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Hazard rate function in dynamic environment

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  • XiaoFei, Lu
  • Min, Liu

Abstract

The hazard rate function is always applied to make maintenance policy, and the usual hazard rate function is computed by the data of failure times of systems working in constant environment, thus for systems working in dynamic environment it cannot be directly applied. In this paper, hazard rate function of system in the dynamic environment is computed, and the effects of current environment status and the environmental history on hazard rate function are explicitly presented. For system with the known degradation process, hazard rate function is studied by the Markov additive process. The environment evolution process is modeled as a stochastic process with two states, one state represents normal environment, the other represents severe environment, and system degrades more quickly under severe environment than under normal environment. The relationship between hazard rate functions of system in time-invariant and dynamic environment is researched, from which three important facts are revealed, firstly hazard rate function jumps as the environment jumps, secondly the form of hazard rate function is determined Wby current environment state, and thirdly the effective age of system is determined by the environmental history. For system with the unknown degradation process, based on the above facts, this paper derives the hazard rate function in dynamic environment, and proposes a method to compute the effective age under given environmental history. Finally the optimal maintenance policy for system in dynamic environment is studied.

Suggested Citation

  • XiaoFei, Lu & Min, Liu, 2014. "Hazard rate function in dynamic environment," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 50-60.
    • Handle: RePEc:eee:reensy:v:130:y:2014:i:c:p:50-60
    DOI: 10.1016/j.ress.2014.04.020
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    3. Coria, V.H. & Maximov, S. & Rivas-Dávalos, F. & Melchor, C.L. & Guardado, J.L., 2015. "Analytical method for optimization of maintenance policy based on available system failure data," Reliability Engineering and System Safety, Elsevier, vol. 135(C), pages 55-63.
    4. Naseri, Masoud & Baraldi, Piero & Compare, Michele & Zio, Enrico, 2016. "Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 66-82.
    5. Reder, Maik & Yürüşen, Nurseda Y. & Melero, Julio J., 2018. "Data-driven learning framework for associating weather conditions and wind turbine failures," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 554-569.
    6. Caballé, N.C. & Castro, I.T. & Pérez, C.J. & Lanza-Gutiérrez, J.M., 2015. "A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 98-109.
    7. Eleftheroglou, Nick & Galanopoulos, Georgios & Loutas, Theodoros, 2024. "Similarity learning hidden semi-Markov model for adaptive prognostics of composite structures," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    8. Sam Efromovich & Jufen Chu, 2018. "Hazard rate estimation for left truncated and right censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 889-917, August.
    9. Dao, Cuong D. & Zuo, Ming J., 2017. "Selective maintenance of multi-state systems with structural dependence," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 184-195.
    10. Dao, Cuong D. & Zuo, Ming J., 2017. "Optimal selective maintenance for multi-state systems in variable loading conditions," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 171-180.

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