| In this thesis a fault detection technique for a high performance hydrostatic actuation system was developed and evaluated. The Extended Kalman Filter (EKF) was used for parameter identification and was applied to an Electrohydraulic Actuator (EHA) and the performance of the technique is discussed. The EHA is a high performance, closed loop actuation system consisting of an AC variable speed electric motor, a bi-directional gear pump, an accumulator, check valves, a cross-over relief valve, connecting tubes and a custom made symmetrical actuator. The EHA has potential applications in the aerospace industry for flight surface actuation and in robotics. Failures in the EHA can pose a safety hazard and unscheduled maintenance can result in costly downtime. Fault detection in the EHA will increase its safety and efficiency.; The proposed preventive maintenance approach involves monitoring the EHA by estimating two parameters of interest, namely the effective bulk modulus and the viscous damping coefficient. Lowering of the effective bulk modulus, as a result of air entrapment, will affect the response of the EHA and may cause stability issues, by lowering the bandwidth of the system. Changes in the damping coefficient for the actuator can indicate deterioration of the oil, wear in the seals or changes in external friction characteristics. The two parameters were estimated using the EKF and changes in the estimated values were related to faults in the system.; Prior to applying the EKF to the EHA prototype, an extensive simulation study was carried out to investigate the feasibility of the approach as well as the level of accuracy to be expected with the experimental system. The simulation study was used to verify that changes in the two parameters were detected and accurately estimated.; In this study, an attempt was also made to visit some of the problems reported with the use of the EKF for fault detection purposes, namely the difficulty in setting the correct values in the matrices to initialize the EKF algorithm and the presence of biases in the estimates. The problem was believed to be linked to system observability which was investigated in this research. (Abstract shortened by UMI.)... |
