作者：许世林¹, 顾建平², 张延兵², 张颖¹, 张潇¹

作者单位：1. 常州大学安全科学与工程学院，江苏 常州 213164;
2. 江苏省特种设备安全监督检验研究院南通分院，江苏 南通 226000

关键词：声发射技术;腐蚀监测;混合信号;盲源分离;时频熵

摘要：

针对管道腐蚀声发射在线监测过程中，管道内介质流动产生的流动信号与管道腐蚀信号相混合，导致管道腐蚀难以识别的问题，提出一种基于VMD-FastICA和时频熵的管道腐蚀声发射信号分离与识别方法。首先利用VMD算法将混合信号分解为多个模态分量，从而满足FastICA算法对观测信号数量的要求，其次将获得的模态分量通过FastICA算法分离为多维独立分量，并根据峭度指标重构出管道流动信号与管道腐蚀信号，然后利用分离信号与样本信号的时频熵进行分离结果的验证，实现对腐蚀信号的提取与流动噪声的剔除。研究结果表明，VMD-FastICA能正确分离出混合信号中的腐蚀声发射信号，并能通过提取信号的时频熵进行腐蚀信号的识别，为管道腐蚀声学在线监测提供一种较好的应用方法。

Study on blind source separation and identification of acoustic emission signal for corrosion of in-service pipeline
XU Shilin¹, GU Jianping², ZHANG Yanbing², ZHANG Ying¹, ZHANG Xiao¹
1. School of Safety Science and Engineering, Changzhou University, Changzhou 213164, China;
2. Nantong Branch of Special Equipment Safety Supervision Inspection Institute of Jiangsu Province, Nantong 226000, China
Abstract: In the process of online monitoring of pipeline corrosion acoustic emission, the flow signal generated by medium flow in the pipeline is mixed with the pipeline corrosion signal, which makes it difficult to identify the pipeline corrosion. A separation and identification method of pipeline corrosion acoustic emission signal based on VMD-FastICA and time-frequency entropy is proposed. Firstly, the mixed signal is decomposed into multiple modal components by VMD algorithm, so as to meet the requirements of FastICA algorithm for the number of observed signals. Then, the obtained modal components are separated into multi-dimensional independent components by FastICA algorithm, and pipeline flow signal and pipeline corrosion signal are reconstructed according to kurtosis index. Finally, the separation results are verified by using the time-frequency entropy of the separated signal and the sample signal. Thus, the extraction of corrosion signal and the elimination of flow noise are realized. The experiment results show that VMD-FastICA can correctly separate the corrosion acoustic emission signal from the mixed signal, and can identify the corrosion signal by extracting the time-frequency entropy of the signal, which provides a promising application method for the online acoustic monitoring of pipeline corrosion.
Keywords: acoustic emission technology;corrosion monitoring;mixed signal;blind source separation;time-frequency entropy
2023, 49(3):53-59  收稿日期: 2021-07-18;收到修改稿日期: 2021-09-23
基金项目: 江苏省市场监督管理局科技计划项目（KJ207515）；江苏省研究生科研创新计划项目（KYCX21_2887）；中国石油化工股份有限公司科技攻关项目（320108）
作者简介: 许世林(1997-),男,安徽桐城市人,硕士研究生,专业方向为特种设备健康监测及评价
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