Deep Learning-Based Detection of Catenary Support Component Defect and Fault in High-Speed Railways
Description
This book focuses on the deep learning technologies and their applications in the catenary detection of high-speed railways. As the only source of power for high-speed trains, the catenary's service performance directly affects the safe operation of high-speed railways. This book systematically shows the latest research results of catenary detection in high-speed railways, especially the detection of catenary support component defect and fault. Some methods or algorithms have been adopted in practical engineering. These methods or algorithms provide important references and help the researcher, scholar, and engineer on pantograph and catenary technology in high-speed railways. Unlike traditional detection methods of catenary support component based on image processing, some advanced methods in the deep learning field, including convolutional neural network, reinforcement learning, generative adversarial network, etc., are adopted and improved in this book. The main contents include the overview of catenary detection of electrified railways, the introduction of some advance of deep learning theories, catenary support components and their characteristics in high-speed railways, the image reprocessing of catenary support components, the positioning of catenary support components, the detection of defect and fault, the detection based on 3D point cloud, etc.
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Wenqiang Liu (IEEE Member) received his Ph.D. degree in electrical engineering from the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China, in 2021. From 2017 to 2019, he was a joint Ph.D. in the Department of Engineering Structures, Delft University of Technology, Delft, the Netherlands. He is currently a postdoc researcher in the Department of National Rail Transit Electrification and Automation Engineering Technology Research Center, the Hong Kong Polytechnic University, Hong Kong, China. His research interests include artificial intelligence, computer vision, imaging, signal processing, and their applications in fault diagnosis and maintenance of railway infrastructures. Dr. Liu is an associate editor of IEEE Transactions on Instrumentation and Measurement (IEEE TIM). He received the IEEE TIM's Outstanding Editor in 2022 and the Outstanding Reviewer in 2021.
Junping Zhong (IEEE Member) received his Ph.D. degree in electrical engineering from Southwest Jiaotong University, Chengdu, China, in 2022. From Oct 2019 to Oct 2020, he is a Ph.D student visitor in the Department of Railway Engineering, Delft University of Technology, Netherlands. From Feb 2023, he is a Postdoctoral Fellow in the Department of Industrial and Systems Engineering, Hong Kong Polytechnic University. His research interests include image processing, signal processing, and their applications in railway infrastructure fault detection. He has published 11 SCI/EI journal papers and 4 conference papers. He severs as a reviewer for IEEE TITS, IEEE TIM, and Applied Soft Computing. He was selected as the Outstanding Reviewer of IEEE Transactions on Instrumentation and Measurement in 2021.