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이민래(Min-Rae Lee),이준현(Joon-Hyun Lee),김정택(Jung-Teak Kim) 대한기계학회 2005 대한기계학회 춘추학술대회 Vol.2005 No.5
The analysis of acoustic emission (AE) signals produced during object leakage is promising for condition monitoring of the components. In this study, an advanced condition monitoring technique based on acoustic emission detection and artificial neural networks was applied to a check valve, one of the components being used extensively in a safety system of a nuclear power plant. AE testing for a check valve under controlled flow loop conditions was performed to detect and evaluate disk movement for valve degradation such as wear and leakage due to foreign object interference in a check valve. It is clearly demonstrated that the evaluation of different types of failure modes suchas disk wear and check valve leakage were successful by systematically analyzing the characteristics of various AE parameters. It is also shown that the leak size can be determined with an artificial neural network