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.

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

Inhalt
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.