Yonghong Liu

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.

This book presents a bibliographical review of the use of Bayesian networks in reliability over the last decade. Bayesian network (BN) is considered to be one of the most powerful models in probabilistic knowledge representation and inference, and it is increasingly used in the field of reliability. After focusing on the engineering systems, the book subsequently discusses twelve important issues in the BN-based reliability methodologies, such as BN structure modeling, BN parameter modeling, BN inference, validation, and verification. As such, it is a valuable resource for researchers and practitioners in the field of reliability engineering.

This book presents a bibliographical review of the use of Bayesian networks in reliability over the last decade. Bayesian network (BN) is considered to be one of the most powerful models in probabilistic knowledge representation and inference, and it is increasingly used in the field of reliability. After focusing on the engineering systems, the book subsequently discusses twelve important issues in the BN-based reliability methodologies, such as BN structure modeling, BN parameter modeling, BN inference, validation, and verification. As such, it is a valuable resource for researchers and practitioners in the field of reliability engineering.