平板导体缺陷复合式涡流检测技术研究
|
摘要
涡流检测技术在航空航天、核工业、石油化工、机械制造等领域应用广泛。本文结合国家自然科学基金项目,围绕板状导电结构缺陷识别和定量化检测开展理论和实验研究,提出了基于远场涡流和常规涡流的复合式涡流检测方法,研究复合式检测系统正向场量数值建模、检测信号与被测材料间映射关系建立、反向缺陷类型判定及尺寸参数预估等关键技术,自主设计研发了新型的复合式涡流检测设备,实现了板状导电结构不同深度缺陷的有效检测,完成了实验验证。主要研究工作和创新点如下：
1.针对板状导电结构表面、亚表面和深层缺陷检测的实际需求,提出了平板远场和常规涡流双模复合式检测方法。利用电磁数值技术,研究平板导体检测系统中涡流、能量、磁力线、磁通密度等电磁场量的分布机理与变化特性。计算求解对应不同检测参数的各类电磁场量分布变化形式,分析总结复合式检测系统中被测导体板材质属性、板中缺陷位置、形状等参数对系统中关键电磁场量分布及其变化的影响规律。研究表明利用电磁场量叠加与相位滞后特性的双模复合式板材检测方式可提高系统的表层缺陷检测精度和深层缺陷检测能力,场量分析结果验证了对板状导体材料进行复合式涡流检测的可行性,为检测系统研发和缺陷参数定量化分析奠定了理论基础。
2.研究了平板远场／常规复合式涡流检测信号特征,建立了探头检测值与被测材料属性、激励频率、提离距离、缺陷形状、位置、尺寸等关键因素间的映射关系。研究结果表明,综合两类不同涡流检测信号有助于提高系统的缺陷检出及缺陷位置判定能力,利用常规涡流探头阻抗信号中电阻和电感分量对缺陷不同方向尺寸变化具有不同敏感性的特点可提高系统对缺陷尺寸参数的反演精度。研究的涡流检测信号变化规律与涡流电磁场量分析结果吻合,检测信号响应特性规律的研究可为系统开发和缺陷反演推算工作提供正向指导。
3.针对无损检测中板状导电结构缺陷类型辨别与缺陷尺寸预估的检测需求,根据探头检测值与缺陷参数间的内在关联与映射关系,利用双模检测信号的互补特性,提出了基于支持向量机和广义回归神经网络技术的复合式涡流检测反演方法。采用群智能算法对分类和回归模型中相关参数进行优化,实现了小样本条件下板状导电结构表层与深层缺陷类型的有效判定和缺陷参数的定量评估。
4.研究开发了综合平板远场涡流与常规涡流技术的复合式涡流检测系统。系统采用复合激励方式,同步采集和处理探头双模检测信号,有效克服了单一检测方法深层缺陷检测信号响应微弱或缺陷位置识别不精确等的局限性。系统可实现板状导电结构表层、下表层缺陷的同步扫描检测。相比于常规涡流,复合式系统大大拓宽了平板导体的缺陷可测范围,对导体板中深层缺陷的检测能力与报道的国外远场涡流系统相当,具有重要的工程应用价值。
Eddy current nondestructive testing is widely used in the fields such as aviation, the nuclear industry, petrochemical engineering, and machine manufacture. Based on defect recognition and quantitative estimation, a theoretical and experimental study on fault detection in slab-like conductors was performed using the eddy current technique. The project was supported by the National Natural Science Funds.The proposed test method combines a plate remote-field eddy current technique and the common eddy current technique. The numerical modeling of the electromagnetic field、the establishment of the mapping between the detection signal of the probe and the materials being tested、the type determination and size assessment of the flaw etc such key technology were studied in the compound testing system. A new compound detecting device was built and experimentally verified. The device realized flaw detection at different depths on the conductor plate. The various aspects of the study covered in the paper and the innovative concepts presented are listed below:
1. To meet the practical needs of fault detection on plate conductive structures in the surface, subsurface, and deep-seated regions, a dual-mode compound testing system was proposed that synthesizes the plate remote-field eddy current technique and the common eddy current technique. Take the advantage of electromagnetic numerical techniques to describe the distribution mechanism and characteristics of the electromagnetic field, such as eddy current, energy, magnetic lines of flux, magnetic flux density, in the system. The distribution form of the field was calculated for different detection parameters. The regular pattern of the key electromagnetic field's distribution form effected by the key parameters of the tested conductor plate, such as the properties of the measured material, the shape, position and size of the defect etc was analysed and summarized. Dual-mode compound testing system improves the precision of defect detection on the surface, as well as the detectability of deep-seated defects, by utilizing the characteristics of field superposition and phase lag. The analyse of the electromagnetic field enabled the validation of the compound testing system used on conductor plate, and established the theoretical foundation for system design and quantitative inversion estimate of the flaw's parameter in the compound eddy current testing.
2. The property of the detection signals from the common and plate remote-field eddy current techniques was analyzed. Establish the mapping between the detection signal of the probe and the key factors in the testing such as the properties of the measured material, the excitation frequency, the lift-off, the shape, position and size of the defect etc. The results of the experiment show that, synthesize different type of eddy current detection signals can improves the ability of the fault detection and the fault's position judgement. Taking advantage of the common eddy current detection signal's characteristics that the resistance and the inductance of the probe has different sensitiveness to the change of the fault's size in different directions, can improves the precision of inversion on fault's size. This enabled the validation of the numerical analysis of field quantities. The study on the tendency of the detection signals can provide guidance for system design and quantitative inversion estimate of the flaw's parameter in the compound eddy current testing.
3. To meet the practical needs of type determination and size assessment of the flaw on plate conductive structures in the NDT testing, According to the intrinsic connection between the probe detection signal and the parameters of the flaw, using the complementary characteristics of the common eddy current signal and the plate remote-field eddy current signal, a compound inversion method was proposed what based on Support Vector Machine and generalized regression neural network technique. Some parameter of the classification model and the estimation model were optimized by swarm intelligence algorithm. This enabled a valid judgement of flaw type and quantitative assessment of flaw size in the plate conductive structure for small sample.
4. The compound detecting device was built which combined plate remote-field eddy current technique and common eddy current technique. The compound excitation mode was adopted, and the dual-mode detection signal was collected and analyzed synchronously. This approach overcomes the limitations of single-detection systems, such as weak responses from deep defects or the inaccurate estimation of the defect position. Thus, we realized flaw detection at different depths on the conductor plate synchronously. Compared with common eddy current detection devices, the combined system greatly broadens the range in which flaws can be detected in a plate conductor. The detectability of flaws in deep-seated regions of the system matches the detectability of reported plate remote-field eddy current detecting systems abroad, and thus the detection system can be expected to have important applications in engineering.
1.针对板状导电结构表面、亚表面和深层缺陷检测的实际需求,提出了平板远场和常规涡流双模复合式检测方法。利用电磁数值技术,研究平板导体检测系统中涡流、能量、磁力线、磁通密度等电磁场量的分布机理与变化特性。计算求解对应不同检测参数的各类电磁场量分布变化形式,分析总结复合式检测系统中被测导体板材质属性、板中缺陷位置、形状等参数对系统中关键电磁场量分布及其变化的影响规律。研究表明利用电磁场量叠加与相位滞后特性的双模复合式板材检测方式可提高系统的表层缺陷检测精度和深层缺陷检测能力,场量分析结果验证了对板状导体材料进行复合式涡流检测的可行性,为检测系统研发和缺陷参数定量化分析奠定了理论基础。
2.研究了平板远场／常规复合式涡流检测信号特征,建立了探头检测值与被测材料属性、激励频率、提离距离、缺陷形状、位置、尺寸等关键因素间的映射关系。研究结果表明,综合两类不同涡流检测信号有助于提高系统的缺陷检出及缺陷位置判定能力,利用常规涡流探头阻抗信号中电阻和电感分量对缺陷不同方向尺寸变化具有不同敏感性的特点可提高系统对缺陷尺寸参数的反演精度。研究的涡流检测信号变化规律与涡流电磁场量分析结果吻合,检测信号响应特性规律的研究可为系统开发和缺陷反演推算工作提供正向指导。
3.针对无损检测中板状导电结构缺陷类型辨别与缺陷尺寸预估的检测需求,根据探头检测值与缺陷参数间的内在关联与映射关系,利用双模检测信号的互补特性,提出了基于支持向量机和广义回归神经网络技术的复合式涡流检测反演方法。采用群智能算法对分类和回归模型中相关参数进行优化,实现了小样本条件下板状导电结构表层与深层缺陷类型的有效判定和缺陷参数的定量评估。
4.研究开发了综合平板远场涡流与常规涡流技术的复合式涡流检测系统。系统采用复合激励方式,同步采集和处理探头双模检测信号,有效克服了单一检测方法深层缺陷检测信号响应微弱或缺陷位置识别不精确等的局限性。系统可实现板状导电结构表层、下表层缺陷的同步扫描检测。相比于常规涡流,复合式系统大大拓宽了平板导体的缺陷可测范围,对导体板中深层缺陷的检测能力与报道的国外远场涡流系统相当,具有重要的工程应用价值。
Eddy current nondestructive testing is widely used in the fields such as aviation, the nuclear industry, petrochemical engineering, and machine manufacture. Based on defect recognition and quantitative estimation, a theoretical and experimental study on fault detection in slab-like conductors was performed using the eddy current technique. The project was supported by the National Natural Science Funds.The proposed test method combines a plate remote-field eddy current technique and the common eddy current technique. The numerical modeling of the electromagnetic field、the establishment of the mapping between the detection signal of the probe and the materials being tested、the type determination and size assessment of the flaw etc such key technology were studied in the compound testing system. A new compound detecting device was built and experimentally verified. The device realized flaw detection at different depths on the conductor plate. The various aspects of the study covered in the paper and the innovative concepts presented are listed below:
1. To meet the practical needs of fault detection on plate conductive structures in the surface, subsurface, and deep-seated regions, a dual-mode compound testing system was proposed that synthesizes the plate remote-field eddy current technique and the common eddy current technique. Take the advantage of electromagnetic numerical techniques to describe the distribution mechanism and characteristics of the electromagnetic field, such as eddy current, energy, magnetic lines of flux, magnetic flux density, in the system. The distribution form of the field was calculated for different detection parameters. The regular pattern of the key electromagnetic field's distribution form effected by the key parameters of the tested conductor plate, such as the properties of the measured material, the shape, position and size of the defect etc was analysed and summarized. Dual-mode compound testing system improves the precision of defect detection on the surface, as well as the detectability of deep-seated defects, by utilizing the characteristics of field superposition and phase lag. The analyse of the electromagnetic field enabled the validation of the compound testing system used on conductor plate, and established the theoretical foundation for system design and quantitative inversion estimate of the flaw's parameter in the compound eddy current testing.
2. The property of the detection signals from the common and plate remote-field eddy current techniques was analyzed. Establish the mapping between the detection signal of the probe and the key factors in the testing such as the properties of the measured material, the excitation frequency, the lift-off, the shape, position and size of the defect etc. The results of the experiment show that, synthesize different type of eddy current detection signals can improves the ability of the fault detection and the fault's position judgement. Taking advantage of the common eddy current detection signal's characteristics that the resistance and the inductance of the probe has different sensitiveness to the change of the fault's size in different directions, can improves the precision of inversion on fault's size. This enabled the validation of the numerical analysis of field quantities. The study on the tendency of the detection signals can provide guidance for system design and quantitative inversion estimate of the flaw's parameter in the compound eddy current testing.
3. To meet the practical needs of type determination and size assessment of the flaw on plate conductive structures in the NDT testing, According to the intrinsic connection between the probe detection signal and the parameters of the flaw, using the complementary characteristics of the common eddy current signal and the plate remote-field eddy current signal, a compound inversion method was proposed what based on Support Vector Machine and generalized regression neural network technique. Some parameter of the classification model and the estimation model were optimized by swarm intelligence algorithm. This enabled a valid judgement of flaw type and quantitative assessment of flaw size in the plate conductive structure for small sample.
4. The compound detecting device was built which combined plate remote-field eddy current technique and common eddy current technique. The compound excitation mode was adopted, and the dual-mode detection signal was collected and analyzed synchronously. This approach overcomes the limitations of single-detection systems, such as weak responses from deep defects or the inaccurate estimation of the defect position. Thus, we realized flaw detection at different depths on the conductor plate synchronously. Compared with common eddy current detection devices, the combined system greatly broadens the range in which flaws can be detected in a plate conductor. The detectability of flaws in deep-seated regions of the system matches the detectability of reported plate remote-field eddy current detecting systems abroad, and thus the detection system can be expected to have important applications in engineering.
引文
[1]孙金立.无损检测及在航空维修中的应用[M].北京：国防工业出版社,2004.
[2]Lavender SJ. NDT and the internet-A five year review[J]. Insight.2005,47(7):430-431.
[3]李家伟,陈积懋.无损检测手册[M].北京：机械工业出版社,2001.
[4]中国机械工程学会无损检测分会.超声波检测[M].北京：机械工业出版社,2000.
[5]Wang Z.D,Gu Y,Wang Y.S. A review of three magnetic NDT technologies[J]. Journalof Magnetism and Magnetic Materials.2012,324(4):382-388.
[6]无损检测学会.磁粉探伤[M].北京：机械工业出版社,1987.
[7]林俊明,林春景,林发炳.基于磁记忆效应的一种无损检测新技术[J].无损检测.2000,7：297-299.
[8]高军哲.多频涡流无损检测的干扰抑制和缺陷检测方法研究[D].湖南：国防科学技术大学,2011.
[9]John Bowler,Marcus Johnson. Pulsed eddy-current response to a conducting half-space[J]. IEEE Transactions on Magnetic.1997,33(3):2258-2264.
[10]杨宾峰.脉冲涡流无损检测若干关键技术研究[D].湖南：国防科学技术大学,,2006.
[11]Maclean MD. Defect characterization by the remote-field eddy-current technique in small-diameter tubing[J]. Materials Evaluation.1989,47(1):24-28.
[12]Sun YS,Udpa L,Udpa S. A novel remote field eddy current technique for inspection of thick-walled aluminum plates[J]. Materials Evaluation.1998,56(1):94-97.
[13]Sun YS,Udpa S,Lord W. A remote field eddy current NDT probe for the inspection of metallic plates[J]. Materials Evaluation.1996,54(4):510-512.
[14]曲民兴,周连文.导电平板的远场涡流现象[J].无损检测.1997,19(8)：216-219.
[15]周连文,曲民兴.导电平板中的远场涡流特性[J].南京航空航天大学学报.1998,30(2)：146-152.
[16]Wang HT, Luo QF, Wang X. Simulation and experimental study of remote field eddy current testing on flat conductive plate [J].International Journal Of Applied Electromagnetics And Mechanics.2010,33(3-4):1261-1266.
[17]崔文岩,朱荣新,杨宾峰等.铁磁性平板导体件远场涡流有限元仿真与分析[J].空军工程大学学报.2012,13(5)：71-75.
[18]Thomas H-M, Heckel T,Hanspach G. Advantage of a combined ultrasonic and eddy current examination for railway inspection trains[J]. Insight-Non-Destructive Testing and Condition Monitoring.2007,49(6):341-344.
[19]D Horn, W.R Mayo. NDE reliability gains from combining eddy-current and ultrasonic testing[J]. NDT & E International.2000,33(6):351-362.
[20]Fathi E, Al-Qadeeb. TUBING INSPECTION USING MULTIPLE NDT TECHNIQUES[C].3rd MENDT-Middle East Nondestructive Testing Conference & Exhibition.2005, Bahrain, Manama.
[21]程玉华,周肇飞,尹伯彪.磁光/涡流实时成像检测系统的研究[J].光学精密工程.2006,14(5)：797-801.
[22]CV Dodd, WE Deeds. Analytical Solutions to Eddy Current Probe Coil Problems [J].Journal of Applied Physics.1968,39(6):2829-2838.
[23]倪光正.工程电磁场数值计算[M].北京：机械工业出版社,2004.
[24]谢德馨,姚缨英,白保东等.三维涡流场的有限元分析[M].北京：机械工业出版社,2008.
[25]刘国强,赵凌志,蒋继娅Ansoft工程电磁场有限元分析[M].北京：电子工业出版社,2005.
[26]赵博Ansoft 12在工程电磁场中的应用[M].北京：水利水电出版社,2010.
[27]叶波.多层导电结构深层缺陷电涡流检测和定量化评估研究[D].杭州：浙江大学,2009.
[28]B.Benoist,P.Gaillard.Characterization of Defect Signals inSteam Generator Tubes[J].Engng Applic.Artif.Intell.1995,8(3):309-318.
[29]M. V. Kreutzbruck K. Allweins. Defect Detection and Classificationusing a SQUID based multiple FrequencyEddy Current NDESystem [J].IEEE Transactions On Applied Superconductivity,2001,2(1):1032-1037.
[30]Radislav Smid, Adam Docekal, Marcel Kreid. Automated classification of eddy current signaturesduring manual inspection[J]. NDT&E International.2005,38:462-470.
[31]Radu C. Popa, Kenzo Miya. Approximate Inverse Mapping in ECT, Based on ApertureShifting and Neural Network Regression[J]. Journal of Nondestructive Evaluation.1998,17(4):209-221.
[32]N. Zavaljevski, S. Bakhtiari.Automated algorithms for eddy current arrayProbes for steam generator inspection[C]. AIP Conference Proceedings.2005,760:728-735.
[33]Andrea Bernieri, Luigi Ferrigno. Crack Shape Reconstruction in Eddy Current TestingUsing Machine Learning Systems for Regression[J]. IEEE Transactions On Instrumentation And Measurement.2008,57(9):1958-1968.
[34]Zheng Liu, Senior Member. A Data-Fusion Scheme for Quantitative Image Analysis by Using Locally Weighted Regression and Dempster-Shafer Theory[J]. IEEE Transactions On Instrumentation And Measurement.2008,57(11):2554-2560.
[35]杨宾峰,罗飞路,曹雄恒等.脉冲涡流无损检测技术在裂纹定量中的应用[J].无损检测.2005,27(6)：291-293.
[36]Ball David W. The Baseline Maxwell's equations, Part I:History[J]. Spectroscopy. 2011,26(4):16.
[37]谢德馨,杨仕友.工程电磁场数值分析与综合[M].北京：机械工业出版社,2009.
[38]陈抗生.电磁场理论与微波工程基础[M].杭州：浙江大学出版社,2009.
[39]倪光正.工程电磁场原理[M].北京：高等教育出版社,2002.
[40]任吉林,林俊明.电磁无损检测[M].北京：科学出版社,2008.
[41]汤蕴谬.电机内的电磁场[M].北京：科学出版社,1981.
[42]BottauscioO, Chiampi M, Manzin A. Element-free galerkin method in eddy-current problems with ferromagnetic media[J].IEEETransactions on Magnetics. 2006,42(5):1577-1584.
[43]Sun YS, Lin HY, Chen MJ. Finite-element modeling and physics of remote field eddy-current responses for axially aligned cracks[J].1992,28(4):1941-1947.
[44]Kim D, Udpa L, Udpa S. Remote field eddy current testing for detection of stress corrosion cracks in gas transmission pipelines[J]. Materials Letters.2004,58(15):2102-2104.
[45]Mackintosh DD, Atherton DL, Sullivan SP. Remote-field eddy-current signal analysis in small-bore ferromagnetic tubes[J]. Materials Evaluation.1993,51(4):492-495
[46]Dodd CV,Deeds WE. Analytical solutions to eddy-current probe-coil problems [J]. JOURNAL OF APPLIED PHYSICS,1968,39(6):2829-&.
[47]Schmidt TR. The remote field dddy-current inspection technique[J]. Materials Evaluation.1984,42(2):225-230.
[48]Shatat A, Atherton DL. Remote field eddy current inspection of support plate fretting wear[J]. Materials Evaluation.1997,55(3):361-366.
[49]郑晖,林树青.超声检测[M].中国劳动社会保障出版社,2008.
[50]Atherton DL, Sullivan S, Daly M. A remote-field eddy-current tool for inspecting nuclear-reactor pressure tubes[J]. British Journal of Non-destructive Testing.1988,30(1): 22-27.
[51]Bhag Singh Guru, Huseyin R. Hiziroglu. Electromagnetic Field Theory Fundamentals [M].2009, USA:Cambridge UniversityPress,2nd Revised edition.
[52]Lord W, Sun Ys, Udpa SS. A finite-element study of the remote field eddy-current phenomenon.IEEE Transactions on Magnetics.1988,24(1):435-438.
[53]颜威利.电气工程电磁场数值分析[M].北京：机械工业出版社,2005.
[54]阎照文ANSYS 10.0工程电磁分析技术与实例详解[M].北京：中国水利出版社,2006.
[55]杨士元.电磁屏蔽理论与实践[M].北京：国防工业出版社,2006.
[56]《国防科技工业无损检测人员资格资定与认证培训教材》编审委员会.涡流检测机械工业出版社2004
[57]Demerdas NA, Gillott DH. New approach for determination of eddy-current and flux penetration in nonlinear ferromagnetic materials[J]. IEEE Transactions on Magnetics. 1974,10(3):682-685.
[58]严密,彭晓领.磁学基础与磁性材料[M].杭州：浙江大学出版社,2006.
[59]Bean CP,Deblois RW,Nesbitt LB. Eddy-current method for measuring the resistivity of metals[J], Journal of Applied Physics.1959,30(12):1976-1980.
[60]黄平捷.多层导电结构厚度与缺陷电涡流检测若干关键技术研究[D].杭州：浙江大学,2004.
[61]张玉华.基于场-路耦合模型的涡流探头设计及提离干扰抑制方法研究[D].湖南：国防科学技术大学,2010.
[62]W Yin, R Binns,S. J.Dickinson, et al. Analysis of the lift-off effect of phase spectra for eddy current sensors[C]. IMTC 2005, Proceedings of the IEEE.
[63]C.L.Davis, M.P.Papaelias, M.Strangwood M,et al. Measurement of phase transformation in steels using electromagnetic sensors[J]. Steelmak.2002,29:469-476.
[64]Tian GY, Sophian A. Reduction of lift-off effects for pulsed eddy current NDT[J]. NDT&E International.2005,38(4):319-324.
[65]Guru B.S. Electromagnetic field theory fundamentals, Second Edition[M]. Beijing:China Machine Press,2005.
[66]钟文定.铁磁学[M].北京：科学出版社,1987.
[67]Haugland SM. Fundamental analysis of the remote-field eddy-current effect[J]. IEEE Transactions on Magnetics.1996,32(4):3195-3211.
[68]鲁述,徐鹏根.电磁场边值问题解析方法[D].湖北：武汉大学出版社,2005.
[69]Cortes C, Vapnik V. Support-Vector Networks[J]. Machine Learning. Machine Learning. 1995,20(3):273-297.
[70]Cherkassky V, Ma YQ. Practical selection of SVM parameters and noise estimation for SVM regression[J]. Neural Networks.2004,17(1):113-126.
[71]Seng TL, Khalid M, Yusof R. Adaptive neuro-fuzzy control system by RBF and GRNN neural networks [J]. Journal of Intelligent & Robotic Systems.
[72]李丽,牛奔.粒子群优化算法[M].北京：冶金工业出版社,2009.
[73]Cheng Zhengjun, Zhang Yuntao, Zhang Wenjun. QSAR studies of imidazopyridine derivatives as Et-PKG inhibitors using the PSO-SVM approach[J]. Medicinal Chemistry Research.2010,19(9):1307-1325.
[74]Huang Cheng-Lung, Dun Jian-Fan. A distributed pso-svm hybrid system with feature selection and parameter optimization[J]. Applied soft computing.2008,8(4):1381-1391.
[75]Liu Li-xia, Zhuang Yi-qi, Liu Xue-yong. Tax forecasting theory and model based on SVM optimized by PSO[J]. Expert Systems with Applications.2011,38(1):116-120.
[76]梁艳春,吴春国,时小虎,等.群智能优化算法理论与应用[M].北京：科学出版社,2009.
[77]Leung H,Dubash N,Xie N. Detection of small objects in clutter using a GA-RBF neural network[J]. IEEE Transactions on Aerospace and Electronic Systems.2002,38(1):98-118.
[78]Harrison Gareth P,Piccolo Antonio, Siano Pierluigi. Hybrid GA and OPF evaluation of network capacity of distributed generation connections[J]. Electric power systems research.2008,78(3):392-398.
[79]Grefenstette JJ. Optimization of control parameters for genetic algorithms[J]. IEEE Transactions on Systems Man and Cybernetics.1986,16(1):122-128.
[80]魏秀业,潘宏侠.粒子群优化及智能故障诊断[M].北京：国防工业出版社,2010.
[81]MATLAB中文论坛Matlab神经网络30个案例分析[M].北京：北京航空航天大学出版社,2010.
[82]Wu Qi. Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM[J]. Expert Systems With Applications.2010,37(1):194-201.
[83]纪震,廖惠连,吴青华.粒子群算法及应用[M].北京：科学出版社,2009.
[84]Yu Jianbo,Wang shijin, Xi llifeng. Evolving artificial neural networks using an improved PSO and DPSo[J]. Neurocomputing.2008,71(4-6):1054-1060.
[85]Assareh E, Behrang M. A. Assari M. R. Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran[J]. Energy.2010,35(12):5223-5229.
[86]Guo Zhiqiang, Wang Huaiqing, Liu Quan. Financial time series forecasting using LPP and SVM optimized by PSO[J]. Soft Computing.2013,17(5):805-818.
[87]Pourbasheer E, Riahi S, Ganjali MR. Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity[J]. European Journal Of Medicinal Chemistry.2009,44(12):5023-5028.
[88]Bazi Y, Melgani F. Semisupervised PSO-SVM regression for biophysical parameter estimation[J]. IEEE Transactions On Geoscience And Remote Sensing.2007,45(6): 1887-1895.
[89]Chau K W. Application of a PSO-based neural network in analysis of outcomes of construction claims[J]. Automation in Construction.2007,16(5):642-646.
[90]Deb k, Pratap A, agarwal S. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Transactions On Evolutionary'Computation.2002.6 (2):182-197.
[91]Chua LO,Yang L. Cellular neural networks-theory[J]. IEEE Transactions on Circuits and Systems.1988,35(10):1257-1272.
[92]Seng TL, Khalid M, Yusof R. Adaptive neuro-fuzzy control system by RBF and GRNN neural networks[J]. Journal of Intelligent & Robotic Systems.
[93]Yuen RKK, Lee EWM, Lim CP. Fusion of GRNN and FA for online noisy data regression[J]. Neural Processing Letters.2004,19(3):227-241.
[94]黄卡玛,赵翔.电磁场中的逆问题及应用[M].北京：科学出版社,2005.
[95]Schmidt TR. History of the remote-field eddy-current inspection technique[J]. Materials Evaluation.1989,47(1):14-22.
[96]Wang Haitao, Luo Qiufeng, Wang Xin. Simulation and experimental study of remote field eddy current testing on flat conductive plate[J]. International Journal of Applied Electromagnetics and Mechanics.2010,33(3-4):1261-1266.
[97]Atherton DL, Mackintosh DD, Sullivan SP. Remote-field eddy-current signal representation[J]. Materials Evaluation.1993,51(7):782-&.
[98]谢嘉奎,宣月清,冯军.电子线路[M].北京：高等教育出版社,1996.
[99]曾庆勇.微弱信号检测[M].杭州：浙江大学出版社,2004.
[100]阎石.数字电子技术基础[M].北京：高等教育出版社,1997.
[101]特拉特斯,克林Lavview大学实用教程[M].北京：电子工业出版社,2008.
[102]岂兴明,田京京,夏宁Labview入门与实战开发100例[M].北京：电子工业出版社,2011.
[103]曲丽荣Labview MATLAB及其混合编程技术[M].北京：机械工业出社,2011.
[104]陈锡辉,张银鸿.Labview8.20程序设计从入门到精通[M].北京：清华大学出版社,2007.
[2]Lavender SJ. NDT and the internet-A five year review[J]. Insight.2005,47(7):430-431.
[3]李家伟,陈积懋.无损检测手册[M].北京：机械工业出版社,2001.
[4]中国机械工程学会无损检测分会.超声波检测[M].北京：机械工业出版社,2000.
[5]Wang Z.D,Gu Y,Wang Y.S. A review of three magnetic NDT technologies[J]. Journalof Magnetism and Magnetic Materials.2012,324(4):382-388.
[6]无损检测学会.磁粉探伤[M].北京：机械工业出版社,1987.
[7]林俊明,林春景,林发炳.基于磁记忆效应的一种无损检测新技术[J].无损检测.2000,7：297-299.
[8]高军哲.多频涡流无损检测的干扰抑制和缺陷检测方法研究[D].湖南：国防科学技术大学,2011.
[9]John Bowler,Marcus Johnson. Pulsed eddy-current response to a conducting half-space[J]. IEEE Transactions on Magnetic.1997,33(3):2258-2264.
[10]杨宾峰.脉冲涡流无损检测若干关键技术研究[D].湖南：国防科学技术大学,,2006.
[11]Maclean MD. Defect characterization by the remote-field eddy-current technique in small-diameter tubing[J]. Materials Evaluation.1989,47(1):24-28.
[12]Sun YS,Udpa L,Udpa S. A novel remote field eddy current technique for inspection of thick-walled aluminum plates[J]. Materials Evaluation.1998,56(1):94-97.
[13]Sun YS,Udpa S,Lord W. A remote field eddy current NDT probe for the inspection of metallic plates[J]. Materials Evaluation.1996,54(4):510-512.
[14]曲民兴,周连文.导电平板的远场涡流现象[J].无损检测.1997,19(8)：216-219.
[15]周连文,曲民兴.导电平板中的远场涡流特性[J].南京航空航天大学学报.1998,30(2)：146-152.
[16]Wang HT, Luo QF, Wang X. Simulation and experimental study of remote field eddy current testing on flat conductive plate [J].International Journal Of Applied Electromagnetics And Mechanics.2010,33(3-4):1261-1266.
[17]崔文岩,朱荣新,杨宾峰等.铁磁性平板导体件远场涡流有限元仿真与分析[J].空军工程大学学报.2012,13(5)：71-75.
[18]Thomas H-M, Heckel T,Hanspach G. Advantage of a combined ultrasonic and eddy current examination for railway inspection trains[J]. Insight-Non-Destructive Testing and Condition Monitoring.2007,49(6):341-344.
[19]D Horn, W.R Mayo. NDE reliability gains from combining eddy-current and ultrasonic testing[J]. NDT & E International.2000,33(6):351-362.
[20]Fathi E, Al-Qadeeb. TUBING INSPECTION USING MULTIPLE NDT TECHNIQUES[C].3rd MENDT-Middle East Nondestructive Testing Conference & Exhibition.2005, Bahrain, Manama.
[21]程玉华,周肇飞,尹伯彪.磁光/涡流实时成像检测系统的研究[J].光学精密工程.2006,14(5)：797-801.
[22]CV Dodd, WE Deeds. Analytical Solutions to Eddy Current Probe Coil Problems [J].Journal of Applied Physics.1968,39(6):2829-2838.
[23]倪光正.工程电磁场数值计算[M].北京：机械工业出版社,2004.
[24]谢德馨,姚缨英,白保东等.三维涡流场的有限元分析[M].北京：机械工业出版社,2008.
[25]刘国强,赵凌志,蒋继娅Ansoft工程电磁场有限元分析[M].北京：电子工业出版社,2005.
[26]赵博Ansoft 12在工程电磁场中的应用[M].北京：水利水电出版社,2010.
[27]叶波.多层导电结构深层缺陷电涡流检测和定量化评估研究[D].杭州：浙江大学,2009.
[28]B.Benoist,P.Gaillard.Characterization of Defect Signals inSteam Generator Tubes[J].Engng Applic.Artif.Intell.1995,8(3):309-318.
[29]M. V. Kreutzbruck K. Allweins. Defect Detection and Classificationusing a SQUID based multiple FrequencyEddy Current NDESystem [J].IEEE Transactions On Applied Superconductivity,2001,2(1):1032-1037.
[30]Radislav Smid, Adam Docekal, Marcel Kreid. Automated classification of eddy current signaturesduring manual inspection[J]. NDT&E International.2005,38:462-470.
[31]Radu C. Popa, Kenzo Miya. Approximate Inverse Mapping in ECT, Based on ApertureShifting and Neural Network Regression[J]. Journal of Nondestructive Evaluation.1998,17(4):209-221.
[32]N. Zavaljevski, S. Bakhtiari.Automated algorithms for eddy current arrayProbes for steam generator inspection[C]. AIP Conference Proceedings.2005,760:728-735.
[33]Andrea Bernieri, Luigi Ferrigno. Crack Shape Reconstruction in Eddy Current TestingUsing Machine Learning Systems for Regression[J]. IEEE Transactions On Instrumentation And Measurement.2008,57(9):1958-1968.
[34]Zheng Liu, Senior Member. A Data-Fusion Scheme for Quantitative Image Analysis by Using Locally Weighted Regression and Dempster-Shafer Theory[J]. IEEE Transactions On Instrumentation And Measurement.2008,57(11):2554-2560.
[35]杨宾峰,罗飞路,曹雄恒等.脉冲涡流无损检测技术在裂纹定量中的应用[J].无损检测.2005,27(6)：291-293.
[36]Ball David W. The Baseline Maxwell's equations, Part I:History[J]. Spectroscopy. 2011,26(4):16.
[37]谢德馨,杨仕友.工程电磁场数值分析与综合[M].北京：机械工业出版社,2009.
[38]陈抗生.电磁场理论与微波工程基础[M].杭州：浙江大学出版社,2009.
[39]倪光正.工程电磁场原理[M].北京：高等教育出版社,2002.
[40]任吉林,林俊明.电磁无损检测[M].北京：科学出版社,2008.
[41]汤蕴谬.电机内的电磁场[M].北京：科学出版社,1981.
[42]BottauscioO, Chiampi M, Manzin A. Element-free galerkin method in eddy-current problems with ferromagnetic media[J].IEEETransactions on Magnetics. 2006,42(5):1577-1584.
[43]Sun YS, Lin HY, Chen MJ. Finite-element modeling and physics of remote field eddy-current responses for axially aligned cracks[J].1992,28(4):1941-1947.
[44]Kim D, Udpa L, Udpa S. Remote field eddy current testing for detection of stress corrosion cracks in gas transmission pipelines[J]. Materials Letters.2004,58(15):2102-2104.
[45]Mackintosh DD, Atherton DL, Sullivan SP. Remote-field eddy-current signal analysis in small-bore ferromagnetic tubes[J]. Materials Evaluation.1993,51(4):492-495
[46]Dodd CV,Deeds WE. Analytical solutions to eddy-current probe-coil problems [J]. JOURNAL OF APPLIED PHYSICS,1968,39(6):2829-&.
[47]Schmidt TR. The remote field dddy-current inspection technique[J]. Materials Evaluation.1984,42(2):225-230.
[48]Shatat A, Atherton DL. Remote field eddy current inspection of support plate fretting wear[J]. Materials Evaluation.1997,55(3):361-366.
[49]郑晖,林树青.超声检测[M].中国劳动社会保障出版社,2008.
[50]Atherton DL, Sullivan S, Daly M. A remote-field eddy-current tool for inspecting nuclear-reactor pressure tubes[J]. British Journal of Non-destructive Testing.1988,30(1): 22-27.
[51]Bhag Singh Guru, Huseyin R. Hiziroglu. Electromagnetic Field Theory Fundamentals [M].2009, USA:Cambridge UniversityPress,2nd Revised edition.
[52]Lord W, Sun Ys, Udpa SS. A finite-element study of the remote field eddy-current phenomenon.IEEE Transactions on Magnetics.1988,24(1):435-438.
[53]颜威利.电气工程电磁场数值分析[M].北京：机械工业出版社,2005.
[54]阎照文ANSYS 10.0工程电磁分析技术与实例详解[M].北京：中国水利出版社,2006.
[55]杨士元.电磁屏蔽理论与实践[M].北京：国防工业出版社,2006.
[56]《国防科技工业无损检测人员资格资定与认证培训教材》编审委员会.涡流检测机械工业出版社2004
[57]Demerdas NA, Gillott DH. New approach for determination of eddy-current and flux penetration in nonlinear ferromagnetic materials[J]. IEEE Transactions on Magnetics. 1974,10(3):682-685.
[58]严密,彭晓领.磁学基础与磁性材料[M].杭州：浙江大学出版社,2006.
[59]Bean CP,Deblois RW,Nesbitt LB. Eddy-current method for measuring the resistivity of metals[J], Journal of Applied Physics.1959,30(12):1976-1980.
[60]黄平捷.多层导电结构厚度与缺陷电涡流检测若干关键技术研究[D].杭州：浙江大学,2004.
[61]张玉华.基于场-路耦合模型的涡流探头设计及提离干扰抑制方法研究[D].湖南：国防科学技术大学,2010.
[62]W Yin, R Binns,S. J.Dickinson, et al. Analysis of the lift-off effect of phase spectra for eddy current sensors[C]. IMTC 2005, Proceedings of the IEEE.
[63]C.L.Davis, M.P.Papaelias, M.Strangwood M,et al. Measurement of phase transformation in steels using electromagnetic sensors[J]. Steelmak.2002,29:469-476.
[64]Tian GY, Sophian A. Reduction of lift-off effects for pulsed eddy current NDT[J]. NDT&E International.2005,38(4):319-324.
[65]Guru B.S. Electromagnetic field theory fundamentals, Second Edition[M]. Beijing:China Machine Press,2005.
[66]钟文定.铁磁学[M].北京：科学出版社,1987.
[67]Haugland SM. Fundamental analysis of the remote-field eddy-current effect[J]. IEEE Transactions on Magnetics.1996,32(4):3195-3211.
[68]鲁述,徐鹏根.电磁场边值问题解析方法[D].湖北：武汉大学出版社,2005.
[69]Cortes C, Vapnik V. Support-Vector Networks[J]. Machine Learning. Machine Learning. 1995,20(3):273-297.
[70]Cherkassky V, Ma YQ. Practical selection of SVM parameters and noise estimation for SVM regression[J]. Neural Networks.2004,17(1):113-126.
[71]Seng TL, Khalid M, Yusof R. Adaptive neuro-fuzzy control system by RBF and GRNN neural networks [J]. Journal of Intelligent & Robotic Systems.
[72]李丽,牛奔.粒子群优化算法[M].北京：冶金工业出版社,2009.
[73]Cheng Zhengjun, Zhang Yuntao, Zhang Wenjun. QSAR studies of imidazopyridine derivatives as Et-PKG inhibitors using the PSO-SVM approach[J]. Medicinal Chemistry Research.2010,19(9):1307-1325.
[74]Huang Cheng-Lung, Dun Jian-Fan. A distributed pso-svm hybrid system with feature selection and parameter optimization[J]. Applied soft computing.2008,8(4):1381-1391.
[75]Liu Li-xia, Zhuang Yi-qi, Liu Xue-yong. Tax forecasting theory and model based on SVM optimized by PSO[J]. Expert Systems with Applications.2011,38(1):116-120.
[76]梁艳春,吴春国,时小虎,等.群智能优化算法理论与应用[M].北京：科学出版社,2009.
[77]Leung H,Dubash N,Xie N. Detection of small objects in clutter using a GA-RBF neural network[J]. IEEE Transactions on Aerospace and Electronic Systems.2002,38(1):98-118.
[78]Harrison Gareth P,Piccolo Antonio, Siano Pierluigi. Hybrid GA and OPF evaluation of network capacity of distributed generation connections[J]. Electric power systems research.2008,78(3):392-398.
[79]Grefenstette JJ. Optimization of control parameters for genetic algorithms[J]. IEEE Transactions on Systems Man and Cybernetics.1986,16(1):122-128.
[80]魏秀业,潘宏侠.粒子群优化及智能故障诊断[M].北京：国防工业出版社,2010.
[81]MATLAB中文论坛Matlab神经网络30个案例分析[M].北京：北京航空航天大学出版社,2010.
[82]Wu Qi. Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM[J]. Expert Systems With Applications.2010,37(1):194-201.
[83]纪震,廖惠连,吴青华.粒子群算法及应用[M].北京：科学出版社,2009.
[84]Yu Jianbo,Wang shijin, Xi llifeng. Evolving artificial neural networks using an improved PSO and DPSo[J]. Neurocomputing.2008,71(4-6):1054-1060.
[85]Assareh E, Behrang M. A. Assari M. R. Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran[J]. Energy.2010,35(12):5223-5229.
[86]Guo Zhiqiang, Wang Huaiqing, Liu Quan. Financial time series forecasting using LPP and SVM optimized by PSO[J]. Soft Computing.2013,17(5):805-818.
[87]Pourbasheer E, Riahi S, Ganjali MR. Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity[J]. European Journal Of Medicinal Chemistry.2009,44(12):5023-5028.
[88]Bazi Y, Melgani F. Semisupervised PSO-SVM regression for biophysical parameter estimation[J]. IEEE Transactions On Geoscience And Remote Sensing.2007,45(6): 1887-1895.
[89]Chau K W. Application of a PSO-based neural network in analysis of outcomes of construction claims[J]. Automation in Construction.2007,16(5):642-646.
[90]Deb k, Pratap A, agarwal S. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Transactions On Evolutionary'Computation.2002.6 (2):182-197.
[91]Chua LO,Yang L. Cellular neural networks-theory[J]. IEEE Transactions on Circuits and Systems.1988,35(10):1257-1272.
[92]Seng TL, Khalid M, Yusof R. Adaptive neuro-fuzzy control system by RBF and GRNN neural networks[J]. Journal of Intelligent & Robotic Systems.
[93]Yuen RKK, Lee EWM, Lim CP. Fusion of GRNN and FA for online noisy data regression[J]. Neural Processing Letters.2004,19(3):227-241.
[94]黄卡玛,赵翔.电磁场中的逆问题及应用[M].北京：科学出版社,2005.
[95]Schmidt TR. History of the remote-field eddy-current inspection technique[J]. Materials Evaluation.1989,47(1):14-22.
[96]Wang Haitao, Luo Qiufeng, Wang Xin. Simulation and experimental study of remote field eddy current testing on flat conductive plate[J]. International Journal of Applied Electromagnetics and Mechanics.2010,33(3-4):1261-1266.
[97]Atherton DL, Mackintosh DD, Sullivan SP. Remote-field eddy-current signal representation[J]. Materials Evaluation.1993,51(7):782-&.
[98]谢嘉奎,宣月清,冯军.电子线路[M].北京：高等教育出版社,1996.
[99]曾庆勇.微弱信号检测[M].杭州：浙江大学出版社,2004.
[100]阎石.数字电子技术基础[M].北京：高等教育出版社,1997.
[101]特拉特斯,克林Lavview大学实用教程[M].北京：电子工业出版社,2008.
[102]岂兴明,田京京,夏宁Labview入门与实战开发100例[M].北京：电子工业出版社,2011.
[103]曲丽荣Labview MATLAB及其混合编程技术[M].北京：机械工业出社,2011.
[104]陈锡辉,张银鸿.Labview8.20程序设计从入门到精通[M].北京：清华大学出版社,2007.
© 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 |