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Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains pp 3–31Cite as

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Abstract

High-speed trains have become one of the most important and advanced branches of intelligent transportation, of which the reliability and safety are still not mature enough for keeping up with other aspects. In the past one decade, data-driven fault detection and diagnosis (FDD) methods for high-speed trains are receiving increasing attention in transportation fields, and have always been a new category which is parallel to the signal analysis-based and model-based FDD methods. In this book, the data-driven FDD methods for high-speed trains are emphatically investigated and presented in both theoretical and practical aspects. Additional contributions of this thesis also cover the comprehensive review on FDD techniques for high-speed trains, the pros and cons of all FDD methods provided for researchers and practitioners with informative guidance, and some challenges and promising issues speculated for future investigation.

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Authors and Affiliations

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Hongtian Chen, Bin Jiang & Ningyun Lu

  2. Division of Engineering Technology, Wayne State University, Detroit, USA

    Wen Chen

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  1. Hongtian Chen
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Chen, H., Jiang, B., Lu, N., Chen, W. (2020). Introduction. In: Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-46263-5_1

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  • DOI: https://doi.org/10.1007/978-3-030-46263-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46262-8

  • Online ISBN: 978-3-030-46263-5

  • eBook Packages: EngineeringEngineering (R0)

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