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Estimation of a log-linear model for the reliability assessment of products under two stress variables

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  • Luis Alberto Rodríguez-Picón

    (Autonomous University of Ciudad Juárez)

  • Víctor Hugo Flores-Ochoa

    (Technological Institute of Ciudad Juárez)

Abstract

In this article, different models for reliability inference of devices affected by more than one accelerating variable in accelerated life tests are presented. General log-linear relationship is modeled with the lognormal and Weibull distributions considering the effect of two accelerating variables. Estimation of the parameters is performed via maximum likelihood estimation using the Newton–Raphson algorithm and through a Bayesian approach defining conjugate prior and initial non-informative distributions. In order to illustrate these models, an example is presented based on an accelerated life test applied to resistances. Obtained results show that although there are slight differences in the estimates of the parameters based on the two models and approaches, it can be noted that they have an important impact in the reliability inference. The best model and estimation approach is selected via information criteria. In addition, reliability information is obtained from the device under study.

Suggested Citation

  • Luis Alberto Rodríguez-Picón & Víctor Hugo Flores-Ochoa, 2017. "Estimation of a log-linear model for the reliability assessment of products under two stress variables," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1026-1040, November.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-016-0564-6
    DOI: 10.1007/s13198-016-0564-6
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    References listed on IDEAS

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    1. Pan, Zhengqiang & Balakrishnan, Narayanaswamy, 2011. "Reliability modeling of degradation of products with multiple performance characteristics based on gamma processes," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 949-957.
    2. Arne Henningsen & Ott Toomet, 2011. "maxLik: A package for maximum likelihood estimation in R," Computational Statistics, Springer, vol. 26(3), pages 443-458, September.
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    Cited by:

    1. Sainath G. Bidikar & Santosh B. Rane & Prathamesh R. Potdar, 2022. "Product development using Design for Six Sigma approach: case study in switchgear industry," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 203-230, February.

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