System State Estimation by Particle Filtering for Fault Diagnosis and Prognosis

Abstract : 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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https://hal-supelec.archives-ouvertes.fr/hal-00610505
Contributor : Yanfu Li <>
Submitted on : Friday, July 22, 2011 - 11:12:56 AM
Last modification on : Tuesday, August 13, 2019 - 11:10:04 AM

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F. Cadini, D. Avram, Enrico Zio. System State Estimation by Particle Filtering for Fault Diagnosis and Prognosis. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, SAGE Publications, 2010, 224 (3), pp.149-158. ⟨10.1243/1748006XJRR309⟩. ⟨hal-00610505⟩

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