基于稀疏建模和SVM的管道缺陷分类方法研究  

Research on pipeline defect classification based on sparse modeling and SVM

作　　者：郑林 张红星 句海洋 Zheng Lin;Zhang Hongxing;Ju Haiyang(Taiyuan Satellite Launch Center,Taiyuan 036303,China;Intelligence Technology of CEC Co.,Ltd.,Beijing 102209,China;National Computer System Engineering Research Institute of China,Beijing 100083,China;Beijing University of Technology,Beijing 100124,China)

机构地区：[1]太原卫星发射中心,山西太原036303 [2]中电智能科技有限公司,北京102209 [3]华北计算机系统工程研究所,北京100083 [4]北京工业大学,北京100124

出　　处：《信息技术与网络安全》2020年第10期67-74,共8页Information Technology and Network Security

基　　金：国家重点研发计划项目(2017YFC0805005);北京市教育委员会科研计划项目(KZ201810005009)。

摘　　要：埋地钢质管道缺陷识别及评估是管道检测领域中长期存在的难点之一,而实现对管道缺陷准确分类的前提是管道损伤信号的精准提取,针对埋地管道缺陷信号特征提出一种基于稀疏建模和支持向量机(SVM)的管道缺陷信号提取与识别方法。通过从原始信号中学习获得字典,将该字典采用正则化正交匹配追踪算法构建缺陷信号稀疏模型,并根据压缩感知理论获得信号的特征向量。进一步,采用多分类SVM将缺陷信号的特征向量与管道实际缺陷类型建立映射关系,并通过遗传粒子群优化算法指导SVM参数选取。结果表明:提出的分类方法可实现对管道缺陷损伤程度的准确划分,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。The defect identification and evaluation of buried steel pipeline is a long-term challenge in the field of pipeline detection,and the prerequisite for efficient identification of defects is the accurate extraction of pipeline damage signals.Aiming at the characteristics of buried pipeline defect signals,a method of pipeline defect signal extraction and recognition is proposed,which is based on sparse modeling and support vector machine(SVM).A dictionary is obtained by learning from the original signal,the dictionary is used to construct a sparse model of the defect signal using a regularized orthogonal matching pursuit algorithm,and the feature vector of the signal is obtained according to the compressed sensing theory.Furthermore,multi-classification SVM is used to establish a mapping relationship between the feature vector of the defect signal and the actual defect type of the pipeline,and Genetic Algorithm-Particle Swarm Optimization is used to guide the selection of SVM parameters.The results showed that the proposed classification method can realize the accurate division of the damage degree of pipeline defects,which has been successfully verified in the laboratory and applied to the engineering field detection of an oil field in North China.

关 键 词：稀疏建模 SVM 管道缺陷 分类方法 

分 类 号：TE973[石油与天然气工程—石油机械设备] TP391.41[自动化与计算机技术—计算机应用技术]