System State Estimation by Particle Filtering for Fault Diagnosis and Prognosis

F. Cadini; D. Avram; Enrico Zio

System State Estimation by Particle Filtering for Fault Diagnosis and Prognosis

F. Cadini ¹ D. Avram ¹ Enrico Zio ^{1, 2, 3}

1 DENG - Dipartimento di Energia [Milano]

2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec

3 LGI - Laboratoire Génie Industriel - EA 2606

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.

Keywords : hybrid system state estimation Monte Carlo particle filtering

Document type :

Journal articles

Complete list of metadatas

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

System State Estimation by Particle Filtering for Fault Diagnosis and Prognosis

Identifiers

Collections

Citation

Export

Metrics

229

Contact

System State Estimation by Particle Filtering for Fault Diagnosis and Prognosis

Identifiers

Collections

Citation

Export

Share

Metrics

229

Contact