Generic characterization of diagnosis and prognosis for complex heterogeneous systems



Published Nov 1, 2020
Pauline Ribot Yannick Pencol´ Michel Combacau


Maintenance efficiency of complex industrial systems is an important economical and business issue. Main difficulties come from the choice of maintenance actions. A wrong choice can lead to maintenance costs that are not acceptable. In this paper, we propose a generic health monitoring system that integrates some diagnostic and prognostic capabilities to determine the current and future state of a large and complex system such as an aircraft. The diagnostic function aims at identifying faulty components that may cause global system failures. The prognostic function estimates the remaining time until the next global system failure. A formal and generic modeling framework for a complex system encapsulating the knowledge required to get the consistent coordination of the diagnostic and prognostic functions is presented. We propose in this framework to take into account component redundancies which is common in systems like aircrafts. Moreover, an original coupling of diagnosis and prognosis is established based on the characterization of the system operational modes and on a decentralized architecture of the monitoring system.

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complex systems, diagnosis, preventive maintenance, failure prognosis

Abbas, M., & Vachtsevanos, G. J. (2009, September 27 October 1). A System-Level Approach to Fault Progression Analysis in Complex Engineering Systems. In the Annual Conference of the Prognostics and Health Management Society 2009. Sans Diego, CA.
Brotherton, T., Grabill, P., Wroblewski, D., Friend, R., Sotomayer, B., & Berry, J. (2002, March 9-16). A testbed for data fusion for engine diagnostics and prognostics. In Proceedings of the IEEE Aerospace Conference (Vol. 6, p. 3029-3042). Big Sky, Montana.
Buchanan, B., & Shortliffe, E. (1984). Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.
Chanthery, E., & Ribot, P. (2013, March). An Integrated Framework for Diagnosis and Prognosis of Hybrid Systems. In the 3rd Workshop on Hybrid Autonomous System (HAS). Roma, Italy.
Chitttaro, L., Guida, G., Tasso, C., & Toppano, E. (1993). Functional and Teleological Knowledge in the Multimodeling Approach for Reasoning about Physical Systems: A Case Study in Diagnosis. IEEE Transactions on Systems, Man and Cybernetics, 23, 1718–1751.
Console, L., & Torasso, P. (1991). A spectrum of logical definitions of model-based diagnosis. Computational intelligence, 7(3), 133–141.
Daigle, M. J., & Goebel, K. (2011, August). A Model-Based Prognostics Approach Applied to Pneumatic Valves. International Journal of Prognostics and Health Management,2.
Dasgupta, A., & Pecht, M. (1991, December). Material Failure Mechanisms and Damage Models. IEEE Transactions on Reliability, 40(5), 531–536.
Dragomir, O., Gouriveau, R., Zerhouni, N., & Dragomir, F. (2007). Framework for a distributed and hybrid prognostic system. In the 4th IFAC Conference Management and Control of Production and Logistics (pp. 431–436). Romania.
Engel, S., Gilmartin, B., Bongort, K., & Hess, A. (2000, March). Prognostics, The Real Issues Involved With Predicting Life Remaining. In IEEE Aerospace Conference (Vol. 6, p. 457-469). USA.
Ferreiro, S., & Arnaiz, A. (2008). Prognosis Based on Probabilistic Models and Reliability Analysis to improve aircraft maintenance. In International Conference on Prognostics and Health Management. Denver, USA.
Fouladirad, M., & Nikiforov, I. (2005, July). Optimal statistical fault detection with nuisance parameters. Automatica, 41(7), 1157–1171.
Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems. Marcel Deker.
Ghelam, S., Simeu-Abazi, Z., Derain, J.-P., Feuillebois, C., Vallet, S., & Glade, M. (2006). Integration of Health Monitoring in the Avionics Maintenance System. In 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process (p. 1519-1524). China.
Goebel, K., & Eklund, N. (2007, 7-10 May). Prognostic Fusion for Uncertainty Reduction. In AIAA Infotech@ Aerospace Conference and Exhibit. Rohnert Park, California.
Goh, K., Tjahjono, B., Baines, T.,&Subramaniam, S. (2006). A Review of Research in Manufacturing Prognostics. In Proceedings of the IEEE International Conference on Industrial Informatics (p. 417-422). New York.
Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P., & Sun, Y. (2009, September 28-30). A review on degradation models in reliability analysis. In the 4th World Congress on Engineering Asset Management. Athens, Greece.
Hamscher, W., Console, L., & De Kleer, J. (1992). Readings in model-based diagnosis. Morgan Kaufmann Publishers Inc.
Heng, A., Zhang, S., Tan, A., & Mathew, J. (2009). Rotating machinery prognostics: Stateoftheart, challenges and opportunities. Mechanical Systems and Signal Processing, 23, 724–739.
Indra, S., Travé-Massuy`es, L., & Chanthery, E. (2011). A decentralized FDI scheme for spacecraft: Bridging the gap between model based FDI research and practice. In EUCASS.
Isermann, R. (2005). Model-based fault-detection and diagnosis – status and applications. Annual Reviews in Control, 29, 71–85.
Jackson, P. (1998). Introduction to Expert Systems. Boston, USA: Addison-Wesley Longman Publishing Co., Inc.
Kacprzynsk, G. J., Sarlashkar, A., Roemer, M. J., Hess, A., & Hardman, W. (2004). Predicting Remaining Life by Fusing the Physics of Failure Modeling with Diagnostics. Journal of the Minerals, Metals and Material Society, 56, 29–35.
Kaufman, A., Grouchko, D., & Cruon, R. (1975). Mod`eles mathématiques pour l’étude de la fiabilité des syst`emes (Masson, Ed.).
Kirkland, L., Pombo, T., Nelson, K., & Berghout, F. (2004, March 6-13). Avionics Health Management: Searching for the Prognostics Grail. In IEEE Aerospace Conference (Vol. 5, p. 3448-3454).
Lamperti, G., & Zanella, M. (2003). Diagnosis of active systems. Kluwer Academic Publishers.
Lebold, M., & Thurston, M. (2001). Open Standards for Condition-Based Maintenance and Prognostic Systems. In 5th Annual Maintenance and Reliability Conference (MARCON 2001). Gatlinburg, USA.
Mosterman, P. J., & Biswas, G. (1999, November). Diagnosis of continuous valued systems in transient operating regions. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 29(6), 554–565.
Pencolé, Y., & Cordier, M.-O. (2005, May). A formal framework for the decentralised diagnosis of large scale discrete event systems and its application to telecommunication networks. Artificial Intelligence, 164, 121–170.
Rausand, M., & Hoyland, A. (2004). System reliability theory: models, statistical methods and applications. Wiley.
Reiter, R. (1987). A theory of diagnostic from first principles. Artificial Intelligence, 32, 57–95.
Ribot, P., Pencolé, Y., & Combacau, M. (2009a, October 11- 14). Diagnosis and prognosis for the maintenance of complex systems. In IEEE International Conference on Systems, Man, and Cybernetics (p. 4146 - 4151). San Antonio, USA.
Ribot, P., Pencolé, Y., & Combacau, M. (2009b, July 1-3). Functional prognostic architecture for the maintenance of complex systems. In the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SafeProcess’09). Barcelona, Spain.
Roemer, M., Byington, C., Kacprzynski, G., & Vachtsevanos, G. (2005). An Overview of Selected Prognostic Technologies with Reference to an Integrated PHM Architecture. In the 1st International Forum on Integrated System Health Engineering and Management in Aerospace.
Roemer, M. J., & Byington, C. S. (2007, May 14-17). Prognostics and Health Management Software for Gas Turbine Engine Bearings. In Proceedings of GT2007 ASME Turbo Expo 2007 : Power for Land, Sea, and Air (p. 795-802). Montreal, Canada.
Roychoudhury, I., & Daigle, M. (2011, October 4-7). An Integrated Model-Based Diagnostic and Prognostic Framework. In 22nd International Workshop on Principle of Diagnosis. Murnau, Germany.
Schwabacher, M.,& Goebel, K. (2007). A survey of Artificial Intelligence for Prognostics. In AAAI Fall Symposium. Arkington VA, USA.
Sheppard, J., Kaufman, M., & Wilmering, T. (2008). IEEE Standards for prognostics and health management. In Proc. IEEE AUTOTESTCON (pp. 97–103).
Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. In IEEE International Conference on Systems, Man and Cybernetics (Vol. 15, pp. 116–132).
Vinson, G., Ribot, P., Prado, T., & Combacau, M. (2013, June 2-5). A Generic Diagnosis and Prognosis Framework: Application to Permanent Magnets Synchronous Machines. In 11th International Conference on Chemical and Process Engineering. Milan, Italy.
Voisin, A., Levrat, E., Cocheteux, P., & Iung, B. (2010, Accepted article). Generic prognosis model for proactive maintenance decision support: application to preindustrial e-maintenance test bed. Journal of Intelligent Manufacturing, 21(2), 177-193.
Wilkinson, C., Humphrey, D., Vermeire, B., & Houston, J. (2004, March). Prognostic and Health Management for Avionics. IEEE Aerospace Conference Proceedings, 5, 3435-3446.
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