摘要
在管道漏磁检测中,缺陷反演是管道故障诊断的核心部分。考虑漏磁信号的复杂性以及管道环境的多变性,常用的缺陷反演方法多采用传感器单轴信息,从而导致缺陷反演面临缺陷估计尺寸精度低、模型通用性差的问题,难以满足实际应用需求。本文提出基于三轴融合的漏磁内检测数据缺陷反演算法,显著提高漏磁缺陷反演精度。该方法主要由两部分组成,首先,利用提出的加权随机森林算法分别实现单轴信号的缺陷反演;其次,通过本文设计的模糊推理系统实现三轴反演结果决策融合,进而得到精确的缺陷估计尺寸。最后,通过仿真数据与实际管道数据实现该方法的评估。实验结果表明,该方法缺陷反演的长度精度提升23%,宽度精度提升13%,深度精度提升14.7%,具有较好的实验效果。
In the pipeline magnetic flux leakage detection,defect inversion is the core part of pipeline fault diagnosis.Considering the complexity of the magnetic flux leakage signal and the variability of the pipeline environment,the commonly defect inversion methods mostly use sensor uniaxial information,which may cause the defect inversion to bring the problems of low defect estimation size accuracy and poor model versatility.It is difficult to meet the requirement of practical application.This article proposes a three-axis fusion-based defect inversion algorithm for magnetic flux leakage internal inspection data,which significantly improves the inversion accuracy of magnetic flux leakage defect.The method mainly consists of two parts.First,the proposed weighted random forest algorithm is used to realize the defect inversion of single-axis signals.Secondly,the three-axis inversion result decision fusion is achieved through the designed fuzzy inference system.Then,the precise defect size is achieved.Finally,the evaluation of the method is realized through simulation data and practical pipeline data.Experimental results show that the length accuracy of the defect inversion method is increased by 23%,the width accuracy is increased by 13%,and the depth accuracy is increased by 14.7%,which have good experimental results.
作者
卢森骧
神祥凯
张俊楠
刘金海
赵可天
Lu Senxiang;Shen Xiangkai;Zhang Junnan;Liu Jinhai;Zhao Ketian(School of Information Science and Engineering,Northeastern University,Shenyang 110004,China;CNOOC Energy Development Equipment Technology Co.,Ltd.,Tianjin 300452,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2021年第12期245-253,共9页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(61627809,61973071,61703087,62003080)
辽宁省自然科学基金(2019-KF-03-04)项目资助。
关键词
漏磁检测
缺陷反演
加权随机森林
三轴决策融合
模糊推理
magnetic flux leakage detection
defect inversion
weighted random forest
three-axis decision fusion
fuzzy reasoning
