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Burn-in Procedure Based on a Dependent Covariate Process

Part of: Special processes Applications

Published online by Cambridge University Press:  22 February 2016

Ji Hwan Cha  and
Gianpaolo Pulcini
Ji Hwan Cha*
Affiliation:
Ewha Womans University
Gianpaolo Pulcini*
Affiliation:
Istituto Motori
*
∗ Postal address: Department of Statistics, Ewha Womans University, Seoul, 120-750, Korea. Email address: jhcha@ewha.ac.kr
∗ Postal address: Department of Statistics, Ewha Womans University, Seoul, 120-750, Korea. Email address: jhcha@ewha.ac.kr
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Abstract

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Burn-in is a method of ‘elimination’ of initial failures (infant mortality). In the conventional burn-in procedures, to burn-in a component or a system means to subject it to a fixed time period of simulated use prior to actual operation. Then those which fail during the burn-in procedure are scrapped and only those which survived the burn-in procedure are considered to be of satisfactory quality. Thus, in this case, the only information used for the elimination procedure is the lifetime of the corresponding item. In this paper we consider a new burn-in procedure which additionally employs a dependent covariate process in the elimination procedure. Through the comparison with the conventional burn-in procedure, we show that the new burn-in procedure is preferable under commonly satisfied conditions. The problem of determining the optimal burn-in parameters is also considered and the properties of the optimal parameters are derived. A numerical example is provided to illustrate the theoretical results obtained in this paper.

Keywords

Burn-independent covariate processpositive quadrant dependentnegative quadrant dependentdegradation phenomenaheterogeneous populationcatastrophic failure

MSC classification

Primary: 60K10: Applications (reliability, demand theory, etc.)
Secondary: 62P30: Applications in engineering and industry

Information

Type
Research Article
Information
Advances in Applied Probability , Volume 47 , Issue 2 , June 2015 , pp. 506 - 529
Copyright
© Applied Probability Trust 

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