This book introduces a new Model-Based Reliability System Engineering (MBRSE) methodology, including its concept connotation, basic theory and technologies, framework of integrated platform and engineering application cases. Different from traditional Reliability System Engineering (RSE) methods that mainly rely on the management of documents, MBRSE is model centric method, with which the design for hexability (including “reliability, maintainability, testability, supportability, environmental adaptability and safety”) is performed by the revolution of united models in complete R&D fields and the process of closed- loop failure mitigation. This method can integrate all techniques and approaches of reliability engineering into the main process of R&D, and therefore is considered as a generalized hexability design and analysis tool. Furthermore, the future perspectives in the development of MBRSE are also presented through the combination of Digital Twin technology.

The main objective of the book is to provide the readers who have engaged in theoretical and technical researches, project management and engineering design applications of reliability engineering, as well as for teachers, senior undergraduates and postgraduates in colleges and universities working on reliability engineering and system engineering.

About the Author

Yi Ren received the Ph.D. degree in reliability engineering and systems engineering from Beihang University. He is currently a professor at the School of Reliability and System Engineering at Beihang University in Beijing, China. He is currently the associate director of the Institute of reliability engineering at Beihang University.He has over 20 years of research and teaching experience in reliability engineering and system engineering. He is the Team Leader of the KW-GARMS� reliability engineering software platform, and a holder of six Chinese ministry-level professional awards and one Chinese nation-level professional award. His recent research interests include reliability of complex systems, physics of failure, model-based reliability systems engineering (MBRSE), computer aided reliability analysis and simulation, reliability digital engineering, and product life-cycle reliability engineering management. He has led over 20 projects supported by government, industries and companies, and has published over 100 papers and 3 books.

Cheng Qian received the Ph.D. degree in aerospaceengineering from Delft University of Technology, and the M.S. and B.S. degrees from Beijing Institute of Technology. He is currently an associate professor at the School of Reliability and Systems Engineering at BeihangUniversity in Beijing, China. He considers himself as an experienced research scientist in reliability modeling, optimization and improvement of complex products and systems. He has held more than 20 national funded and industrial projects, published over 80 research papers and 5 book chapters. He has also been active in academic activities as the associate editor and guest editor in a couple of journals, and the committee member in the reliability branch of Chinese Institute of Electronics.

Dezhen Yang received the Ph.D. degree in reliability engineering and systems engineering from BeihangUniversity. She is currently an associate professor at the School of Reliability and System Engineering at BeihangUniversity in Beijing, China. She teaches the course “Product Reliability and Maintainability Design” for undergraduate students. Her recent research interests include reliability of complex systems, model-based reliability systems engineering (MBRSE), and computer aided reliability analysis and simulation. She has led or participated in more than 20 projects supported by government, industries and companies, and has published over 40 papers and 1 book.

Qiang Feng received the Ph.D. degree in reliability engineering and systems engineering from BeihangUniversity, Beijing, China; and the B.S. degree in mechanical engineering from Beihang University, Beijing, China. He is currently an associate professor at the School of Reliability and Systems Engineering at BeihangUniversity in Beijing, China. He is a supervisor of MS and Ph.D students. He teaches three courses for undergraduate and master students. He is also a senior member of IEEE society and a reviewer of more than 20 SCI journals. His current research interests include reliability modeling and intelligent maintenance of complex swarm system. Dr. Feng has led over 20 projects supported by companies and industries. In the past few years, he has also participated in more than 15 projects supported by government and national commissions. He has published more than 100 papers and 3 book chapters.

Bo Sun received the Ph.D. degree in reliability engineering and systems engineering from BeihangUniversity. He is currently a professor at the School of Reliability and Systems Engineering at BeihangUniversity in Beijing, China. He is a supervisor of MS and PhD students. He has over 20 years of research and teaching experience in reliability engineering and systems engineering. He is the team leader of the RISA-PofEra� and RISA MechRel� reliability simulation software platform, and a holder of three Chinese ministry-level professional awards. He has led over 20 projects supported by government, industries, and companies. He has published more than 120 papers, 50 patents and 5 book chapters. His current research interests include physics of failure, computer-aided reliability design, reliability digital engineering (digital twins), prognostics and health management, and reliability engineering.

Zili Wang is the Academician of Chinese Academy of Engineering, and serves as a professor and PhD supervisor of Beihang University. Currently, he is the Director of National Key Laboratory of Reliability and Environmental Engineering. He has been engaged, for a long time, in theoretical and engineering researches of reliability systems engineering (RSE). He puts forward the synthesis and integration theory of reliability, and develops the corresponding software platform, and the results have been widely used.

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