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Incipient fault diagnosis of chemical processes via artificial neural networks
Author(s) -
Watanabe Kajiro,
Matsuura Ichiro,
Abe Masahiro,
Kubota Makoto,
Himmelblau D. M.
Publication year - 1989
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690351106
Subject(s) - artificial neural network , fault (geology) , process (computing) , content addressable memory , computer science , artificial intelligence , associative property , fault detection and isolation , machine learning , pattern recognition (psychology) , mathematics , seismology , pure mathematics , actuator , geology , operating system
Abstract Artificial neural networks have capacity to learn and store information about process faults via associative memory, and thus have an associative diagnostic ability with respect to faults that occur in a process. Knowledge of the faults to be learned by the network evolves from sets of data, namely values of steady‐state process variables collected under normal operating condition and those collected under faulty conditions, together with information about the degree of the faults and their causes. Here, we describe how to apply artificial neural networks to fault diagnosis. A suitable two‐stage multilayer neural network is proposed as the network to be used for diagnosis. The first stage of the network discriminates between the causes of faults when fed the noisy process measurements. Once the fault is identified, the second stage of the network estimates the degree of the fault. Thus, the diagnosis of incipient faults becomes possible.