Overview
- Offers essential guidance on fault detection and diagnosis (FDD) for traction systems in high-speed trains
- Focuses on FDD strategies with novel data modeling methods and novel test statistics
- Presents data-driven fault detection and diagnosis (FDD) techniques and their practical applications to high-speed trains in China
- Expands the application scope of data-driven FDD techniques, and provides researchers and practitioners with informative guidance for FDD of traction systems in high-speed trains
Part of the book series: Lecture Notes in Intelligent Transportation and Infrastructure (LNITI)
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Table of contents (9 chapters)
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Front Matter
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Background and Basic Methods
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Front Matter
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Data-Driven Designs Focusing on Multivariate Analysis
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Front Matter
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Data-Driven Designs Focusing on Test Statistics
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Front Matter
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Conclusions and Perspectives
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Front Matter
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Keywords
About this book
This book addresses the needs of researchers and practitioners in the field of high-speed trains, especially those whose work involves safety and reliability issues in traction systems. It will appeal to researchers and graduate students at institutions of higher learning, research labs, and in the industrial R&D sector, catering to a readership from a broad range of disciplines including intelligent transportation, electrical engineering, mechanical engineering, chemical engineering, the biological sciences and engineering, economics, ecology, and the mathematical sciences.
Authors and Affiliations
Bibliographic Information
Book Title: Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains
Authors: Hongtian Chen, Bin Jiang, Ningyun Lu, Wen Chen
Series Title: Lecture Notes in Intelligent Transportation and Infrastructure
DOI: https://doi.org/10.1007/978-3-030-46263-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-46262-8Published: 26 April 2020
Softcover ISBN: 978-3-030-46265-9Published: 26 April 2021
eBook ISBN: 978-3-030-46263-5Published: 25 April 2020
Series ISSN: 2523-3440
Series E-ISSN: 2523-3459
Edition Number: 1
Number of Pages: XIII, 160
Number of Illustrations: 6 b/w illustrations, 47 illustrations in colour
Topics: Transportation Technology and Traffic Engineering, Control and Systems Theory, Computational Intelligence
Industry Sectors: Aerospace, Automotive, Engineering