A Study on the Development of Robust Fault Diagnostic System Based on Neuro-Fuzzy Scheme

Kim, Sung Hoon; Lee, Kee-Sang

doi:10.1016/s1474-6670(17)37555-9

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…Yousefi S (with 9 papers) and Rezaee MJ (with 7 papers) have been among the most productive researchers in the field since 2017. Papageorgiou EI and Salmeron JL have 7 and 4 [79,116,73,144,145,74,132,11,98,159,115,164,124,143], Rezaee et al (2018b), [21], [86], [127], [102] 19…”

Section: Resultsmentioning

confidence: 99%

“…Lee et al [79] used FCM to diagnose the on-line fault of the system and implemented it in a tank-pipe system. In a similar study, [73] proposed an FCM-based fault diagnosis scheme and used a fuzzy set theory and a neural network to find out the root causes of faults and to make the diagnosis scheme robust in the face of changes. In another study, [74] offered a fault diagnostic approach using FCM and applied principal component analysis to enhance the diagnostic results.…”

Section: Decision Makingmentioning

confidence: 99%

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Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Bakhtavar

Valipour

Yousefi

et al. 2020

Complex Intell. Syst.

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Fuzzy cognitive maps (FCMs) have been widely applied to analyze complex, causal-based systems in terms of modeling, decision making, analysis, prediction, classification, etc. This study reviews the applications and trends of FCMs in the field of systems risk analysis to the end of August 2020. To this end, the concepts of failure, accident, incident, hazard, risk, error, and fault are focused in the context of the conventional risks of the systems. After reviewing risk-based articles, a bibliographic study of the reviewed articles was carried out. The survey indicated that the main applications of FCMs in the systems risk field were in management sciences, engineering sciences and industrial applications, and medical and biological sciences. A general trend for potential FCMs’ applications in the systems risk field is provided by discussing the results obtained from different parts of the survey study.

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Section: Resultsmentioning

confidence: 99%

Section: Decision Makingmentioning

confidence: 99%

Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Bakhtavar

Valipour

Yousefi

et al. 2020

Complex Intell. Syst.

View full text Add to dashboard Cite

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“…Some representative methods are Neural Network [3], [4], Neuro-Fuzzy system [5], [6] and Fourier Transform [7], [8], etc.…”

Section: Existingphase Failure Detectormentioning

confidence: 99%

New Method for Detecting Failures of Power Converter Module in Control Rod Control System

Cheon

Kim

Lee

et al. 2006

2006 IEEE International Symposium on Industrial Electronics

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In this paper, we introduce a new method for detecting failures of a power converter module in Control Rod Control System of a nuclear power plant. The failure called the phase failure and occurring in a power converter module can exert harmful influence upon the operation of the associated Control Rod Drive Mechanism for driving neutron-absorbing control rods controlling the thermal output of the reactor. Because of this adverse condition, it is necessary that the phase failure should be detected quickly and correctly. We devised a new method for detecting the phase failure for the digital power controller and solved the drawbacks of the existing failure detector.

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A Study on the Development of Robust Fault Diagnostic System Based on Neuro-Fuzzy Scheme

Cited by 2 publications

References 5 publications

Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Fuzzy cognitive maps in systems risk analysis: a comprehensive review

New Method for Detecting Failures of Power Converter Module in Control Rod Control System

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