Enhancing Decisions in Prognostics and Health Management Framework



Published Mar 23, 2021
Omar Bougacha Christophe Varnier


Prognostics and health management have become increasingly important in recent years. Many research studies focus on a crucial phase consisting of predicting the remaining useful life of equipment or a component. However, this step is often carried out without taking into account the decisions that will be taken later. This article aims to propose a modification of the existing PHM framework to combine the prognostics and decision-making phases in a closed loop. In this paper, the presented framework is described and some elements for its implementation are proposed. A simplified
example is developed to illustrate the presented methodology of post-prognostic decision enhancement.

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decision support, prognostics and health management (PHM), Post-prognostics decision

An, D., Kim, N. H., & Choi, J.-H. (2015). Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliability Engineering & System Safety, 133, 223–236.
Balaban, E., & Alonso, J. J. (2012). An approach to prognostic decision-making in the aerospace domain (Tech. Rep.). National Aeronautics and Space Administration MOFFETT Field CA AMES Research Centre.
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).
Byington, C. S., Roemer, M. J., & Galie, T. (2002). Prognostic enhancements to diagnostic systems for improved condition-based maintenance. 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.
Camci, F. (2009, September). System maintenance scheduling with prognostics information using genetic algorithm. IEEE Transactions on Reliability, 58(3), 539–552.
Cheng, G. Q., Zhou, B. H., & Li, L. (2018, July). Integrated production, quality control and condition-based maintenance for imperfect production systems. Reliability Engineering & System Safety, 175, 251–264.
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 9th annual ieee international systems conference (syscon), 2015 (pp. 182–188). IEEE.
De Medeiros, I. P., Rodrigues, L. R., Santos, R., Shiguemori, E. H., & J´unior, C. L. N. (2014). Phm-based multi-uav task assignment. In 8th annual ieee systems conference (syscon), 2014 (pp. 42–49). IEEE.
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.
Dong, H., Jin, X., Lou, Y., & Wang, C. (2014). Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regressionparticle filter. Journal of power sources, 271, 114–123.
Dorigo, M., & Caro, G. D. (1999, July). Ant colony optimization: a new meta-heuristic. Proceedings of the 1999 Congress on Evolutionary Computation.
Fitouri, C., Fnaiech, N., Varnier, C., Fnaiech, F., & Zerhouni, N. (2016). A decison-making approach for job shop scheduling with job depending degradation and predictive maintenance. In 8h ifac conference on manufacturing modelling, management and control mim (pp. 1490–1495). Troyes, France.
Frost, S., Goebel, K., Frost, A., Trinh, K., & Balas, M. (2013). Integrating systems health management with adaptive controls for a utility-scale wind turbine. In 51st aiaa aerospace sciences meeting including the new horizons forum and aerospace exposition (p. 314).
Gertler, J. (2013). Fault detection and diagnosis. In Encyclopedia of systems and control (pp. 1–7). Springer London.
Goebel, K., Celaya, J., Sankararaman, S., Roychoudhury, I., Daigle, M. J., & Saxena, A. S. a. G. G. R. A. (2017). Prognostics: The science of making predictions. CreateSpace Independent Publishing Platform.
Goebel, K., Iyer, N., & Bonissone, P. (2006, March). Framework for post-prognostic decision support. Aerospace Conference, 2006 IEEE.
Gouriveau, R., Medjaher, K., & Noureddine, Z. (2016). From prognostics and health systems management to predictive maintenance 1. John Wiley & Sons.
Herr, N. (2015). Post-prognostic scheduling of heterogeneous distributed platforms (Unpublished doctoral dissertation). Franche-Comté University.
Herr, N., Nicod, J.-M., & Varnier, C. (2014). Prognosticsbased scheduling in a distributed platform: Model, complexity and resolution. In Case, 2014 ieee international conference on automation science and engineering (pp. 1054–1059). IEEE.
Herr, N., Nicod, J.-M., Varnier, C., Zerhouni, N., Cherif, M., & Fnaiech, N. (2017). Joint optimization of train assignment and predictive maintenance scheduling. In 7th international conference on railway operations modelling and analysis. Lille, France.
Huynh, K. T., Grall, A., & Bérenguer, C. (2017). Assessment of diagnostic and prognostic condition indices for efficient and robust maintenance decision-making of systems subject to stress corrosion cracking. Reliability Engineering & System Safety, 159, 237–254.
Kandukuri, S. T., Klausen, A., Karimi, H. R., & Robbersmyr, K. G. (2016). A review of diagnostics and prognostics of low-speed machinery towards wind turbine farmlevel health management. Renewable and Sustainable Energy Reviews, 53, 697–708.
Khoury, E., Deloux, E., Grall, A., & Bérenguer, 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., 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 Prognostics and Health Management, 8(2).
Langeron, Y., Grall, A., & Barros, A. (2013). Actuator health prognosis for designing lqr control in feedback systems. Chemical engineering transactions, 33.
Langeron, Y., Grall, A., & Barros, A. (2015, August). A modeling framework for deteriorating control system and predictive maintenance of actuators. Reliability Engineering & System Safety, 140, 22–36.
Lebold, M., & Thurston, M. (2001, January). Open standards for condition-based maintenance and prognostic systems. Maintenance and Reliability Conference (MARCON).
Lei, X., & Sandborn, P. A. (2016). Phm-based wind turbine maintenance optimization using real options. International Journal of Prognostics and Health Management, 7(1), 1–14.
Lin, X., Basten, R. J. I., Kranenburg, A., & van Houtum, G.-J. (2017). Condition based spare parts supply. Reliability Engineering & System Safety, 168, 240–248.
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, 30(3), 1155—1173.
Moore, J. M. (1968). An n job, one machine sequencing algorithm for minimizing the number of late jobs. Management Science, 15(1), 102–109.
Mosallam, A., Medjaher, K., & Zerhouni, N. (2016). Data-driven prognostic method based on bayesian approaches for direct remaining useful life prediction. Journal of Intelligent Manufacturing, 27(5), 1037–1048.
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., & Varnier, C. (2012). Pronostia: An experimental platform for bearings accelerated degradation tests. In Ieee international conference on prognostics and health management, phm’12. (pp. 1–8).
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.
Nicolai, R. P., & Dekker, R. (2008). Optimal maintenance of multi-component systems: A review. in: Complex system maintenance handbook. Springer, London: Springer Series in Reliability Engineering.
Pereira, E. B., Kawakami, R., Galvao, H., & Yoneyama, T. (2010, July). Model predictive control using prognosis and health monitoring of actuators. International Symposium on Industrial Electronics, 237–243.
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.
Schwabacher, M. (2005). A survey of data-driven prognostics (Tech. Rep.). Aerospace Research Central.
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011). Remaining useful life estimation–a review on the statistical data driven approaches. European journal of operational research, 213(1), 1–14.
Sierra, G., Orchard, M., Goebel, K., & Kulkarni, C. (2019). Battery health management for small-size rotary-wing electric unmanned aerial vehicles: An efficient approach for constrained computing platforms. Reliability Engineering & System Safety, 182, 166–178.
Skima, H. (2016). Prognostics and distributed algorithms for post-prognostic decision making in mems based systems (Unpublished doctoral dissertation). Franche-Comté University.
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.
Tamilselvan, P., & Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety, 115, 124–135.
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). Fault prognosis using dynamic wavelet neural networks. In In autotestcon proceedings, 2001. ieee systems readiness technology conference (pp. 857–870). IEEE.
Van Horenbeek, A.,&Pintelon, L. (2013). A dynamic predictive maintenance policy for complex multi-component systems. Reliability Engineering & System Safety, 45(50), 39–50.
Vieira, J. P., Kawakami, R., Galvao, 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 & System Safety, 165, 62–73.
Wang, Z., Hu, C., Wang, W., Kong, X., & Zhang, W. (2015). 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.
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.
Wu, G., Vachtsevanos, F., Lewis, M., Roemer, A., & Hess, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. Wiley, Hoboken, NJ. 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., Roemer, M., & Goebel, K. (2014, July). Autonomous vehicle battery state-ofcharge prognostics enhanced mission planning. International Journal of Prognostics and Health Management, 5(8).
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