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Title: Incipient fault diagnosis of chemical processes via artificial neural networks

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. The authors 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.

Authors:
;
Publication Date:
OSTI Identifier:
6988082
Resource Type:
Journal Article
Journal Name:
A.I.Ch.E. Journal (American Institute of Chemical Engineers); (USA)
Additional Journal Information:
Journal Volume: 35:11; Journal ID: ISSN 0001-1541
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ARTIFICIAL INTELLIGENCE; PERFORMANCE; NEURAL NETWORKS; COMPUTERIZED SIMULATION; FAULT TOLERANT COMPUTERS; LEARNING; USES; COMPUTERS; DIGITAL COMPUTERS; SIMULATION; 990200* - Mathematics & Computers

Citation Formats

Watanabe, K, Matsuura, I, and Abe, M. Kubota, M. Incipient fault diagnosis of chemical processes via artificial neural networks. United States: N. p., 1989. Web. doi:10.1002/aic.690351106.
Watanabe, K, Matsuura, I, & Abe, M. Kubota, M. Incipient fault diagnosis of chemical processes via artificial neural networks. United States. https://doi.org/10.1002/aic.690351106
Watanabe, K, Matsuura, I, and Abe, M. Kubota, M. 1989. "Incipient fault diagnosis of chemical processes via artificial neural networks". United States. https://doi.org/10.1002/aic.690351106.
@article{osti_6988082,
title = {Incipient fault diagnosis of chemical processes via artificial neural networks},
author = {Watanabe, K and Matsuura, I and Abe, M. Kubota, M.},
abstractNote = {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. The authors 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.},
doi = {10.1002/aic.690351106},
url = {https://www.osti.gov/biblio/6988082}, journal = {A.I.Ch.E. Journal (American Institute of Chemical Engineers); (USA)},
issn = {0001-1541},
number = ,
volume = 35:11,
place = {United States},
year = {Wed Nov 01 00:00:00 EST 1989},
month = {Wed Nov 01 00:00:00 EST 1989}
}