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Abstract

Zero-failure testing for Bayesian reliability demonstration (BRD) is studied, aiming at minimal expected costs of testing and process failure. For the process after testing, reliability is formulated in terms of failure-free periods, based on the assumption that failure in the process would be catastrophic, causing the process to be discontinued and incurring high costs. BRD is considered for systems with redundancy, which are required to perform multiple independent types of task. Optimal BRD testing is also considered in combination with flexibility in the system redundancy, where more components can be installed to reduce test effort. Attention is restricted to systems with exchangeable components, with testing only at the component level. Generalizations are briefly discussed at the end of this paper.

Keywords

multiple independent tasks optimal redundancy predictive inference zero-failure testing

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References

Martz

H. F.

Waller

R. A.

A Bayesian zero-failure (BAZE) reliability demonstration testing procedure

J. Qual. Technol. 1979, 11, 128–138.

Rahrouh

Bayesian zero-failure reliability demonstration. PhD thesis, University of Durham, 2005.

Martz

H. F.

Waller

R. A.

Bayesian reliability analysis 1982 (John Wiley, New York).

Meeker

W. Q.

Escobar

L. A.

Statistical methods for reliability data 1998 (John Wiley, New York).

Coolen

F. P.A.

Coolen-Schrijner

On zero-failure testing for Bayesian high-reliability demonstration. Proc. IMechE, Part O: J. Risk and Reliability 2006, 220 (O1), 35–44.

Coolen

F. P. A.

Coolen-Schrijner

Rahrouh

Bayesian reliability demonstration for failure-free periods

Reliability Engng System Safety 2005, 88, 81–91.

Coolen

F. P. A.

Coolen-Schrijner

Rahrouh

Bayesian reliability demonstration with multiple independent tasks

IMA J. Managmt Math. 2006, 17, 131–142.

Coolen

F. P.A.

Coolen-Schrijner

Non-parametric predictive reliability demonstration for failure-free periods

IMA J. Managmt Math. 2005, 16, 1–11.

Geisser

Predictive inference: an introduction, 1993 (Chapman & Hall, London).

10.

Bernardo

J. M.

Smith

A. F. M.

Bayesian theory 1994 (John Wiley, Chichester, West Sussex).

11.

Gelman

Carlin

J. B.

Stern

H. S.

Rubin

D. B.

Bayesian data analysis 1995 (Chapman & Hall, London).

12.

Bedford

Cooke

Probabilistic risk analysis: Foundations and Methods 2001 (Cambridge University Press, Cambridge).

13.

Percy

D. F.

Bayesian enhanced strategic decision making for reliability

Eur. J. Opl Res. 2002 139, 133–145.

14.

French

Rios Insua

Statistical decision theory 2000 (Arnold, London).

15.

Andrews

J. D.

Moss

T. R.

Reliability and risk assessment, 2nd edition, 2002 (Professional Engineering Publishing Limited, London).

16.

Hartigan

J. A.

Bayes theory, 1983 (Springer-Verlag, New York).

17.

Luenberger

D. G.

Introduction to linear and nonlinear programming, 1973 (Addison-Wesley, Menlo Park, California).

18.

Gamerman

Markov chain Monte Carlo: stochastic simulation for Bayesian inference 1997 (Chapman & Hall, London).

19.

Coit

D. W.

Cold-standby redundancy optimization for nonrepairable systems

IIE Trans. 2001, 33, 471–478.

20.

Coit

D. W.

Liu

System reliability optimization with k-out-of-n subsystems

Int. J. Reliability, Qual. Safety Engng 2000, 7, 129–142.

21.

Optimal active redundancy allocation in k-out-of-n system

J. Appl. Probability 1999, 36, 927–933.

Bayesian reliability demonstration for systems with redundancy

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