A Novel Approach for Detection and Diagnosis of Process and Sensor Faults in Electro-Hydraulic Actuator

This paper presents an novel approach for fault detection and diagnosis (FDD) of sensor as well as process faults for Electro-Hydraulic Actuators (EHA) using a bank of residual generators, each of which employs an Extended Kalman Filter (EKF)-based parameter estimator. In traditional sensor fault detection schemes, actual sensor measurements are compared with measurements reconstructed using state estimators following an analytical redundancy approach. In contrast, we propose detection of sensor faults by comparing estimated values of plant parameters, which deviate under fault, with their nominal values. Since process faults usually manifest themselves in deviation of process parameters, this leads to a unified approach to fault detection using parameter estimators. Fault isolation is then achieved by using the set of detection flags, obtained by thresholding each of the residuals, in a so-called diagnosis matrix (D-Matrix). Unlike several earlier works on FDI for electrohydraulic actuator systems, which do not address sensor faults, the present approach is capable of detection and identification of both sensor and process faults. Numerical simulation results for an EHA of a rocket demonstrate the efficacy of the method.