A generic ageing model for prognosis - Application to Permanent Magnet Synchronous Machines



Published Jul 8, 2014
Garance Vinson Pauline Ribot Michel Combacau


In the context of more electrical aircrafts, Permanent Magnet Synchronous Machines are used in a more and more aggressive environment. It becomes necessary to supervise their health state and to predict their future evolution and remaining useful life in order to anticipate any requested maintenance operation. Model-based prognosis is a solution to this issue. Any prognosis method must rely on knowledge about the system ageing. A review of existing ageing laws is presented. The generic ageing model proposed in (Vinson, Ribot, Prado, & Combacau, 2013) is extended in this paper. It allows representing the ageing of any equipment and the impact of this ageing on its environment. The model includes the possible retroaction of the system health state to itself through stress increase in case of damage. The proposed ageing model is then illustrated with Permanent Magnet Synchronous Machines (PMSM). Two critical faults are characterized and modeled : inter-turns short-circuits and rotor demagnetization. Stator and rotor ageing are well represented by the proposed ageing model. The prognosis method developed in (Vinson et al., 2013) is extended to consider this new generic ageing model. In order to test the prognosis algorithm, ageing data are needed Since no real measurements are available, a virtual prototype of PMSM is developed. It is a realistic model which allows running a fictive but realistic scenario of stator ageing. The scenario comprises apparition and progression of an inter-turns short-circuit and its impact on stator temperature, which value has an impact on the ageing speed. The prognosis method is applied successfully to the PMSM during this scenario and allows estimating the Remaining
Useful Life (RUL) of the stator and the machine.

How to Cite

Vinson, G., Ribot, P., & Combacau, M. (2014). A generic ageing model for prognosis - Application to Permanent Magnet Synchronous Machines. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1480
Abstract 474 | PDF Downloads 129



failure prognosis, damage prognosis, ageing law

Adeline, R., Gouriveau, R., & Zerhouni, N. (2008). Pronostic de défaillances: Maitrise de l’erreur de prédiction. In 7`eme Conference Internationale de Mobilisation et Simulation, (MOSIM’08).
Bansal, D., Evans, D.-J., & Jones, B. (2005). Application of a real-time predictive maintenance system to a production machine system. International Journal ofMachine Tools and Manufacture, 45(10), 1210-1221.
Bouaziz, M.-F., Zamai, E., & Duvivier, F. (2013). Towards bayesian network methodology for predicting the equipment health factor of complex semiconduc-tor systems. International Journal of Production Research, 51(15).
Bregon, A., Daigle, M., & Roychoudhury, I. (2012). An integrated framework for model-based distributed diagnosis and prognosis. In Annual Conference of the Prognostics and Health Management Society (PHM’12).
Brissaud, F., Lanternier, B., Charpentier, D., & Lyonnet, P. (2007). Modélisation des taux de défaillance en mécanique, combinaison d’une loi de weibull et d’un mod`ele de cox pour la modélisation des taux de défaillance en fonction du temps et des facteurs d’influence. In 3`eme congr`es Performances et Nouvelles Technologies en Maintenance (PENTOM’07.
Byington, C. S., Roemer, M. J., & Galie, T. (2002). Prognostic enhancements to diagnostic systems for improved condition-based maintenance. IEEE Aerospace Conference Proceedings, 6, 2815-2824.
Byington, C. S., & Stoelting, P. (2004). A model-based approach to prognostics and health management for flight control actuators. In IEEE Aerospace Conference.
Das, S., Hall, R., Herzog, S., Harrison, G., & Bodkin, M. (2011). Essential steps in prognostic health management. In IEEE Conference on Prognostics and Health Management (PHM’11) (p. 1-9).
El-Koujok, M., Gouriveau, R., & Zerhouni, N. (2010). A neuro-fuzzy self built system for prognostics: a way to ensure good prediction accuracy by balancing complexity and generalization. In International Conference on Prognostics and Health Management (PHM’10).
Gebraeel, N., Elwany, A., & Pan, J. (2009). Residual life predictions in the basence of prior degradation knowledge. IEEE Transactions on Reliability, 58, 106-117.
Goh, K., Tjahjono, B., Baines, T.,&Subramaniam, S. (2006). Ra review of research in manufacturing prognostics. In IEEE International Conference on Industrial Informatics (p. 412-422).
Greitzer, F. L., & Pawlowski, R. A. (2002). Embedded prognostics health monitoring. In International instrumentation symposium on embedded health monitoring workshop.
Gucik-Derigny, D., Outbib, R., & Ouladsine, M. (2011). Observer design applied to prognosis of system. In International Conference on Prognostics and Health Management (PHM’11).
Hall, P., & Strutt, J. (2003). Probabilistic physics-of-failure models for component reliabilities using monte carlo simulation and weibull analysis: a parametric study. Reliability Engineering and System Safety, 30, 233-242.
Hu, C. (2011). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. In IEEE International Conference on Prognostics and Health Management (PHM’11).
Huynh, K., Castro, I., Barros, A., & Berenguer, C. (2012). On the construction of mean residual life for maintenance decision-making. In 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS’12).
Khorasgani, H., Kulkarni, C., Biswas, G., Celaya, J. R., & Goebel, K. (2013). Degredation modeling and remaining useful life prediction of electrolytic capacitors under thermal overstress condition using particle filters. In Annual Conference of the Prognostics and Health Management Society (PHM’13). New Orelans, USA.
Lacaille, J., Gouby, A., & Piol, O. (2013). Wear prognostic on turbofan engines. In Annual Conference of the Prognostics and Health Management Society (PHM’13). New Orleans, USA.
Lahoud, N., Faucher, J., Malec, D., & Maussion, P. (2011). Electrical ageing modeling of the insulation of low voltage rotating machines fed by inverters with the design of experiments (doe) method. In IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED).
Lawless. (2004). Covariates and random effects in a gamma process model with application to degradation and failure. Lifetime Data Analysis, 10, 213-227.
Muller, A., Suhner, M.-C., & Iung, B. (2008). Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system. Reliability Engineering and System Safety, 93, 234-253.
Nima, G., Lin,M.,Murthy,M., Prasad, Y.,&Yong, S. (2009). A review on degradation models in reliability analysis. In Proceedings of the 4th world Congress on Engineering Asset Management.
Onori, S., Rizzoni, G., & Cordoba-Arenas, A. (2012). A prognostic methodology for interconnected systems: preliminary results. In 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS’12).
Pommier, S. (2009-2010). Mecanique des materiaux. ENS Cachan.
Ray, A. (1999, Jan). Stochastic modeling of fatigue crack damage for risk analysis and remaining life prediction. Journal of Dynamic Systems, Measurement, and Control (ASME), 121(3).
van Noortwijk, J., Kallen, M., & Pandey, M. (2005). Gamma processes for time-dependant reliability of structures. In European Safety and Reliability Conference (ESREL’05).
van Noortwijk, J., & Klatter, H. (2002). The use of lifetime distributions in bridge replacement modelling. In 1rst International Conference on Bridge Maintenance, Safety and Management (IABMAS).
Venet, P. (2007). Hdr: Amelioration de la srete de fonctionnement des dispositifs de stockage d’energie. Unpublished doctoral dissertation, Universite Claude Bernard - Lyon 1.
Vinson, G., Combacau, M., & Prado, T. (2012). Permanent magnet synchronous machines faults detection and identification. In 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS’12).
Vinson, G., Combacau, M., Prado, T., & Ribot, P. (2012). Synchronous machine faults detection and diagnosis for electromechanical actuators in aeronautics. In 38th Annual Conference of IEEE Industrial Electronics (IECON’12).
Vinson, G., Ribot, P., Prado, T., & Combacau, M. (2013). A generic diagnosis and prognosis framework: application to permanent magnets synchronous machines. In IEEE Prognostics and System Health Management Conference (PHM’13).
Weber, P., P.Munteanu, & Jouffe, L. (2004). Dynamic bayesian networks modelling the dependability of systems with degradations and exogenous constraints. In 11th IFAC Symposium on Information Control Problems in Manufacturing (INCOM’04).
Technical Papers

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.