Network approach to modelling and analysing failure propagation in high-speed train systems

Abstract

Since a high-speed train (HST) system is a complex integration of multiple minimum maintenance units (MMUs), a single faulty MMU can trigger a cascade of failures (specifically, in the form of fault spreading) and damage a substantial part of the HST system. To investigate the failure propagation in an HST system, the system is first abstracted to a weighted directed network composed of MMUs, connections and functional properties. Next, a failure propagation model is constructed from the perspective of load redistribution to mimic the process of failure propagation originating from a single failed MMU. The node reliability and the topological relationships among nodes are introduced to quantify the initial load and the capacity of nodes in the proposed model. Subsequently, this model is then applied to analyse the failure propagation in a China railway high-speed X (CRHX) HST under four types of attack strategies for a single MMU failure. The simulation results demonstrate that the proposed approach is highly effective in predicting the failure propagation, estimating the influence of the failure propagation on the HST system, and diagnosing large-scale failures. The results can provide guiding significance for maintenance personnel to develop strategies to reduce losses and optimise the management scheme.

KEYWORDS:

• Failure propagation
• network science
• high-speed train systems
• network efficiency
• risk management

Acknowledgements

We would like to thank the anonymous reviewers for their constructive comments and suggestions, which can help to improve this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research is partially supported by a project funded by the Youth Program of the National Natural Science Foundation of China [award number 71901019] and Shanghai Post-doctoral Excellence Program (Shanghai Municipal Human Resources and Social Security Bureau) [award number 2020242].

Notes on contributors

Shuai Lin

Shuai Lin is a post-doctor in Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai, China. She completed her Ph.D. and postdoctoral program in safety science and engineering from Beijing Jiaotong University, Beijing, China, in 2018 and 2019. Her research interests include the reliability and safety of complex electromechanical systems.

Limin Jia

Limin Jia is a professor in the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University. He received the Ph.D. degree from the China Academy of Railway Sciences, Beijing, China, in 1991. His current research interests include safety science and engineering, control science and engineering, and transportation engineering.

Hengrun Zhang

Hengrun Zhang is pursuing his Ph.D. degree in the Department of Computer Science, Volgenau School of Engineering, George Mason University, Fairfax, VA, USA. He received his Master's degree from Shanghai Jiao Tong University, Shanghai, China, in 2015, and George Mason University, Fairfax, VA, USA, in 2018. His research interests include machine learning and data mining, as well as network communication.

Pengzhu Zhang

Pengzhu Zhang is a professor in the Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai, China. His articles have appeared in Decision Support Systems, Information Systems Engineering and many journals in Chinese. His research interests include group decision/team support systems, financial management information systems, E-government & E-commerce.