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ID 15391
Eprint ID
15391
FullText URL
Author
Shimada, Yukiyasu
Sayama, Hayatoshi
Abstract
Neural nets have recently become the focus of much attention, largely because of their wide range of complex and nonlinear problems. This paper presents a new integrated approach using neural networks for diagnosing process failures. The fault propagation in process is modeled by causal relationships from the fault tree and its minimal cut sets. The measurement patterns required for training and testing the neural network were obtained from fault propagation model. The network is able to diagnose even in the presence of malfunction of certain sensors. We demonstrate via a nitric acid cooler process how the neural network can learn and successfully diagnose the faults.
Published Date
1992-11-25
Publication Title
Memoirs of the Faculty of Engineering, Okayama University
Publication Title Alternative
岡山大学工学部紀要
Volume
volume27
Issue
issue1
Publisher
Faculty of Engineering, Okayama University
Publisher Alternative
岡山大学工学部
Start Page
33
End Page
44
ISSN
0475-0071
NCID
AA10699856
Content Type
Departmental Bulletin Paper
language
英語
File Version
publisher
Refereed
False
Eprints Journal Name
mfe