Overview
- First monograph which presents shock and damage models in reliability from introduction to application
- The practical applications of shock and damage models are demonstrated using case studies
- Professor Nakagawa is a leading researcher in this field with more than thirty year’s experience
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Series in Reliability Engineering (RELIABILITY)
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Keywords
Table of contents (10 chapters)
Reviews
From the reviews:
"The book describes various models of shock and damage in reliability theory, and derives the optimal policies for such models. … covers a large number of shock and damage models, and opens new areas of research. It can be found to be useful to graduate students and researchers in reliability theory." (Moshe Shaked, Zentralblatt MATH, Vol. 1144, 2008)
Authors and Affiliations
About the author
Toshio Nakagawa is a Professor of Engineering at Aichi Institute of Technology, Japan. He is considered the world leader in maintenance reliability engineering and has worked in this area for thirty years. He has published more than a hundred research papers mainly on the subject of maintenance and reliability theory.
Bibliographic Information
Book Title: Shock and Damage Models in Reliability Theory
Authors: Toshio Nakagawa
Series Title: Springer Series in Reliability Engineering
DOI: https://doi.org/10.1007/978-1-84628-442-7
Publisher: Springer London
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag London 2007
Hardcover ISBN: 978-1-84628-441-0Published: 08 December 2006
Softcover ISBN: 978-1-84996-601-6Published: 28 October 2010
eBook ISBN: 978-1-84628-442-7Published: 01 February 2007
Series ISSN: 1614-7839
Series E-ISSN: 2196-999X
Edition Number: 1
Number of Pages: VIII, 188
Topics: Quality Control, Reliability, Safety and Risk, Industrial and Production Engineering, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Engineering Design, Probability Theory and Stochastic Processes, Simulation and Modeling