Stochastic Modeling for Reliability: Shocks, Burn-in and Heterogeneous populations (Springer Series in Reliability Engineering) - Tapa dura

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Dr. Maxim Finkelstein received his M.Sc degree in Mathematical Physics from Leningrad State University (1971), Ph.D in OR from Leningrad Elektropribor Institute, 1979 and Doctor of Science degree (habilitation) in OR from St.-Petersburg Elektropribor Institute (1993).

     Currently he is a Senior Professor at the Department of Mathematical Statistics, University of the Free State, Republic of South Africa and a visiting researcher at the Max Planck Institute for Demographic Research, Rostock, Germany.

     His main research interests are: reliability theory and applications, survival analysis, risk and safety modelling, applied stochastic processes. He had published more than 160 papers and 5 books on different aspects of reliability and stochastic modelling. His last book "Failure rate modelling for reliability and risk" was published by Springer in 2008. He is the Editor of “South African Statistical Journal”  and serves as a board member and Associate Editor for a number of international journals.

Dr. Ji Hwan Cha received his B.Sc degree in Computer Science and Statistics from Seoul National University, Korea, in 1993 and his M.Sc and Ph.D degrees in Statistics from Seoul National University in 1995 and 1999, respectively.

     He is an Associate Professor at the Department of Statistics, Ewha Womans University, Seoul, Korea. He is an associate editor of Journal of the Korean Statistical Society. He has published numerous papers in various international journals and two books on Introductory Statistics and Reliability Theory, respectively (in Korean). His research interests include applied probability, reliability theory, stochastic processes, stochastic operations research and management science.

De la contraportada

Focusing on shocks modeling, burn-in and heterogeneous populations, Stochastic Modeling for Reliability naturally combines these three topics in the unified stochastic framework and presents numerous practical examples that illustrate recent theoretical findings of the authors.

The populations of manufactured items in industry are usually heterogeneous. However, the conventional reliability analysis is performed under the implicit assumption of homogeneity, which can result in distortion of the corresponding reliability indices and various misconceptions. Stochastic Modeling for Reliability fills this gap and presents the basics and further developments of reliability theory for heterogeneous populations. Specifically, the authors consider burn-in as a method of elimination of weak items from heterogeneous populations. The real life objects are operating in a changing environment. One of the ways to model an impact of this environment is via the external shocks occurring in accordance with some stochastic point processes. The basic theory for Poisson shock processes is developed and also shocks as a method of burn-in and of the environmental stress screening for manufactured items are considered.

Stochastic Modeling for Reliability introduces and explores the concept of burn-in in heterogeneous populations and its recent development, providing a sound reference for reliability engineers, applied mathematicians, product managers and manufacturers alike.