Data-Driven Methods for Reliability and Safety Engineering: Applications in Industrial Systems : Leveraging AI, Machine Learning, and Advanced Analytics to Enhance Risk Assessment, Decision-Making, and System Performance
Language: English
Published by Palgrave Macmillan, 2026
- Hardcover
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a comprehensive guide to using data-driven methods in reliability and safety engineering for industrial systems. It explores how modern technologies like data analytics, machine learning, and artificial intelligence can enhance decision-making, predict failures, and improve system resilience.In an era of increasingly complex industrial systems, traditional methods often fail to address reliability and safety challenges. This book highlights how integrating data-driven techniques can optimize system performance, reduce risks, and enhance safety outcomes. Key topics include predictive maintenance, risk assessment, AI integration, and the challenges of implementing these technologies in real-world environments. Case studies across industries like energy and manufacturing illustrate the practical applications of these methods.This book is aimed at professionals in reliability engineering, safety, risk management, and industrial systems, as well as researchers and students seeking to understand the role of data-driven methods in modern engineering practices.…
Seller Inventory # 9783032228727
- Title
- Data-Driven Methods for Reliability and Safety Engineering: Applications in Industrial Systems : Leveraging AI, Machine Learning, and Advanced Analytics to Enhance Risk Assessment, Decision-Making, and System Performance
- Author
- He Li
- Publisher
- Palgrave Macmillan
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3032228727
- ISBN 13
- 9783032228727
- Item weight
- 1,401 grams
- Dimensions
- 241x160x43 mm
This book provides a comprehensive guide to using data-driven methods in reliability and safety engineering for industrial systems. It explores how modern technologies like data analytics, machine learning, and artificial intelligence can enhance decision-making, predict failures, and improve system resilience.
In an era of increasingly complex industrial systems, traditional methods often fail to address reliability and safety challenges. This book highlights how integrating data-driven techniques can optimize system performance, reduce risks, and enhance safety outcomes. Key topics include predictive maintenance, risk assessment, AI integration, and the challenges of implementing these technologies in real-world environments. Case studies across industries like energy and manufacturing illustrate the practical applications of these methods.
This book is aimed at professionals in reliability engineering, safety, risk management, and industrial systems, as well as researchers and students seeking to understand the role of data-driven methods in modern engineering practices.
"Synopsis" may belong to another edition of this title.
About the Author
Dr. He Li obtained his Ph.D. degrees from the University of Electronic Science and Technology of China, China (2021), and the University of Lisbon, Portugal (2025), and has been a researcher at Liverpool John Moores University, UK (2024-). He is a fellow of the International Society of Engineering Asset Management (ISEAM Fellow), a technical committee member of the European Safety and Reliability Association (Marine Engineering, ESRA Fellow), and an editor/associate editor/guest editor/editorial board member for more than 10 Journals. He has been selected as a Marie Curie fellow and recognized as a World's Top 2% Scientist (2024, 2025). Dr. Li has a selection of publications, e.g., monographs and journal/conference papers with several highly cited/hot papers and best paper awards. His research focuses on the reliability and maintainability of marine energy systems.
Professor Ke Feng is a full professor at Xi’an Jiaotong University, China. He is a Marie Curie fellow, a World's Top 2% Scientist, and received a Ph.D. degree from the University of New South Wales, Australia. He worked at the University of British Columbia and the National University of Singapore in 2022 and 2023, respectively. His main research interests include digital twins, vibration analysis, structural health monitoring, dynamics, tribology, signal processing, and machine learning. He is recognized as the emerging leader (2023) by the Measurement Science and Technology journal. He has been the associate editor and guest editor of several journals, including IEEE Transactions on Industrial Informatics, Information Fusion, Mechanical Systems and Signal Processing, IEEE Transactions on Industrial Cyber-Physical Systems, etc.
Professor Mohammad Yazdi is an assistant professor at Macquarie University, Australia. He earned a dual Ph.D. degree from Memorial University of Newfoundland, Canada, and Macquarie University, Australia. His research interests and professional background converge at the nexus of system safety, risk assessment, resilience, process integrity, and asset management, especially concerning renewable and non-renewable energy infrastructure. With an impressive track record of leading large-scale energy projects and technology-rich initiatives, Mohammad offers invaluable support to asset operators, developers, and maintainers and has been recognized as a World's Top 2% Scientist for many years.
Professor Hong-Zhong Huang is a full professor and director of the Center for System Reliability and Safety, at the University of Electronic Science and Technology of China. He has held visiting appointments at several universities in the USA, Canada, and Asia. He received a Ph.D. degree in reliability engineering from Shanghai Jiaotong University, China. He has published more than 200 journal papers and 5 books in the fields of reliability engineering, optimization design, fuzzy sets theory, and product development. His main research interests include reliability design, optimization design, condition monitoring, fault diagnosis, and life prediction.
"About the title" may belong to another edition of this title.
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