Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-23T14:37:42.185Z Has data issue: false hasContentIssue false
  • English
  • Français

Stochastic Modeling for Environmental Stress Screening

Part of: Applications Special processes

Published online by Cambridge University Press:  19 February 2016

Ji Hwan Cha  and
Maxim Finkelstein
Ji Hwan Cha*
Affiliation:
Ewha Womans University
Maxim Finkelstein*
Affiliation:
University of the Free State and University ITMO
*
∗ Postal address: Department of Statistics, Ewha Womans University, Seoul, 120-750, Korea. Email address: [email protected].
∗∗ Postal address: Department of Mathematical Statistics, University of the Free State, 339 Bloemfontein 9300, South Africa. Email address: [email protected].
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Environmental stress screening (ESS) of manufactured items is used to reduce the occurrence of future failures that are caused by latent defects by eliminating the items with these defects. Some practical descriptions of the relevant ESS procedures can be found in the literature; however, the appropriate stochastic modeling and the corresponding thorough analysis have not been reported. In this paper we develop a stochastic model for the ESS, analyze the effect of this operation on the population characteristics of the screened items, and also consider the relevant optimization issues.

Keywords

Environmental stress screeningburn-instress-strength modelshock modelnonhomogeneous Poisson process

MSC classification

Primary: 60K10: Applications (reliability, demand theory, etc.)
Secondary: 62P30: Applications in engineering and industry
Type
Research Article
Information
Journal of Applied Probability , Volume 51 , Issue 2 , June 2014 , pp. 387 - 399
Copyright
© Applied Probability Trust 

References

Andersen, P. K., Borgan, Ø., Gill, R. D. and Keiding, N. (1993). Statistical Models Based on Counting Processes. Springer, New York.CrossRefGoogle Scholar
Brown, M. and Proschan, F. (1983). Imperfect repair. J. Appl. Prob. 20, 851–859.CrossRefGoogle Scholar
Cha, J. H. (2009). Burn-in models: recent issues, development and future topics. Commun. Korean Statist. Soc. 16, 871–880.Google Scholar
Cha, J. H. and Finkelstein, M. (2010). Burn-in by environmental shocks for two ordered subpopulations. Europ. J. Operat. Res. 206, 111–117.CrossRefGoogle Scholar
Cha, J. H. and Finkelstein, M. (2011). On new classes of extreme shock models and some generalizations. J. Appl. Prob. 48, 258–270.CrossRefGoogle Scholar
Cha, J. H. and Finkelstein, M. (2012). Information-based thinning of point processes and its application to shock models. J. Statist. Planning Infer. 142, 2345–2350.CrossRefGoogle Scholar
Cha, J. H. and Finkelstein, M. (2013). On history-dependent shock models. Operat. Res. Lett. 41, 232–237.CrossRefGoogle Scholar
Cox, D. R. and Isham, V. (1980). Point Processes. Chapman & Hall, London.Google Scholar
Finkelstein, M. (2008). Failure Rate Modelling for Reliability and Risk. Springer, London.Google Scholar
Fiorentino, E. and Saari, A. E. (1983). Planning of production reliability stress-screening programs. IEEE Trans. Reliab. 32, 247–252.CrossRefGoogle Scholar
Fleming, T. R. and Harrington, D. P. (1991). Counting Processes and Survival Analysis. John Wiley, New York.Google Scholar
Landers, T. L., Malstrom, R. M., Schmitt, N. and Fant, E. (1994). Electronics Manufacturing Processes. Prentice Hall, Englewood Cliffs, NJ.Google Scholar
Mi, J. (1991). Optimal Burn-in. , University of Pittsburgh.Google Scholar
Mi, J. (1994a). Burn-in and maintenance policies. Adv. Appl. Prob. 26, 207–221.CrossRefGoogle Scholar
Mi, J. (1994b). Maximization of a survival probability and its application. J. Appl. Prob. 31, 1026–1033.CrossRefGoogle Scholar
Mi, J. (1996). Minimizing some cost functions related to both burn-in and field use. Operat. Res. 44, 497–500.CrossRefGoogle Scholar
Shaked, M. and Shanthikumar, J. G. (2007). Stochastic Orders. Springer, New York.CrossRefGoogle Scholar
Yan, L and English, J. R. (1997). Economic cost modeling of environmental-stress-screening and burn-in. IEEE Trans. Reliab. 46, 275–282.CrossRefGoogle Scholar