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Description
This book provides a unified and forward looking framework for reliability centred asset management under uncertainty, responding to the increasing complexity and risk exposure of modern engineering systems. It integrates reliability engineering, risk analysis, artificial intelligence, and human factors to support robust and transparent decision making across the entire asset lifecycle.
The book moves beyond traditional reliability based approaches by explicitly addressing both aleatory and epistemic uncertainty and their impact on engineering decisions. It presents state of the art methods for uncertainty modelling, risk assessment, and multi criteria decision making, alongside data driven and AI based techniques for predictive maintenance, condition monitoring, and asset performance optimisation. Equal emphasis is placed on technical models and the role of human judgement, cognitive bias, and organisational influences on safety, reliability, and resilience.
Real world case studies from energy, manufacturing, infrastructure, and maritime sectors demonstrate how the proposed methods can be implemented in practice, highlighting both benefits and challenges. The book also explores emerging developments such as digital twins, Industry 4.0 technologies, and resilience engineering, positioning asset management within the broader context of digital transformation.
Designed as both a reference and a learning resource, this book is intended for researchers, practicing engineers, asset managers, and advanced students seeking an integrated, practical, and scientifically rigorous approach to managing complex engineering assets under uncertainty.
Introduction to Reliability-Centred Asset Management.- Foundations of Reliability Engineering and Asset Management.- Uncertainty Modelling in Engineering Systems.- Risk Assessment and Decision-Making Frameworks.- Multi-Criteria Decision-Making Methods.- Artificial Intelligence in Reliability and Predictive Maintenance.- Human Factors and Cognitive Bias in Reliability.- Resilience Engineering and Adaptive Systems.- Lifecycle Reliability: Design, Manufacturing, and Operations.- Case Studies in Complex Engineering Systems.- Digital Transformation and Smart Asset Management.- Future Directions in Reliability and Asset Management.
Dr. He Li is a researcher in reliability and maintainability engineering at Liverpool John Moores University, UK. He received his PhD from the University of Electronic Science and Technology of China and the University of Lisbon, respectively. He has led or contributed to more than ten international projects funded by the EU, UK, Portugal, and China, and has published a number of monographs and papers. Dr Li is a Fellow of the International Society of Engineering Asset Management and a Technical Committee member of the European Safety and Reliability Association. He is a Marie Curie Fellow and a World's Top 2% Scientist (2024-2026). His honours include the Emerging Leader Award given by Measurement Science and Technology Journal of IOP, the global Gold Award of A' Design (2023), and LJMU's Ringsing Star Award (2025). He serves as an Associate Editor and an editorial board member or guest editor for 12 journals. He has held leadership roles in more than 20 international conferences.
Professor Mingwei Li is a Professor at the College of Shipbuilding Engineering, Harbin Engineering University, and a nationally recognised Young Changjiang Scholar. He is Director of the Institute of Ocean Engineering Technology, CTO of Harbin Electric Machinery Company Limited, and Deputy Director of the MIIT Key Laboratory of Deep Sea Engineering Equipment. His research focuses on digital and intelligent water transportation engineering and green energy technologies. He has led more than 50 projects, published over 80 papers and four monographs, and served on editorial boards of five journals and committees of six international conferences. He has received six scientific and technological awards and five teaching awards.
Professor Mohammad Yazdi is an academic at Macquarie University, Australia, specialising in digital transformation, systems engineering, and data driven decision making. His work centres on digital twins, data analytics, and intelligent systems for improving organisational and infrastructure performance. He has published more than 180 papers and chapters and has authored or edited five books with major publishers.
Professor Hong Zhong Huang is Director of the Centre for System Reliability and Safety at the University of Electronic Science and Technology of China. He has held visiting positions internationally and has published more than 200 journal papers and five books in reliability engineering, optimisation design, fuzzy systems, and product development. His research interests include reliability design, condition monitoring, fault diagnosis, and life prediction.
Professor Jin Wang is the Director of the Liverpool Logistics, Offshore and Marine Research Institute. His research interests are designing and operating sizeable maritime engineering systems. He has been actively involved in a variety of research and scholarly activities such as the organisation of international conferences (e.g., ICTIS 2019), provision of advice in national and international organisations as an expert (e.g., IMO since 2010), management of professional bodies in UK/Europe (e.g., Chairman of the Maritime and Offshore Technology Committee of the European Safety and Reliability Society since 2010), research quality assessment (e.g., engineering sub-panel member in UK Research Excellence Framework 2014 and 2021), and implementation of research programmes (e.g., coordination of 3 EU funded and 7 UK research council funded projects). He is a Fellow of the Institute of Marine Engineering, Science and Technology, the UK Safety and Reliability Society, the Royal Institute of Naval Architects, as well as a Member of the Academia Europaea and a Member of the European Academy of Sciences and Arts.
