Kalman filter for parametric fault detection: An internal model principle-based approach

The paramount importance of fault detection (FD) in complex engineering systems has undoubtedly been the main driver behind the development of a plethora of techniques in the FD area. In this study, the authors propose an internal model principle-based Kalman filter (IMP-KF) structure for use in the detection of parametric faults. The authors show that the closed-loop structure of the IMP-KF is indeed a necessary and sufficient condition for generating residuals upon which the FD process hinges. They advocate a residual generator structure similar to that used in the standard Kalman filtering (KF), and judiciously exploit the non-robustness to model mismatch of the proposed IMP-KF scheme to detect faults in the presence of noise and disturbances. With no model mismatch, the KF residual's whiteness is exploited to derive a composite hypothesis testing that accounts for a low probability for false alarm and a high probability of correct decision for various reference inputs. The proposed scheme was successfully evaluated on both simulated and physical systems.