Deep Learning-Based Detection of Catenary Support Component Defect and Fault in High-Speed Railways (Advances in High-speed Rail Technology)

Liu, Zhigang; Liu, Wenqiang; Zhong, Junping

ISBN 10: 9819909554 / ISBN 13: 9789819909551

Editore: Springer, 2024

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Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.

Codice articolo 42807235/1

Dati bibliografici

• Titolo: Deep Learning-Based Detection of Catenary Support Component Defect and Fault in High-Speed Railways (Advances in High-speed Rail Technology)
• Editore: Springer
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 9819909554
• ISBN 13: 9789819909551
• Rilegatura: Brossura
• Condizione: Hervorragend
• Cataloghi dei venditori:
  • Bücher

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Zhigang Liu (IEEE Fellow, IET Fellow, AAIA Fellow) received the Ph.D. degree in Power system and its Automation from Southwest Jiaotong University, China in 2003. He is currently a Full Professor of the School of Electrical Engineering, Southwest Jiaotong University, Chengdu. He is also a Guest Professor of Tongji University. Shanghai. He has authored three books and published more than 200 peer-reviewed journal and conference articles. His research interests include the electrical relationship of EMUs and traction, detection, and assessment of pantograph-catenary in high-speed railway. Dr. Liu is an Associate Editor-in-Chief of IEEE Transactions on Instrumentation and Measurement, Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Vehicular Technology and IEEE Access. He received the IEEE TIM's Outstanding Associate Editors for 2019, 2020 and 2021, and the Outstanding Reviewer of IEEE Transactions on Instrumentation and Measurement in 2018. 

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.