基于贝叶斯估计的漏磁缺陷轮廓重构方法研究

doi:10.3969/j.issn.1000-1093.2012.01.019

兵工学报 ›› 2012, Vol. 33 ›› Issue (1): 116-120.doi: 10.3969/j.issn.1000-1093.2012.01.019

基于贝叶斯估计的漏磁缺陷轮廓重构方法研究

苑希超，王长龙，王建斌

（军械工程学院电气工程系，河北石家庄 050003）

收稿日期:2010-11-18 修回日期:2010-11-18 上线日期:2014-03-04
作者简介:苑希超（1985—），男，硕士研究生
基金资助:
河北省自然科学基金项目(E2008001258); 军内科研项目

Defect Profile Reconstruction from Magnetic Flux Leakage Signals Based on Bayesian Estimation

YUAN Xi-chao, WANG Chang-long, WANG Jian-bin

(Department of Electrical Engineering,Ordnance Engineering College, Shijiazhuang 050003, Hebei, China)

Received:2010-11-18 Revised:2010-11-18 Online:2014-03-04

摘要/Abstract

摘要： 漏磁缺陷轮廓重构是指由检测到的漏磁信号重构缺陷轮廓及参数，是实现漏磁反演的关键。目前常用的反演方法包括神经网络法和优化法，但神经网络法的计算精度受噪声影响严重，优化法计算量大。针对这些问题，提出基于递推贝叶斯估计的漏磁缺陷重构算法。建立缺陷轮廓与漏磁信号的状态空间模型，将反演问题描述为基于状态和观测方程的典型的离散时间跟踪问题，对漏磁信号进行了反演，并在不同信噪比下对神经网络法和所提方法进行了反演效果的比较。结果表明：基于递推贝叶斯估计方法的漏磁信号反演算法精度高，同时对噪声具有鲁棒性，是一种有效可行的漏磁反演新方法。

关键词: 电磁学, 漏磁检测, 轮廓重构, 粒子滤波, 重采样

Abstract: The reconstruction of magnetic flux leakage (MFL) defect profiles means the reconstruction of defect profiles and parameters from MFL inspection signals. It is the key for the inversion of MFL inspection signals. The studies of MFL inversion problem mainly based on neural network and optimization method. But these two methods have certain shortages. The precision of neural networks may be influenced by noises, and the optimization method is computational demanding. To overcome these shortages, a reconstruction approach for solving such inversion problems based on Bayesian estimation method is proposed. It formulates the inversion problem as a classical discrete-time tracking problem with state and measurement equations. State-space model of defect profile and MFL signals is established, the proposed method is adapted to reconstruct defect profile and the comparison between neural network and proposed method under different SNR. Results indicate that the proposed method has high accuracy and robustness against noise, and it is an effective and feasible approach for solving inverse problems.

Key words: electromagnetic, magnetic flux leakage inspection, profile reconstruction, particle filter, resampling

中图分类号:

TG115.28

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, 33(1): 116-120.

YUAN Xi-chao, WANG Chang-long, WANG Jian-bin. Defect Profile Reconstruction from Magnetic Flux Leakage Signals Based on Bayesian Estimation[J]. Acta Armamentarii, 2012, 33(1): 116-120.

参考文献

［1］ Snarskii A A, Zhenirovskyy M, Meinert D, et al. An integral equation model for the magnetic flux leakage method[J]. NDT & E International, 2010, 43: 343-347.
[2] Ji Fengzhu, Wang Changlong, Zuo Xianzhang, et al. LS-SVM- based reconstruction of 3-D defect profile from magnetic flux leakage signals[J]. Insight, 2007, 49(9): 516-520.
[3] Ravan M, Amineh R K, Koziel S, et al. Sizing of 3-D arbitrary defects using magnetic flux leakage measurements[J]. IEEE Transactions on Magnetics, 2010, 46(4):1024-1033.
[4] Amineh R K, Koziel S, Nikolova N K, et al. A space mapping methodology for defect characterization from magnetic flux leakage measurements[J]. IEEE Transactions on Magnetics, 2008, 44(8):2058-2065.
[5] 刘美全, 徐章遂, 王建斌. 基于磁偶极子能级分布的缺陷反演成像[J]. 中国机械工程, 2005,16(11): 952-955.
LIU Mei-quan, XU Zhang-sui, WANG Jian-bin. Inverse imaging for defects based on energy level of the magnetic dipole[J]. China Mechanical Engineering, 2005, 16(11):952-955. (in Chinese)
[6] Carvalhoa A A, Rebelloa J M A, Sagrilo L V S. MFL signals and artificial neural networks applied to detection and classification of pipe weld defects[J]. NDT & E International, 2006, 39:661-667.
[7] 纪凤珠, 王长龙, 王瑾, 等. 基于稀疏化LS-SVM的漏磁缺陷三维轮廓重构[J]. 兵工学报,2008,29(5):592-595.
JI Feng-zhu, WANG Chang-long, WANG Jin, et al. 3-D defect profile reconstruction from magnetic flux leakage signals based on sparsity LS-SVM [J]. ACTA Armamentarii, 2008, 29(5): 592-595. (in Chinese)
[8] 韩文花, 阙沛文, 梁巍. 改进的遗传局部搜索算法在漏磁逆问题中的应用研究[J]. 上海交通大学学报,2007,41(5):751-754.
HAN Wen-hua, QUE Pei-wen, LIANG Wei. The application of an improved genetic local search algorithm to MFL inverse problem[J]. Journal of Shanghai Jiao Tong University, 2007, 41(5): 751-754. (in Chinese)
[9] Djuric P M, Kotecha J H, ZHANG Jianqui, et al. Particle filtering [J]. IEEE Signal Processing, 2003: 19-38.
[10] Arulampalam S, Maskell S, Gordon N, et al. A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking [J]. IEEE Trans. Signal Process, 2002,(50): 174-189.
[11] 王来雄, 黄士坦. 一种新的粒子滤波算法[J]. 武汉大学学报(工学版), 2006,39(1):118-120.
WANG Lai-xiong, HUANG Shi-tan. A novel particle filter algorithm[J]. Engineering Journal of Wuhan University, 2006, 39(1): 118-120. (in Chinese)
[12] 胡昭华, 宋耀良. 一种用于运动跟踪的加窗粒子滤波新算法研究[J]. 南京理工大学学报, 2007,31(3):337-341.
HU Zhao-hua, SONG Yao-liang. Motjion tracking based on novel winding particle filter[J]. Journal of Nanjing University of Science and Technology, 2007,31(3): 337-341. (in Chinese)

[1]	刘望生, 潘海鹏, 王明环. 多特征下室内声源定位的复合模型粒子滤波[J]. 兵工学报, 2024, 45(3): 975-985.
[2]	徐熙毅，谭鸽伟，李彪. 基于空变分离的两步聚焦双基曲线合成孔径雷达成像[J]. 兵工学报, 2022, 43(6): 1365-1375.
[3]	郭永红，宋彪，赵东阳，李旭光，南怀良. 基于极限学习机模型的粒子滤波无设备定位方法研究[J]. 兵工学报, 2019, 40(8): 1740-1746.
[4]	闫理跃, 王厚军, 刘震. 一种结合噪声辅助技术和现场数据的模拟电路实时可靠度预测方法[J]. 兵工学报, 2019, 40(2): 326-333.
[5]	贾文抖，林春生，孙玉绘，翟国君. 基于单个磁梯度计的磁目标定位方法研究[J]. 兵工学报, 2017, 38(8): 1572-1577.
[6]	朱甦，薄煜明，何亮. 基于冗余字典的多特征压缩感知目标跟踪算法[J]. 兵工学报, 2017, 38(6): 1140-1146.
[7]	陈海龙，王长龙，左宪章，朱红运. 基于磁记忆梯度张量信号的缺陷二维反演研究[J]. 兵工学报, 2017, 38(5): 995-1001.
[8]	姜润翔，张伽伟，林春生. 基于点电荷模型的腐蚀相关静电场快速预测方法研究[J]. 兵工学报, 2017, 38(4): 735-743.
[9]	郭成豹，周炜昶. 舰船消磁绕组磁特征数值计算与验证研究[J]. 兵工学报, 2017, 38(10): 1988-1994.
[10]	李子高，李淑秋，闻疏琳. 一种基于独立成分分析的水下宽带信号波达方位估计方法[J]. 兵工学报, 2017, 38(1): 114-122.
[11]	唐波，何闻，贾叔仕. 基于等效磁化强度法的一种电磁式角振动台气隙磁场分析方法[J]. 兵工学报, 2017, 38(1): 202-208.
[12]	朱红运，王长龙，王建斌，江涛. 一种消除材料属性对脉冲涡流缺陷轮廓重构影响的方法[J]. 兵工学报, 2015, 36(9): 1766-1771.
[13]	朱明强，侯建军，刘颖，李旭，田洪娟. 适于无线传感网络目标追踪的一种改进无迹粒子滤波时延差估计算法[J]. 兵工学报, 2015, 36(7): 1266-1272.
[14]	石剑，刘忠乐，文无敌. 海水中带电极电缆空中磁场的实场测量研究[J]. 兵工学报, 2015, 36(7): 1295-1301.
[15]	刘忠乐，石剑，文无敌. 磁诱饵空中磁场验证的测量区域研究[J]. 兵工学报, 2015, 36(6): 1046-1051.

基于贝叶斯估计的漏磁缺陷轮廓重构方法研究

Defect Profile Reconstruction from Magnetic Flux Leakage Signals Based on Bayesian Estimation

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价