Intelligent Operation and Maintenance for Subsea Production Systems
Description
This book systematically explores the challenges and advancements in integrating intelligent technologies with ocean engineering, with a particular focus on three core topics of fault diagnosis, fault prognosis, and maintenance for subsea production systems in harsh environments. It specifically addresses subsea engineering, focusing on the intersection with intelligent technologies in operation and maintenance, and also appeals to scholars and engineers from various disciplines, including Mechanical Engineering, Electrical Engineering, Oil and Gas Production, Reliability Engineering, and other related fields. This book introduces the latest algorithmic models for fault diagnosis, prognosis and maintenance, grounded in advanced methodologies such as big data, digital twin, Bayesian Networks. It features comprehensive figures, detailed tables, and a novel presentation style, making complex research more accessible. Additionally, this book stands out for its systematic approach to integrating cutting-edge methodologies with practical applications, providing practical insights and demonstrating foresight in the field of intelligent operation and maintenance for subsea production systems. The book is intended for graduate students, researchers, practitioners, industry engineers, and maintenance professionals specializing in subsea engineering, marine technology, intelligent systems, oil and gas production systems, and alike.
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Baoping Cai received the Ph.D. degree in Mechanical and Electronic Engineering from the China University of Petroleum (East China), Qingdao, China, in 2012. He is currently Dean, Professor, and Ph.D. Supervisor of the College of Mechanical and Electrical Engineering at China University of Petroleum (East China), the recipient of the National Science Fund for Distinguished Young Scholars, Distinguished Young Scholar of Shandong Province, Taishan Scholar of Shandong Province, and Hong Kong Scholar. He has been selected into the top 2% of scientists in the world for 4 consecutive years. In recent years, he has led 4 projects funded by the National Natural Science Foundation of China. Additionally, he has led 1 project and 3 special topics of National Key Research and Development Program, 1 sub-project of the National 863 Program, 2 special projects of the Ministry of Industry and Information Technology for high-tech ship research, and over 10 provincial and ministerial-level projects.
Yiliu Liu received the Ph.D. degree in Management Science and Engineering from Tianjin University, Tianjin, China. He is currently Professor at the Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU). The main application areas of his research results are in the energy industries, including offshore oil and gas production, and renewable energies, such as wind power and green hydrogen. Prof. Liu has published more than 180 papers in international journals and conferences and one book. Currently, he is Co-chair of Sweden-Norway joint section of IEEE Reliability Society, Editor-in-Chief for International Journal of Reliability and Safety, Subject Editor for the journal of Process Safety and Environmental Protection, and Director of International RAMS (reliability, availability, maintainability, and safety) master program at NTNU. He also manages several national, EU, and international research projects related to accident prevention and sustainability development in the offshore and energy industries.
Yonghong Liu received the Ph.D. degree in Mechanical Manufacturing and Automation from the Harbin Institute of Technology, Harbin, China, in 1996. He is currently Professor and Ph.D. Supervisor of the College of Mechanical and Electrical Engineering at the China University of Petroleum (East China), China, National Candidate of New Century Hundred Million Talents Project, Elsevier Highly Cited Chinese Researchers, Taishan Scholar Distinguished Professor, and Vice Chairman of the Nontraditional Manufacturing branch of the Chinese Mechanical Engineering Society. He has published more than 300 SCI journal papers and 7 English academic monographs. He has been granted 32 invention patents, including 12 international invention patents. He has won 4 first-prize award and 7 second-prize awards of provincial and ministerial-level science and technology, and 1 China Patent Excellence Award. His research interests include nontraditional machining, intelligent manufacturing, and fault diagnosis methodology.
Yixin Zhao received the Ph.D. degree in RAMS (reliability, availability, maintainability, and safety) from Norwegian University of Science and Technology (NTNU). She is currently Post-doc and Lecturer in the College of Mechanical and Electrical Engineering at China University of Petroleum (East China). She has published over 10 peer-reviewed papers and 1 English academic monograph in the field of reliability and maintenance. Her research interests include subsea systems, cascading failures, reliability engineering, and maintenance optimization.
Xiaoyan Shao is Ph.D. Candidate in Mechanical Engineering at China University of Petroleum (East China). During his doctoral studies, he focused on the remaining useful life (RUL) prediction and life-extension optimization of key equipment in subsea production systems. He established a systematic methodology spanning data augmentation, predictive accuracy improvement, and optimized maintenance strategies, addressing critical challenges such as missing monitoring data, low precision in small-sample data, and difficulties in equipment degradation modeling. He has published over 20 SCI-indexed papers in journals such as IEEE Transactions on Industrial Electronics, Journal of Industrial Information Integration, Mechanical Systems and Signal Processing, Reliability Engineering & System Safety, and Ocean Engineering. His work includes two ESI highly cited papers. Shao holds 9 Chinese invention patents, 3 U.S. patents, and 3 Australian patents. He led a key project funded by the Graduate Innovation Fund of China University of Petroleum (East China).
Content
1. Fault diagnosis for composite faults and minor faults.- 2. Digital twin-assisted intelligent fault diagnosis.- 3. Optimal sensor placement for fault diagnosis.- 4. Fault diagnosis for subsea control system.- 5. Concurrent fault diagnosis for electric-hydraulic system.