Defect 3D Profile Reconstruction Using Magnetic Flux Leakage Signals Based on Error Estimation
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摘要: 漏磁检测是一种广泛应用于在役管道检测中的无损检测技术，有效的缺陷轮廓三维重构方法对于漏磁检测非常重要。提出了一种基于偏差估计的随机森林缺陷三维轮廓重构方法。该方法利用随机森林，以漏磁信号偏差估计重构轮廓偏差，并根据估计信号和实际信号之间的偏差更新缺陷轮廓，最终实现缺陷的三维轮廓重构。试验结果表明：提出的方法具有良好的缺陷轮廓重构精度。Abstract: Magnetic leakage detection as a nondestructive testing technology, is widely used in in-service pipeline. Effective 3-D profile reconstruction of defect is very important for magnetic flux leakage (MFL) detection. In this paper, a novel 3-D profile reconstruction method using random forest (RF) based on error estimation is proposed. RF is developed to estimate reconstructed profile errors by inputting signal errors. Then errors between estimated signals and real signals are applied to update defect profiles. Finally, 3-D reconstructed profiles are obtained. Experimental results show that the proposed method has good precision of defect reconstruction.
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Keywords:
- magnetic leakage detection /
- profile reconstruction /
- finite element /
- random forest
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[1] 唐建华,孙少欣,刘金海,等.一种漏磁内检测器数据的自适应滤波方法[J].无损检测,2017,39(5):1-5,56. [2] 邓炜,高斌,田贵云,等.基于脉冲涡流的多层异种金属材料内部缺陷检测[J].无损检测,2018,40(4):1-6. [3] 纪凤珠,王长龙,梁四洋,等. 基于LS-SVM的管道二维漏磁缺陷重构[J]. 西南石油大学学报,2007(5):134-136,208. [4] 苑希超,王长龙,王建斌. 基于贝叶斯估计的漏磁缺陷轮廓重构方法研究[J]. 兵工学报,2012(1):116-120. [5] 韩文花,汪胜兵,王建,等. 基于改进人工蜂群算法的漏磁缺陷轮廓重构[J]. 火力与指挥控制,2016(6):15-18. [6] 朱红运,王长龙,王建斌,等. 基于径向基神经网络的脉冲涡流缺陷轮廓重构[J]. 仪表技术与传感器,2016(6):98-101. [7] 杨理践.管道漏磁在线检测技术[J].沈阳工业大学学报,2005,27(5):522-525. [8] 杨理践,毕大伟,高松巍.油气管道漏磁检测的缺陷量化技术的研究[J].计算机测量与控制,2009,17(8):1489-1491. [9] 梁志珊,孟祥萍,张化光. 从一类数据信息中产生模糊规则的有限元方法[J]. 信息与控制,1998(4):28-32. [10] MANDACHE C, CLAPHAM L. A model formagnetic flux leakage signal predictions[J].Journal of Physics D:Applied Physics, 2003, 36:2427-2431.
[11] HUANG Z Y, QVE P W, CHEN L. 3D FEM analysis in magnetic flux leakage method[J]. NDT&E International,2006,39:61-66.
[12] 肖敏,史忠科. 水雷出水突变非线性滑模自适应反演弹道控制[J]. 信息与控制,2012(6):687-694. [13] BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1):5-32.
[14] GISLASON P O, BENEDIKTSSON J A, SVEINSSON J R. Random forests for land cover classification[J]. Pattern Recognition Letters, 2006, 27(4):294-300.
[15] LEUNG T, MALIK J. Representing and recognizing the visual appearance of materials using three-dimensional textons[J]. International Journal of Computer Vision, 2001, 43(1):29-44.
[16] CHAN J C W, PAELINCKX D. Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery[J]. Remote Sensing of Environment, 2008, 112(6):2999-3011.
[17] CUTLER D R, STEVENS J. Random forests for classification in ecology[J]. Ecology, 2007,88(11):2783-2792.
[18] LEI Z, FANG T, LI D. Land cover classification for remote sensing imagery using conditional texton forest with historical land cover map[J]. Geoscience and Remote Sensing Letters,IEEE,2011(99):720-724.
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