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A novel optimal preventive maintenance policy for a cold standby system based on semi-Markov theory

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  • Zhong, Chongquan
  • Jin, Haibo

Abstract

A novel optimal preventive maintenance policy for a cold standby system consisting of two components and a repairman is described herein. The repairman is to be responsible for repairing either failed component and maintaining the working components under certain guidelines. To model the operational process of the system, some reasonable assumptions are made and all times involved in the assumptions are considered to be arbitrary and independent. Under these assumptions, all system states and transition probabilities between them are analyzed based on a semi-Markov theory and a regenerative point technique. Markov renewal equations are constructed with the convolution of the cumulative distribution function of system time in each state and corresponding transition probability. By using the Laplace transform to solve these equations, the mean time from the initial state to system failure is derived. The optimal preventive maintenance policy that will provide the optimal preventive maintenance cycle is identified by maximizing the mean time from the initial state to system failure, and is determined in the form of a theorem. Finally, a numerical example and simulation experiments are shown which validated the effectiveness of the policy.

Suggested Citation

  • Zhong, Chongquan & Jin, Haibo, 2014. "A novel optimal preventive maintenance policy for a cold standby system based on semi-Markov theory," European Journal of Operational Research, Elsevier, vol. 232(2), pages 405-411.
    • Handle: RePEc:eee:ejores:v:232:y:2014:i:2:p:405-411
    DOI: 10.1016/j.ejor.2013.07.020
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    6. Ardakan, Mostafa Abouei & Amini, Hanieh & Juybari, Mohammad N., 2022. "Prescheduled switching time: A new strategy for systems with standby components," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    7. Ruiz-Castro, Juan Eloy, 2016. "Complex multi-state systems modelled through marked Markovian arrival processes," European Journal of Operational Research, Elsevier, vol. 252(3), pages 852-865.
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