Prognostics Assessment Using Fleet-wide Ontology



Gabriela Medina-Oliva Alexandre Voisin Maxime Monnin Flavien Peysson Jean-Baptiste Leger


Large complex systems, such as power plants, ships and aircraft, are composed of multiple systems, subsystems and components. When they are considered as embedded in operating systems such as a fleet, mission readiness and maintenance management issues are raised. PHM (Prognostics and Health Management) plays a key role in controlling the performance level of such systems, at least on the basis of adapted PHM strategies and system developments. Moreover considering a fleet implies to provide managers and engineers a relevant synthesis of information and to keep this information updated in terms of the global health of the fleet as well as the current status of their maintenance efforts. In order to achieve PHM at a fleet level, it is thus necessary to manage relevant knowledge arising from both modeling and monitoring of the fleet. In that way, this paper presents a knowledge structuring scheme based on ontologies for fleet PHM management applied to marine domain, with emphasis on prognostics modeling.

How to Cite

Medina-Oliva, G. ., Voisin, . A. ., Monnin, M. ., Peysson, F., & Leger, . J.-B. . (2012). Prognostics Assessment Using Fleet-wide Ontology. Annual Conference of the PHM Society, 4(1).
Abstract 52 | PDF Downloads 43



prognostics, PHM, Fleet, Semantic

Alsyouf, I. (2007). The role of maintenance in improving companies’ productivity and profitability. International Journal of Production Economics, 105, 70–78.

Azam M., Tu F. and Pattipati, K. R. (2002) “Condition Based Predictive Maintenance of Industrial Power Systems”, SPIE conference on Fault Diagnosis, Prognosis and System Health Management, Orlando, April 2002.

Barros A., Bérenguer C., Grall A. (2009). A maintenance policy for two-unit parallel systems based on imperfect monitoring information. Reliability Engineering and System Safety 91 (2006) 131–136.

Bonissone, P.P., Varma, A. (2005). Predicting the Best Unit within a Fleet: Prognostics Capabilities Enabled by Peer Learning, Fuzzy Similarity, and Evolutionary Design Process. In Proceedings of the 14th IEEE International Conference on Fuzzy Systems, IEEE, pp. 312-318.

Gruber, T. (2009), Ontology. In: the Encyclopedia of Database Systems, Ling Liu and M. Tamer Özsu (Eds.), Springer-Verlag.

IEC 60812. Analysis techniques for system reliability – Procedure for failure mode and effects analysis (FMEA). 2006

ISO 13381-1:2004. Condition monitoring and diagnostics of machines- Prognostics- Part 1: General guidelines

Kleindorfer PR, Singhal K, Van Wassenhove LN. (2005). Sustainable operations management. Production and Operations Management; winter, 14(4):482–492.

Léger J-B. (2004). A case study of remote diagnosis and e- maintenance information system, Keynote speech of IMS’2004, International Conference on Intelligent Maintenance Systems, Arles, France.

Liu J., Djurdjanovic D., Ni J., Casoetto N., Lee J. (2007). Similarity based method for manufacturing process performance prediction and diagnosis. Computer in Industry 58, Pages 558-566.

Medina-Oliva G., Weber P., Levrat E., Iung B. (2012) Using probabilistic relational models for knowledge representation of production systems: A new approach to assessing maintenance strategies. CIRP Annals - Manufacturing Technology. in press. DOI: 10.1016/j.cirp.2012.03.059

Monnin M., Abichou B., Voisin A., Mozzati C. (2011b). Fleet historical cases for predictive maintenance. The International Conference Surveillance 6. October 25- 26. Compiègne, France.

Monnin M., Voisin A., Leger JB., Iung B. (2011a). Fleet- wide health management architecture. Annual Conference of the Prognostics and Health Management Society. Montreal, Quebec, Canada.

Monnin, M, Leger, J-B., Morel, D. (2011c). KASEM®: e- Maintenance SOA Platform, in Proceedings of 24th International Congress on Condition Monitoring and Diagnostics Engineering Management, 29th May – 1st June, Stavanger, Norway.

Noy N. F. and McGuinness D. L. (2001). Ontology development 101: A guide to creating your first ontology. Technical Report SMI-2001-0880, Stanford Medical Informatics.

Patrick, R., Smith, M J., Byington, C S., Vachtsevanos, G J., Tom, K., Ly, C. (2010). Integrated Software Platform for Fleet Data Analysis, Enhanced Diagnostics, and Safe Transition to Prognostics for Helicopter Component CBM, in Proceedings of Annual Conference of the Prognostics and Health Management Society, October 10-16, Portland, Oregon.

Pecht, M. (2010). A prognostics and health management roadmap for information and electronics-rich systems, IEICE Fundamentals Review, vol. 3, no. 4, pp. 25 – 32.

Peysson F, Ouladsine M, Outbib R, Leger JB, Myx O, Allemand C (2009). "A generic prognostics methodology using damage trajectory models." IEEE Trans. Reliab., 58(2) (277 - 285), June.

Peysson F., Léger J-B., Allemand C., Ouladsine M., Iung B. (2012) New approaches for ships fleet-wide management and naval mission prognostics. MFTP 2012: The Prognostics and Health Management Solutions Conference. April 24-26. Dayton, Ohio, USA.

Provan, G., (2003). Prognosis and Condition-Based Monitoring: an open systems architecture. 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, 57--62, Washington, USA.

Rajkumar and Bardina. Training data requirement for a neural network to predict aerodynamic coefficients. Proceedings of SPIE, 2003.

Reymonet, A., Thomas, J., Aussenac-Gilles, N. (2009). Ontology Based Information Retrieval: an application to automotive diagnosis, in Proceedings of International Workshop on Principles of Diagnosis, June 14-17, Stockholm, Sweden, pp. 9-14.

Umiliacchi, P., Lane, D., Romano, F. (2011). Predictive Maintenance of railway subsystems using an Ontology based modelling approach, in Proceedings of 9th world Conference on Railway Research, May 22-26, Lille, France.

Verma, A. K. and Srividya, A. and Ramesh, P. (2010). A systemic approach to integrated E-maintenance of large engineering plants, International Journal of Automation and Computing, vol. 7, pp. 173-179.

Voisin, A., Levrat, E., Cocheteux, P., & Iung, B. (2010). Generic prognosis model for proactive maintenance decision support: Application to pre-industrial e- Maintenance test bed. Journal of Intelligent Manufacturing. 21 (2): 177-193.

Wang P., Youn B., Byeng D. A generic probabilistic framework for structural health prognostics and uncertainty management. Mechanical Systems and Signal Processing. 28, Pages 622–637

Wang T., Yu J., Siegel D., Lee J. (2008). A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems. International Conference on Prognostics and Health Management. Denver, USA.

Weber P., Medina-Oliva G., Simon C., Iung B. (2012). Overview on Bayesian networks Applications for Dependability, Risk Analysis and Maintenance areas. Engineering Applications of Artificial Intelligence, vol. 25 (4), (671-682).

Wheeler, K., Kurtoglu, T., Poll, S.D. (2009). A survey of health management user objectives related to diagnostic and prognostics metrics, in Proceedings of International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, August 30–September 2, San Diego, California, USA.

Williams, Z., Gilbertson, D. & Sheffield, G., (2008). Fleet analysis and planning using CBM+ open architecture, in Proceedings of IEEE International Conference on Prognostics and Health Management, Denver, CO.
Poster Presentations

Most read articles by the same author(s)