H
Hongtian Chen
Researcher at University of Alberta
Publications - 94
Citations - 1578
Hongtian Chen is an academic researcher from University of Alberta. The author has contributed to research in topics: Computer science & Fault detection and isolation. The author has an hindex of 13, co-authored 41 publications receiving 616 citations. Previous affiliations of Hongtian Chen include Nanjing University of Aeronautics and Astronautics.
Papers
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Journal ArticleDOI
A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains
Hongtian Chen,Bin Jiang +1 more
TL;DR: A comprehensive review on the fault detection and diagnosis techniques for high-speed trains is presented using data-driven methods which are receiving increasing attention in transportation fields over the past ten years.
Journal ArticleDOI
Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives
TL;DR: To ensure the reliability and safety of high-speed trains, detection and diagnosis of faults (FDD) in traction systems have become an active issue in the transportation area over the past two decades and most of the mainstream methods are reviewed.
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Deep PCA Based Real-Time Incipient Fault Detection and Diagnosis Methodology for Electrical Drive in High-Speed Trains
TL;DR: A real-time incipient FDD method named deep principal component analysis (DPCA) for electrical drive in high-speed trains that can effectively detect incipient faults in electrical drive before they develop into faults or failures is developed.
Journal ArticleDOI
Data-driven Detection and Diagnosis of Incipient Faults in Electrical Drives of High-Speed Trains
TL;DR: Under principal component analysis (PCA) framework, a new data-driven FDD method is proposed, which is named probability-relevant PCA (PRPCA), for electrical drives in high-speed trains and can greatly improve the fault detectability and achieve accurate fault diagnosis via support vector machine.
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An improved incipient fault detection method based on Kullback-Leibler divergence.
TL;DR: An improved incipient fault detection method based on Kullback-Leibler (KL) divergence under multivariate statistical analysis frame that can detect slight anomalous behaviors by comparing the online probability density function online with the reference PDF obtained from large scale off-line data set is presented.