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A novel data-driven approach to optimizing replacement policy

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  • Ahmadi, Reza
  • Wu, Shaomin

Abstract

Parallel systems are a commonly used structure in reliability engineering. A common characteristic of such systems is that the failure of a component may not cause its system to fail. As such, the failure may not be immediately detected and the random (disruption) time at which the number of failed components reaches a certain predefined number d may therefore be unknown. For such systems, scheduling maintenance policy is a difficult task, which is tackled in this paper. The paper assumes that times between inspections conform to a modulated Poisson process. This assumption allows the frequency of inspection responds to the variation of the disruption state. The paper then estimates the disruption time on the basis of inspection point process observations in the framework of filtering theorem. The paper develops a unified cost structure to jointly optimise inspection frequency and replacement time for the system when the lifetime distribution of a component follows the Pareto or exponential distribution. Numerical results are provided to show the application of the proposed model.

Suggested Citation

  • Ahmadi, Reza & Wu, Shaomin, 2017. "A novel data-driven approach to optimizing replacement policy," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 506-516.
    • Handle: RePEc:eee:reensy:v:167:y:2017:i:c:p:506-516
    DOI: 10.1016/j.ress.2017.06.027
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    References listed on IDEAS

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    1. 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 122-135.
    2. E. Arjas & I. Norros, 1989. "Change of Life Distribution Via a Hazard Transformation: An Inequality with Application to Minimal Repair," Mathematics of Operations Research, INFORMS, vol. 14(2), pages 355-361, May.
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    4. Ahmadi, Reza & Newby, Martin, 2011. "Maintenance scheduling of a manufacturing system subject to deterioration," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1411-1420.
    5. Ahmadi, Reza & Fouladirad, Mitra, 2017. "Maintenance planning for a deteriorating production process," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 108-118.
    6. Zhao, Xufeng & Chen, Mingchih & Nakagawa, Toshio, 2016. "Replacement policies for a parallel system with shortage and excess costs," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 89-95.
    7. Taghipour, Sharareh & Banjevic, Dragan, 2012. "Optimal inspection of a complex system subject to periodic and opportunistic inspections and preventive replacements," European Journal of Operational Research, Elsevier, vol. 220(3), pages 649-660.
    8. Xuejuan Liu & Wenbin Wang & Rui Peng & Fei Zhao, 2015. "A delay-time-based inspection model for parallel systems," Journal of Risk and Reliability, , vol. 229(6), pages 556-567, December.
    9. Hajipour, Yassin & Taghipour, Sharareh, 2016. "Non-periodic inspection optimization of multi-component and k-out-of-m systems," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 228-243.
    10. Zhao, Xufeng & Al-Khalifa, Khalifa N. & Magid Hamouda, Abdel & Nakagawa, Toshio, 2017. "Age replacement models: A summary with new perspectives and methods," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 95-105.
    11. Zhao, Xufeng & Qian, Cunhua & Nakagawa, Toshio, 2017. "Comparisons of replacement policies with periodic times and repair numbers," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 161-170.
    12. 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.
    13. Giovanni Parmigiani, 1996. "Optimal Scheduling of Fallible Inspections," Operations Research, INFORMS, vol. 44(2), pages 360-367, April.
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