A fast training neural network and its updation for incipient fault detection and diagnosis

Fast incipient fault diagnosis is becoming one of the key requirements for safe and optimal process operations. There has been considerable work done in this area with a variety of approaches being proposed for incipient faul t detection and diagnosis (FDD). Incipient FDD problem is particularly diff icult in the case of chemical processes as these processes are usually char acterized by complex operations, high dimensionality and inherent nonlinear ity. Neural networks have been shown to solve FDD problems in chemical proc esses as they develop inherently non-linear input-output maps and are well suited for high dimensionality problems. In this work, to enhance the neura l network framework, we address the following three issues, (i) speed of tr aining; (ii) introduction of time explicitly into the classifier design; an d (iii) online updation using a mirror-like process model. (C) 2000 Elsevie r Science Ltd. All rights reserved.