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ECOLE SUPÉRIEURE D'ÉLECTRICITÉ Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec |
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ECOLE SUPÉRIEURE D'ÉLECTRICITÉ Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec |
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| HAL : hal-00610505, version 1 |
| DOI : 10.1243/1748006XJRR309 |
| Fiche détaillée |  Récupérer au format |
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| Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 224, 3 (2010) 149-158 |
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| System State Estimation by Particle Filtering for Fault Diagnosis and Prognosis |
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| F. Cadini 1D. Avram 1 |
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| (2010) |
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| Efficient diagnosis and prognosis of system faults depend on the ability to estimate the system state. In many real applications, the system dynamics is typically characterized by transitions among discrete modes of operation, each one giving rise to a specific continuous dynamics of evolution. The estimation of the state of these hybrid dynamic systems is a particularly challenging task because it requires tracking the system dynamics corresponding to the different modes of operation. In the present paper a Monte Carlo-based estimation method, called particle filtering, is illustrated with reference to a case study of a hybrid system from the literature. |
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| 1 : | Dipartimento di Energia |
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Politecnico di Milano |
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| 2 : | Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec (SSEC) |
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EDF – Ecole Centrale Paris – SUPELEC |
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| 3 : | Laboratoire Génie Industriel - EA 2606 (LGI) |
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Ecole Centrale Paris |
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| hybrid system – state estimation – Monte Carlo – particle filtering |
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| hal-00610505, version 1 | |
| http://hal-supelec.archives-ouvertes.fr/hal-00610505 | |
| oai:hal-supelec.archives-ouvertes.fr:hal-00610505 | |
| Contributeur : Yanfu Li | |
| Soumis le : Vendredi 22 Juillet 2011, 11:12:56 | |
| Dernière modification le : Mercredi 7 Septembre 2011, 14:14:03 | |
