基于卷积注意力机制的阀门内漏声发射识别方法

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (9) : 105-114.

论文

黄鑫，屈文忠，肖黎

作者信息 +

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

HUANG Xin, QU Wenzhong, XIAO Li

Author information +

文章历史 +

摘要

阀门结构作为核电厂的关键设备部件之一，因长期处于高温高压环境下，其闸板或阀瓣易发生热变形或磨损导致密封不良，进而引发内漏事故。实时在线识别阀门的内漏状态，对提升核电机组热效率、提高阀门可靠性具有重要意义。因实际工业现场的基底噪声极易掩盖阀门内漏的声发射信号，进而造成阀门内漏状态的误判。为实现阀门内漏状态的快速准确识别，搭建了阀门内漏检测试验台架，开发了基于声发射方法的阀门内漏监检测分析系统，将卷积注意力机制引入卷积神经网络中，实现高效快速地识别阀门内漏状态。结果表明：基于阀门内漏的声发射信号频域数据，利用卷积注意力机制神经网络能有效准确地识别阀门内漏状态，在内漏率为26L/h时，识别准确率高达98%，并且具有较好的可靠性和鲁棒性。

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.

导出引用

黄鑫，屈文忠，肖黎. 基于卷积注意力机制的阀门内漏声发射识别方法[J]. 振动与冲击, 2024, 43(9): 105-114

HUANG Xin, QU Wenzhong, XIAO Li. Acoustic emission recognition method for valve internal leakage based on convolutional attention mechanism[J]. Journal of Vibration and Shock, 2024, 43(9): 105-114

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