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Fault detection for linear parameter varying systems under changes in the process noise covariance

Résumé : Detecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varying parameter, an interpolation approach is pursued, where a limited number of reference models { each estimated from data measured in a reference state { are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex whenthe different points of interpolation correspond to dierent noise conditions. Then conicts may arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to dierent noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system.
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Submitted on : Monday, July 20, 2020 - 3:29:25 PM
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Eva Viefhues, Michael Döhler, Falk Hille, Laurent Mevel. Fault detection for linear parameter varying systems under changes in the process noise covariance. IFAC 2020, 21st International Federation of Automatic Control World Congress, Jul 2020, Berlin, Germany. ⟨10.1016/j.ifacol.2020.12.868⟩. ⟨hal-02903013⟩

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