文章基本信息

标题：A new scheme for fault detection based on Optimal Upper Bounded Interval Kalman Filter
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作者：Quoc Hung Lu ; Soheib Fergani ; Carine Jauberthie 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：7
页码：292-297
DOI：10.1016/j.ifacol.2021.08.374
语种：English
出版社：Elsevier
摘要：AbstractThis 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.
关键词：KeywordsFDI for linear systemFilteringestimation