A Comparison of Two Methods for Stochastic Fault Detection : the Parity Space Approach and Principal Component Analysis

摘要

This paper compares two methods for fault detection and isolation in a stochastic setting. We assume additive faults on input and output signals, and stochastic unmeasurable disturbances. The first method is the parity space approach, analyzed in a stochastic setting. The stochastic parity space approach is similar to a Kalman filter, but uses an FIR fiter, while the Kalman filter is IIR. This enables faster response to changes. The second method is to use PCA, principal component analysis. In this case no model is needed, but fault isolation will be more difficult. The methods are illustrated on a simulation model of an F-16 aircraft. The fault detection probabilities can be calculated explicitly for the parity space approach, and are verified by simulations. The simulations of the PCA method suggest that the residuals have similar fault detection and isolation capabilities as for the stochastic parity space approach.