Fleet-wide Diagnostic and Prognostic Assessment

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Alexandre Voisin Gabriela Medina-Oliva Maxime Monnin Jean-Baptiste Leger Benoit Iung

Abstract

In order to anticipate failures and reduce downtime, “predictive diagnostic” aims not only at warning about the failure events before they occur but also at identifying the causes of degradation leading to such detections. Then, based on the results of predictive diagnostic, “prognostic” aims at estimating the remaining useful life in order to plan a maintenance action before unit performances are affected. However, these are complex tasks. To overcome these difficulties, the notion of fleet may be very useful. In the present paper a fleet is composed of heterogeneous units (mainly components but could be systems or sub-systems) that are grouped together considering some similarities. The fleet can provide capitalized data and information coming from other members of the fleet for the improvement/development of the diagnostic/prognostic models. In order to achieve PHM with a fleet-wide dimension, it is thus necessary to manage relevant knowledge arising from the fleet taking into account heterogeneities and similarities amongst components, operational context, behaviours, etc. This paper will focus mainly in the formalization of a data-driven prognostic model considering a fleet-wide approach. The model is based on a prognostic approach of the system health using Relevant Vector Machine. The proposed model is based on historical data coming from similar units of a fleet. The heterogeneity of the monitored data is treated by assessing a global health index of the units. The proposed approach is shown on a case study. This case study illustrates how the fleet dimension facilitates predictive diagnostic and the definition of the prognostic model in the marine domain.

How to Cite

Voisin, A. ., Medina-Oliva , G., Monnin, M. ., Leger, J.-B. ., & Iung, B. . (2013). Fleet-wide Diagnostic and Prognostic Assessment. Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2311
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Keywords

Prognostic, Fleet-wide management, proactive maintenance, predictive diagnostic, health assessment

References
Bonissone, P .P ., V arma, A. (2005). Predicting the Best Unit within a Fleet: Prognostic 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.

Byington C.S., M.J. Roemer, G.J. Kacprzynski et T. Galie (2002). Prognostic Enhancements to Diagnostic Systems for Improved Condition-­‐‑Based Maintenance. 2002 IEEE Aerospace Conference, Big Sky, USA.

G.E.P. Box, G.M. Jenkins (1976), and Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco.

Heng A., S. Zhang, A.C.C. Tan et J. Mathew. (2009). Rotating machinery prognostic: State of the art, challenges and opportunities, Mechanical Systems and Signal Processing, vol. 23 (3), pp. 724-­‐‑739.

Jardine A.K.S., D. Lin et D. Banjevic (2006). A review on machinery diagnostics and prognostic implementing condition-­‐‑based maintenance. Mechanical Systems and Signal Processing, vol. 20, pp.1483-­‐‑1510.

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

Medina-Oliva G., Léger J-B, Voisin A., Monnin M., (2012) Predictive Diagnostic based on a Fleet-wide Ontology Approach, MFPT 2013, 13-17 May 2013 - Cleveland, Ohio, USA.

Medina-Oliva G., Weber P., Levrat E., Iung B. (2012a) 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

Medina-Oliva G., Voisin A., Monnin M., Peysson F., Leger JB. (2012b). Prognostic Assessment Using Fleet-wide Ontology. PHM Conference 2012, Minneapolis, Minnesota, USA.

Medina-Oliva G., Peysson F., Voisin A., Monnin M., Léger J-B. (2013). Ships and marine diesel engines fleet-wide predictive diagnostic based on ontology, improvement feedback loop and continuous analytics. Proceedings of 25th International Congress on Condition Monitoring and Diagnostics Engineering Management, 11–13 June, 2013, Helsinki, Finland.

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

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.

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(2) 234- 253.

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 Prognostic for Helicopter Component CBM, in Proceedings of Annual Conference of the Prognostic and Health Management Society, October 10-16, Portland, Oregon.

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.

Rizzolo L., Abichou B., Voisin A., Kosayyer N. (2011), Aggregation of Health Assessment Indicators of Industrial Systems. In Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology, EUSFLAT-2011, Aix-Les-Bains, France.

Tipping M.E. (2001) Sparse Bayesian Learning and the Relevance Vector Machine, J ournal of Machine Learning Research 1, 211 244

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.

Wang P., Youn B., Hu C. (2012) A generic probabilistic framework for structural health prognostic and uncertainty management. Mechanical Systems and Signal Processing. 28, Pages 622–637

Wang T., Yu J., Siegel D., Lee J. (2008). A similarity-based prognostic approach for Remaining Useful Life estimation of engineered systems. International Conference on Prognostic 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).
Section
Technical Papers