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Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains [electronic resource] / by Hongtian Chen, Bin Jiang, Ningyun Lu, Wen Chen.

By: Chen, Hongtian [author.].
Contributor(s): Jiang, Bin [author.] | Lu, Ningyun [author.] | Chen, Wen [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Intelligent Transportation and Infrastructure: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XIII, 160 p. 53 illus., 47 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030462635.Subject(s): Transportation engineering | Traffic engineering | Control engineering | Computational intelligence | Transportation Technology and Traffic Engineering | Control and Systems Theory | Computational IntelligenceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 629.04 Online resources: Click here to access online
Contents:
Introduction -- Traction Systems and Experimental Platforms -- Basics of Data-driven FDD Methods -- Multi-mode PCA-based FDD Methods -- Probability-relevant PCA-based FDD Methods -- Deep PCA-based FDD Methods -- PCA and Kull back-Leibler Divergence-based FDD Methods -- PCA and Hellinger Distance-based FDD Methods -- Conclusions and Further Work. .
In: Springer Nature eBookSummary: 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. .
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Introduction -- Traction Systems and Experimental Platforms -- Basics of Data-driven FDD Methods -- Multi-mode PCA-based FDD Methods -- Probability-relevant PCA-based FDD Methods -- Deep PCA-based FDD Methods -- PCA and Kull back-Leibler Divergence-based FDD Methods -- PCA and Hellinger Distance-based FDD Methods -- Conclusions and Further Work. .

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. .

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