Observer-Based Fault Diagnosis for Virtually-Coupled Electric Multiple Units Trains. Control Engineering Practice, 147105921.
Zhang X, Wang Z, Du M, et al., 2024, Research on Data-Driven Model for Power Grid Fault Diagnosis Fusing Topological Quantification Information. Engineering Applications of Artificial Intelligence, 133(PC): 108286.
Zhang Y, Cao Y, Zhao Y, 2024, Research on Fault Location Method of Train Operation Control System Based on Delta Debugging. Journal of Railway, 46 (11): 84–92.
Zhang J, 2024, Simulation Model Analysis of Train Communication Network Fault Diagnosis. Heilongjiang Science, 15(18): 91–93 + 97.