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- W3200581919 abstract "This paper deals with a sensor fault detection approach using the Optimal Upper Bounded Interval Kalman Filter (OUBIKF) and an adaptive degree of freedom χ2-statistics method. It is devoted to discrete time linear model subjected to mixed uncertainties in terms of observations and noises. Mixed uncertainties mean both bounded and stochastic uncertainties. The degrees of freedom of this χ2 hypothesis test method are adaptively chosen thanks to amplifier coefficients improving the detection of the sensor faults. The proposed approach is an extension of a result developped in Lu et al. (2019). Application on a vehicle bicycle model highlights the efficiency of the proposed approach. Comparisons with other efficient estimation and fault detection strategies are provided to discuss the accuracy of the obtained results." @default.
- W3200581919 created "2021-09-27" @default.
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- W3200581919 date "2021-01-01" @default.
- W3200581919 modified "2023-10-18" @default.
- W3200581919 title "A new scheme for fault detection based on Optimal Upper Bounded Interval Kalman Filter" @default.
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- W3200581919 doi "https://doi.org/10.1016/j.ifacol.2021.08.374" @default.
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