Optimal actuator fault detection via MLP neural network for PDFs

In this paper a new type of fault detection (FD) problem is considered where the measured information is the stochastic distribution of the system output rather than its value. A multi-layer perceptron (MLP) neural network is adopted to approximate the probability density function (PDF) of the system outputs and nonlinear principal component analysis (NLPCA) is applied to reduce the model order for a lower-order model. For such a dynamic model in discrete-time context, where nonlinearities, uncertainties and time delays are included, the concerned FD problem is investigated. The measure of estimation errors, which is represented by the distances between two output PDFs, will be optimized to find the detection filter gain. Guaranteed cost detection filter are designed based on LMI formulations.