Component fault diagnosis using wavelet neural networks with local recurrent structure

Abstract

This chapter investigates the development of the wavelet neural network (WNN) with local recurrent structure and its application to fault detection and isolation (FDI) of components of a dynamic process by including a recurrent connection into the static WNN structure. Hybrid learning based on orthogonal least-squares and the steepest-descent method, is used to train the proposed neural network. The experimental case study concerns the component fault diagnosis of a three-tank system. A neural simplified observer scheme is used to generate the residuals (symptoms) in the form of one step-ahead prediction errors. These are further analyzed by a neural classifier to take the appropriate decision regarding the actual behavior of the process.