Abstract
It is very important to detect and identify small anomalies and component failures for the safe operation of complex and large-scale artifacts such as nuclear power plants. Each diagnostic technique has its own advantages and limitations. These facts inspire us not only to enhance the capability of diagnostic techniques but also to integrate the results of diagnostic subsystems in order to obtain more accurate diagnostic results. The article describes the outline of four diagnostic techniques developed for the condition monitoring of the fast breeder reactor “Monju”. The techniques are (1) estimation technique of important state variables based on a physical model of the component, (2) a state identification technique by non-linear discrimination function applying SVM (Support Vector Machine), (3) a diagnostic technique applying WT (Wavelet Transformation) to detect changes in the characteristics of measurement signals, and (4) a state identification technique effectively using past cases. In addition, a hybrid diagnostic system in which a final diagnostic result is given by integrating the results from subsystems is introduced, where two sets of values called confidence values and trust values are used. A technique to determine the trust value is investigated under the condition that the confidence value is determined by each subsystem
| Original language | English |
|---|---|
| Pages (from-to) | 725-856 |
| Number of pages | 132 |
| Journal | Nuclear Engineering and Technology |
| Volume | 46 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2014 |
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Keywords
- Diagnosis
- Fast Breeder Reactor
- Hybrid Diagnostic System
- Information Integration
- Subsystem
ASJC Scopus subject areas
- Nuclear Energy and Engineering
Cite this
Integrated diagnostic technique for nuclear power plants. / Gofuku, Akio.
In: Nuclear Engineering and Technology, Vol. 46, No. 6, 2014, p. 725-856.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Integrated diagnostic technique for nuclear power plants
AU - Gofuku, Akio
PY - 2014
Y1 - 2014
N2 - It is very important to detect and identify small anomalies and component failures for the safe operation of complex and large-scale artifacts such as nuclear power plants. Each diagnostic technique has its own advantages and limitations. These facts inspire us not only to enhance the capability of diagnostic techniques but also to integrate the results of diagnostic subsystems in order to obtain more accurate diagnostic results. The article describes the outline of four diagnostic techniques developed for the condition monitoring of the fast breeder reactor “Monju”. The techniques are (1) estimation technique of important state variables based on a physical model of the component, (2) a state identification technique by non-linear discrimination function applying SVM (Support Vector Machine), (3) a diagnostic technique applying WT (Wavelet Transformation) to detect changes in the characteristics of measurement signals, and (4) a state identification technique effectively using past cases. In addition, a hybrid diagnostic system in which a final diagnostic result is given by integrating the results from subsystems is introduced, where two sets of values called confidence values and trust values are used. A technique to determine the trust value is investigated under the condition that the confidence value is determined by each subsystem
AB - It is very important to detect and identify small anomalies and component failures for the safe operation of complex and large-scale artifacts such as nuclear power plants. Each diagnostic technique has its own advantages and limitations. These facts inspire us not only to enhance the capability of diagnostic techniques but also to integrate the results of diagnostic subsystems in order to obtain more accurate diagnostic results. The article describes the outline of four diagnostic techniques developed for the condition monitoring of the fast breeder reactor “Monju”. The techniques are (1) estimation technique of important state variables based on a physical model of the component, (2) a state identification technique by non-linear discrimination function applying SVM (Support Vector Machine), (3) a diagnostic technique applying WT (Wavelet Transformation) to detect changes in the characteristics of measurement signals, and (4) a state identification technique effectively using past cases. In addition, a hybrid diagnostic system in which a final diagnostic result is given by integrating the results from subsystems is introduced, where two sets of values called confidence values and trust values are used. A technique to determine the trust value is investigated under the condition that the confidence value is determined by each subsystem
KW - Diagnosis
KW - Fast Breeder Reactor
KW - Hybrid Diagnostic System
KW - Information Integration
KW - Subsystem
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UR - http://www.scopus.com/inward/citedby.url?scp=84919880527&partnerID=8YFLogxK
U2 - 10.5516/NET.04.2014.719
DO - 10.5516/NET.04.2014.719
M3 - Article
AN - SCOPUS:84919880527
VL - 46
SP - 725
EP - 856
JO - Nuclear Engineering and Technology
JF - Nuclear Engineering and Technology
SN - 1738-5733
IS - 6
ER -