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Change points estimations of bathtub-shaped hazard functions

Author

Listed:
  • Robab Aghazadeh Chakherloo

    (Islamic Azad University, Science and Research Branch)

  • Mohammad Pourgol-Mohammad

    (Sahand University of Technology)

  • Kamran Sepanloo

    (Nuclear Science and Technology Research Institute)

Abstract

The life data analysis has been finding increasing importance for every industry, resulting in higher quality and cost reduction in current fierce competitive market. The hazard function for life models have three distinct phases of burn-in, useful life and wear-out shown in the bath-tub curves. Change points estimation is important for bathtub-shaped hazard function models in reliability and life data analysis for the product developer and designers to have a relatively accurate estimation of the burn-in, useful life and onset of the wear-out life phases. The applications are for determination and assisting to plan appropriate burn-in, guarantee, maintenance, repair and replacement strategies. In this research, life time interval is studied for bathtub shaped hazard function. Two change points are calculated for burn-in and useful life phases, as well as useful life and wear out phases. Two criteria are used in this study for determination of the change point including (1) minimum of hazard function and (2) maximum change in slope of hazard function. This research is structured as a parametric approach with Bayesian inference utilized as the parameter estimation. The modified Weibull distribution are determined as the suitable models for simulation of all three life phases. In this paper, failure data of an electronic system case is used for the method demonstration.

Suggested Citation

  • Robab Aghazadeh Chakherloo & Mohammad Pourgol-Mohammad & Kamran Sepanloo, 2017. "Change points estimations of bathtub-shaped hazard functions," 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(3), pages 553-559, September.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:3:d:10.1007_s13198-016-0567-3
    DOI: 10.1007/s13198-016-0567-3
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    References listed on IDEAS

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    1. Murari Mitra & Sujit Basu, 1995. "Change point estimation in non-monotonic aging models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 47(3), pages 483-491, September.
    2. Bebbington, Mark & Lai, Chin-Diew & Zitikis, RiÄ ardas, 2007. "A flexible Weibull extension," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 719-726.
    3. Almalki, Saad J. & Yuan, Jingsong, 2013. "A new modified Weibull distribution," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 164-170.
    4. Bebbington, Mark & Lai, Chin-Diew & Wellington, Morgan & Zitikis, RiÄ ardas, 2012. "The discrete additive Weibull distribution: A bathtub-shaped hazard for discontinuous failure data," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 37-44.
    5. Tuan Pham & Hung Nguyen, 1993. "Bootstrapping the change-point of a hazard rate," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(2), pages 331-340, June.
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