基于支持向量机和磁记忆技术的管道缺陷深度的定量化反演研究

金属管道表面往往存在不同深度的腐蚀缺陷。金属磁记忆检测技术是目前唯一能对铁磁性构件的早期损伤进行诊断的无损检测技术,然而磁记忆原始信号本身并不能直接实现对管道腐蚀缺陷深度特征的定量化识别,进而无法实现对管道腐蚀程度的预警。针对该问题,采用支持向量机方法建立了管道缺陷深度的定量化反演模型,利用该模型对管道上深度为1~15mm的腐蚀缺陷进行了多次反演,反演结果的平均误差为2.398mm,平均均方根误差为3.205mm,结果表明,模型对管道腐蚀缺陷深度的定量化反演是可行的。研究结果可为该领域的研究提供一定的参考,且具有较高的实际应用价值。

Quantitative Inversion of Pipeline Defect Depth Based on Support Vector Machine and Magnetic Memory Technology

Various depths of corrosion defects often occurred on the surfaces of metal pipelines.At present metal magnetic memory detection technology is the only non-destructive testing technology that can diagnose the early damage of ferromagnetic components.However,the original signal of magnetic memory cannot directly realize the quantitative identification of pipeline corrosion defects,and thus it is impossible to realize the warning of the degree of corrosion of the pipeline.Aiming at this problem,aquantitative inversion model of pipeline defect depth was established using support vector machine method.The model was used to identify and predict the corrosion defects with a depth of 1-15 mm on the metal pipelines.The average error of the prediction results was 2.398 mm,and the average root mean square error was 3.205 mm.The results demonstrate that the model was feasible for quantitative inversion of pipeline corrosion depth.The research results could provide a certain reference for the research in this field,and had high practical application value.