Distributed Fault Diagnosis using Sensor Networks and Consensus-based Filters

Autor:	Reza Olfati-Saber, Elisa Franco, Thomas Parisini, Marios M. Polycarpou
Přispěvatelé:	E., Franco, R., OLFATI SABER, Parisini, Thoma, M. M., Polycarpou
Rok vydání:	2006
Předmět:	Consensus algorithm Engineering Brooks–Iyengar algorithm business.industry Distributed algorithm Distributed computing Kalman filter business Sensor fusion Fault (power engineering) Wireless sensor network Statistical hypothesis testing
Zdroj:	CDC
DOI:	10.1109/cdc.2006.376797
Popis:	This paper considers the problem of designing distributed fault diagnosis algorithms for dynamic systems using sensor networks. A network of distributed estimation agents is designed where a bank of local Kalman filters is embedded into each sensor. The diagnosis decision is performed by a distributed hypothesis testing method that relies on a belief consensus algorithm. Under certain assumptions, both the distributed estimation and the diagnosis algorithms are derived from their centralized counterparts thanks to dynamic average-consensus techniques. Simulation results are provided to demonstrate the effectiveness of the proposed architecture and algorithm.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47ef4c6ecefb182b3f4a7cc9f8ab8e9a https://doi.org/10.1109/cdc.2006.376797 Zobrazit plný text záznamu