Classification of abnormal plant operation using multiple process variabletrends

This paper illustrates two strategies for the detection and classification of abnormal process operating conditions in which multiple process variable trends are available. The first strategy uses a hidden Markov model (HMM) for overall process classification while the second method uses a back-prop agation neural network (BPNN) to determine the overall process classificati on. The methods are compared in terms of their ability to detect and correc tly diagnose a variety of abnormal operating conditions for a non-isotherma l CSTR simulation. For the case study problem, the BPNN method resulted in better classification accuracy with a moderate increase in training time co mpared with the HMM approach. (C) 2001 Elsevier Science Ltd. All rights res erved.