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 system operating as a fleet, it raises mission readiness and maintenance management issues. PHM (Prognostics and Health Management) plays a key role for controlling the performance level of such systems, at least on the basis of adapted PHM strategies and system developments. However, considering a fleet implies to provide managers and engineers with a relevant synthesis of information and keep it updated regarding both the global health of the fleet and the current status of their maintenance efforts. For achieving PHM at a fleet level, it is thus necessary to manage relevant corresponding knowledge arising both from modeling and monitoring of the fleet. In that way, this paper presents a knowledge structuring scheme for fleet PHM management applied to marine domain.
How to Cite
PHM, Fleet, Knowledge modeling, Semantic
Campos J. (2009). Development in the application of ICT in condition monitoring and maintenance, Computers in Industry, vol. 60, pp. 1–20.
Charles-Owaba O.E., Oluleye A.E., Oyawale F.A., Oke S.A. (2008). An opportunity cost maintenance scheduling framework for a fleet of ships: a case study, Journal of Industrial Engineering International, vol. 4, pp. 64-77.
Emmannouilidis, C., Fumagalli, F., Jantunen, E., Pistofidis, P., Macchi, M., Garetti, M. (2010). Condition monitoring based on incremental learning and domain ontology for condition-based Maintenance, in Proceedings of 11th international Conference on advances in Production Management Systems, October 11-13, Cernobbio, Como, Italy.
Cocheteux, P., Voisin, A., Levrat, E., Iung, B. (2009). Prognostic Design: Requirements and Tools, in Proceedings of 11th International Conference on The Modern Information, Technology in the Innovation Processes of the Industrial Enterprises, Bergame, Italy.
Gebraeel, N.( 2010). Prognostics-Based Identification of the Top-k Units in a Fleet, IEEE transactions on automation science and engineering, vol. 7, pp. 37- 48.
Gu J., Lau D., Pecht M. (2009). Health assessment and prognostics of electronic products, in Proceedings of 8th International Conference on Reliability, Maintainability and Safety, July 21-25, Chengdu, China, pp. 912-919.
Hwang, W.T., Tien S.W. and Shu,C.M. (2007). Building an Executive Information System for Maintenance Efficiency in Petrochemical Plants— An Evaluation, Process Safety and Environmental Protection, vol. 85, pp 139-146.
Iung, B., Morel, G. and Léger, J.B. (2003). Proactive maintenance strategy for harbour crane operation improvement. n: H. Erbe, Editor, Robotica. Special Issue on Cost Effective Automation, vol. 21.
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.
Monnin, M, Leger, J-B., Morel, D. (2011a). Proactive facility fleet/plant monitoring and management, in Proceedings of 24th International Congress on Condition Monitoring and Diagnostics Engineering Management, 29th May – 1st June, Stavanger, Norway.
Monnin, M, Leger, J-B., Morel, D. (2011b). KASEM®: e-Maintenance SOA Platform, in Proceedings of 24th International Congress on Condition Monitoring and Diagnostics Engineering Management, 29th May – 1st June, Stavanger, Norway.
Moore, W.J., Starr, A.G. (2006). An intelligent maintenance system for continuous cost-based priorisation of maintenance activities, Computers in Industry, vol. 57, pp. 595–606.
Niu G, Yang B, Pecht M. (2010). Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance, Reliability Engineering and System Safety, vol. 95, pp. 786–796.
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.
Peysson, F., Ouladsine, M., Outbib, R., Leger, J-B., Myx, O., Allemand, C. (2008). Damage Trajectory Analysis based Prognostic, in Proceedings of IEEE International Conference on Prognostics and Health Management, October 6-9, Denver, CO, pp. 1-8.
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.
Roemer, M.J., Kacprzynski, G.J. and Orsagh, R.F. (2001). Assessment of data and knowledge fusion strategies for prognostics and health management, in Proceedings of IEEE Aerospace Conference Proceedings, Big Sky, MT, USA, pp. 2979–2988.
Thurston, M., Lebold, M. (2001). Open standards for condition-based maintenance and prognostic systems, in Proceedings of MARCON2001, http://wwsw.osacbm.org.
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.
Wheeler, K., Kurtoglu, T., Poll, S.D. (2009). A survey of health management user objectives related to diagnostic and prognostic 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
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.