Statistical Methods For The Reliability Of Repairable Systems

A unique, practical guide for industry professionals who need to improve product quality and reliability in repairable systems

Owing to its vital role in product quality, reliability has been intensely studied in recent decades. Most of this research, however, addresses systems that are nonrepairable and therefore discarded upon failure. Statistical Methods for the Reliability of Repairable Systems fills the gap in the field, focusing exclusively on an important yet long-neglected area of reliability. Written by two highly recognized members of the reliability and statistics community, this new work offers a unique, systematic treatment of probabilistic models used for repairable systems as well as the statistical methods for analyzing data generated from them.

Liberally supplemented with examples as well as exercises boasting real data, the book clearly explains the difference between repairable and nonrepairable systems and helps readers develop an understanding of stochastic point processes. Data analysis methods are discussed for both single and multiple systems and include graphical methods, point estimation, interval estimation, hypothesis tests, goodness-of-fit tests, and reliability prediction. Complete with extensive graphs, tables, and references, Statistical Methods for the Reliability of Repairable Systems is an excellent working resource for industry professionals involved in producing reliable systems and a handy reference for practitioners and researchers in the field.

STEVEN E. RIGDON, PhD, is Professor of Statistics at Southern Illinois University Edwardsville. ASIT P. BASU, PhD, is Professor of Statistics at the University of Missouri Columbia.

Terminology and Notation for Repairable Systems.

Probabilistic Models: The Poisson Process.

Probabilistic Models: Renewal and Other Processes.

Analyzing Data from a Single Repairable System.

Analyzing Data from Multiple Repairable Systems.

Appendix.

References.