Kalman-based strategies for Fault Detection and Identification (FDI): Extensions and critical evaluation for a buffer tank system

Abstract

Abstract This paper is concerned with the application of Kalman filter based methods for Fault Detection and Identification (FDI). The original Kalman based method, formulated for bias faults only, is extended for three more fault types, namely the actuator or sensor being stuck, sticky or drifting. To benchmark the proposed method, a nonlinear buffer tank system is simulated as well as its linearized version. This method based on the Kalman filter delivers good results for the linear version of the system and much worse for the nonlinear version, as expected. To alleviate this problem, the Extended Kalman Filter (EKF) is investigated as a better alternative to the Kalman filter. Next to the evaluation of detection and diagnosis performance for several faults, the effect of dynamics on fault identification and diagnosis as well as the effect of including the time of fault occurrence as a parameter in the diagnosis task are investigated.