Miscellaneous Neural Networks Applied to Fault Detection and Isolation of an Evaporation Station

Abstract

The problem of robust diagnosis of process faults in an evaporation station of a sugar factory is addressed by means of a neural-network approach. The main emphasis is placed upon the development of generalised observer schemes. These are designed based on neural nets with internal dynamics. The goal is to achieve an adequate approximation of process outputs corresponding to the normal behaviour of the plant. Symptoms characterising the current state of the process are obtained based on the prediction errors. Static artificial networks further evaluate these. Appropriate decision mechanisms lead to fault detection and isolation. Experimental results using real data supplied by industry assess the efficiency of the approach.