Fault detection method for nonlinear systems based on probabilistic neural network filtering

A fault detection method for nonlinear systems, which is based on Probabilistic Neural Network Filtering (PNNF), is presented. PNNF limits the maximum estimation error of the asymptotic Bayes optimal result and describes the tracking process with an expression. On the basis of these properties of PNNF and the statistical characteristics of the noise of the system, a fault threshold can be better calculated, especially for the tracking process corresponding to a strong disturbance. According to the threshold, the fault can be detected by evaluating the residuals. Also, for some special cases when a fault is potential but the system is in steady state, which causes the information for fault detection may be insufficient and a group of disturbances are artificially input with definite amplitudes, so that the result of detection can be enhanced by comparing the real with the expected tracking processes of the filter. Examples are given to demonstrate the method of fault detection based on PNNF.