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Preventive Maintenance Policies for Equipment Under Condition Monitoring Based on Two Types of Failure Rate

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Abstract

In this article, we deal with optimal preventive maintenance policies based on online condition monitoring. The failure rate function is important for maintenance decisions. Two concepts of failure are proposed and the computing method based on condition monitoring data is given. Both un-repairable and repairable equipment are taken into consideration. For repairable equipment, the degree of degradation and failure rate will decrease after maintenance. The result of the simulation shows that taking the two types of failure rate functions into account will make the expected cost rate less than the classical method. So we draw a conclusion that the two types of failure rate functions are advantageous in maintenance decisions.

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References

  1. B. Bergquist, P. Soderholm, Data analysis for condition-based railway infrastructure maintenance. Qual. Reliab. Eng. Int. 31(5), 773–781 (2015)

    Article  Google Scholar 

  2. X. Liu, J. Li, K.N. Al-Khalifa, A.S. Hamouda, D.W. Coit, E.A. Elsayed, Condition-based maintenance for continuously monitored degrading systems with multiple failure modes. IIE Trans. 45(4), 422–435 (2013)

    Article  Google Scholar 

  3. J. Koochaki, A.C. Jos, H. Wortmann, W. Klingenberg, The influence of condition-based maintenance on workforce planning and maintenance scheduling. Int. J. Prod. Res. 51(8), 2339–2351 (2013)

    Article  Google Scholar 

  4. P. Ashok, G. Subra, Application of statistical techniques and neural networks in condition-based maintenance. Qual. Reliab. Eng. Int. 29(3), 439–461 (2013)

    Article  Google Scholar 

  5. D.V. Phuc, B. Christophe, Condition-based maintenance with imperfect preventive repairs for a deteriorating production system. Qual. Reliab. Eng. Int. 28(6), 624–633 (2012)

    Article  Google Scholar 

  6. P. Do, A. Voisin, E. Levrat, A.B. Iung, A Proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions. Reliab. Eng. Syst. Saf. 133(1), 22–32 (2015)

    Article  Google Scholar 

  7. N.J.M. Van, A survey of the application of gamma processes in maintenance. Reliab. Eng. Syst. Saf. 94(1), 2–21 (2009)

    Article  Google Scholar 

  8. Y. Peng, M. Dong, M.J. Zuo, Current status of machine prognostics in condition-based maintenance: a review. Int. J. Adv. Manuf. Technol. 50(1), 297–313 (2010)

    Article  Google Scholar 

  9. W. Wang, Overview of a semi-stochastic filtering approach for residual life estimation with applications in condition based maintenance. Proc. Inst. Mech. Eng. O J. Risk Reliab. 255(2), 185–197 (2011)

    Google Scholar 

  10. E. Deloux, B. Castanier, Condition-based maintenance approaches for deteriorating system evolving in a stressful environment. Proc. Inst. Mech. Eng. O J. Risk Reliab. 222(4), 613–622 (2008)

    Google Scholar 

  11. N. Gebraeel, J. Pan, Prognostic degradation models for computing and updating residual life distributions in a time-varying environment. IEEE Trans. Reliab. 54(4), 539–550 (2008)

    Article  Google Scholar 

  12. I.T. Yu, C.D. Fuh, Estimation of time to hard failure distributions using a three-stage method. IEEE Trans. Reliab. 59(2), 405–412 (2010)

    Article  Google Scholar 

  13. L. Axel, Joint modeling of degradation and failure time data. J. Stat. Plan. Inference 193(5), 1693–1706 (2009)

    Google Scholar 

  14. M.J. Zuo, R.Y. Jiang, R.C.M. Yam, Approaches for reliability modeling of continuous-state devices. IEEE Trans. Reliab. 48(1), 9–18 (1999)

    Article  Google Scholar 

  15. D. Lin, M.J. Zuo, R.C.M. Yam, General sequential imperfect preventive maintenance models. Int. J. Reliab. Qual. Saf. Eng. 7(3), 253–266 (2000)

    Article  Google Scholar 

  16. D. Lin, M.J. Zuo, R.C.M. Yam, Sequential imperfect preventive maintenance models with two categories of failure modes. Naval Res. Logist. 48, 172–183 (2001)

    Article  Google Scholar 

  17. S. Elferik, D.M. Ben, Age-based hybrid model for imperfect preventive maintenance. IIE Trans. 38(4), 365–375 (2006)

    Article  Google Scholar 

  18. X.J. Zhou, L.F. Xi, A dynamic opportunistic maintenance policy for continuously monitored systems. J. Qual. Maint. Eng. 12(3), 294–305 (2006)

    Article  Google Scholar 

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Correspondence to Yunzhi Yao.

Appendix

Appendix

Figure 6 shows the procedure to compute the expected cost rate using Monte Carlo method. In the flow chart, cr represents the cost rate, cr = tcost/ttime.

Fig. 6
figure 6

The procedure to compute the expected cost rate using Monte Carlo method

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Yao, Y., Meng, C., Wang, C. et al. Preventive Maintenance Policies for Equipment Under Condition Monitoring Based on Two Types of Failure Rate. J Fail. Anal. and Preven. 16, 457–466 (2016). https://doi.org/10.1007/s11668-016-0111-4

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  • DOI: https://doi.org/10.1007/s11668-016-0111-4

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