IEICE TRANSACTIONS on Fundamentals
On-Line Fault Diagnosis by Using Fuzzy Cognitive Map
Summary :
A system based on application of Fuzzy Cognitive Map (FCM) to perform on-line fault diagnosis is presented. The diagnostic part of the system is composed of two diagnostic schemes. The first one (basic diagnostic algorithm) can be considered as a simple transition of Shiozaki's signed directed graph approach to FCM framework. The second one is an extended version of the basic diagnostic algorithm where an important concept, the temporal associative memories (TAM) recall of FCM, is adopted. In on-line application, self-generated fault FCM model generates predicted pattern sequence through the TAM recall process, which is compared with observed pattern sequence to declare the origin of fault. As the resultant diagnosis scheme takes short computation time, it can be used for on-line fault diagnosis of large and complex processes, and even for incipient fault diagnosis. In practical case, since real observed pattern sequence may be different from predicted one through the TAM recall owing to propagation delay between process variables, the time indexed fault FCM model incorporating delay time is proposed. The utility of the proposed system is illustrated in fault diagnosis of a tank-pipe system.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E79-A No.6 pp.921-927
- Publication Date
- 1996/06/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Reliability and Fault Analysis
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Keesang LEE, Sungho KIM, Masatoshi SAKAWA, "On-Line Fault Diagnosis by Using Fuzzy Cognitive Map" in IEICE TRANSACTIONS on Fundamentals,
vol. E79-A, no. 6, pp. 921-927, June 1996, doi: .
Abstract: A system based on application of Fuzzy Cognitive Map (FCM) to perform on-line fault diagnosis is presented. The diagnostic part of the system is composed of two diagnostic schemes. The first one (basic diagnostic algorithm) can be considered as a simple transition of Shiozaki's signed directed graph approach to FCM framework. The second one is an extended version of the basic diagnostic algorithm where an important concept, the temporal associative memories (TAM) recall of FCM, is adopted. In on-line application, self-generated fault FCM model generates predicted pattern sequence through the TAM recall process, which is compared with observed pattern sequence to declare the origin of fault. As the resultant diagnosis scheme takes short computation time, it can be used for on-line fault diagnosis of large and complex processes, and even for incipient fault diagnosis. In practical case, since real observed pattern sequence may be different from predicted one through the TAM recall owing to propagation delay between process variables, the time indexed fault FCM model incorporating delay time is proposed. The utility of the proposed system is illustrated in fault diagnosis of a tank-pipe system.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_6_921/_p
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@ARTICLE{e79-a_6_921,
author={Keesang LEE, Sungho KIM, Masatoshi SAKAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={On-Line Fault Diagnosis by Using Fuzzy Cognitive Map},
year={1996},
volume={E79-A},
number={6},
pages={921-927},
abstract={A system based on application of Fuzzy Cognitive Map (FCM) to perform on-line fault diagnosis is presented. The diagnostic part of the system is composed of two diagnostic schemes. The first one (basic diagnostic algorithm) can be considered as a simple transition of Shiozaki's signed directed graph approach to FCM framework. The second one is an extended version of the basic diagnostic algorithm where an important concept, the temporal associative memories (TAM) recall of FCM, is adopted. In on-line application, self-generated fault FCM model generates predicted pattern sequence through the TAM recall process, which is compared with observed pattern sequence to declare the origin of fault. As the resultant diagnosis scheme takes short computation time, it can be used for on-line fault diagnosis of large and complex processes, and even for incipient fault diagnosis. In practical case, since real observed pattern sequence may be different from predicted one through the TAM recall owing to propagation delay between process variables, the time indexed fault FCM model incorporating delay time is proposed. The utility of the proposed system is illustrated in fault diagnosis of a tank-pipe system.},
keywords={},
doi={},
ISSN={},
month={June},}
Copy
TY - JOUR
TI - On-Line Fault Diagnosis by Using Fuzzy Cognitive Map
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 921
EP - 927
AU - Keesang LEE
AU - Sungho KIM
AU - Masatoshi SAKAWA
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 1996
AB - A system based on application of Fuzzy Cognitive Map (FCM) to perform on-line fault diagnosis is presented. The diagnostic part of the system is composed of two diagnostic schemes. The first one (basic diagnostic algorithm) can be considered as a simple transition of Shiozaki's signed directed graph approach to FCM framework. The second one is an extended version of the basic diagnostic algorithm where an important concept, the temporal associative memories (TAM) recall of FCM, is adopted. In on-line application, self-generated fault FCM model generates predicted pattern sequence through the TAM recall process, which is compared with observed pattern sequence to declare the origin of fault. As the resultant diagnosis scheme takes short computation time, it can be used for on-line fault diagnosis of large and complex processes, and even for incipient fault diagnosis. In practical case, since real observed pattern sequence may be different from predicted one through the TAM recall owing to propagation delay between process variables, the time indexed fault FCM model incorporating delay time is proposed. The utility of the proposed system is illustrated in fault diagnosis of a tank-pipe system.
ER -
English
