Acoustic emission recognition method for valve internal leakage based on convolutional attention mechanism

As one of key components of nuclear power plant, valve structure is prone to thermal deformation or wear of its gate or valve disc due to long-term exposure to high-temperature and high-pressure environment to cause poor sealing and ultimately lead to internal leakage accident. Real time online identifying valve internal leakage status is of great significance for improving thermal efficiency of nuclear power units and enhancing valve reliability. Due to substrate noise in actual industrial site being easy to cover acoustic emission signals of valve internal leakage, valve leakage status was easy to misjudge. To realize fast and correct recognition of valve leakage status, a valve internal leakage detection test bench was built, and a valve leakage monitoring and analysis system based on acoustic emission was developed. Convolutional attention mechanism was introduced into convolutional neural network to realize efficient and fast identification of valve leakage status. The results showed that based on frequency domain data of acoustic emission signals of valve internal leakage, the convolutional attention mechanism neural network can effectively and accurately identify valve internal leakage status; when the internal leakage rate is 26 L/h, the proposed method' s recognition accuracy is up to 98% with better reliability and robustness. © 2024 Chinese Vibration Engineering Society. All rights reserved.