Identification and supervision of a thermal plant based on multi-layer perceptron networks with locally distributed dynamics

This paper considers the application of neural networks with distributed dynamics to the identification of nonlinear systems. The primary objective is to establish a neural model bank which generates prediction errors. These estimation residuals can be treated as analytic symptoms to supervise the plant operating state. The practical approach applicability has been illustrated using a thermal plant. Here, two sensor faults of interest are localized by means of the so-called residual pattern.