articleIEEE Transactions on Intelligent Transportation SystemsOct 22, 2020Closed access

Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives

HCHongtian ChenBJBin JiangSXSteven X. DingBHBiao Huang

University of Alberta · Nanjing University of Aeronautics and Astronautics · +1 more institution

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Abstract

Recently, to ensure the reliability and safety of high-speed trains, detection and diagnosis of faults (FDD) in traction systems have become an active issue in the transportation area over the past two decades. Among these FDD methods, data-driven designs, that can be directly implemented without a logical or mathematical description of traction systems, have received special attention because of their overwhelming advantages. Based on the existing data-driven FDD methods for traction systems in high-speed trains, the first objective of this paper is to systematically review and categorize most of the mainstream methods. By analyzing the characteristic of observations from sensors equipped in traction systems,…

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Topics & keywords

Topics
Keywords
  • Train
  • Traction (geology)
  • Computer science
  • Implementation
  • Control engineering
  • Engineering
  • Fault detection and isolation
  • Artificial intelligence
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