Stochastic Models in Reliability Engineering

By: Lirong Cui (Editor), Ilia Frenkel (Editor), Anatoly Lisnianski (Editor)

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Hardcover | 30 July 2020 | Edition Number 1

At a Glance

Hardcover
484 Pages

Dimensions(cm)
23.4 x 15.6 x 2.69

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This book is a collective work by many leading scientists, analysts, mathematicians, and engineers who have been working at the front end of reliability science and engineering. The book covers conventional and contemporary topics in reliability science, all of which have seen extended research activities in recent years.

The methods presented in this book are real-world examples that demonstrate improvements in essential reliability and availability for industrial equipment such as medical magnetic resonance imaging, power systems, traction drives for a search and rescue helicopter, and air conditioning systems.

The book presents real case studies of redundant multi-state air conditioning systems for chemical laboratories and covers assessments of reliability and fault tolerance and availability calculations. Conventional and contemporary topics in reliability engineering are discussed, including degradation, networks, dynamic reliability, resilience, and multi-state systems, all of which are relatively new topics to the field.

The book is aimed at engineers and scientists, as well as postgraduate students involved in reliability design, analysis, experiments, and applied probability and statistics.

Industry Reviews

"This book provides a good coverage of topics in the field of stochastic modelling in reliability engineering with pointers to more in-depth material. It is noteworthy that there are future research directions provided in most of the book chapters, which is a useful feature for readers who are looking for research problems in the area of reliability engineering. Academics, engineers and practitioners who are interested in modelling and analysis of data in reliability engineering will appreciate this book for covering a wide range of topics with practical engineering applications."

- International Statistical Review (2021)

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