A Predictive Maintenance Approach for Complex Equipment Based on a Failure Mechanism Propagation Model



Published Jan 1, 2019
Olivier Blancke Amélie Combette Normand Amyot Dragan Komljenovic Mélanie Lévesque Claude Hudon Antoine Tahan Noureddine Zerhouni


The aim of this paper is to propose a comprehensive approach for the predictive maintenance of complex equipment. The approach relies on a physics of failure ( PoF ) model based on expert knowledge and data. The model can be represented as a multi-state Petri Net where different failure mechanisms have been discretized using physical degradation states. Each physical state can be detected by a unique combination of symptoms that are measurable using diagnostic tools. Based on actual diagnostic information, a diagnostic algorithm is used to identify active failure mechanisms and estimate their propagation using the Petri Net technique. Specific maintenance actions and their potential effects on the system can be associated with target states. A prognostic algorithm using a colored Petri Net propagates active failure mechanisms through the target physical states. A predictive maintenance approach is therefore proposed by allowing specific maintenance actions to be determined in a reasonable timeframe. A case study is presented for an actual hydro-generator. Finally, model limits are discussed and potential areas for further research are identified.

Abstract 233 | PDF Downloads 94



complex systems, prognostics, systems engineering, diagnostics, predictive maintenance, Physics-of-Failure, Expert elicitation, Hydroelectric generators, Graph theory

Ammour, R., Leclercq, E., Sanlaville, E., & Lefebvre, D. (2016). Faults prognosis using partially observed stochastic Petri nets. Paper presented at the Discrete Event Systems (WODES), 2016 13th International Workshop on.
Amyot, N., Hudon, C., Lévesque, M., Bélec, M., Brabant, F., & St-Louis, C. (2014). Development of a Hydrogenerator Prognosis Approach. CIGRE.
Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Zerhouni, N. (2017). Prognostics and health management for maintenance practitioners - review, implementation and tools evaluation. International Journal of Prognostics and Health Management, 8, 31.
Blancke, O., Amyot, N., Hudon, C., Lévesque, M., & Tahan, A. (2015). A New Generic Approach to Convert FMEA in Causal Trees for the Purpose of Hydro-Generator Rotor Failure Mechanisms Identification. Paper presented at the Annual Conference of the Prognostics and Health Management Society, San Diego, CA, US.
Blancke, O., Combette, A., Amyot, N., Komljenovic, D., Lévesque, M., Hudon, C., . . . Zerhouni, N. (2018). A Predictive Maintenance Approach for Complex Equipment Based on Petri Net Failure Mechanism Propagation Model. Paper presented at the PHM Society European Conference.
Blancke, O., Tahan, A., Komljenovic, D., Amyot, N., Lévesque, M., & Hudon, C. (2018). A holistic multi-failure mode prognosis approach for complex equipment. Reliability Engineering & System Safety, 180, 136-151. doi:https://doi.org/10.1016/j.ress.2018.07.006
Chemweno, P., Pintelon, L., Muchiri, P. N., & Van Horenbeek, A. (2018). Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches. Reliability Engineering & System Safety, 173, 64-77.
Chiachío, J., Chiachío, M., Sankararaman, S., Saxena, A., & Goebel, K. (2015). Condition-based prediction of time-dependent reliability in composites. Reliability Engineering & System Safety, 142, 134-147. doi:https://doi.org/10.1016/j.ress.2015.04.018
Chiachío, M., Chiachío, J., Sankararaman, S., Andrews, J., & Target:, P. (2017). Integration of prognostics at a system level: a petri net approach. Paper presented at the Annual Conference of the Prognostics and Health Management Society.
Corbetta, M., Sbarufatti, C., Manes, A., & Giglio, M. (2014). On dynamic state-space models for fatigue-induced structural degradation. International Journal of Fatigue, 61, 202-219.
Elattar, H. M., Elminir, H. K., & Riad, A. M. (2016). Prognostics: a literature review. Complex & Intelligent Systems, 2(2), 125-154. doi:10.1007/s40747-016-0019-3
EPRI. (2002). Reliability and Preventive Maintenance: Balancing Risk and Reliability: For Maintenance and Reliability Professionals at Nuclear Power Plants (1002936). Retrieved from Palo Alto, CA:
Goebel, K., Daigle, M., Saxena, A., Sankararaman, S., Roychoudhury, I., & Celaya, J. (2017). Prognostics: NASA.
Gu, J., & Pecht, M. (2008, 28-31 Jan. 2008). Prognostics and health management using physics-of-failure. Paper presented at the 2008 Annual Reliability and Maintainability Symposium.
IAM. (2015). Asset Management - an anatomy V3. Retrieved from
ISO. (2014). ISO 55000, 55001 and 55002 Asset Management Standards. In: BSI Standards.
Javed, K., Gouriveau, R., & Zerhouni, N. (2017). State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels. Mechanical Systems and Signal Processing, 94, 214-236.
Kulkarni, C. S., Biswas, G., Celaya, J. R., & Goebel, K. (2013). Physics based degradation models for electrolytic capacitor prognostics under thermal overstress conditions. International Journal of Prognostics and Health Management, 825.
Kwon, D., Hodkiewicz, M. R., Fan, J., Shibutani, T., & Pecht, M. G. (2016). IoT-Based Prognostics and Systems Health Management for Industrial Applications. IEEE Access, 4, 3659-3670. doi:10.1109/ACCESS.2016.2587754
Murata, T. (1989). Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4), 541-580.
Peterson, J. L. (1981). Petri net theory and the modeling of systems.
Petri, C. A. (1966). Communication with automata.
Zhouhang, W., Maen, A., & H., K. A. (2014). Coloured stochastic Petri nets modelling for the reliability and maintenance analysis of multi-state multi-unit systems. Journal of Manufacturing Technology Management, 25(4), 476-490. doi:doi:10.1108/JMTM-04-2013-0045
Technical Papers