• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 57 Issue 5
Oct.  2022
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Article Contents
LYU Biao, LIU Yumeng. Resilience Assessment Based on Bayesian Network for on-Board Subsystem of CTCS-3 Train Control System[J]. Journal of Southwest Jiaotong University, 2022, 57(5): 949-959. doi: 10.3969/j.issn.0258-2724.20210102
Citation: LYU Biao, LIU Yumeng. Resilience Assessment Based on Bayesian Network for on-Board Subsystem of CTCS-3 Train Control System[J]. Journal of Southwest Jiaotong University, 2022, 57(5): 949-959. doi: 10.3969/j.issn.0258-2724.20210102

Resilience Assessment Based on Bayesian Network for on-Board Subsystem of CTCS-3 Train Control System

doi: 10.3969/j.issn.0258-2724.20210102
  • Received Date: 02 Feb 2021
  • Rev Recd Date: 30 Jun 2021
  • Available Online: 22 Aug 2022
  • Publish Date: 06 Sep 2021
  • To make up the deficiency of existing indexes, resilience is introduced as the operation stability index for CTCS-3 (China train control system-3) on-board subsystem under abnormal events. The quantitative evaluation method of on-board subsystem resilience is proposed, the resilience evaluation model based on Bayesian network (BN) is constructed, and five kinds of component importance indexes based on resilience are defined. The bi-directional reasoning function of Bayesian network is used to evaluate the resilience of on-board subsystem under different disturbances and calculate the component importance indexes. The results show that, the resilience index can fully describe the capability of on-board subsystem to resist disturbance or recover from disturbance, and under the disturbance of abnormal events, resilience and availability indexes have marked differences. Different disturbance scenarios lead to obviously different resilience. When disturbance occurs, the resilience of the on-board subsystem is 0.8017 when it is affected by magnetic storm, 0.8819 when it is affected by thunder, and 0.9880 when it is disturbed by snow and ice. The component importance depends on the scenario, specifically, the same component may be varied in the importance ranking in different disturbance scenarios, and may change dynamically with time.

     

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