This book addresses a modern topic in reliability: multi-state and continuous-state system reliability, which has been intensively developed in recent years. It offers an up-to-date overview of the latest developments in reliability theory for multi-state systems, engineering applications to a variety of technical problems, and case studies that will be of interest to reliability engineers and industrial managers. It also covers corresponding theoretical issues, as well as case studies illustrating the applications of the corresponding theoretical advances.
The book is divided into two parts: Modern Mathematical Methods for Multi-state System Reliability Analysis (Part 1), and Applications and Case Studies (Part 2), which examines real-world multi-state systems. It will greatly benefit scientists and researchers working in reliability, as well as practitioners and managers with an interest in reliability and performability analysis. It can also be used as a textbook or as a supporting text for postgraduate courses in Industrial Engineering, Electrical Engineering, Mechanical Engineering, Applied Mathematics, and Operations Research.
Anatoly Lisnianski is an engineering expert at the Reliability Department of the Israel Electric Corporation Ltd., an adjunct senior lecturer at Haifa University, and scientific supervisor of the Centre for Reliability and Risk Management at Shamoon College of Engineering, Israel. He received his MSc degree (1975) in Electrical Engineering from the University of Information Technology, Precision Mechanics and Optics, St. Petersburg, and his PhD degree (1984) in Reliability from the Federal Scientific & Production Centre "Aurora" in St. Petersburg, where he served as a senior researcher until 1989. Since 1991 he has been working at the Israel Electric Corporation, Haifa, Israel, where he has specialized in reliability and applied probability. He is a Senior Member of the IEEE and Member of the Israel Society of Quality and Israel Statistical Association. He is the author or co-author of more than 150 research papers in the field of reliability and applied probability and co-author of two books.
Alex Karagrigoriou is a Professor of Probability and Statistics and Director of Graduate Studies in Statistics and Actuarial-Financial Mathematics at the Department of Mathematics of the University of the Aegean, Greece. He studied at the University of Maryland, USA (MA, 1988, PhD, 1992), worked at the United States Department of Agriculture (USDA) and the Institute of Statistical Sciences, Taiwan, and taught at the Universities of Maryland, Athens, Aegean, Cyprus and Hellenic Open University. His research activities cover areas such as statistical modeling, model selection criteria, biostatistics, information theory, time series analysis, stochastic modeling, economic demography, finance and reliability theory. He has published over 100 research articles and has extensive experience in the design and execution of research projects involving the statistical analysis of medical, biomedical, technological, socioeconomic and economic data.
Ilia Frenkel is the Chair of the Center for Reliability and Risk Management and a Senior Lecturer at the Industrial Engineering and Management Department, Shamoon College of Engineering, Israel. He received his MSc in Applied Mathematics from Voronezh State University, Russia, and his PhD in Operational Research and Computer Science from the Institute of Economy, Ukrainian Academy of Science, former USSR. He has more than 40 years of academic and teaching experience at universities and institutions in Russia and Israel.
He has specialized in applied statistics and reliability with applications to preventive maintenance. He is an Editor and a member of the editorial board for numerous scientific and professional journals.
He has published one book and more than 50 scientific articles and book chapters in the fields of reliability, applied statistics, and production and operation management.
Reliability of a Network with Heterogeneous Components.- Reliability Analysis of Complex Multi-State System with Common Cause Failure Based on DS Evidence Theory and Bayesian Network.- A D-MMAP to Model a Complex Multi-state System with Loss of Units.- Modeling and Inference for Multi-state Systems.- Optimizing Availability and Performance of a Two-unit Redundant Multi-state Deteriorating System.- Phase-type Models and Their Extension to Competing Risks.