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缺陷的智能识别与分类专题
缺陷的智能识别与分类专题

DOI： 10.11973/wsjc202406008

基于 VMD-HT 和深度学习的流噪环境

腐蚀损伤声发射识别模型

顾建平 ，许世林 ，张延兵 ，张 颖 ，王雪琴 2
1
2
1
2
（1. 江苏省特种设备安全监督检验研究院，南京 210000；2. 常州大学 安全科学与工程学院，常州 213164）
摘 要：对在役管道进行腐蚀声发射监测的过程中，管内介质流动产生的噪声同样会被传感器
接收，导致腐蚀信号被覆盖从而引发误判。针对这一问题，提出了一种基于变分模态分解（VMD）、
希尔伯特变换（HT）和深度双向门限循环单元神经网络（BiGRU）的流噪环境腐蚀损伤声发射识别
模型。该模型能够将原始信号自适应地转化成多个本征模态分量，并提取各分量的瞬时频率及谱
熵构建多维时序特征矩阵，进而建立原始信号与多维特征之间的映射关系。为验证该方法的有效
性，对在役管道进行腐蚀声发射监测试验。结果表明，所提模型在流噪环境下具有良好的鲁棒性，
监测数据的识别准确率达96. 3%，可作为一种解决在役管道腐蚀声发射监测的新方案。
关键词：在役管道；腐蚀监测；声发射技术；变分模态分解
中图分类号：TG115.28 文献标志码：A 文章编号：1000-6656(2024)06-0043-06

Acoustic emission recognition model for corrosion damage in flow noise environment based

on VMD-HT and deep learning

GU Jianping , XU Shilin , ZHANG Yanbing , ZHANG Ying , WANG Xueqin 2
2
1
2
1
（1. Special Equipment Safety Supervision Inspection Institute of Jiangsu Province, Nanjing 210000, China;
2. School of Safety Science and Engineering, Changzhou University, Changzhou 213164, China）
Abstract：In the process of corrosion acoustic emission monitoring of in-service pipelines, the flow noise due to
the flow of the medium inside the pipe will also be picked up by the sensors, which results in the corrosion signals being
overwritten and thus making it difficult to determine whether corrosion has occurred or not. To address this problem,
a corrosion damage acoustic emission identification model based on variational mode decomposition (VMD), Hilbert
transform (HT), and deep bi-directional GRU neural network (BiGRU) was proposed. The model was able to adaptively
transform the original signal into multiple eigenmode components, and extracted the instantaneous frequency and spectral
entropy of each component to construct a multidimensional time-series feature matrix, and then establish the mapping
relationship between the original signal and the multidimensional features. In order to verify the effectiveness of the method,
corrosion acoustic emission monitoring experiments of in-service pipelines were carried out, and the experimental results
showed that the proposed model had good robustness under the flow noise environment, and the recognition accuracy of the
monitoring data reached 96.3%, and it was able to provide a new solution for the corrosion acoustic emission monitoring
of in-service pipelines.
Key words：in-service pipeline; corrosion monitoring; acoustic emission technology; variational mode decomposition

收稿日期：2023-07-21 腐蚀是管道服役过程中常见的问题，在腐蚀的
基金项目：江苏省特种设备安全监督检验研究院2023年科技项 长期作用下，管道易出现腐蚀减薄、腐蚀开裂、穿孔
目[KJ（Y）2023004] [1]
等缺陷，而带来安全隐患 。由于管道腐蚀是一个
作者简介：顾建平（1966—），男，硕士，正高级工程师，主要从事
动态发展和长期积累的过程，常规的定期检测方式
化工机械安全技术和特种设备检验工作
难以对管道腐蚀进行连续、完整的评估，所以，发展
通信作者：张 颖，aezy163@163. com
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2024 年 第 46 卷 第 6 期
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