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Fault Detection using Interval Kalman Filtering enhanced by Constraint Propagation

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Xiong, Jun | Jauberthie, Carine | Travé-Massuyès, Louise | Le Gall, Françoise

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International audience. In this paper, we consider an extension of conventional Kalman filtering to discrete time linear models with bounded uncertainties on parameters and gaussian measurement noise. To solve the interval matrix inversion problem involved in the equations of the Kalman filter and the over-bounding problem due to interval calculus, we propose an original approach combining the set inversion algorithm SIVIA and constraint propagation. The improved interval Kalman filter is applied in a fault detection schema illustrated by a simple case study.

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