Fault Estimator and Diagnosis for Generalized Linear Discrete-Time System via Self-constructing Fuzzy UKF Method

This study investigated fault estimation and diagnosis using a novel approach based on an integrated fault estimator and state estimator for generalized linear discrete-time systems. The proposed scheme uses a self-constructing fuzzy unscented Kalman filter (UKF) system to simultaneously estimate the system state and approximate the fault information. To achieve this, a generalized linear discrete-time system without faults was first transformed into an equivalent standard state-space system with faults. Then, the self-constructing fuzzy UKF system was designed in order to obtain the fault information. According to fault information obtained using the proposed scheme, fault detection experiments based on fuzzy clustering were performed and the fault feature parameters required for fault isolation were determined. Finally, the scheme was applied to a direct current (DC) motor to demonstrate the effectiveness of the proposed fault estimation and diagnosis approach. Results of the simulation illustrate the effectiveness of the proposed method.