Impactability and susceptibility assessment based on D-S evidence theory for analyzing the risk of fault propagation among catenary components
As an important part of the traction power supply system, the research on fault prevention of the catenary system has become a crucial issue for efficient operation and maintenance. In this paper, we propose a data-driven approach to investigate the underlying correlations among catenary components...
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Published in | Reliability engineering & system safety Vol. 251; p. 110389 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.11.2024
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Subjects | |
Online Access | Get full text |
ISSN | 0951-8320 |
DOI | 10.1016/j.ress.2024.110389 |
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Abstract | As an important part of the traction power supply system, the research on fault prevention of the catenary system has become a crucial issue for efficient operation and maintenance. In this paper, we propose a data-driven approach to investigate the underlying correlations among catenary components from the historical fault data, so that the fault propagation mechanisms among components can be revealed. Initially, based on the different roles played by components in the fault propagation process, we define fault impactability and susceptibility of components under different mechanical coupling relationships to capture the fault propagation mechanisms. Then, we propose a risk trust function model based on the D-S evidence theory to assess the fault impactability and susceptibility. Meanwhile, a belief and disbelief-based risk coefficient is proposed in the risk trust function model to construct the evidence source. Finally, the case study, based on the fault database of the Chengdu Railway Bureau, demonstrates that the proposed method can effectively assess the fault impactability and susceptibility of components to reveal the fault propagation mechanisms, which provides valuable references for formulating fault prevention strategies. |
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AbstractList | As an important part of the traction power supply system, the research on fault prevention of the catenary system has become a crucial issue for efficient operation and maintenance. In this paper, we propose a data-driven approach to investigate the underlying correlations among catenary components from the historical fault data, so that the fault propagation mechanisms among components can be revealed. Initially, based on the different roles played by components in the fault propagation process, we define fault impactability and susceptibility of components under different mechanical coupling relationships to capture the fault propagation mechanisms. Then, we propose a risk trust function model based on the D-S evidence theory to assess the fault impactability and susceptibility. Meanwhile, a belief and disbelief-based risk coefficient is proposed in the risk trust function model to construct the evidence source. Finally, the case study, based on the fault database of the Chengdu Railway Bureau, demonstrates that the proposed method can effectively assess the fault impactability and susceptibility of components to reveal the fault propagation mechanisms, which provides valuable references for formulating fault prevention strategies. |
ArticleNumber | 110389 |
Author | Shibin, Gao Diyang, Liu Jian, Shi Jiaming, Luo Xiaoguang, Wei |
Author_xml | – sequence: 1 givenname: Liu surname: Diyang fullname: Diyang, Liu organization: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China – sequence: 2 givenname: Gao surname: Shibin fullname: Shibin, Gao organization: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China – sequence: 3 givenname: Wei orcidid: 0000-0001-9091-3345 surname: Xiaoguang fullname: Xiaoguang, Wei email: wei_xiaoguang@126.com organization: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China – sequence: 4 givenname: Luo surname: Jiaming fullname: Jiaming, Luo organization: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China – sequence: 5 givenname: Shi orcidid: 0000-0001-5777-3678 surname: Jian fullname: Jian, Shi organization: Department of Engineering Technology, University of Houston, Houston, TX 77004, USA |
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