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A trivariate optimal replacement policy for a deteriorating system based on cumulative damage and inspections

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  • Tsai, Hsin-Nan
  • Sheu, Shey-Huei
  • Zhang, Zhe George

Abstract

In this article, we study a trivariate replacement model for a deteriorating system consisting of two units. Failures of unit 1 can be classified into two types. Type I failure (minor failure) is fixed by a minimal repair and type II failure (catastrophic failure) is removed by a replacement. Both types of failures can only be detected through inspection. Each type I failure of unit 1 will result in a random amount of damage to unit 2 and the damages are cumulative. The probability of type I failure or type II failure is assumed to depend on the number of failures since the last replacement. We formulate a replacement policy based on the number of type I failure, the occurrence of the first type II failure, and the amount of accumulative damages. Hence the system is replaced either preventively or correctively at any of the following four conditions depend on whichever occurs first; preventively (a) at the Nth type I failure; or (b) when the total damage of unit 2 exceeds a pre-specified level Z (but less than the failure level l); and, correctively (c) at the first type II failure; or (d) when the total damage of unit 2 exceeds a failure level l, where Z and l represent the thresholds of total damage level for unit 2 to preventive and corrective replacements, respectively. Although a type I failure can be fixed by a minimal repair, but the operating period is stochastically decreasing and repair time is stochastically increasing as time goes on. The minimal total expected long-run net cost per unit time of the system is derived and a computational algorithm for determining the optimal policy is developed. A real-world application from electric power industry is provided. Several past studied are shown to be special cases of our model. Finally, a numerical example is presented.

Suggested Citation

  • Tsai, Hsin-Nan & Sheu, Shey-Huei & Zhang, Zhe George, 2017. "A trivariate optimal replacement policy for a deteriorating system based on cumulative damage and inspections," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 74-88.
    • Handle: RePEc:eee:reensy:v:160:y:2017:i:c:p:74-88
    DOI: 10.1016/j.ress.2016.10.031
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    3. Song, Cen & Zhuang, Jun, 2017. "N-stage security screening strategies in the face of strategic applicants," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 292-301.
    4. Sheu, Shey-Huei & Tsai, Hsin-Nan & Sheu, Uan-Yu & Zhang, Zhe George, 2019. "Optimal replacement policies for a system based on a one-cycle criterion," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    5. Junyuan Wang & Jimin Ye, 2022. "A new repair model and its optimization for cold standby system," Operational Research, Springer, vol. 22(1), pages 105-122, March.
    6. Sheu, Shey-Huei & Liu, Tzu-Hsin & Sheu, Wei-Teng & Zhang, Zhe-George & Ke, Jau-Chuan, 2021. "Optimal replacement policy with replacement last under cumulative damage models," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    7. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    8. Qinglai Dong & Lirong Cui & Hongda Gao, 2019. "A bivariate replacement policy for an imperfect repair system based on geometric processes," Journal of Risk and Reliability, , vol. 233(4), pages 670-681, August.

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