Titlebook: Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains; Hongtian Chen,Bin Jiang,Wen Chen Book 2020 The Edi

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https://doi.org/10.1007/978-3-030-46263-5Data-driven Methods; Fault Detection and Diagnosis; Traction Systems; High-speed Trains; Principal Compo

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Deep PCA-Based FDD MethodsThis chapter develops a real-time incipient FDD method named deep PCA (DPCA) for traction systems in high-speed trains. This scheme adopting multivariate statistics is composed of multiple data processing layers to extract more accurate signal features of traction systems.

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PCA and Kullback-Leibler Divergence-Based FDD MethodsThis chapter presents an improved incipient FD method based on Kullback-Leibler divergence (KLD) under multivariate statistical analysis frame. Different from the traditional MVA-based FD methods, this methodology can detect slight anomalous behaviors by comparing the PDF online with the reference PDF obtained from large scale off-line datasets.

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PCA and Hellinger Distance-Based FDD MethodsIncipient faults in high-speed trains are usually masked by noises and disturbances from both process and sensors, which severely increases the difficulty of incipient FDD tasks.