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Basics of Data-Driven FDD Methods

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Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains

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Abstract

Compared with signal analysis-based and model-based methods, data-driven FDD schemes can be implemented directly by sufficient use of information hidden in the abundant recorded data. Nowadays, a variety of advanced sensors can ensure implementation and promote development of the data-driven FDD methods. To our best knowledge, multivariate statistical analysis (MSA) and subspace identification method (SIM) are two parallel methods providing basic tools and techniques to deal with FDD problems in both stationary and dynamic operating conditions. Therefore, this chapter firstly describes the basics including MSA, SIM, together with the used test statistic, which serves as the fundamentals of this work; based on these basics, challenging topics of FDD applications to high-speed trains is then summarized.

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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

Authors
  1. Hongtian Chen
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  2. Bin Jiang
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  3. Ningyun Lu
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  4. Wen Chen
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Correspondence to Hongtian Chen .

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Chen, H., Jiang, B., Lu, N., Chen, W. (2020). Basics of Data-Driven FDD Methods. 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_3

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  • DOI: https://doi.org/10.1007/978-3-030-46263-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46262-8

  • Online ISBN: 978-3-030-46263-5

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