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 such as 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.
This paper presents an approach to leak detection of pipeline review in term of theoretical analysis such as acoustics and hydromechanics that should be accompanied by explanation of leakage. The acoustic emission signals during leak from a circular hole of different geometries were studied both analytically and experimentally. The relationships between acoustic parameters and fluid mechanical parameters also were derived analytically. A quadrupole aerodynamic model was applied in the analysis of leak form the circular hole. Computer simulation results demonstrate the effectiveness of the proposed approach. In addition, the leak source location results are also presented by employing the wavelet transform.
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