A Joint Predictive Maintenance and Spare Parts Provisioning Policy for Multi-component Systems Using RUL Prediction and Importance Measure
The paper presents a joint predictive maintenance and spare parts provisioning policy for gradually deteriorating multicomponent systems with complex structure. The decisionmaking process related to maintenance, spare parts ordering, as well as inspections scheduling is based on both RUL prediction and structural importance measure. Moreover, economic dependency between components is studied and integrated in decision rules. In addition, the impacts of the system structure on components deterioration process are also investigated. This dependency may have a significant influence on the RUL estimation of components. In order to evaluate the performance of the proposed joint predictive policy, a cost model is used. Finally, a numerical example of a 6-
component system is introduced to illustrate the use and the advantages of the proposed joint maintenance and spare parts provisioning policy.
How to Cite
complex systems, remaining useful life (RUL), decision making, spare parts, predictive maintenance, importance measure
Birnbaum, L. (1969). On the importance of different elements in a multielement system. multivariate analysis. Academic Press, 2.
Boudhar, H., Dahane, M., & Rezg, N. (2013). Order/remanufacturing policy of spare part with recovery option for stochastic deteriorating system. In Ieee 18th conference on emerging technologies & factory automation (etfa) (pp. 1–7).
Do Van, P., Levrat, E., Voisin, A., Iung, B., et al. (2012). Remaining useful life (rul) based maintenance decision making for deteriorating systems. In 2nd ifac workshop on advanced maintenance engineering, service and technology, a-mest’12.
Elwany, A. H., & Gebraeel, N. Z. (2008). Sensor-driven prognostic models for equipment replacement and spare parts inventory. IIE Transactions, 40(7), 629–639.
Grall, A., Dieulle, L., Bérenguer, C., & Roussignol, M. (2002). Continuous-time predictive-maintenance scheduling for a deteriorating system. IEEE Transactions on Reliability, 51(2), 141-150.
Le Son, K., Fouladirad, M., Barros, A., Levrat, E., & Iung, B. (2013). Remaining useful life estimation based on stochastic deterioration models: A comparative study. Reliability Engineering & System Safety, 112, 165–175.
Moinzadeh, K., & Schmidt, C. P. (1991). An (s- 1, s) inventory system with emergency orders. Operations Research, 39(2), 308-321.
Nguyen, K.-A., Do Van, P., & Grall, A. (2013a). Predictive grouping maintenance strategy for complex structure systems using importance measure. In Ieee 2013 international conference on quality, reliability, risk, maintenance, and safety engineering (qr2mse) (pp. 582–588).
Nguyen, K.-A., Do Van, P., & Grall, A. (2013b). A predictive maintenance strategy for multi-component systems using importance measure. In Proc. of the european safety and reliability conference, esrel 2013 (pp. 967–975).
Rausand, M., & Høyland, A. (2004). System reliability theory: Models, statistical methods and applications (Second ed.). John Wiley & Sons.
Van Horenbeek, A., Scarf, P. A., Cavalcante, C. A., & Pintelon, L. (n.d.). The effect of maintenance quality on spare parts inventory for a fleet of assets. IEEE Transactions on Reliability, 62(3), 596 - 607.
Van Noortwijk, J. M. (2009). A survey of the application of gamma processes in maintenance. Reliability Engineering & System Safety, 94(1), 2-21.
Wang, L., Chu, J., & Mao,W. (2008). An optimum conditionbased replacement and spare provisioning policy based on markov chains. Journal of Quality in Maintenance Engineering, 14(4), 387–401.
Wang, L., Chu, J., & Mao, W. (2009). A condition-based replacement and spare provisioning policy for deteriorating systems with uncertain deterioration to failure. European Journal of Operational Research, 194(1), 184–205.
Wang, W., Pecht, M. G., & Liu, Y. (2012). Cost optimization for canary-equipped electronic systems in terms of inventory control and maintenance decisions. Reliability, IEEE Transactions on, 61(2), 466–478.
Xie, J., & Wang, H. (2008). Joint optimization of conditionbased preventive maintenance and spare ordering policy. In Wireless communications, networking and mobile computing, 2008. wicom’08. 4th international conference on (pp. 1–5).
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