Über den Autor
Toshio Nakagawa has published more than 150 papers, mainly on the subject of reliability theory, in research journals. He has already published Maintenance Theory of Reliability (2005), Shock and Damage Models in Reliability (2007) and Advanced Reliability Models and Optimum Policies (2008) with Springer.His research group in Nagoya has studied reliability theory and its applications continuously since 1988, and has presented a large number of papers in reliability journals and at international conferences. Most papers have been written using some techniques and results of stochastic processes.
1. Introduction.- 2. Poisson Processes.- 3. Renewal Processes.- 4. Markov Chains.- 5. Semi-Markov and Markov Renewal Processes.- 6. Cumulative Processes.- 7. Brownian Motion and Levy Processes.- 8. Redundant Systems.
Reliability theory is of fundamental importance for engineers and managers involved in the manufacture of high-quality products and the design of reliable systems. In order to make sense of the theory, however, and to apply it to real systems, an understanding of the basic stochastic processes is indispensable.
As well as providing readers with useful reliability studies and applications, Stochastic Processes also gives a basic treatment of such stochastic processes as:the Poisson process,
the renewal process,
the Markov chain,
the Markov process, and
the Markov renewal process.
Many examples are cited from reliability models to show the reader how to apply stochastic processes. Furthermore, Stochastic Processes gives a simple introduction to other stochastic processes such as the cumulative process, the Wiener process, the Brownian motion and reliability applications.
Stochastic Processes is suitable for use as a reliability textbook by advanced undergraduate and graduate students. It is also of interest to researchers, engineers and managers who study or practise reliability and maintenance.
Provides a basic treatment of stochastic processes
Includes reliability studies and their applications
Shows readers how to apply stochastic processes