The updating stopping condition (USC) has great impact on the effectiveness of a predictive maintenance (PdM) policy, but did not receive enough attention. This paper reviews the common USCs, proposes a residual life based USC, and evaluates the influence of the USCs on the effectiveness of a PdM policy. The commonly used USCs are concretely defined in a PdM policy based on the stochastic linear degradation model. An extensive numerical investigation compares the performances of the PdM policy using different USCs. The investigation results verify the importance of optimizing the USC for a PdM policy.
References
Zio E, Compare M. Evaluating maintenance policies by quantitative modeling and analysis. Reliability Engineering and System Safety 2013; 109: 53-65. http://dx.doi.org/10.1016/j.ress.2012.08.002
Jardine AKS, Lin DM, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 2006; 20(7): 1483-1510. http://dx.doi.org/10.1016/j.ymssp.2005.09.012
Liao H, Elsayed EA, Chan LY. Maintenance of continuously monitored degradation systems. European Journal of Operational Research 2006; 175(2): 821-835. http://dx.doi.org/10.1016/j.ejor.2005.05.017
Camci F. System maintenance scheduling with prognostics information using genetic algorithm. IEEE Transactions on Reliability 2009; 58(3): 539-552. http://dx.doi.org/10.1109/TR.2009.2026818
You MY, Li L, Meng G, Ni J. Cost-effective updated sequential predictive maintenance policy for continuously monitored degrading systems. IEEE Transactions on Automation Science and Engineering 2010; 7(2): 257-265. http://dx.doi.org/10.1109/TASE.2009.2019964
Kaiser KA, Gebraeel NZ. Predictive maintenance management using sensor-based degradation models. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 2009; 39(4): 840-849. http://dx.doi.org/10.1109/TSMCA.2009.2016429
Curcuru G, Galante G, Lombardo A. A predictive maintenance policy with imperfect monitoring. Reliability Engineering and System Safety 2010; 95: 989-997. http://dx.doi.org/10.1016/j.ress.2010.04.010
Gebraeel NZ. Sensory-updated residual life distributions for components with exponential degradation patterns. IEEE Transactions on Automation Science and Engineering 2006; 3(4): 382-393. http://dx.doi.org/10.1109/TASE.2006.876609
Meeker WQ, Escobar LA. Statistical Methods for Reliability Data M. New York: Wiley 1998.
Kim YS, Kolarik WJ. Real-time conditional reliability prediction from on-line tool performance data. International Journal of Production Research 1992; 30(8): 1831-1844. http://dx.doi.org/10.1080/00207549208948125
Gebraeel NZ, Lawley MA, Li R, et al. Residual-life distributions from component degradation signals: a Bayesian approach. IIE Transactions 2005; 37(6): 543-557. http://dx.doi.org/10.1080/07408170590929018