Neural networks applied to fault detection of a biotechnological process

This paper deals with a new method for the detection and diagnosis of faults when they develop in different parts of a wastewater treatment plant situated in Manresa, Spain. When a fault occurs, estimates of parameters in a non-linear mathematical model of the plant change. In this paper, a new approach to fault detection and diagnosis is presented and assessed by using real data from experiments on the plant. The method is based on the use of an on-line estimation technique and a backpropagation neural network to analyse the frequency content of some fault-indicating signal derived from the identification step, allowing correct fault detection, diagnosis and isolation. (C) 1999 Elsevier Science Ltd. All rights reserved.