Fault Detection in a Three Tanks Hydraulic System using Unknown Input Observer and Extended Kalman Filter
This paper presents the problem of fault diagnosis in three-tank hydraulic system. A mathematical model of the system is developed in order to apply two different observing algorithms. Unknown input observer (UIO) and Extended Kalman filter (EKF) have been used to (diagnosis) detected and isolated actuator and sensor faults. For Unknown Input Observer (UIO), residuals are calculated from the measured and estimated output according to the eigenvalues of the system. Extended Kalman filter (EKF) uses process and measurement noise variances for state estimation. The performance of Unknown Input Observer and Extended Kalman Filter in fault estimation and isolation is evaluated under seven different scenarios. By using Extended Kalman filter, (EKF) faults can be diagnosed effectively in the presence of noise while Unknown Input Observer (UIO) is working better in the absence of noise and simulation results illustrate that clearly.