脉冲涡流缺陷分类识别技术研究
|
摘要
脉冲涡流是近几年迅速发展起来的一种无损检测新技术,其宽频谱激励方式在大面积复杂结构的检测中可获得较多的缺陷信息。本文在分析了国内外脉冲涡流无损检测技术研究现状的基础上,对脉冲涡流无损检测系统的研制、检测信号去噪及特征提取以及缺陷的分类识别等若干关键技术进行了研究,这对脉冲涡流检测技术的发展具有重要的学术价值和实际意义。论文研究内容得到广东省科技攻关计划项目(2006B12401001)资助,论文主要研究成果包括:
(1)系统设计。设计了整个脉冲涡流缺陷检测系统,主要包括数据采集与存储系统,并对检测系统中的关键模块:脉冲涡流信号产生模块、信号调理模块及检测探头进行研制。本系统采用DDS技术,在FPGA芯片上设计脉冲涡流激励信号,降低了系统设计成本的同时提高了设计灵活性。
(2)针对脉冲涡流检测信号噪声污染较严重的情况,分析比较了不同小波系的优缺点,采用Morlet小波对检测信号进行去噪,并采用信噪比、均方根误差及平滑度指标对去噪效果进行评价。针对传统时域波形特征提取容易受到噪声干扰的问题,采用Daubech -ies小波对去噪后的信号进行3层小波分解,并计算各层小波信号的能量值作为脉冲涡流信号的特征值,相对于传统的信号处理方法,可大大减少运算量。
(3)分析蚁群算法和遗传算法的优缺点,结合其在模式识别中的应用,创新性地把蚁群算法和遗传算法应用于脉冲涡流表面缺陷和亚表面缺陷的识别。对比了单独采用蚁群算法和遗传算法进行缺陷分类识别的优点及不足,提出两种算法相结合的蚁群-遗传算法,实验证明,这种算法的分类识别能力优于采用单一的算法。
Pulsed Eddy Current(PEC) nondestructive testing(NDT) is a new rapidly developing NDT technologies. Due to its broadband spectrum Excitation, it can get more defects information in large-scale testing of complex structures. Based on the current situation of Pulsed Eddy Current NDT technique both abroad and at home, the progress in the research of Nondestructive Test System, test signal denoising and feature extraction,and recognition of different defects are presented. The research work is funded under the research contract with the Guangdong government (2006B12401001) and is important to PEC inspection not only in theory but also in practice.The main research work is described as follows:
(1) System design. The paper built the entire system PEC flaw detection including data collection, storage systems, and the key modules of detection system, such as PEC signal generation module, signal conditioning module and development of detection probes. Based on DDS technology and FPGA chip, the PEC excitation signal module can reduce design costs and improve design flexibility.
(2) Addressing on the situation that PEC testing signals are disturbed seriously by environment noise pollution,comparing the advantages and disadvantages of different wavelets, this paper used Morlet wavelet to denoise the detection signals, and used signal to noise ratio, root mean square error and smoothness indicators to evaluate the denoising effect. As Traditional time-domain waveform feature extraction is susceptible to noise interference, the paper chose Daubechies wavelet on layer 3 to decompesite denoised signals, calculated the energy of each level of PEC wavelet signal and set the energy value as characteristic value. Compared with traditional signal compression, the progress can significantly reduce the computation.
(3)Based on the analysis of advantages and disadvantages in ant colony algorithm and genetic algorithm, combining with its application to pattern recognition, the paper innovatively presented using ant colony algorithm and genetic algorithm in PEC surface defects and sub-surface defects recognition. Compared separately using ant colony algorithm or genetic algorithm in defects classification and recognition, the paper proposed the combination of the two algorithms--ant colony - the genetic algorithm. Experiments showed that this algorithm is better than both ant colony algorithm and genetic algorithm.
(1)系统设计。设计了整个脉冲涡流缺陷检测系统,主要包括数据采集与存储系统,并对检测系统中的关键模块:脉冲涡流信号产生模块、信号调理模块及检测探头进行研制。本系统采用DDS技术,在FPGA芯片上设计脉冲涡流激励信号,降低了系统设计成本的同时提高了设计灵活性。
(2)针对脉冲涡流检测信号噪声污染较严重的情况,分析比较了不同小波系的优缺点,采用Morlet小波对检测信号进行去噪,并采用信噪比、均方根误差及平滑度指标对去噪效果进行评价。针对传统时域波形特征提取容易受到噪声干扰的问题,采用Daubech -ies小波对去噪后的信号进行3层小波分解,并计算各层小波信号的能量值作为脉冲涡流信号的特征值,相对于传统的信号处理方法,可大大减少运算量。
(3)分析蚁群算法和遗传算法的优缺点,结合其在模式识别中的应用,创新性地把蚁群算法和遗传算法应用于脉冲涡流表面缺陷和亚表面缺陷的识别。对比了单独采用蚁群算法和遗传算法进行缺陷分类识别的优点及不足,提出两种算法相结合的蚁群-遗传算法,实验证明,这种算法的分类识别能力优于采用单一的算法。
Pulsed Eddy Current(PEC) nondestructive testing(NDT) is a new rapidly developing NDT technologies. Due to its broadband spectrum Excitation, it can get more defects information in large-scale testing of complex structures. Based on the current situation of Pulsed Eddy Current NDT technique both abroad and at home, the progress in the research of Nondestructive Test System, test signal denoising and feature extraction,and recognition of different defects are presented. The research work is funded under the research contract with the Guangdong government (2006B12401001) and is important to PEC inspection not only in theory but also in practice.The main research work is described as follows:
(1) System design. The paper built the entire system PEC flaw detection including data collection, storage systems, and the key modules of detection system, such as PEC signal generation module, signal conditioning module and development of detection probes. Based on DDS technology and FPGA chip, the PEC excitation signal module can reduce design costs and improve design flexibility.
(2) Addressing on the situation that PEC testing signals are disturbed seriously by environment noise pollution,comparing the advantages and disadvantages of different wavelets, this paper used Morlet wavelet to denoise the detection signals, and used signal to noise ratio, root mean square error and smoothness indicators to evaluate the denoising effect. As Traditional time-domain waveform feature extraction is susceptible to noise interference, the paper chose Daubechies wavelet on layer 3 to decompesite denoised signals, calculated the energy of each level of PEC wavelet signal and set the energy value as characteristic value. Compared with traditional signal compression, the progress can significantly reduce the computation.
(3)Based on the analysis of advantages and disadvantages in ant colony algorithm and genetic algorithm, combining with its application to pattern recognition, the paper innovatively presented using ant colony algorithm and genetic algorithm in PEC surface defects and sub-surface defects recognition. Compared separately using ant colony algorithm or genetic algorithm in defects classification and recognition, the paper proposed the combination of the two algorithms--ant colony - the genetic algorithm. Experiments showed that this algorithm is better than both ant colony algorithm and genetic algorithm.
引文
[1] Z Mottl. The quantitative relations between true and standard depth of penetration for air-cored probe coils in eddy current testing. NDT Internation, 1990, 23(2): 11-18.
[2] D Hagemaier. Eddy current depth of penetration[J]. Materials Evaluation, 2004, 62(10): 1028-1029.
[3] S Sullivan, D L Atherton, T R Schmidt. Comparing a one-dimensional skin effect equation with through transmission eddy current phenomena. British journal of NDT, 1990, 32(2): 71-75.
[4] V Sundararaghavan, K Balasubramaniam, N R Babu, N Rajesh. A multi-frequency eddy current inversion method for characterizing conductivity gradients on water jet peened components[J]. NDT and E International, 2005, 38(7): 541-547.
[5]冯婷婷,罗飞路,何赟泽.双频涡流检测中微弱信号的二维信息提取[J].中国测试技术, 2008, 34(4): 59-62
[6]程婷婷,宋文爱,陈以方,张世雄.基于LabVIEW的燃料元件远场涡流式在线检测系统研究[J]. .核动力工程, 2009, 30(1): 90-94
[7]周德强,张斌强,田贵云,王海涛.脉冲涡流检测中裂纹的深度定量及分类识别[J].仪器仪表学报, 2009(6): 78-80.
[8]周德强,田贵云,王海涛,姚恩涛.小波变换在脉冲涡流检测信号中的应用[J].传感器与微系统, 2008(10): 29-31.
[9]张斌强,田贵云,王海涛,王平.脉冲涡流检测技术的研究[J].无损检测, 2008, (10): 241-243.
[10]徐晨曦,阙沛文,毛义梅.脉冲涡流检测系统的设计[J].传感器与微系统, 2009, (6): 89-91.
[11]何赟泽,罗飞路,胡祥超,刘波,高军哲.脉冲涡流矩形传感器的多维信号特征分析与缺陷识别[J].传感技术学报, 2009, (5): 65-67.
[12] LI Y, TTIAN G Y. Fast analytical method for pulsededdy current evaluation[C]. Proceeding of 45th AnnualBritish Conference on NDT 2006,UK:Stratford-upon-Avon, 2006.
[13]郑岗,赵亮.金属厚度的脉冲涡流无损检测研究[J].传感器与微系统, 2006(4): 281-284.
[14] Giguere S. Pulsed eddy current technology: Characterizing material loss with gap and lift-off variations[J]. Research in Nondestructive Evaluation, 2001, 13(3): 119-129.
[15]杨宾峰.脉冲涡流无损检测若干关键技术研究[D].国防科学技术大学, 2006.
[16] Zhang Y H, Sun H X, Luo F L. 3D magnetic field responses to a defect using a tangential driver-coil for pulsed eddy current testing[C]. 17th World Conference Nondestructive Testing, Shanghai, China. 2008.
[17] Yang BinFeng, Luo FeiLu, Han Dan. Research on edge identification of a defect using pulsed eddy current based on pricipal component analysis[J]. NDT&E Internaltional, 2007, 40: 294-299.
[18]张玉华,孙慧贤,罗飞路,杨宾峰.脉冲涡流检测中三维磁场量的特征分析与缺陷定量评估[J].传感技术学报, 2008(5): 259-263 .
[19] HE Y Z, LUO F L, PAN M C. Defect classification based on rectangular pulsed eddy current sensor in dif-ferent directions[J] . Sensors&Actuators:A. Physica, 2010, 157: 26-31.
[20] Auld B A, Moulder J C. Review of advances in quantitative eddy current nondestructive evaluation[J]. JOURNAL OF NONDESTRUCTIVE EVALUATION, 1999, 18(1): 3-36
[21]刘清洪,黄松岭,陆文娟.大口径管道远场涡流缺陷检测仿真研究[J].无损检测, 2008, 30(4): 241- 245.
[22]徐小杰.铁磁性管道中轴向裂纹的远场涡流检测技术研究[D].国防科学技术大学, 2007.
[23] M Muck, M Korn, C Welzel, S Grawunder, F Scholz. Nondestructive evaluation of various materialsusing a SQUID-based eddy-current system[J]. IEEE Transactions on Applied Superconductivity, 2005, 15(2): 733-738
[24] C Carr, M A Espy, T G Abeln, J Kraus, H Robert. Characterization of stainless steel inertia welds using HTS SQUID NDE[J]. IEEE Transactions on Applied Superconductivity,2005,15(2): 719-722.
[25] Krause H J, Panaitov G I, Zhang Y. Appearance of sign reversal in geophysical transient electromagnetics with a SQUID due to stacking[J]. IEEE Transactions On Applied Superconductivity, 2007, 15(2): 745-748.
[26] Krause H J, Panaitov G I, Wolters N. Detection of magnetic contaminations in industrial products using HTS SQUIDs[J]. IEEE Transactions On Applied Superconductivity, 2005, 15(2): 729-732.
[27]白世武,丁胜红.直流超导量子干涉器无损检测的原理与应用[J].无损检测, 2006, 28(5): 242-246.
[28]何东风,立木实,系崎秀夫.手持移动型高温超导SQUID在无损检测中的应用[J].低温与超导, 2008, 36(12): 44-47.
[29] R A Smith, G R Hugo. Deep Corrosion and Crack Detection in Aging Aircraft using Transient Eddy-current NDE[J]. Aging Aircraft, 2001, 78(23):96-101.
[30] J C Moulder, Sk Shaligram, JA Bieberm. Pulsed Eddy Current Inspections[J]. Foundation, Inc.2000.
[31] W D Rummel, J R Bowler. Integrated Quantitative Nondestructive Evaluation(NDE) and Reliability Assessment of Aging Aircraft Structures[J]. Final Report for The United States Air Force Office of Scientific Research 27, 2001.
[32] B Lebrun, Y Jayet, J C Baboux. Pulsed eddy current signal analysis: application to the experimental detection and characterization of deep flaws in highly conductive materials[J]. NDT & E International, 1997, 30: 163-170.
[33] Giguere S, Lepine B A, Dubois J M S. Pulsed eddy current technology: Characterizing material loss with gap and lift-off variations[J]. RESEARCH IN NONDESTRUCTIVE EVALUATION. 2001, 13(3): 119-121.
[34] R A Smith, G R Hugo. Transient eddy current NDE for aging aircraft-capabilities and limitations[J]. Insight, 2001, 43(1): 14-25.
[35] Yang G, Tian GY, Que PW. Independent Component Analysis-Based Feature Extraction Technique for Defect Classification Applied for Pulsed Eddy Current NDE[J]. Research In Nondestructive Evaluation, 2009, 20(4): 230-245.
[36] Abidin IZ, Mandache C, Tian GY. Pulsed eddy current testing with variable duty cycle on rivet joints[J]. NDT & E INTERNATIONAL, 2009, 42(7): 599-605.
[37] Tian GY, Li Y, Mandache C. Study of Lift-Off Invariance for Pulsed Eddy-Current Signals[J]. IEEE TRANSACTIONS ON MAGNETICS, 2009, 45(1): 184-191
[38] Chen TL, Tian GY, Sophian A. Feature extraction and selection for defect classification of pulsed eddy current[J]. NDT & E INTERNATIONAL, 2008, 41(6): 467-476.
[39] Lang ZQ, Agurto A, Tian GY. A system identification based approach for pulsed eddy current non- destructive evaluation[J]. MEASUREMENT SCIENCE & TECHNOLOGY, 2007, 18(7): 2083-2091.
[40] Tian GY, Li Y, Mandache C. Study of Lift-Off Invariance for Pulsed Eddy-Current Signals[J]. IEEE TRANSACTIONS ON MAGNETICS, 2009, 45(1): 184-191
[41] Li Y, Tian GY, Simm A. Fast analytical modelling for pulsed eddy current evaluation[J]. NDT & E INTERNATIONAL, 2008, 41(6): 477-483.
[42] Yang G, Tian GY, Que PW. Data fusion algorithm for pulsed eddy current detection[J]. IET SCIENCE MEASUREMENT & TECHNOLOGY, 2007, 1(6): 312-316.
[43] Edwards RS, Sophian A, Dixon S. Data fusion for defect characterisation using a dual probe system[J]. SENSORS AND ACTUATORS A-PHYSICAL, 2008, 144(1): 222-228
[44] Liu Z, Forsyth D S, Lepine B A. Investigations on classifying pulsed eddy current signals with a neural network[J]. INSIGHT, 2003, 45(9): 608-614.
[45] Wei JX, Liu H, Sun YH. Application of Genetic Algorithm in Document Clustering[J]. 2009 International Conference On Information Technology And Computer Science, 2009, 1:145-148.
[46] Mukhopadhyay A, Maulik U, Bandyopadhyay S. Multiobjective Genetic Algorithm-Based Fuzzy Clustering of Categorical Attributes[J]. IEEE Transactions On Evolutionary Computation, 2009, 13(5): 991-1005.
[47] Chen YH, Ho YW, Wu CH. Aerial Image Clustering using Genetic Algorithm[J]. IEEE International Conference On Computational Intelligence For Measurment Systems And Applications, 2009, 7: 42- 45.
[48] Wen F, Gen MS, Yu XJ. Multilayer Traffic Network Optimized by Multiobjective Genetic Clustering Algorithm[J]. Ieice Transactions On Fundamentals Of Electronics Communi- Cations And Computer Sciences, 2008, 92(8): 2107-2115.
[49] Song W, Li CH, Park SC. Genetic algorithm for text clustering using ontology and evaluating the validity of various semantic similarity measures[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36(5) :9095-9104.
[50]魏武,张亚楠,武林林.基于遗传算法的改进AdaBoost算法在汽车识别中的应用[J].公路交通科技, 2010, 27(2): 511-518.
[51] Ayvaz MT, Karahan H, Aral MM. Aquifer parameter and zone structure estimation using kernel-based fuzzy c-means clustering and genetic algorithm[J]. JOURNAL OF HYDROLOGY, 2007, 343(3-4): 240-253.
[52] Dong YY, Zhang YJ, Chang CL .Multistage random sampling genetic-algorithm-based fuzzy C-means clustering algorithm[J]. PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, 2004, 1(7): 2069-2073.
[53] Mukhopadhyay A, Maulik U, Bandyopadhyay S. Multiobjective Genetic Algorithm-Based Fuzzy Clustering of Categorical Attributes[J]. PATTERN RECOGNITION, 2009, 13(5): 991-1005.
[54] Gan G, Wu J, Yang Z. A genetic fuzzy k-Modes algorithm for clustering categorical data[J] EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36(2): 1615-1620.
[55]孙红艳,王英博.一种改进的小生境遗传聚类算法[J].计算机系统应用, 2009, 19(2): 37-39.
[56] Chang DX, Zhang XD, Zheng CW. A genetic algorithm with gene rearrangement for K-means clustering[J] .PATTERN RECOGNITION, 2009, 42(7): 1210-1222.
[57] Wang YN, Ge F. An Improved Genetic K-Means Algorithm for Spatial Clustering[J]. PROGRESS IN INTELLIGENCE COMPUTATION AND APPLICATIONS, 2008: 123-126.
[58]史奎凡,陈月辉.提高遗传聚类收敛速度的方法[J].信息与控制, 1998, 27(4): 271-275.
[59] Dorigo M. Optimization learning and naturalal gorithms[M]. PhDThesis,Department of Eleetronies, Politeenieo di Milano,Italy,1992.
[60]张军英,敖磊,贾江涛,高琳.求解TSP问题的改进蚁群算法[J].西安电子科技大学学报, 2005, 32(5): 681-684.
[61]徐金荣,李允,刘海涛,刘攀.一种求解TSP的混合遗传蚁群算法[J].计算机应用, 2008, 28(8): 2084-2088.
[62] Blum C. Ant colony optimization: Introduetion and recent trends[J]. Physies of Life Reviews, 2005(2): 353-373.
[63]李瑞,邱玉辉.基于离散点的蚁群聚类算法的研究[J].计算机科学, 2005, 32(6): 111-115.
[64]陈立潮,刘佳,吕亚男.带杂交算子的蚁群算法求解动态网络中的最短路径问题[J].计算机工程与科学, 2007, 29(5): 81-83.
[65] Chi SC, Yang CC. A two-stage clustering method combining ant colony SOM and K-means[J]. Journal Of Information Science And Engineering, 2008, 24(5): 1445-1460.
[66] Upda L, S Upda. Eddy current defect characterization using neural networks[J]. Materials Evaluation, 1990, 48: 342-347.
[67] Upda L, S Upda. Neural Networks for the classification of Nondestructive Evaluation Signals[J]. IEE Proceedings Pt-F, 1991, 138(1): 41-45.
[68] Spanner J, L Udpa, R Polikar, P Ramuhalli. Neural networks for ultrasonic detection of intergranular stress corrosion cracking[J]. the e-Journal of Nondestructive Testing And Ultrasonics, 2000, 5(7): 591-594.
[69] Simone,G.. RBFNN-based hole identification system in conducting plates[J]. IEEE Transactions onNeural Networks,2001,12(6):1445-1454.
[70] Zhenmao Chen, Ladislav Janousek,Noritaka Yusa,Kenzo Miya. A Nondestructive Strategy for the Distinction of Natural Fatigue and Stress Corrosion Cracks Based on Signals From Eddy Current Testing. Journal of Pressure Vessel Technology[J], 2007, 129(11):719-728.
[71] Xiang P, S Ramakrishnan, et al. Automated analysis of rotating probe multi-frequency eddy current data from steam generator tubes[J]. International Journal of Applied Electromagnetics and Mechanics, 2001, 12: 151-164
[72] Simone G. RBFNN-based hole identification system in conducting plates[J]. IEEE Transactions on Neural Networks, 2001, 12(6): 1445-1454.
[73] Souza JR, Ludermir TB, Almeida LM .A Two Stage Clustering Method Combining Self-Organizing Maps and Ant K-Means[J]. ARTIFICIAL NEURAL NETWORKS - ICANN, 2009(57): 485-494.
[74] Zhenmao Chen, Ladislav Janousek, Noritaka Yusa, Kenzo Miya. A nondestructive strategy for the distinction of natural fatigue and stress corrosion cracks based on signals from eddy current testing[J]. Journal of Pressure Vessel Technology, 2007, 129(11): 719-728.
[75]孙群,宋卿基.基于DDS技术的便携式波形信号发生器[J].仪表技术与传感器, 2009(4): 67-70.
[76]张思全,陈铁群,朱佳震.脉冲涡流检测技术的进展[J].无损检测, 2008(11): 981-984.
[77]孙多,陈志敏,沈洁.一种新的基于蚁群原理的聚类算法[J].扬州大学学报(自然科学版), 2009, 11(2):57-60.
[78]张春蕾.小波变换在GPS变形监测中的应用[D].武汉:武汉大学, 2007
[79]岳林,张令弥.脉冲激励下信号去噪新方法研究和仿真[J].振动工程学报, 2003, 16(3): 335-338.
[80]陈强,黄声享,王韦.小波去噪效果评价的另一指标[J].测绘信息与工程, 2008 ,33 (5): 13-17.
[81] Dorigo M, Blum C. Ant colony optimization theory:A survey Theoretieal Computer[J]. Science 2005, 344(2-3): 243-278.
[82]宋存利,薛倩.混合蚁群遗传算法在车间作业调度的应用研究[J].科学技术与工程, 2009, 9(11): 2109-2112.
[83]吴静波,张承宁,邹渊,李军求.基于遗传蚁群算法的履带式混合动力车辆控制策略参数优化[J].车用发动机, 2009(6): 41-45.
[84]王刚,钟志水,黄永青.基于蚁群遗传算法的网格资源调度研究[J].计算机仿真, 2009, 26(4): 240-242.
[85]张思全.自然裂纹涡流检测及形状重构若干关键技术研究[D].华南理工大学, 2008.
[2] D Hagemaier. Eddy current depth of penetration[J]. Materials Evaluation, 2004, 62(10): 1028-1029.
[3] S Sullivan, D L Atherton, T R Schmidt. Comparing a one-dimensional skin effect equation with through transmission eddy current phenomena. British journal of NDT, 1990, 32(2): 71-75.
[4] V Sundararaghavan, K Balasubramaniam, N R Babu, N Rajesh. A multi-frequency eddy current inversion method for characterizing conductivity gradients on water jet peened components[J]. NDT and E International, 2005, 38(7): 541-547.
[5]冯婷婷,罗飞路,何赟泽.双频涡流检测中微弱信号的二维信息提取[J].中国测试技术, 2008, 34(4): 59-62
[6]程婷婷,宋文爱,陈以方,张世雄.基于LabVIEW的燃料元件远场涡流式在线检测系统研究[J]. .核动力工程, 2009, 30(1): 90-94
[7]周德强,张斌强,田贵云,王海涛.脉冲涡流检测中裂纹的深度定量及分类识别[J].仪器仪表学报, 2009(6): 78-80.
[8]周德强,田贵云,王海涛,姚恩涛.小波变换在脉冲涡流检测信号中的应用[J].传感器与微系统, 2008(10): 29-31.
[9]张斌强,田贵云,王海涛,王平.脉冲涡流检测技术的研究[J].无损检测, 2008, (10): 241-243.
[10]徐晨曦,阙沛文,毛义梅.脉冲涡流检测系统的设计[J].传感器与微系统, 2009, (6): 89-91.
[11]何赟泽,罗飞路,胡祥超,刘波,高军哲.脉冲涡流矩形传感器的多维信号特征分析与缺陷识别[J].传感技术学报, 2009, (5): 65-67.
[12] LI Y, TTIAN G Y. Fast analytical method for pulsededdy current evaluation[C]. Proceeding of 45th AnnualBritish Conference on NDT 2006,UK:Stratford-upon-Avon, 2006.
[13]郑岗,赵亮.金属厚度的脉冲涡流无损检测研究[J].传感器与微系统, 2006(4): 281-284.
[14] Giguere S. Pulsed eddy current technology: Characterizing material loss with gap and lift-off variations[J]. Research in Nondestructive Evaluation, 2001, 13(3): 119-129.
[15]杨宾峰.脉冲涡流无损检测若干关键技术研究[D].国防科学技术大学, 2006.
[16] Zhang Y H, Sun H X, Luo F L. 3D magnetic field responses to a defect using a tangential driver-coil for pulsed eddy current testing[C]. 17th World Conference Nondestructive Testing, Shanghai, China. 2008.
[17] Yang BinFeng, Luo FeiLu, Han Dan. Research on edge identification of a defect using pulsed eddy current based on pricipal component analysis[J]. NDT&E Internaltional, 2007, 40: 294-299.
[18]张玉华,孙慧贤,罗飞路,杨宾峰.脉冲涡流检测中三维磁场量的特征分析与缺陷定量评估[J].传感技术学报, 2008(5): 259-263 .
[19] HE Y Z, LUO F L, PAN M C. Defect classification based on rectangular pulsed eddy current sensor in dif-ferent directions[J] . Sensors&Actuators:A. Physica, 2010, 157: 26-31.
[20] Auld B A, Moulder J C. Review of advances in quantitative eddy current nondestructive evaluation[J]. JOURNAL OF NONDESTRUCTIVE EVALUATION, 1999, 18(1): 3-36
[21]刘清洪,黄松岭,陆文娟.大口径管道远场涡流缺陷检测仿真研究[J].无损检测, 2008, 30(4): 241- 245.
[22]徐小杰.铁磁性管道中轴向裂纹的远场涡流检测技术研究[D].国防科学技术大学, 2007.
[23] M Muck, M Korn, C Welzel, S Grawunder, F Scholz. Nondestructive evaluation of various materialsusing a SQUID-based eddy-current system[J]. IEEE Transactions on Applied Superconductivity, 2005, 15(2): 733-738
[24] C Carr, M A Espy, T G Abeln, J Kraus, H Robert. Characterization of stainless steel inertia welds using HTS SQUID NDE[J]. IEEE Transactions on Applied Superconductivity,2005,15(2): 719-722.
[25] Krause H J, Panaitov G I, Zhang Y. Appearance of sign reversal in geophysical transient electromagnetics with a SQUID due to stacking[J]. IEEE Transactions On Applied Superconductivity, 2007, 15(2): 745-748.
[26] Krause H J, Panaitov G I, Wolters N. Detection of magnetic contaminations in industrial products using HTS SQUIDs[J]. IEEE Transactions On Applied Superconductivity, 2005, 15(2): 729-732.
[27]白世武,丁胜红.直流超导量子干涉器无损检测的原理与应用[J].无损检测, 2006, 28(5): 242-246.
[28]何东风,立木实,系崎秀夫.手持移动型高温超导SQUID在无损检测中的应用[J].低温与超导, 2008, 36(12): 44-47.
[29] R A Smith, G R Hugo. Deep Corrosion and Crack Detection in Aging Aircraft using Transient Eddy-current NDE[J]. Aging Aircraft, 2001, 78(23):96-101.
[30] J C Moulder, Sk Shaligram, JA Bieberm. Pulsed Eddy Current Inspections[J]. Foundation, Inc.2000.
[31] W D Rummel, J R Bowler. Integrated Quantitative Nondestructive Evaluation(NDE) and Reliability Assessment of Aging Aircraft Structures[J]. Final Report for The United States Air Force Office of Scientific Research 27, 2001.
[32] B Lebrun, Y Jayet, J C Baboux. Pulsed eddy current signal analysis: application to the experimental detection and characterization of deep flaws in highly conductive materials[J]. NDT & E International, 1997, 30: 163-170.
[33] Giguere S, Lepine B A, Dubois J M S. Pulsed eddy current technology: Characterizing material loss with gap and lift-off variations[J]. RESEARCH IN NONDESTRUCTIVE EVALUATION. 2001, 13(3): 119-121.
[34] R A Smith, G R Hugo. Transient eddy current NDE for aging aircraft-capabilities and limitations[J]. Insight, 2001, 43(1): 14-25.
[35] Yang G, Tian GY, Que PW. Independent Component Analysis-Based Feature Extraction Technique for Defect Classification Applied for Pulsed Eddy Current NDE[J]. Research In Nondestructive Evaluation, 2009, 20(4): 230-245.
[36] Abidin IZ, Mandache C, Tian GY. Pulsed eddy current testing with variable duty cycle on rivet joints[J]. NDT & E INTERNATIONAL, 2009, 42(7): 599-605.
[37] Tian GY, Li Y, Mandache C. Study of Lift-Off Invariance for Pulsed Eddy-Current Signals[J]. IEEE TRANSACTIONS ON MAGNETICS, 2009, 45(1): 184-191
[38] Chen TL, Tian GY, Sophian A. Feature extraction and selection for defect classification of pulsed eddy current[J]. NDT & E INTERNATIONAL, 2008, 41(6): 467-476.
[39] Lang ZQ, Agurto A, Tian GY. A system identification based approach for pulsed eddy current non- destructive evaluation[J]. MEASUREMENT SCIENCE & TECHNOLOGY, 2007, 18(7): 2083-2091.
[40] Tian GY, Li Y, Mandache C. Study of Lift-Off Invariance for Pulsed Eddy-Current Signals[J]. IEEE TRANSACTIONS ON MAGNETICS, 2009, 45(1): 184-191
[41] Li Y, Tian GY, Simm A. Fast analytical modelling for pulsed eddy current evaluation[J]. NDT & E INTERNATIONAL, 2008, 41(6): 477-483.
[42] Yang G, Tian GY, Que PW. Data fusion algorithm for pulsed eddy current detection[J]. IET SCIENCE MEASUREMENT & TECHNOLOGY, 2007, 1(6): 312-316.
[43] Edwards RS, Sophian A, Dixon S. Data fusion for defect characterisation using a dual probe system[J]. SENSORS AND ACTUATORS A-PHYSICAL, 2008, 144(1): 222-228
[44] Liu Z, Forsyth D S, Lepine B A. Investigations on classifying pulsed eddy current signals with a neural network[J]. INSIGHT, 2003, 45(9): 608-614.
[45] Wei JX, Liu H, Sun YH. Application of Genetic Algorithm in Document Clustering[J]. 2009 International Conference On Information Technology And Computer Science, 2009, 1:145-148.
[46] Mukhopadhyay A, Maulik U, Bandyopadhyay S. Multiobjective Genetic Algorithm-Based Fuzzy Clustering of Categorical Attributes[J]. IEEE Transactions On Evolutionary Computation, 2009, 13(5): 991-1005.
[47] Chen YH, Ho YW, Wu CH. Aerial Image Clustering using Genetic Algorithm[J]. IEEE International Conference On Computational Intelligence For Measurment Systems And Applications, 2009, 7: 42- 45.
[48] Wen F, Gen MS, Yu XJ. Multilayer Traffic Network Optimized by Multiobjective Genetic Clustering Algorithm[J]. Ieice Transactions On Fundamentals Of Electronics Communi- Cations And Computer Sciences, 2008, 92(8): 2107-2115.
[49] Song W, Li CH, Park SC. Genetic algorithm for text clustering using ontology and evaluating the validity of various semantic similarity measures[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36(5) :9095-9104.
[50]魏武,张亚楠,武林林.基于遗传算法的改进AdaBoost算法在汽车识别中的应用[J].公路交通科技, 2010, 27(2): 511-518.
[51] Ayvaz MT, Karahan H, Aral MM. Aquifer parameter and zone structure estimation using kernel-based fuzzy c-means clustering and genetic algorithm[J]. JOURNAL OF HYDROLOGY, 2007, 343(3-4): 240-253.
[52] Dong YY, Zhang YJ, Chang CL .Multistage random sampling genetic-algorithm-based fuzzy C-means clustering algorithm[J]. PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, 2004, 1(7): 2069-2073.
[53] Mukhopadhyay A, Maulik U, Bandyopadhyay S. Multiobjective Genetic Algorithm-Based Fuzzy Clustering of Categorical Attributes[J]. PATTERN RECOGNITION, 2009, 13(5): 991-1005.
[54] Gan G, Wu J, Yang Z. A genetic fuzzy k-Modes algorithm for clustering categorical data[J] EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36(2): 1615-1620.
[55]孙红艳,王英博.一种改进的小生境遗传聚类算法[J].计算机系统应用, 2009, 19(2): 37-39.
[56] Chang DX, Zhang XD, Zheng CW. A genetic algorithm with gene rearrangement for K-means clustering[J] .PATTERN RECOGNITION, 2009, 42(7): 1210-1222.
[57] Wang YN, Ge F. An Improved Genetic K-Means Algorithm for Spatial Clustering[J]. PROGRESS IN INTELLIGENCE COMPUTATION AND APPLICATIONS, 2008: 123-126.
[58]史奎凡,陈月辉.提高遗传聚类收敛速度的方法[J].信息与控制, 1998, 27(4): 271-275.
[59] Dorigo M. Optimization learning and naturalal gorithms[M]. PhDThesis,Department of Eleetronies, Politeenieo di Milano,Italy,1992.
[60]张军英,敖磊,贾江涛,高琳.求解TSP问题的改进蚁群算法[J].西安电子科技大学学报, 2005, 32(5): 681-684.
[61]徐金荣,李允,刘海涛,刘攀.一种求解TSP的混合遗传蚁群算法[J].计算机应用, 2008, 28(8): 2084-2088.
[62] Blum C. Ant colony optimization: Introduetion and recent trends[J]. Physies of Life Reviews, 2005(2): 353-373.
[63]李瑞,邱玉辉.基于离散点的蚁群聚类算法的研究[J].计算机科学, 2005, 32(6): 111-115.
[64]陈立潮,刘佳,吕亚男.带杂交算子的蚁群算法求解动态网络中的最短路径问题[J].计算机工程与科学, 2007, 29(5): 81-83.
[65] Chi SC, Yang CC. A two-stage clustering method combining ant colony SOM and K-means[J]. Journal Of Information Science And Engineering, 2008, 24(5): 1445-1460.
[66] Upda L, S Upda. Eddy current defect characterization using neural networks[J]. Materials Evaluation, 1990, 48: 342-347.
[67] Upda L, S Upda. Neural Networks for the classification of Nondestructive Evaluation Signals[J]. IEE Proceedings Pt-F, 1991, 138(1): 41-45.
[68] Spanner J, L Udpa, R Polikar, P Ramuhalli. Neural networks for ultrasonic detection of intergranular stress corrosion cracking[J]. the e-Journal of Nondestructive Testing And Ultrasonics, 2000, 5(7): 591-594.
[69] Simone,G.. RBFNN-based hole identification system in conducting plates[J]. IEEE Transactions onNeural Networks,2001,12(6):1445-1454.
[70] Zhenmao Chen, Ladislav Janousek,Noritaka Yusa,Kenzo Miya. A Nondestructive Strategy for the Distinction of Natural Fatigue and Stress Corrosion Cracks Based on Signals From Eddy Current Testing. Journal of Pressure Vessel Technology[J], 2007, 129(11):719-728.
[71] Xiang P, S Ramakrishnan, et al. Automated analysis of rotating probe multi-frequency eddy current data from steam generator tubes[J]. International Journal of Applied Electromagnetics and Mechanics, 2001, 12: 151-164
[72] Simone G. RBFNN-based hole identification system in conducting plates[J]. IEEE Transactions on Neural Networks, 2001, 12(6): 1445-1454.
[73] Souza JR, Ludermir TB, Almeida LM .A Two Stage Clustering Method Combining Self-Organizing Maps and Ant K-Means[J]. ARTIFICIAL NEURAL NETWORKS - ICANN, 2009(57): 485-494.
[74] Zhenmao Chen, Ladislav Janousek, Noritaka Yusa, Kenzo Miya. A nondestructive strategy for the distinction of natural fatigue and stress corrosion cracks based on signals from eddy current testing[J]. Journal of Pressure Vessel Technology, 2007, 129(11): 719-728.
[75]孙群,宋卿基.基于DDS技术的便携式波形信号发生器[J].仪表技术与传感器, 2009(4): 67-70.
[76]张思全,陈铁群,朱佳震.脉冲涡流检测技术的进展[J].无损检测, 2008(11): 981-984.
[77]孙多,陈志敏,沈洁.一种新的基于蚁群原理的聚类算法[J].扬州大学学报(自然科学版), 2009, 11(2):57-60.
[78]张春蕾.小波变换在GPS变形监测中的应用[D].武汉:武汉大学, 2007
[79]岳林,张令弥.脉冲激励下信号去噪新方法研究和仿真[J].振动工程学报, 2003, 16(3): 335-338.
[80]陈强,黄声享,王韦.小波去噪效果评价的另一指标[J].测绘信息与工程, 2008 ,33 (5): 13-17.
[81] Dorigo M, Blum C. Ant colony optimization theory:A survey Theoretieal Computer[J]. Science 2005, 344(2-3): 243-278.
[82]宋存利,薛倩.混合蚁群遗传算法在车间作业调度的应用研究[J].科学技术与工程, 2009, 9(11): 2109-2112.
[83]吴静波,张承宁,邹渊,李军求.基于遗传蚁群算法的履带式混合动力车辆控制策略参数优化[J].车用发动机, 2009(6): 41-45.
[84]王刚,钟志水,黄永青.基于蚁群遗传算法的网格资源调度研究[J].计算机仿真, 2009, 26(4): 240-242.
[85]张思全.自然裂纹涡流检测及形状重构若干关键技术研究[D].华南理工大学, 2008.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |