中文版 | English
题名

管道漏磁检测缺陷信号的智能提取方法研究

姓名
姓名拼音
YANG Jie
学号
11930329
学位类型
硕士
学位专业
0801 力学
学科门类/专业学位类别
08 工学
导师
赵春田
导师单位
前沿与交叉科学研究院
论文答辩日期
2022-05-09
论文提交日期
2022-06-14
学位授予单位
南方科技大学
学位授予地点
深圳
摘要

  漏磁检测技术被广泛应用于铁磁构件中缺陷的检测和量化评估。其对缺陷形状、尺寸的三维反演成像、量化能力,尤其是对常见的自然(腐蚀、疲劳)复杂缺陷的三维成像能力,是漏磁检测技术水平的核心标志之一。由于实际缺陷具有形状复杂、不规则和多重缺陷复合的特点,致使其漏磁检测信号彼此影响。为提升复杂缺陷的成像精度和数据处理速度,从漏磁信号里自动分解并剥离出对应缺陷的信号是很重要的前置工作。

  本文提出三种漏磁检测缺陷信号的智能提取算法,用于自动识别并提取缺陷的漏磁信号。第一种方法是基于相似波形的小波分解提取法,该算法基于漏磁检测典型缺陷信号的形态特征,匹配与其波形相似的小波基,用选取的小波基对单通道漏磁信号进行多尺度分解,得到小波高频系数,然后建立了小波系数特征与缺陷个数、位置的对应关系,据此实现任意单通道缺陷信号的提取,最后通过判定缺陷信号相邻通道的连通性,对连通区域内的缺陷信号集成,实现对二维缺陷信号的提取;第二种方法是基于图像边缘检测的缺陷提取法,该算法将漏磁数字信号图像化,利用图像处理算法对其进行预处理,包括去噪、增强及二值化,然后对预处理后得到的二值漏磁图像进行边缘检测,获取每个缺陷区域的轮廓及其位置信息,最后通过对图像中缺陷位置信息的归一化处理,实现从原漏磁信号中提取缺陷信号;第三种方法是基于EMD(经验模态分解)算法的缺陷提取法,该算法基于单通道漏磁检测信号的本征波动模态,对其进行分解,得到本征模态分量IMF,选取其中的一阶分量IMF1分析缺陷的个数及位置,根据IMF1的幅值特征实现缺陷信号的拾取,然后根据缺陷信号的连通性判定,实现对二维缺陷漏磁信号的提取。

  最后,本文对上述三种方法进行了缺陷提取结果验证,分析比较了每种方法的优缺点。结果表明,第一种方法对二维漏磁缺陷信号的识别提取准确率为98%,其能够实现对密集缺陷信号的单独剥离提取;第二种方法的准确率为92%,其能够实现缺陷区域的初步提取;第三种方法的准确率为99%,进一步提高了缺陷检测的精度。上述三种算法都能够有效识别并提取漏磁检测缺陷信号,在一定程度上提高了缺陷检测的准确率和速度,并能实现复杂密集型缺陷的单独提取,为缺陷的三维反演成像及量化评定奠定了基础,在实际管道检测工程中有广泛的应用前景

关键词
语种
中文
培养类别
独立培养
入学年份
2019
学位授予年份
2022-06
参考文献列表

[1] 王洪博.复合材料构件的超声无损检测关键技术研究[D].北京:北京理工大学,2014.
[2] 甘文成.转向架金属橡胶件粘接状态超声无损检测方法研究[D].成都:西南交通大学,2019.
[3] LAVRENTYEV A I, ROKHLIN S I . An ultrasonic method for determination of elastic moduli, density, attenuation and thickness of a polymer coating on a stiff plate[J]. Ultrasonics, 2001, 39(3):211-221.
[4] BRUNE, KAI, et al. Surface analytical approaches contributing to quality assurance during manufacture of functional interfaces[J]. Applied Adhesion Science, 2015(03): 1-17.
[5] 戴光,吴忠义,朱祥军,等.管道内外壁缺陷的漏磁检测[J].无损检测2018,40(03):19-23+28.
[6] 杨理践,耿浩,高松巍.长输油气管道漏磁内检测技术[J].仪器仪表学报,2016,37(8):1736-1746.
[7] 孟祥吉,宋兵臣,刘健,等.管道部件及典型缺陷漏磁内检测图像化显示研究[J].管道技术与设备,2021(01):26-32.
[8] LI HM, ZHANG FC, YANG B, et al. Distribution characteristics of calculated magnetic charges around discontinuous structures in magnetic memory testing[J]. International Journal of Applied Electromagnetics and Mechanics, 2019(59):1321-1329.
[9] LI HM, HUANG RR, ZHAO CT, et al. 3D reconstructing of arbitrary defects with magnetic flux leakage testing signals[J]. 2020 IEEE Far East NDT New Technology and Application Forum( FENDT), 2020:51-55.
[10] 王婷婷.金属表面缺陷特征智能提取及特征分析的方法研究[D].东北:东北大学,2017.
[11] LI M, LI X, GAO CX, et al. Acoustic microscopy signal processing method for detecting near-surface defects in metal materials[J]. NDT and E International, 2019(103):133-140.
[12] MALLET S G. Multiresolution Approximations and Wavelet Orthonormal Bases of L2(R)[J]. Transactions of the American Mathematical Society,1989,315(1):69-87.
[13] 徐柳.EMD算法发展及应用综述[J].长江信息通信,2022,35(01):61-64.
[14] CAO J, LI Z, LI J. Financial time series forecasting model based on CEEMDAN and LSTM[J]. Physica A: Statistical Mechanics and its Applications. 2019(519):127-139.
[15] 夏希林.图像滤波去噪及边缘检测技术研究与实验分析[D].吉林.吉林大学.2021.
[16] 迟慧智,田宇.图像边缘检测算法的分析与研究[J].电子产品可靠性与环境试验,2021,39(04):92-97.
[17] 王玉凡.基于Canny边缘检测和Harris角点检测的图像拼接方法[J].内蒙古科技与经济,2019(17):90-91.
[18] HE JZ, ZHANG SL, YANG M, et al. Bi-directional cascade network for perceptual edge detection[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019:3823-3832.
[19] EDWARDS C, PALMER S B. The prod magnetization method of magnetic particle inspection[J]. British Journal of Non-Destructive Testing, 1983, 25(06):305-308.
[20] HWANG J, LORD W. Finite element analysis of the magnetic field distribution inside a rotating ferromagnetic bar[J]. IEEE Transactions on Magnetics, 1974,10(04):1113-1118.
[21] SHCHERBININ V E, ZATSEPIN N N. Calculation of the magnetostatic field of surface defects. I. Field topography of defect models[J]. Defectoscopy,1966, (05):385-393.
[22] SHCHHERBININ V E, ZATSEPIN N N. Calculation of the magnetostatic field of surface defects. IⅡ. Experimental verification of the principal theoretical relationships[J]. Defectoscopy, 1966, (05):394-399.
[23] EDWARDS C, Palmer S B. The magnetic leakage field of surface-breaking cracks[J]. Journal of Physics D: Applied Physics, 1986, 19(04):657-673.
[24] ATHERTON D L. Finite element calculations and computer measurements of magnetic flux leakage patternsfor pits[J]. British Journal of Non-Destructive Testing, 1988,30(03):159-162.
[25] Ong J K, Kerr D, BOUAZZA-MAROUF K. Design of a semi-autonomous modular robotic vehicle for gas pipeline inspection[J]. Proceedings of the Institution of Mechanical Engineers Part l-Journal of Systems and Control Engineering, 2003, 217(12):109-122.
[26] CSLEYO F, HALLEN J M, Gonzalez J L. Pipeline inspection-1-reliability-based method assesses corroding pipelines[J]. 0il & Gas Journal, 2003, 101(01): 54-58.
[27] ANON. Pipeline inspection failure blamed for oil spil[J]. Pipeline&Gas Journal, 2002, 229(09):2-5.
[28] MOHAMED L, HAMDI M S, TAHAR S. Detection and Sizing of Metal-loss Defects in Oil and Gas Pipelines Using Pattern-adapted Waveiets and Machine Learning[M] [S.1.]: Elsevier Science Publishers B.V., 2017:19-31.
[29] LDRD W. Residual and Active Leakage Fields around Defects in Ferromagnetic Materials[J]. Materail Evalution, 1978:36.
[30] DANIEL J, MOHANAGAYATHRIAND R, ABUDHAHIR A. Characterization of Defects in Magnetic Flux Leakage(MFL)Images Using Wavelet Transform and Neural Network[C]. Elec-tronics and Communication Systems(ICECS),v2014 International Conference on. IEEE, 2014:1-5.
[31] NARAT, FUJIEDA M, GOTOH Y. Non-destructive Inspection of Ferromagnetic Pipes based on the Discrete Fourier Coefficients of Magnetic Flux Leakage[J]. Journal of Applied Physics, 2014, 115(17):17-509.
[32] 刘志平,康宜华,武新军,等.储罐底板漏磁检测传感器设计[J].无损检测,2004,26(12):612-615.
[33] 李路明,黄松龄,李振星,等.铸铁件的漏磁检测方法[J].清华大学学报,2002,42(4):474-476.
[34] 黄松龄.管道磁化的有限元优化设计[J].清华大学学报,2000,40(02):67-69.
[35] 李路明,黄松龄,施克仁.漏磁检测的交直流磁化问题[J].清华大学学报,2002,42(2):154-156.
[36] 杨理践,马凤铭,高松巍.管道漏磁在线检测系统的研究[J].仪器仪表学报,2004(S1):1052-1054.
[37] 杨理践,李松松,王玉梅,等.小波包在管道漏磁信号分析中的应用[J].仪器仪表学报,2002(S2):484-485+491.
[38] 杨理践,马凤铭,高松巍.基于神经网络及数据融合的管道缺陷定量识别[J].无损检测,2006(06):281-284.
[39] 王晓红,吴德会,李雪松,等.小型磁化器条件下的变励磁MFL检测新方法[J].仪器仪表学报,2015,36(1):70-77.
[40] 周燕,董怀荣,周志刚,谢慧.油气管道内检测技术的发展[J].石油机械,2011,39(03):74-77.
[41] 王丹丹,林晓,骆秀媛,等.海底管道两轮漏磁内检测数据的比对方法[J].船海工程,2016,45(3):122-126.
[42] 张博.基于小波去噪的BP神经网络在变形监测中的应用[J].北京测绘,2021,35(12):1592-1596.
[43] 墙梓薇.基于贝叶斯方法的铁路站房结构损伤识别研究[D].大连:大连理工大学,2021.
[44] 王长龙,梁四洋,左宪章,等.漏磁检测的研究现状及发展[J].军械工程学院学报,2007,19(04):13-16.
[45] SHI T, KONG J, WANG X, et al. Improved sobel algorithm for defect detection of rail surfaces with enhanced efficiency and accuracy[J]. Journal of Central South University, 2016, 23(11):2867-2875.
[46] SI X, FENG J, ZHOU J, et al. Detection and rectification of distorted fingerprints[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3):555-568.
[47] GU S, FENG J, LU J, et al. Efficient rectification of distorted fingerprints[J]. IEEE Transactions on Information Forensics and Security, 2018, 13(1):156-169.
[48] DUAN T D, DUC D A,DU T L H. Combining Hough transform and contour algorithm for detecting vehicles' license-plates[C]. International Symposium on Intelligent Multimedia, Video and Speech Processing, 2005:747-750.
[49] 张莉,焦宇倩,续婷,等.基于CGLCM和GA-SVM的混凝土图像分类方法[J].中北大学学报(自然科学版),2022,43(01):84-90.
[50] HARALICK R. Textural features for image classification[J]. Studies in Media and Communication, 1973, 3(6):610-621.
[51] WANG Y, XIA H, YUAN X, et al. Distributed defect recognition on steel surfaces using an improved random forest algorithm with optimal multi-feature-set fusion [J]. Multimedia Tools and Applications, 2017, 77(13):16741-16770.
[52] XIE S, SHAN S, CHEN X, et al. Fusing local patterns of Gabor magnitude and phase for face recognition[J]. IEEE Transactions on Image Processing, 2010, 19(5):1349-1361.
[53] CHARLS K. Chui, An Introduction to Wavelets[M]. American: Academic Press, 1995:109-229.
[54] JEON Y J, YUN J P, CHOI D C. Defect detection algorithm for corner cracks in steel billet using discrete wavelet transform[J]. 2009 ICROS-SICE International Joint Conference, 2009:2769-2773.
[55] SONG K C, HU S P, YAN Y H. Automatic recognition of surface defects on hot-rolled steel strip using scattering convolution network[J]. Journal of Computational Information Systems, 2014, 10(7) :3049-3055.
[56] XUAN B, XIE Q W, PENG S L. EMD sifting based on bandwidth[J]. IEEE Signal Processing Letters 2007, 14(8): 537-540.
[57] WU ZH, HUANG Norden E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2004, 460(2046):1597-1611
[58] CROSS G R, JAIN A K. Markov random field texture models[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983, 5(1): 25-39.
[59] GAYUBO F, GONZ LEZ J L, DE LA FUENTE E, et al. On-line machine vision system for detect split defects in sheet-metal forming processes[C]. 18th International Conference on Pattern Recognition, 2006: 723-726.
[60] ZHANG X W, DING Y Q, DUAN D Q, et al. Surface defects inspection of copper strips based on vision bionics[J].Journal of Image and Graphics, 2011, 16(4): 593-599.
[61] 徐科,宋敏,杨朝霖,等.隐马尔可夫树模型在带钢表面缺陷在线检测中的应用[J].机械工程学报,2013,49(22):34-40.
[62] YAZDCHI M, YAZDI M, MAHYARI A G. Steel surface defect detection using texture segmentation based on multifractal dimension[C]. International Conference on Digital Image Processing, 2009:346-350.
[63] ZHIZNYAKOV A L, PRIVEZENTSEV D G, ZAKHAROV A A. Using fractal features of digital images for the detection of surface defects[J]. Pattern Recognition and Image Analysis, 2015, 25(1):122-131.
[64] SHI B K, QIAO P Z. A new surface fractal dimension for displacement mode shape-based damage identification of plate-type structures[J]. Mechanical Systems and Signal Processing, 2018(103):139-161.
[65] ONG Q, OSKOUI E A, TAYLOR T. Visual saliency-based image binarization approach for detection of surface microcracks by distributed optical fiber sensors[J]. Structural Health Monitoring-an International Journal, 2019, 18(5-6):1590-1601.
[66] ZHOU S, WU S, LIU H, et al. Double low-rank and sparse decomposition for surface defect segmentation of steel sheet[J]. Applied Sciences, 2018, 8(9):1628-1643.
[67] SUSAN S, SHARMA M. Automatic texture defect detection using Gaussian mixture entropy modeling[J]. Neurocomputing, 2017, 239:232-237.
[68] WANG J Z, LI Q Y, GAN J R, et al. Surface defect detection via entity sparsity pursuit with intrinsic priors[J]. IEEE Transactions on Industrial Informatics, 2019:1-10.
[69] MALLET S.G. A wavelet tour of signal processing [M]. Printed in the United States of America, 1998:120-125
[70] 胡昌化,李国华,刘涛,等.基于Matlab 6.x 的系统分析与设计—小波分析[M].西安:西安电子科技大学出版社,2004.
[71] 彭海.皮尔逊相关系数应用于医学信号相关度测量[J].电子世界,2017(07):163.
[72] 陈世群,高伟,陈孝琪,等.一种基于极限学习机和皮尔逊相关系数的光伏阵列故障快速诊断方法[J].电气技术,2021,22(10):57-64.
[73] 殷宇殿.基于数学形态学的管道缺陷特征提取方法研究[D].东北:东北大学,2012.
[74] 刘喆.基于图像分析的管道缺陷特征提取方法研究[D].东北:东北大学,2014.

所在学位评定分委会
力学与航空航天工程系
国内图书分类号
O39
来源库
人工提交
成果类型学位论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/335760
专题工学院_海洋科学与工程系
推荐引用方式
GB/T 7714
杨杰. 管道漏磁检测缺陷信号的智能提取方法研究[D]. 深圳. 南方科技大学,2022.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可 操作
11930329-杨杰-海洋科学与工程系(4748KB)----限制开放--请求全文
个性服务
原文链接
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
导出为Excel格式
导出为Csv格式
Altmetrics Score
谷歌学术
谷歌学术中相似的文章
[杨杰]的文章
百度学术
百度学术中相似的文章
[杨杰]的文章
必应学术
必应学术中相似的文章
[杨杰]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
[发表评论/异议/意见]
暂无评论

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。