A distributed Architecture to implement a Prognostic Function for Complex Systems
The proactivity in maintenance management is improved by the implementation of CBM (Condition-Based Maintenance) and of PHM (Prognostic and Health Management). These implementations use data about the health status of the systems. Among them, prognostic data make it possible to evaluate the future health of the systems. The Remaining Useful Lifetimes (RULs) of the components is frequently required to prognose systems. However, the availability of complex systems for productive tasks is often expressed in terms of RULs of functions and/or subsystems; those RULs provide information about the components. Indeed, the maintenance operators must know what components need maintenance actions in order to increase the RULs of the functions or subsystems, and consequently the availability of the complex systems. This paper aims at defining a generic prognostic function of complex systems aiming at prognosing its subsystems, functions and at enabling the isolation of components that needs maintenance actions. The proposed function requires knowledge about the system to be prognosed. The corresponding models are detailed. The proposed prognostic function contains graph traversal so its distribution is proposed to increase the calculation speed. It is carried out by generic agents.
How to Cite
distributed prognostics, multi-agent system
Chittaro, L., Ranon, R. (2003). Hierarchical model-based diagnosis based on structural abstraction, Artificial Intelligence, vol. 155, pp. 147–182
Dragomir, O., Gouriveau, R., Zerhouni, N., Dragomir, F. (2007). Framework for a distributed and hybrid prognostic system, Proceedings of 4th IFAC Conference on Management and Control of Production and Logistics.
Dunjo, J., Fthenakis, V., Vilchez, J.A., Arnaldos, J. (2010). Hazard and operability (HAZOP) analysis. A literature review, Journal of Hazardous Materials, vol. 173, pp. 19–32.
Engel, S., Gilmartin, B., Bongort, K., Hess, A. (2000). Prognostics, the real issues involved with predicting life remaining, Proceedings of the IEEE Aerospace Conference, vol. 6, pp. 457-469.
Iung, B., Monnin, M., Voisin, A., Cocheteux, P., Levrat, E. (2008). Degradation state model-based prognosis for proactively maintaining product performance, CIRP Annals - Manufacturing Technology, vol. 57, pp.49–52.
Jardine, A., Lin, D. and Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, vol. 20, pp. 1483-1510.
Jennings, N.R., Wooldridge, M. (1995) Applying agent technology, Applied Artificial Intelligence, vol. 9, pp. 357-369.
Lebold, M., Thurston, M. (2001) Open standards for condition-based maintenance and prognostics systems, Proceedings of the 5th annual maintenance and reliability conference (MARCON 2001).
Muller, A., Crespo Marquez, A., Iung, B. (2008). On the concept of e-maintenance: Review and current research, Reliability Engineering and System Safety, vol. 93, pp. 1165–1187.
Reiter, R. (1992). A theory of diagnosis from RST principles, Readings in model-based diagnosis, Morgan Kaufmann Publishers, pp. 29-48.
Roemer, M., Byington, C., Kacprzynski, G.J., Vachtsevanos, G. (2007). An overview of selected prognostic technologies with reference to an integrated PHM architecture. Technical Report, Impakt Technologies.
Saha, B., Saha, S., Goebel, K. (2009). A distributed prognostic health management architecture, Proceedings of the Conference of the Society for Machinery Failure.
Scarf, P. (2007). A Framework for Condition Monitoring and Condition Based Maintenance, Quality Technology & Quantitative Management, vol 4, pp. 301-312.
Takai, S. Kumar, R. (2011). Inference-Based Decentralized Prognosis in Discrete Event Systems, IEEE Transactions on Automatic, vol. 56, pp.165-171.
Vachtsevanos, G., Wang, P. (2001). Fault prognosis using dynamic wavelet neural networks. Proceedings of AUTOTESTCON IEEE Systems Readiness Technology Conference, pp. 857-870.
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering system. Hoboken, NJ: John Wiley & Sons, Inc.
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, vol. 21, pp. 177–193.
Worn, H., Langle, T., Albert, M., Kazi, A., Brighenti, A., Revuelta Seijo, S., Senior, C., Sanz-Bobi, M.A., Villar Collado, J. (2004). Diamond: distributed multi-agent architecture for monitoring and diagnosis. Production Planning and Control, vol. 5, pp. 189-200.
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