电子测量与仪器学报

张杰,陈世利.基于 FCN 的阀门内泄漏声发射信号识别方法[J].电子测量与仪器学报,2023,37(8):84-93

基于 FCN 的阀门内泄漏声发射信号识别方法

Identification method of valve internal leakage acoustic emission signal based on FCN

DOI：

英文关键词:full convolutional neural network valve internal leakage acoustic emission classification and identification

基金项目:

作者	单位
张杰	1.天津大学精密测试技术及仪器国家重点实验室
陈世利	1.天津大学精密测试技术及仪器国家重点实验室

Author	Institution
Zhang Jie	1.State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University
Chen Shili	1.State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University

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中文摘要:

针对石化工业中输气管道阀门的内泄漏故障,将声发射检测技术与深度学习技术相结合,提出了一种基于全卷积神经网络(FCN)的阀门内泄漏声发射信号识别方法。该方法利用声发射技术采集阀门内泄漏的声发射信号,基于 FCN 搭建阀门内泄漏分类诊断模型,充分发挥了声发射技术在阀门内泄漏检测领域的优越性,以及 FCN 在时间序列分类任务上的高性能。该方法相较于传统的识别方法,无需对原始采集数据进行特征提取或繁重复杂的预处理,而是将特征提取的任务也交于神经网络模型来学习和完成,可实现端到端的阀门内泄漏声发射信号分类识别。搭建阀门内泄漏检测实验平台,采集并制作阀门内泄漏声发射信号数据集,建立了基于 FCN 的阀门内泄漏声发射信号的二分类模型,实验结果表明,该模型的分类识别准确率可达 98. 72%,相比较于其他先进的分类模型在数据集上表现出了更加优越的分类识别性能和训练效率,同时对环境噪声具有良好的抗干扰性能。

英文摘要:

Aiming at internal leakage failures of gas transmission pipeline valves in petrochemical industry, this paper proposes an identification method of acoustic emission signal of valve internal leakage based on full convolutional neural network ( FCN) by combining acoustic emission detection technology with deep learning technology. The method uses acoustic emission technology to collect acoustic emission signal of valve internal leakage and builds a valve internal leakage classification and diagnosis model based on FCN, which fully exploits the superiority of acoustic emission technology in the field of valve internal leakage detection and the high performance of FCN in time series classification tasks. Compared with traditional identification methods, this method does not require any feature extraction or complex preprocessing of the original collected data. Instead, the task of feature extraction is also handed over to the neural network model to learn and complete, which can realize end-to-end classification and identification of valve internal leakage acoustic emission signal. The data sets of valve internal leakage acoustic emission signal are collected and produced through the experimental platform of valve internal leakage detection, and the binary classification model of valve internal leakage acoustic emission signal based on FCN is established as well. The experimental results show that the accuracy of classification and recognition of the model can reach 98. 72%. Compared with other advanced classification models, the model shows more superior recognition performance on the data sets and has higher training efficiency, which also has good anti-interference performance against environmental noise at the same time.

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