A Review of Post-Prognostics Decision-Making in Prognostics and Health Management
Mainly, the prognostics and health management (PHM) process is based on three processes: the data acquisition and health assessment process in which sensors signals are acquired and processed, the diagnostic and prognostic process in which the source of failure is detected and the remaining useful life (RUL) is predicted and finally the decision- making process that refers to the term management in prognostics and health management. This paper reviews in the literature about the different aspects of decision-making in the context of PHM. The selected papers are subject to con- tent assessment and grouped according to the decision type. Additionally, this paper presents a synthesis of the previous works that helps identify new trends and deficiencies in the decision-making process. The synthesis can guide efforts for future work.
Prognostic Decision Making, Prognostics and Health Management, Post-Prognostics Decisions
Ambani, S., Li, L., & Ni, J. (2009). Condition-Based Maintenance Decision-Making for Multiple Machine Systems. Journal of Manufacturing Science and Engineering, 131(3), 031009.
Balaban, E., & Alonso, J. J. (2012). An approach to prognostic decision making in the aerospace domain. In Annual conference of the prognostics and health management society.
Benaggoune, K., Meraghni, S., Ma, J., Mouss, L., & Zerhouni, N. (2020). Post prognostic decision for predictive maintenance planning with remaining useful life uncertainty. In 2020 prognostics and health management conference (phm-besanc¸on) (pp. 194–199).
Bencheikh, G., Letouzey, A., & Desforges, X. (2018, august). Process for joint scheduling based on health assessment of technical resources. In 10th ifac symposium on fault detection, supervision and safety for technical processes safeprocess 2018 (Vol. 51, pp. 192–199). Warsaw, Poland.
Bogdanov, A., Chiu, S., Gokdere, L. U., & Vian, J. (2007, May). Stochastic Optimal Control of a Servo Motor with a Lifetime Constraint. In Proceedings of the 45th IEEE Conference on Decision and Control. San Diego, CA, USA: IEEE.
Bole, B., Tang, L., Goebel, K., & Vachtsevanos, G. (2011, July). Adaptive Load-Allocation for Prognosis-Based Risk Management. In Annual Conference of the Prognostics and Health Management Society, 2011. Montreal, Canada.
Bougacha, O., Varnier, C., Zerhouni, N., & Hajri-Gabouj, S. (2018, dec). A post-prognostic decision approach for production and maintenance planning. In 48th conference on computers & industrial engineering (2018).
Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2017). A Proactive Event-driven Decision Model for Joint Equipment Predictive Maintenance and Spare Parts Inventory Optimization. Procedia CIRP, 59, 184–189.
Brown, D. W., Georgoulas, G., Bole, B., Pei, H.-L., Orchard, M., Tang, L., . . . Vachtsevanos, G. (2009, September). Prognostics Enhanced Reconfigurable Control of Electro-Mechanical Actuators. In Annual Conference of the Prognostics and Health Management Society, 2009. San Diego, CA, USA.
Brown, D. W., & Vachtsevanos, G. (n.d., July). A Prognostic Health Management Based Framework for Fault- Tolerant Control. In Annual Conference of the Prognostics and Health Management Society, 2011. Montreal, Canada.
Byington, C. S., Roemer, M. J., & Galie, T. (2002). Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft]. In Proceedings, ieee aerospace conference, 2002. IEEE.
Cai, J., Li, X., & Chen, X. (2016). Optimization of Aeroengine Shop Visit Decisions Based on Remaining Useful Life and Stochastic Repair Time. Mathematical Problems in Engineering, 2016, 1–11.
Cai, J., Li, X., & Chen, X. (2017, December). Joint optimization of maintenance inspection and spare provisioning for aircraft deteriorating parts. Journal of Systems Engineering and Electronics, 28(6), 1133 – 1140.
Cai, J., Yin, Y., Zhang, L., & Chen, X. (2017). Joint Optimization of Preventive Maintenance and Spare Parts Inventory with Appointment Policy. Mathematical Problems in Engineering, 2017, 1–12.
Camci, F. (2009, September). System Maintenance Scheduling With Prognostics Information Using Genetic Algorithm. IEEE Transactions on Reliability, 58(3), 539– 552.
Camci, F. (2014, September). The travelling maintainer problem: integration of condition-based maintenance with the travelling salesman problem. Journal of the Operational Research Society, 65(9), 1423–1436.
Camci, F. (2015, September). Maintenance scheduling of geographically distributed assets with prognostics information. European Journal of Operational Research, 245(2), 506–516.
Chebel-Morello, B., Nicod, J.-M., & Varnier, C. (2017). From Prognostics and Health Systems Management to Predictive Maintenance 2. John Wiley & Sons.
Chen, X., Xu, D., & Xiao, L. (2016, December). Joint optimization of replacement and spare ordering for critical rotary component based on condition signal to date. Eksploatacja i Niezawodnosc - Maintenance and Reliability, 19(1), 76–85.
Cheng, G. Q., Zhou, B. H., & Li, L. (2018, July). Integrated production, quality control and condition-based maintenance for imperfect production systems. Reliability Engineering and System Safety, 175, 251–264.
Cholette, M. E., Celen, M., Djurdjanovic, D., & Rasberry, J. D. (2013, November). Condition Monitoring and Operational Decision Making in Semiconductor Manufacturing. IEEE Transactions on Semiconductor Manufacturing, 26(4), 454–464.
Choo, B. Y., Adams, S. C., Weiss, B. A., Marvel, J. A., & Beling, P. A. (2016). Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems. International Journal of Prognostics and Health Management.
Choo, B. Y., Beling, P. A., LaViers, A. E., Marvel, J. A., & Weiss, B. A. (2015). Adaptive Multi-scale PHM for Robotic Assembly Processes. In Proceedings of the Annual Conference in Prognostics and Health Management Society. San Diego, CA, USA.
Choo, B. Y., Weiss, B. A., & Beling, P. A. (2017, June). Health-Aware Hierarchical Control for Smart Manufacturing using Reinforcement Learning. In Proceedings of the IEEE International Conference on Prognostics and Health Management. Dallas, TX , USA: IEEE.
Chre´tien, S., Herr, N., Nicod, J.-M., & Varnier, C. (2016, october). Post-Prognostics Decision for Optimizing the Commitment of Fuel Cell Systems. In 3rd ifac workshop on advanced maintenance engineering, services and technology amest 2016 (Vol. 49, pp. 168–173). Biarritz, France.
Cui, Y., Shi, J., & Wang, Z. (2015, September). Discrete Event Logistics Systems (DELS) simulation modeling incorporating two-step Remaining Useful Life (RUL) estimation. Computers in Industry, 72, 68–81.
Daigle, M., & Goebel, K. (2010). improving computational efficiency of prediction in model-based prognostics using the unscented transform. Annual Conference of the Prognostics and Health Management Society 2010.
De Medeiros, I. P., Rodrigues, L. R., Kern, C. S., Dos Santos, R. D. C., & Shiguemori, E. H. (2015). Integrated task assignment and maintenance recommendation based on system architecture and PHM information for UAVs. In Systems Conference (SysCon), 2015 9th Annual IEEE International (pp. 182–188). IEEE.
de Medeiros, I. P., Rodrigues, L. R., Santos, R., Shiguemori, E. H., & Ju´nior, C. L. N. (2014). PHM-based Multi-UAV task assignment. In Systems Conference (SysCon), 2014 8th Annual IEEE (pp. 42–49). IEEE.
Desforges, X., Die´vart, M., & Archime`de, B. (2017, April). A prognostic function for complex systems to support production and maintenance co-operative planning based on an extension of object oriented Bayesian networks. Computers in Industry, 86, 34–51.
Do, P., Voisin, A., Levrat, E., & Iung, B. (2015, January). A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions. Reliability Engineering and System Safety, 133, 22–32.
Durazo-Cardenas, I., Starr, A., Turner, C. J., Tiwari, A., Kirkwood, L., Bevilacqua, M., . . . Emmanouilidis, C. (2018, April). An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition, planning and cost. Transportation Research Part C: Emerging Technologies, 89, 234–253.
Feng, D., Lin, S., He, Z., & Sun, X. (2017, July). A technical framework of phm and active maintenance for modern high-speed railway traction power supply systems. International Journal of Rail Transportation, 5(3), 145– 169.
Feng, Q., Bi, X., Zhao, X., Chen, Y., & Sun, B. (2017, January). Heuristic hybrid game approach for fleet condition-based maintenance planning. Reliability Engineering and System Safety, 157, 166–176.
Fitouri, C., Fnaiech, N., Varnier, C., Fnaiech, F., & Zerhouni, N. (2016). A Decison-Making Approach for Job Shop Scheduling with Job Depending Degradation and PredictiveMaintenance. In (Vol. 49, pp. 1490–1495). Troyes, France.
Goebel, K., Celaya, J., Sankararaman, S., Roychoudhury, I., Daigle, M. J., & Saxena, A. (2017). Prognostics: The Science of Making Predictions. CreateSpace Independent Publishing Platform.
Gouriveau, R., Medjaher, K., & Zerhouni, N. (2016). From Prognostics and Health Systems Management to Predictive Maintenance 1. John Wiley & Sons.
Griffith, D. T., Yoder, N. C., Resor, B., White, J., & Paquette, J. (2014, November). Structural health and prognostics management for the enhancement of offshore wind turbine operations and maintenance strategies: Structural health and prognostics management for offshore O&M. Wind Energy, 17(11), 1737–1751.
Grosso, J. M., Ocampo-Martinez, C., & Puig, V. (2016, January). Reliability–based economic model predictive control for generalised flow–based networks including actuators’ health–aware capabilities. International Journal of Applied Mathematics and Computer Science, 26(3). doi: 10.1515/amcs-2016-0044
Haddad, G., Peter, S., & Pecht, M. (2011). A Real Options Optimization Model to Meet Availability Requirements for Offshore Wind Turbines. In Proceedings of MFPT: The Applied Systems Health Management Conference. Virginia Beach, VA, USA.
Herr, N. (2015). Post-Prognostic scheduling of heterogeneous distributed platforms (Unpublished doctoral dissertation). Franche-Comte University, Besancon, France.
Herr, N., Nicod, J.-M., & Varnier, C. (2014). Prognostics- based scheduling in a distributed platform: Model, complexity and resolution. In Automation Science and Engineering (CASE), 2014 IEEE International Conference on (pp. 1054–1059). IEEE.
Herr, N., Nicod, J.-M., Varnier, C., Jardin, L., Sorrentino, A., Hissel, D., & Pe´ra, M.-C. (2017, May). Decision process to manage useful life of multi-stacks fuel cell systems under service constraint. Renewable Energy, 105, 590–600.
Herr, N., Nicod, J.-M., Varnier, C., Zerhouni, N., Cherif, M., & Fnaiech, N. (2017). Joint optimization of train assignment and predictive maintenance scheduling. Lille, France.
Huynh, K., Grall, A., & Be´renguer, C. (2017, March). Assessment of diagnostic and prognostic condition indices for efficient and robust maintenance decision-making of systems subject to stress corrosion cracking. Reliability Engineering and System Safety, 159, 237– 254.
Huynh, K. T., Barros, A., & Berenguer, C. (2015, March). Multi-Level Decision-Making for The Predictive Maintenance of $k$ -Out-of-$n$ :F Deteriorating Systems. IEEE Transactions on Reliability, 64(1), 94– 117.
Huynh, K. T., Castro, I. T., Barros, A., & Berenguer, C. (2014, July). On the Use of Mean Residual Life as a Condition Index for Condition-Based Maintenance Decision-Making. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(7), 877–893.
Iyer, N., Goebel, K., & Bonissone, P. (2006). Framework for post-prognostic decision support. In Aerospace Conference, 2006 IEEE (pp. 10–pp). IEEE.
Jain, A. K., & Lad, B. K. (2017, June). Dynamic Optimization of Process Quality Control and Maintenance Planning. IEEE Transactions on Reliability, 66(2), 502– 517.
Jin, C., Djurdjanovic, D., Ardakani, H. D., Wang, K., Buzza, M., Begheri, B., . . . Lee, J. (2015). A comprehensive framework of factory-to-factory dynamic fleet- level prognostics and operation management for geographically distributed assets. In Automation Science and Engineering (CASE), 2015 IEEE International Conference on (pp. 225–230). IEEE.
Julka, N., Thirunavukkarasu, A., Lendermann, P., Gan, B. P., Schirrmann, A., Fromm, H., & Wong, E. (2011, August). Making use of prognostics health management information for aerospace spare components logistics network optimisation. Computers in Industry, 62(6), 613–622.
Khoury, E., Deloux, E., Grall, A., & Be´renguer, C. (2013). On the Use of Time-Limited Information for Maintenance Decision Support: A Predictive Approach under Maintenance Constraints. Mathematical Problems in Engineering, 2013, 1–11.
Ladj, A., Benbouzid-Si Tayeb, F., Varnier, C., Dridi, A. A., & Selmane, N. (2017). A Hybrid of Variable Neighbor Search and Fuzzy Logic for the permutation flowshop scheduling problem with predictive maintenance. Procedia Computer Science, 112, 663–672.
Ladj, A., Varnier, C., Tayeb, F. B. S., & Zerhouni, N. (2017). Exact and heuristic algorithms for post prognostic decision in a single multifunctional machine. International Journal of of Prognostics and Health Management, 8(2).
Langeron, Y., Fouladirad, M., & Grall, A. (2016, june). Controlled systems, failure prediction and maintenance. In 8th ifac conference on manufacturing modelling, management and control mim (Vol. 49, pp. 805–808). Troyes, France.
Langeron, Y., Grall, A., & Barros, A. (2013). Actuator Health Prognosis for Designing LQR Control in Feedback Systems. Chemical engineering transactions, 33. doi: 10.3303/CET1333164
Langeron, Y., Grall, A., & Barros, A. (2015, August). A modeling framework for deteriorating control system and predictive maintenance of actuators. Reliability Engineering and System Safety, 140, 22–36.
Langeron, Y., Grall, A., & Barros, A. (2017, August). Joint maintenance and controller reconfiguration policy for a gradually deteriorating control system. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 231(4), 339–349.
Lebold, M., & Thruston, M. (2001, January). Open Standards For Condition-Based Maintenance and Prognostic Systems. In Maintenance and Reliability Conference (MARCON). Gatlinburg, USA.
Lei, X., & Sandborn, P. A. (2016). PHM-based wind turbine maintenance optimization using real options. International Journal of of Prognostics and Health Management, 7(1), 1–14.
Lei, X., & Sandborn, P. A. (2018, February). Maintenance scheduling based on remaining useful life predictions for wind farms managed using power purchase agreements. Renewable Energy, 116, 188–198.
Li, R., & Ryan, J. K. (2011, September). A Bayesian Inventory Model Using Real-Time Condition Monitoring Information. Production and Operations Management, 20(5), 754–771.
Li, Z., Guo, J., & Zhou, R. (2016). Maintenance scheduling optimization based on reliability and prognostics information. In Reliability and Maintainability Symposium (RAMS), 2016 Annual (pp. 1–5). IEEE.
Lin, L., Luo, B., & Zhong, S. (2017, December). Development and application of maintenance decision-making support system for aircraft fleet. Advances in Engineering Software, 114, 192–207.
Lin, S., Zhang, A., & Feng, D. (2016, October). Maintenance decision-making model based on POMDP for traction power supply equipment and its application. In 2016 Prognostics and System Health Management Conference (PHM-Chengdu) (pp. 1–6). Chengdu, China: IEEE.
Lin, X., Basten, R., Kranenburg, A., & van Houtum, G. (2017, December). Condition based spare parts supply. Reliability Engineering and System Safety, 168, 240–248.
Liu, Q., Dong, M., Chen, F., Lv, W., & Ye, C. (2019, February). Single-machine-based joint optimization of predictive maintenance planning and production scheduling. Robotics and Computer-Integrated Manufacturing, 55, 173–182.
Liu, Q., Dong, M., Lv, W., & Ye, C. (2017, February). Manufacturing system maintenance based on dynamic programming model with prognostics information. Journal of Intelligent Manufacturing.
Liu, X., Yang, T., Pei, J., Liao, H., & Pohl, E. A. (2019, March). Replacement and Inventory Control for a Multi-Customer Product Service System with Decreasing Replacement Costs. European Journal of Operational Research, 273(2), 561–574.
Luo, B., & Lin, L. (2018). Multi-objective decision-making model based on CBM for an aircraft fleet. Xi’an City, China.
Matyas, K., Nemeth, T., Kovacs, K., & Glawar, R. (2017). A procedural approach for realizing prescriptive maintenance planning in manufacturing industries. CIRP Annals, 66(1), 461–464.
Mazidi, P., Bertling Tjernberg, L., & Sanz Bobi, M. A. (2017, April). Wind turbine prognostics and maintenance management based on a hybrid approach of neural networks and a proportional hazards model. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 231(2), 121–129.
Meraghni, S., Terrissa, L. S., Ayad, S., Zerhouni, N., & Varnier, C. (2018, March). Post-prognostics decision in cyber-physical systems. In 2018 International Conference on Advanced Systems and Electric Technologies (IC aset) (pp. 201–205). Hammamet: IEEE.
Meraghni, S., Terrissa, L. S., Zerhouni, N., Varnier, C., & Ayad, S. (2016). A post-prognostics decision framework for cell site using Cloud computing and Internet of Things. In Cloud Computing Technologies and Applications (CloudTech), 2016 2nd International Conference on (pp. 310–315). IEEE.
Moghaddass, R., & Ertekin, S. (2018, April). Joint optimization of ordering and maintenance with condition monitoring data. Annals of Operations Reseach, 263(1), 271–310.
Nguyen, D. N., Dieulle, L., & Grall, A. (2014, August). Feedback Control System with Stochastically Deteriorating Actuator: Remaining Useful Life Assessment. In Proceedings of the 19th World Congress The International Federation of Automatic Control. Cape Town, South Africa.
Nguyen, K.-A., Do, P., & Grall, A. (2015, December). Multilevel predictive maintenance for multi-component systems. Reliability Engineering and System Safety, 144, 83–94.
Nguyen, K. T., & Medjaher, K. (2019). A new dynamic predictive maintenance framework using deep learning for failure prognostics. Reliability Engineering & System Safety, 188, 251–262.
Niknam, S. A., Kobza, J. E., & Hines, J. W. (2015). Operation and maintenance decision-making using prognostic information. In Reliability and Maintainability Symposium (RAMS), 2015 Annual (pp. 1–7). IEEE.
Niu, G., & Jiang, J. (2017). Prognostic control-enhanced maintenance optimization for multi-component systems. Reliability Engineering & System Safety, 168, 218–226.
Niu, G., & Liu, S. (2018). Demagnetization monitoring and life extending control for permanent magnet-driven traction systems. Mechanical Systems and Signal Processing, 103, 264–279.
Nzukam, C., Voisin, A., Levrat, E., Sauter, D., & Iung, B. (2017, July). A dynamic maintenance decision approach based on maintenance action grouping for HVAC maintenance costs savings in Non-residential buildings. In 20th ifac world congress (Vol. 50, pp. 13722–13727).
Nzukam, C., Voisin, A., Levrat, E., Sauter, D., & Iung, B. (2018, june). Opportunistic maintenance scheduling with stochastic opportunities duration in a predictive maintenance strategy. In 16th ifac symposium on information control problems in manufacturing incom (Vol. 51, pp. 453–458). Bergamo, Italy.
Pan, E., Liao, W., & Xi, L. (2012, June). A joint model of production scheduling and predictive maintenance for minimizing job tardiness. The International Journal of Advanced Manufacturing Technology, 60(9), 1049–1061.
Pereira, E. B., Galva˜o, R. K. H., & Yoneyama, T. (2010). Model predictive control using prognosis and health monitoring of actuators. In Industrial Electronics (ISIE), 2010 IEEE International Symposium on (pp. 237–243). IEEE.
Ramos Rodrigues, L., Paixao de Medeiros, I., & Strottmann Kern, C. (2015, April). Maintenance cost optimization for multiple components using a condition based method. In 2015 Annual IEEE Systems Conference (SysCon) Proceedings (pp. 164–169). Vancouver, BC, Canada: IEEE.
Rodrigues, L. R., Gomes, J. P. P., & Alcaˆntara, J. F. L. (2018, May). Embedding Remaining Useful Life Predictions into a Modified Receding Horizon Task Assignment Algorithm to Solve Task Allocation Problems. Journal of Intelligent & Robotic Systems, 90(1-2), 133–145.
Rodrigues, L. R., Gomes, J. P. P., Ferri, F. A. S., Medeiros, I. P., Galvao, R. K. H., & Nascimento Junior, C. L. (2015, December). Use of PHM Information and System Architecture for Optimized Aircraft Maintenance Planning. IEEE Systems Journal, 9(4), 1197–1207.
Shi, H., & Zeng, J. (2016, March). Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence. Computers & Industrial Engineering, 93, 192–204.
Si, X., Li, T., Zhang, Q., & Hu, X. (2018, September). An Optimal Condition-Based Replacement Method for Systems With Observed Degradation Signals. IEEE Transactions on Reliability, 67(3), 1281–1293.
Skima, H. (2016). Prognostics and distributed algorithms for post-prognostic decision making in MEMS based Systems (Unpublished doctoral dissertation). Franche- Comte University, Besancon, France.
Skima, H., Varnier, C., Dedu, E., Medjaher, K., & Bourgeois, J. (2017, February). Post-prognostics decision making in distributed MEMS-based systems. Journal of Intelligent Manufacturing.
Sun, B., Zeng, S., Kang, R., & Pecht, M. (2010, January). Benefits analysis of prognostics in systems. 2010 Prognostics and System Health Management Conference, 1–10.
Tamssaouet, F., Nguyen, K., & Medjaher, K. (2019). System remaining useful life maximization through mission profile optimization. In Asia-pacific conference of the prognostics and health management society.
Tang, D., Makis, V., Jafari, L., & Yu, J. (2015, February). Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring. Reliability Engineering and System Safety, 134, 198–207.
Tang, L., Hettler, E., Zhang, B., & DeCastro, J. (2011, July). A Testbed for Real-Time Autonomous Vehicle PHM and Contingency Management Applications. In Annual Conference of the Prognostics and Health Management Society, 2011. Montreal, Canada.
Tian, Z., Jin, T., Wu, B., & Ding, F. (2011, May). Condition based maintenance optimization for wind power generation systems under continuous monitoring. Renewable Energy, 36(5), 1502–1509.
Tian, Z., & Liao, H. (2011, May). Condition based maintenance optimization for multi-component systems using proportional hazards model. Reliability Engineering and System Safety, 96(5), 581–589.
Uckun, S., Goebel, K., & Lucas, P. J. (2008, October). Standardizing research methods for prognostics. In International Conference on Prognostics and Health Management, 2008. PHM 2008., 1–10.
Vachtsevanos, G., & Wang, P. (2001, August). Fault prognosis using dynamic wavelet neural networks. In IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference. (pp. 857–870). Valley Forge, PA, USA.
Van Horenbeek, A., & Pintelon, L. (2013, December). A dynamic predictive maintenance policy for complex multi-component systems. Reliability Engineering and System Safety, 120, 39–50.
Verbert, K., De Schutter, B., & Babuška, R. (2017, March). Timely condition-based maintenance planning for multi-component systems. Reliability Engineering and System Safety, 159, 310–321.
Vianna, W. O. L., & Yoneyama, T. (2018, June). Predictive Maintenance Optimization for Aircraft Redundant Systems Subjected to Multiple Wear Profiles. IEEE Systems Journal, 12(2), 1170–1181.
Vieira, J. P., Galva˜o, R. K. H., & Yoneyama, T. (2015, December). Predictive Control for Systems with Loss of Actuator Effectiveness Resulting from Degradation Effects. Journal of Control, Automation and Electrical Systems, 26(6), 589–598.
Vileiniskis, M., & Remenyte-Prescott, R. (2017). Quantitative risk prognostics framework based on petri net and bow-tie models. Reliability Engineering and System Safety, 165, 62–73.
Villarejo, R., Johansson, C.-A., Galar, D., Sandborn, P., & Kumar, U. (2016, July). Context-driven decisions for railway maintenance. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 230(5), 1469–1483.
Wang, C., Xu, J., Wang, H., & Zhang, Z. (2018, September). A criticality importance-based spare ordering policy for multi-component degraded systems. Eksploatacja i Niezawodnosc - Maintenance and Reliability, 20(4), 662–670.
Wang, K., Tian, J., Pecht, M., & Xu, A. (2015). A Prognostics and Health Management Based Method for Refurbishment Decision Making for Electromechanical Systems. In 15th ifac symposium oninformation control problems inmanufacturing (Vol. 48, pp. 454–459).
Wang, P., Tamilselvan, P., Twomey, J., & Youn, B. D. (2013, June). Prognosis-informed wind farm operation and maintenance for concurrent economic and environmental benefits. International Journal of Precision Engineering and Manufacturing, 14(6), 1049–1056.
Wang, W. (2014, October). A scheduling model for systems with task and health dependent remaining useful life prognostics. International Journal of Production Research, 52(19), 5764–5779. doi: 10.1080/00207543.2014.910629
Wang, Y., Gogu, C., Binaud, N., Bes, C., Haftka, R. T., & Kim, N. H. (2017, June). A cost driven predictive maintenance policy for structural airframe maintenance. Chinese Journal of Aeronautics, 30(3), 1242– 1257.
Wang, Y., Gogu, C., Binaud, N., Bes, C., Haftka, R. T., & Kim, N.-H. (2018, December). Predictive airframe maintenance strategies using model-based prognostics. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 232(6), 690–709.
Wang, Z., Hu, C., Wang, W., Kong, X., & Zhang, W. (2015, August). A prognostics-based spare part ordering and system replacement policy for a deteriorating system subjected to a random lead time. International Journal of Production Research, 53(15), 4511–4527.
Wang, Z.-Q., Wang, W., Hu, C.-H., Si, X.-S., & Zhang, W. (2015, June). A Prognostic-Information-Based Order-Replacement Policy for a Non-Repairable Critical System in Service. IEEE Transactions on Reliability, 64(2), 721–735.
Welz, Z., Coble, J., Upadhyaya, B., & Hines, W. (2017, August). Maintenance-based prognostics of nuclear plant equipment for long-term operation. Nuclear Engineering and Technology, 49(5), 914–919.
Yang, Z. M., Djurdjanovic, D., & Ni, J. (2008, February). Maintenance scheduling in manufacturing systems based on predicted machine degradation. Journal of Intelligent Manufacturing, 19(1), 87–98.
Zhang, B., Tang, L., Decastro, J., & Goebel, K. (2011). Prognostics-enhanced receding horizon mission planning for field unmanned vehicles. In Aiaa guidance, navigation, and control conference (p. 6294).
Zhang, B., Tang, L., DeCastro, J., Roemer, M., & Goebel, K. (2014). Autonomous vehicle battery state-of-charge prognostics enhanced mission planning. International Journal of of Prognostics and Health Management, 5, 1–11.
Zhao, X., Fouladirad, M., Be´renguer, C., & Bordes, L. (2009). Maintenance policy for deteriorating system with explanatory variables. R&RATA.
Zuo, J., Cadet, C., Li, Z., Be´renguer, C., & Outbib, R. (2020). Post-prognostics decision making for a two-stacks fuel cell system based on a load-dependent deterioration model. In European conference of the prognostics and health management (phm) society (Vol. 5, p. 9).