Neural networks applied to fault detection of a biotechnological process

This paper deals with a new method for the detection and diagnosis of fault s when they develop in different parts of a wastewater treatment plant situ ated in Manresa, Spain. When a fault occurs, estimates of parameters in a n on-linear mathematical model of the plant change. In this paper, a new appr oach to fault detection and diagnosis is presented and assessed by using re al 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 analy se the frequency content of some fault-indicating signal derived from the i dentification step, allowing correct fault detection, diagnosis and isolati on. (C) 1999 Elsevier Science Ltd. All rights reserved.