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Decentralized and distributed active fault diagnosis: Multiple model estimation algorithms

Straka Ondřej, Punčochář Ivo

International Journal of Applied Mathematics and Computer Science

2020

Vol. 30, no. 2

239--249

The paper focuses on active fault diagnosis (AFD) of large scale systems. The multiple model framework is considered and two architectures are treated: the decentralized and the distributed one. An essential part of the AFD algorithm is state estimation, which must be supplemented with a mechanism to achieve feasible implementation in the multiple model framework. In the paper, the generalized pseudo Bayes and interacting multiple model estimation algorithms are considered. They are reformulated for a given model of a large scale system. Performance of both AFD architectures is analyzed for different combinations of multiple model estimation algorithms using a numerical example.