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Cost optimization of reliability testing by a bayesian approach

Published online by Cambridge University Press:  28 August 2014

S. Beleulmi ,
A. Bellaouar  and
M. Lachi
S. Beleulmi
Affiliation:
Laboratoire ingénierie des transports et environnement Département de Génie Mécanique Faculté des sciences de la technologie Université Constantine 1 Constantine, Algeria
A. Bellaouar
Affiliation:
Laboratoire ingénierie des transports et environnement, Faculté des sciences de la technologie Université Constantine 1Constantine, Algeria
M. Lachi
Affiliation:
Laboratoire GRESPI, UFR Sciences, Université de Reims Champagne Ardenne, Reims, France
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Abstract

The Bayesian approach is a stochastic method, allowing to establish trend studies on the behavior of materials between two periods or after a break in the life of these materials. It naturally integrates the inclusion of the information partially uncertain to support in modeling problem. The method is therefore particularly suitable for the analysis of the reliability tests, especially for equipment and organs whose different tests are costly. Bayesian techniques are used to reduce the size of estimation tests, improving the evaluation of the parameters of product reliability by the integration of the past (data available on the product concerned) and process, the case “zero” failure observed, difficult to treat with conventional statistical approach. This study will concern the reduction in the number of tests on electronic or mechanical components installed in a mechanical lift knowing their a priori behavior in order to determine their a posteriori behavior.

Keywords

Bayesian approachreliabilityoptimisationfailure rateaccelerated tests
Type
Research Article
Information
Mechanics & Industry , Volume 15 , Issue 5 , 2014 , pp. 449 - 454
Copyright
© AFM, EDP Sciences 2014

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References

D.C. Li, F.M. Chang, K.C. Chen, Building Reliability Growth Model Using Sequential Experiments and the Bayesian Theorem for Small Datasets, Expert Systems with Applications (2009), DOI: 10.1016/j.eswa.2009.10.001
Maurizio, G., Gianpaolo, P., Bayesian Analysis Of Repairable Syst. Showing A Bounded Failure Intensity, Reliab. Eng. Syst. Safe. 91 (2006) 828–838 Google Scholar
Makota, A., Bayesian Analysis For Results Of Fatigue Test Using Full-Scale Models To Obtain The Accurate Failure Probabilities Of The Shinkansen Vehicle Axle, Reliab. Eng. Syst. Safe. 75 (2002) 321–33 Google Scholar
F. Corset, Aide à l’optimisation de maintenance à partir de réseaux bayésiens et fiabilité dans un contexte doublement censuré. Thèse de doctorat, Université Joseph Fourrier, Grenoble 1, 2003
A. Muller, Contribution à la maintenance prévisionnelle des systèmes de production par la formalisation d’un processus de pronostic, Thèse de doctorat, Université Henri Poincaré, Nancy 1, 2005
P. Weber, L. Jouffe, Reliability modeling with Dynamic Bayesian Networks. 5th IFAC Sysmposium on Fault Detection, Supervision and Safety of Technical Processes, D.C. Washington, USA, 2003
C. Robert, L’analyse statistique bayésienne. Edition Economica, 1992
C.P. Robert, The Bayesian choice. Ed. Springer, 2001
P. Congdon, Bayesian statistical modelling. Ed. Wiley, 2001
J. Ibrahim, M. Chen, D. Sinha, Bayesian survival analysis. Ed. Springer, 2001
J. Ringler, Utilisation des techniques bayésiennes dans le cas de système complexe. Revue de Statistique Appliquée XXVII (1979)
H. Procaccia, P. Morilhat, Fiabilité des structures des installations industrielles. Ed. Eyrolles, (1996)
P. Gondran, Fiabilité des systèmes, Eyrolles, 1980
W. Nelson, Accelerated Testing: Statistical Models, Test Plans and Data Analysis, Eds. Wiley Series in Probability and Mathematical Statistics, 1990
P. O’Connor, Testing for reliability, Qual. Reliab. Eng. Int. 19 (2003) 73–84
H. Caruso, A. Dasgupta, A fundamental overview of accelerated testing analytical models. In IEEE Proceedings Annual Reliability and Maintainability Symposium, Tutorial notes, 1998
A. Vassilious, A. Mettas, Understanding accelerated life testing analysis. In IEEE Proceedings Annual Reliability and Maintainability Symposium, Tutorial notes, USA, 2001
H. Procaccia, L. Piepszownik, C. Clarotti, Fiabilité des équipements et théorie de la décision statistique fréquentielle et bayésienne. Ed. Eyrolles, 1992
Ross, T.D., Accurate confidence intervals for binomial proportion Poisson rate estimation, Comput. Biol. Med. 33 (2003) 509–531 CrossRefGoogle ScholarPubMed
Military Handbook-Reliability Prediction of Electronic Equipment. United States Department Of Defense, notice 2. [MIL-HDBK-217 F], 1995