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Acoustic emission recognition method for valve internal leakage based on convolutional attention mechanism |
HUANG Xin, QU Wenzhong, XIAO Li |
Department of Engineering Mechanics, Wuhan University, Wuhan 430072, China |
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Abstract Because of long-term exposure to high temperature and high-pressure environment, the gate or disc of the valve structure, one of the key equipment components of nuclear power plant, is prone to thermal deformation or wear, resulting in poor sealing, which lead to internal leakage accidents. Real-time on-line identification of internal leakage state of valve is of great significance for improving thermal efficiency and valve reliability of nuclear power units. It’s easy for the acoustic emission signal of valve leakage to be covered by the base noise of the actual industrial site, which result in the misjudgment of valve leakage state. In order to realize quick and accurate identification of valve internal leakage state, in present paper, the valve internal leakage detection test bench is built, the valve internal leakage monitoring and analysis system based on acoustic emission method is developed, and the convolutional block attention module is introduced into the convolutional neural network to realize efficient and rapid identification of valve internal leakage state. The results show that: Based on the acoustic emission signal frequency domain data of the valve leakage, the convolutional block attention module can effectively and accurately identify the valve leakage state. When the internal leakage rate is 26L/h, the recognition accuracy is up to 98%, with good reliability and robustness.
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Received: 27 March 2023
Published: 15 May 2024
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