Optimal maintenance policy incorporating system level and unit level for mechanical systems

References

• Alaswad, S., & Xiang, Y. (2017). A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliability Engineering & System Safety, 157, 54–63.
   Web of Science ®Google Scholar
• Ba, H. T., Cholette, M. E., Borghesani, P., Zhou, Y., & Ma, L. (2017). Opportunistic maintenance considering non-homogenous opportunity arrivals and stochastic opportunity durations. Reliability Engineering & System Safety, 160, 151–161.
   Web of Science ®Google Scholar
• Bishop, C. M. (2006). Pattern recognition and machine learning. Heidelberg: Springer.
   Google Scholar
• Chen, N., Ye, Z. S., Xiang, Y., & Zhang, L. (2015). Condition-based maintenance using the inverse Gaussian degradation model. European Journal of Operational Research, 243(1), 190–199.
   Web of Science ®Google Scholar
• Cho, D. I., & Parlar, M. (1991). A survey of maintenance models for multi-unit systems. European Journal of Operational Research, 51(1), 1–23.
   Web of Science ®Google Scholar
• Cinlar, E., Bazant, Z. P., & Osman, E. M. (1977). Stochastic process for extrapolating concrete creep.103(6), 1069–1088. ( ASCE 13447 Proceeding).
   Google Scholar
• Crocker, J., & Kumar, U. D. (2000). Age-related maintenance versus reliability centred maintenance: A case study on aero-engines. Reliability Engineering & System Safety, 67(2), 113–118.
   Web of Science ®Google Scholar
• Dao, C. D., Zuo, M. J., & Pandey, M. (2014). Selective maintenance for multi-state series-parallel systems under economic dependence. Reliability Engineering & System Safety, 121, 240–249.
   Web of Science ®Google Scholar
• de Jonge, B., Klingenberg, W., Teunter, R., & Tinga, T. (2015). Optimum maintenance strategy under uncertainty in the lifetime distribution. Reliability Engineering & System Safety, 133, 59–67.
   Web of Science ®Google Scholar
• Dekker, R., Wildeman, R. E., & Van der Duyn Schouten, F. A. (1997). A review of multi-component maintenance models with economic dependence. Mathematical Methods of Operations Research, 45(3), 411–435.
   Web of Science ®Google Scholar
• Do, P., Voisin, A., Levrat, E., & Iung, B. (2015). A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions. Reliability Engineering & System Safety, 133, 22–32.
   Web of Science ®Google Scholar
• Doostparast, M., Kolahan, F., & Doostparast, M. (2015). Optimisation of PM scheduling for multi-component systems: A simulated annealing approach. International Journal of Systems Science, 46(7), 1199–1207.
   Web of Science ®Google Scholar
• Duan, C., Deng, C., Gong, Q., & Wang, Y. (2017). Optimal failure mode-based preventive maintenance scheduling for a complex mechanical device. International Journal of Advanced Manufacturing Technology, 1–12. doi:10.1007/s00170-017-1419-2
   Google Scholar
• Duan, C., Deng, C., & Wang, B. (2017a). Optimal multi-level condition-based maintenance policy for multi-unit systems under economic dependence. International Journal of Advanced Manufacturing Technology, 91(9), 4299–4312.
   Web of Science ®Google Scholar
• Duan, C., Deng, C., & Wang, B. (2017b). Multi-phase sequential preventive maintenance scheduling for deteriorating repairable systems. Journal of Intelligent Manufacturing, 1–15. doi:10.1007/s10845-017-1353-z
   Web of Science ®Google Scholar
• Fan, H., Xu, Z., & Chen, S. (2015). Optimally maintaining a multi-state system with limited imperfect preventive repairs. International Journal of Systems Science, 46(10), 1729–1740.
   Web of Science ®Google Scholar
• Hu, C. H., Lee, M. Y., & Tang, J. (2015). Optimum step-stress accelerated degradation test for Wiener degradation process under constraints. European Journal of Operational Research, 241(2), 412–421.
   Web of Science ®Google Scholar
• Jaber, M. Y. (2016). Learning curves: Theory, models, and applications. CRC Press, FL: Baco Raton.
   Google Scholar
• Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.
   Web of Science ®Google Scholar
• Khaleghei, A., & Makis, V. (2015). Model parameter estimation and residual life prediction for a partially observable failing system. Naval Research Logistics, 62(3), 190–205.
   Web of Science ®Google Scholar
• Kim, M. J., Jiang, R., Makis, V., & Lee, C.-G. (2011). Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure. European Journal of Operational Research, 214(2), 331–339.
   Web of Science ®Google Scholar
• Klaine, P. V., Imran, M. A., Onireti, O., & Souza, R. D. (2017). A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Communications Surveys & Tutorials, 19(4), 2392–2431. doi:10.1109/COMST.2017.2727878
   Web of Science ®Google Scholar
• Laggoune, R., Chateauneuf, A., & Aissani, D. (2009). Opportunistic policy for optimal preventive maintenance of a multi-component system in continuous operating units. Computers & Chemical Engineering, 33(9), 1499–1510.
   Web of Science ®Google Scholar
• Laggoune, R., Chateauneuf, A., & Aissani, D. (2010). Preventive maintenance scheduling for a multi-component system with non-negligible replacement time. International Journal of Systems Science, 41(7), 747–761.
   Web of Science ®Google Scholar
• Lawless, J. F. (2011). Statistical models and methods for lifetime data (Vol. 362). New York: Wiley.
   Google Scholar
• Lawless, J., & Crowder, M. (2004). Covariates and random effects in a gamma process model with application to degradation and failure. Lifetime Data Analysis, 10(3), 213–227.
   PubMed Web of Science ®Google Scholar
• Liu, Q., Dong, M., Lv, W., Geng, X., & Li, Y. (2015). A novel method using adaptive hidden semi-Markov model for multi-sensor monitoring equipment health prognosis. Mechanical Systems and Signal Processing, 64, 217–232.
   Web of Science ®Google Scholar
• Mohamed-Salah, O., Daoud, A.-K., & Ali, G. (1999). A simulation model for opportunistic maintenance strategies. IEEE International Conference on Emerging Technologies and Factory Automation., 1, 703–708, Barcelona, Spain.
   Google Scholar
• Nowakowski, T. & Werbinka, S. (2009). On problems of multicomponent system maintenance modelling. International Journal of Automation and Computing, 6(4), 364–378.
   Google Scholar
• Shafiee, M., & Finkelstein, M. (2015). An optimal age-based group maintenance policy for multi-unit degrading systems. Reliability Engineering & System Safety, 134, 230–238.
   Web of Science ®Google Scholar
• Sheu, S. H., & Jhang, J. P. (1997). A generalized group maintenance policy. European Journal of Operational Research, 96(2), 232–247.
   Web of Science ®Google Scholar
• Si, X. S., Wang, W., Hu, C. H., & Zhou, D. H. (2011). Remaining useful life estimation review on the statistical data driven approaches. European Journal of Operational Research, 213(1), 1–14.
   Web of Science ®Google Scholar
• Taghipour, S., & Banjevic, D. (2012). Optimal inspection of a complex system subject to periodic and opportunistic inspections and preventive replacements. European Journal of Operational Research, 220(3), 649–660.
   Web of Science ®Google Scholar
• Tijms, H. C. (1994). Stochastic models: An algorithmic approach (Vol. 303). New York: Wiley.
   Google Scholar
• Tobon-Mejia, D. A., Medjaher, K., & Zerhouni, N. (2010). The iso 13381-1 standard's failure prognostics process through an example. IEEE Prognostics and System Health Management Conference. Macau, China: University of Macau. pp. 1–12. .
   Google Scholar
• Van Noortwijk, J. (2009). A survey of the application of gamma processes in maintenance. Reliability Engineering & System Safety, 94(1), 2–21.
   Web of Science ®Google Scholar
• Vlok, P. J., Wnek, M., & Zygmunt, M. (2004). Utilising statistical residual life estimates of bearings to quantify the influence of preventive maintenance actions. Mechanical Systems and Signal Processing, 18(4), 833–847.
   Web of Science ®Google Scholar
• Wang, W. B. (2000). A model to determine the optimal critical level and the monitoring intervals in condition-based maintenance. International Journal of Production Research, 38(6), 1425–1436.
   Web of Science ®Google Scholar
• Wang, W., & Christer, A. H. (2000). Towards a general condition based maintenance model for a stochastic dynamic system. Journal of the Operational Research Society, 51(2), 145–155.
   Web of Science ®Google Scholar
• Wang, Y., Deng, C., Wu, J., Wang, Y., & Xiong, Y. (2014). A corrective maintenance scheme for engineering equipment. Engineering Failure Analysis, 36, 269–283.
   Web of Science ®Google Scholar
• Wang, S., & Liu, M. (2016). Two-machine flow shop scheduling integrated with preventive maintenance planning. International Journal of Systems Science, 47(3), 672–690.
   Web of Science ®Google Scholar
• Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: Practical machine learning tools and techniques. San Francisco, CA: Morgan Kaufmann Publishers Inc.
   Google Scholar
• Xia, T., Xi, L., Pan, E., & Ni, J. (2016). Reconfiguration-oriented opportunistic maintenance policy for reconfigurable manufacturing systems. Reliability Engineering & System Safety, 166, 87–98.
   Web of Science ®Google Scholar
• Xu, H., & Hu, W. (2008). Availability optimisation of repairable system with preventive maintenance policy. International Journal of Systems Science, 39(6), 655–664.
   Web of Science ®Google Scholar
• You, M.-Y., Liu, F., Wang, W., & Meng, G. (2010). Statistically planned and individually improved predictive maintenance management for continuously monitored degrading systems. IEEE Transactions on Reliability, 59(4), 744–753.
   Web of Science ®Google Scholar
• Zhang, J., Huang, X., Fang, Y., Zhou, J., Zhang, H., & Li, J. (2016). Optimal inspection-based preventive maintenance policy for three-state mechanical components under competing failure modes. Reliability Engineering & System Safety, 152, 95–103.
   Web of Science ®Google Scholar