Active Fault Diagnosis for Hybrid Systems Based on Sensitivity Analysis and EKF

An active fault diagnosis approach for different
kinds of faults is proposed. The input of the approach is
designed off-line based on sensitivity analysis such that the
maximum sensitivity for each individual system parameter is
obtained. Using maximum sensitivity, results in a better
precision in the estimation of the corresponding parameter. The
fault detection and isolation is done by comparing the nominal
parameters with those estimated by Extended Kalman Filter
(EKF). In study, Gaussian noise is used as the input disturbance
as well as the measurement noise for simulation. The method is
implemented on the large scale livestock hybrid ventilation
model which was obtained during previous research