Fault detection combining adaptive degrees of freedom χ2-statistics and interval approach for nonlinear systems

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An enlargement of the Adaptive degrees of freedom χ2-statistics (ADFC) method to fault detection for nonlinear systems with mixed uncertainties (stochastic and bounded uncertainties) is presented in this paper. The ADFC approach, primarily developped for fault detection in case of linear systems, is then combined with the Reinforced likelihood box particle filter (RLBPF). A residual generator is used, followed by the adaptive amplifier coefficient (a.a.c.) concept in the decision making stage. Then, the proposed approach is applied to a nonlinear Magneto-Rheological damper model to illustrate the efficiency of the method.