作者：句海洋, 王新华, 赵以振

作者单位：北京工业大学, 北京 100124

关键词：管道损伤;信号提取;变分模态分解;非开挖;近似熵;尺度空间

摘要：

埋地钢质管道非开挖管体缺陷检测是管道检测领域中长期存在的一个难点，而实现对缺陷有效识别的前提是管道损伤信号的精准提取，针对管道缺陷信号特征提出一种改进的变分模态分解（VMD）算法。该算法采用近似熵噪声抑制方法提升VMD对特征信号的提取效果，进一步用尺度空间算法对VMD的设定参数进行优化并对其抗噪性能进行比较分析，最后通过能量梯度算子实现对缺陷信号的识别。结果表明：改进后的VMD算法可实现对管道损伤信号的有效提取，滤波性能和计算效率方面优于原VMD算法。该方法已经成功通过实验室验证，并成功应用于华北某油田的工程领域检测，为埋地钢质管道非开挖管体缺陷被动式检测提供一种有效途径。

Research on defect signal extraction algorithm of steel pipeline based on improved VMD
JU Haiyang, WANG Xinhua, ZHAO Yizhen
Beijing University of Technology, Beijing 100124, China
Abstract: The detection of non-excavated is a long-term challenge forbody defects of buried steel pipeline, and the prerequisite for efficient identification of defects is the accurate extraction of pipeline damage signals. An improved variational mode decomposition (VMD) algorithm is proposed for the characteristics of pipeline defect signals. The algorithm adopts approximate entropy noise suppressionmethod to improve the extraction effect of VMD for feature signal. Furthermore, the scale space algorithm is used to optimize the setting parameters of VMD, and its anti-noise performance is compared and analyzed. Finally, the defect signal is identified by energy gradient operator. The results show that the improved VMD algorithm can effectively extract the pipeline damage signal, and its filtering performance and calculation efficiency are better than the original VMD algorithm. This method has successfully passed laboratory verification and has been successfully applied to the engineering field detection of an oil field in North China, which provides an effective way for passive detection of non-excavated pipeline defects of buried steel pipelines.
Keywords: pipeline defect;signal extraction;variational mode decomposition;non-excavation;approximate entropy;scale space
2020, 46(5):100-107  收稿日期: 2019-12-01;收到修改稿日期: 2020-01-20
基金项目: 国家重点研发计划项目（2017YFC0805005-1）；北京市教育委员会科研计划项目资助（KZ201810005009）
作者简介: 句海洋(1990-),男,河北衡水市人,博士研究生,研究方向为管道损伤地磁检测、信号与信息处理
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