James C. Wong، نويسنده , , Karen A. McDonald and Ahmet Palazoglu، نويسنده , , Ahmet Palazoglu، نويسنده ,

1384402

Classification of abnormal plant operation using multiple process variable trends

11185

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.

409

process diagnosis , Hidden Markov models , back-propagation neural network

Studia Iranica

418

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