Electro-Hydrostatic Actuator Fault Detection and Diagnosis

Abstract

<p><h1>Abstract</h1></p> <p>As a compact, robust, and reliable power distribution method, hydraulic systems have been used for flight surface control for decades. Electro-hydrostatic Actuator (EHA) is increasingly replacing the conventional valve-controlled system for better performance, lighter weight and higher energy efficiency. The EHA is increasingly being used for flight control. As such its reliability is thereby critical important for flight safety. This research focuses on fault detection and diagnosis (FDD) for the EHA to enable predictive unscheduled maintenance when fault detected at its inception.</p> <p>An EHA prototype previously built at McMaster University is studied in this research and modified to physically simulate two faults conditions pertaining to leakage and friction. Nine different working conditions including normal running and eight fault conditions are simulated. Physical model has been derived mathematically capable of numerically simulating the fault conditions. Furthermore, for comparison, parametric model was obtained through system identification for each fault condition. This comparison revealed that parametric models are not suitable for fault detection and diagnosis due to the computation complexity.</p> <p>The FDD approach in this research uses model-based state estimation using filters. The filter based combined with the Interacting Multiple Model fault detection and diagnosis algorithm is introduced. Based on this algorithm, three FDD strategies are developed using a combination of the Extended Kalman Filter and IMM (IMM-EKF), the Smooth Variable Structure Filter with Varying Boundary and IMM (IMM-SVSF (VBL)), and the Smooth Variable Structure Filter with Fixed Boundary and IMM (IMM-SVSF (FBL)). All the three FDD strategies were implemented on the EHA prototype. Based on the results, the IMM-SVSF (VBL) provided the best performance. It detected and diagnosed faults correctly at high mode probabilities with excellent robustness to modeling uncertainties. It also was able to detect slow growing leakage fault, and predicted the changing trend of fault conditions.</p>