基于漏磁信号深度特性的缺陷深度轮廓迭代优化方法  

Iterative Optimization Method of Defect Depth Profile Based on Depth Characteristics of MFL Signal

作　　者：缪立恒[1] 潘峰 彭丽莎[2] 黄松岭[2] MIU Liheng;PAN Feng;PENG Lisha;HUANG Songling(State Grid Wuxi Power Supply Co.,Wuxi 214061,Jiangsu Province,China;State Key Laboratory of Power System and Generation Equipment(Department of Electrical Engineering,Tsinghua University),Haidian District,Beijing 100084,China)

机构地区：[1]国网无锡供电公司,江苏省无锡市214061 [2]电力系统及大型发电设备安全控制和仿真国家重点实验室(清华大学电机系),北京市海淀区100084

出　　处：《中国电机工程学报》2022年第8期3077-3085,共9页Proceedings of the CSEE

基　　金：国网江苏省电力有限公司科技项目(J2020039);国家自然科学基金面上项目(52077088,52077110)。

摘　　要：漏磁检测是一种广泛应用于钢结构件缺陷检测与评估中的无损检测方法。缺陷漏磁信号反演是漏磁检测的关键环节,其中有效的缺陷深度轮廓优化对于提高反演效率至关重要。该文提出一种基于漏磁信号深度特性的缺陷深度轮廓迭代优化方法,该方法基于缺陷区域漏磁信号平均值与深度间的近似线性关系,对缺陷初始深度序列进行估计,并进一步根据缺陷计算漏磁信号与检测漏磁信号间的误差分析对缺陷深度序列进行优化更新,最终实现缺陷深度轮廓的反演重构。通过仿真与试验分别对不同类型缺陷的漏磁检测信号进行反演,仿真与试验结果均验证了该方法的有效性。Magnetic flux leakage testing(MFL) is a nondestructive testing method widely used in defect detection and evaluation of steel structures. The inversion of defect signal is the key part of MFL testing, and effective defect depth profile optimization is very important to improve the efficiency of defect inversion. In this paper, an iterative optimization method of defect depth profile based on the depth characteristics of MFL signal was proposed. Based on the approximate linear relationship between the average value of MFL signal and the depth, the initial depth sequence of defect was estimated, and the defect depth sequence was optimized and updated according to the error analysis between MFL signal and MFL signal. The defect depth profile was reconstructed by inversion. Through the simulation and experiment, the MFL detection signals of different types of defects were inversed. The simulation and test results verify the effectiveness of the method.

关 键 词：漏磁检测信号 深度特性 优化算法 缺陷深度轮廓反演 

分 类 号：TG115.28[金属学及工艺—物理冶金]