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Classification of abnormal plant operation using multiple process variable trends
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DOI：10.1016/S0959-1524(00)00011-1.png)
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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-propagation neural network (BPNN) to determine the overall process classification. The methods are compared in terms of their ability to detect and correctly diagnose a variety of abnormal operating conditions for a non-isothermal CSTR simulation. For the case study problem, the BPNN method resulted in better classification accuracy with a moderate increase in training time compared with the HMM approach. (C) 2001 Elsevier Science Ltd. All rights reserved.
Keyword:
process diagnosis
hidden Markov models
back-propagation neural network
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3.9
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被引数:
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Classification of process trends based on fuzzified symbolic representation and hidden Markov models
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