金属板裂纹的电磁声发射信号检测与处理技术研究
|
摘要
电磁声发射技术是一项基于声发射检测原理的无损检测新方法,它通过对金属材料的局部电磁加载激发缺陷部位产生声发射,进而实现对金属结构不连续的无损检测。本文以电磁声发射技术在非磁性金属板裂纹检测中的应用为主要研究内容,在理论研究与实验研究相结合的基础上,对电磁声发射信号进行了深入分析,并构建了电磁声发射信号的神经网络识别系统。
首先,在分析电磁声发射激发原理的基础上,进行了电磁声发射缺陷检测实验。设计了脉冲大电流加载装置,并将其应用于铝板裂纹检测实验中,详细分析了检测条件、加载条件和试件类型的改变对电磁声发射信号的影响。
其次,对电磁声发射信号的处理进行了系统研究,采用联合时频分析方法对电磁声发射信号进行处理,引入时频重排技术以便于有效地提取信号特征,同时深入探讨了小波包变换在电磁声发射信号处理及特征提取中的应用,并基于此提出了电磁声发射信号的预处理和特征提取方案,给出了电磁声发射信号的能量特征判据。
最后,对电磁声发射信号的模式识别问题进行了研究,分别构建了BP神经网络和小波神经网络对电磁声发射信号进行识别,并提出了自适应优化输入特征向量的方法,以达到改进神经网络性能的目的。
Electromagnetically induced acoustic emission (EMAE) is a new method for nondestructive testing based on acoustic emission. It did nondestructive detection with the effect of dynamic electromagnetic loading to generate a stress field stimulating stress waves from the defects. This thesis mainly focuses on the application of the EMAE in the nonmagnetic metal plate crack detection. It adopts the study methods of combining theory with practice. It deeply analyses the EMAE signal and builds a recognition system based on neural network technology to identify the EMAE signal.
At first, based on the analysis of the generate principle of EMAE, EMAE experiment of defect detection is achieved. The pulse-current loading unit is designed and applied in the detection experiment of Aluminum crack. It analyses the influence of detection conditions, loading conditions and specimen type on the EMAE signal in detail.
Secondly, a full research on the EMAE signal processing has been done in this thesis. It adopts Joint Time-Frequency Analysis methods (JTFA) to process the EMAE signal. For extracting the signal feature effectively, the time-frequency reassignment method is introduced. It deeply discusses the application of wavelet packet transform in the signal processing and feature extracting of EMAE. Meanwhile the program of preprocessing and feature extracting of the EMAE signal is proposed, and a criterion of the metal plate with the crack based on the wavelet packet transform and energy partition is given.
Finally, the recognition issue of EMAE signal is studied. It builds BP neural network and wavelet neural network to identify the EMAE signals, and an adaptive optimization method of input feature vector of neural network is presented for achieving good performance of the recognition system.
首先,在分析电磁声发射激发原理的基础上,进行了电磁声发射缺陷检测实验。设计了脉冲大电流加载装置,并将其应用于铝板裂纹检测实验中,详细分析了检测条件、加载条件和试件类型的改变对电磁声发射信号的影响。
其次,对电磁声发射信号的处理进行了系统研究,采用联合时频分析方法对电磁声发射信号进行处理,引入时频重排技术以便于有效地提取信号特征,同时深入探讨了小波包变换在电磁声发射信号处理及特征提取中的应用,并基于此提出了电磁声发射信号的预处理和特征提取方案,给出了电磁声发射信号的能量特征判据。
最后,对电磁声发射信号的模式识别问题进行了研究,分别构建了BP神经网络和小波神经网络对电磁声发射信号进行识别,并提出了自适应优化输入特征向量的方法,以达到改进神经网络性能的目的。
Electromagnetically induced acoustic emission (EMAE) is a new method for nondestructive testing based on acoustic emission. It did nondestructive detection with the effect of dynamic electromagnetic loading to generate a stress field stimulating stress waves from the defects. This thesis mainly focuses on the application of the EMAE in the nonmagnetic metal plate crack detection. It adopts the study methods of combining theory with practice. It deeply analyses the EMAE signal and builds a recognition system based on neural network technology to identify the EMAE signal.
At first, based on the analysis of the generate principle of EMAE, EMAE experiment of defect detection is achieved. The pulse-current loading unit is designed and applied in the detection experiment of Aluminum crack. It analyses the influence of detection conditions, loading conditions and specimen type on the EMAE signal in detail.
Secondly, a full research on the EMAE signal processing has been done in this thesis. It adopts Joint Time-Frequency Analysis methods (JTFA) to process the EMAE signal. For extracting the signal feature effectively, the time-frequency reassignment method is introduced. It deeply discusses the application of wavelet packet transform in the signal processing and feature extracting of EMAE. Meanwhile the program of preprocessing and feature extracting of the EMAE signal is proposed, and a criterion of the metal plate with the crack based on the wavelet packet transform and energy partition is given.
Finally, the recognition issue of EMAE signal is studied. It builds BP neural network and wavelet neural network to identify the EMAE signals, and an adaptive optimization method of input feature vector of neural network is presented for achieving good performance of the recognition system.
引文
[1]刘尚慈.火力发电厂金属断裂与失效分析[M].北京:水利水电出版社, 1992年. 24-26
[2]陈国华.结构完整性评估[M].北京:科学出版社, 2002年. 96-97
[3]冉启芳.无损检测方法的分类及其特征简介[J].无损检测, 1999, 21(2): 75-80
[4] Baby Sony, Balasubramanian T., Pardikar R. J., et al. Time-of-flight diffraction (TOFD) technique for accurate sizing of cracks embedded in sub-cladding[J]. Insight Non-Destructive Testing and Condition Monitoring, 2003, 45(9): 600-604
[5]迟大钊,刚铁,姚英学,袁媛.基于超声波法的紫铜焊接结构缺陷检测[J].焊接学报, 2010, 31(5): 21-24
[6]陈国华,张新梅,谢常欢,何湘柏.超声检测中裂纹型缺陷深度的智能识别[J].华南理工大学学报(自然科学版),2005, 33(8): 1-5
[7]陈明,马跃洲,陈光. X射线线阵实时成像焊缝缺陷检测方法[J].焊接学报, 2007, 28(6): 81-84
[8]王百荣,张国华,蒋以山. X射线无损检测在炮弹识别中的应用[J].核电子学与探测技术, 2006, 26(1):14-15
[9]侯润石,都东,邵家鑫,朱新杰.用于焊缝识别的X射线实时图像序列时空域滤波[J].清华大学学报(自然科学版), 2009, 49(5): 643-646
[10]杨涛,王太勇,李清,冷永刚.油气管道缺陷漏磁检测实验[J].天津大学学报, 2004, 37(8): 686-689
[11]郭付吉,武修明.油管漏磁无损检测中的磁化、剩磁及退磁技术[J].石油天然气学报, 2005, 27(4): 723-724
[12] Mustafa G?ktepe. Non-destructive crack detection by capturing local flux leakage field[J]. Sensors and Actuators A: Physical, 2001, 91(1): 70-72
[13] Guiyun Tian, Ali Sophian. Defect classification using a new feature for pulsed eddy current sensors[J]. NDT & E International, 1997, 30(3): 163-170
[14]薛光辉,赵国瑞,朱世钢,苏明辉等.基于金属磁记忆检测和涡流检测技术的大型振动筛下横梁裂纹检测方法[J].煤炭学报, 2010, 35(7): 1215-1218
[15]姚运萍,韩捷,廖述圣.核电站管道缺陷涡流定量检测的可靠性分析[J].核动力工程, 2009, 30(4): 17-20
[16]成建国,毛汉领,黄振峰,黄云奇.金属材料声发射信号特征提取方法[J].声学技术, 2008, 27(3): 309-314
[17]何舒,马羽宽,杨建波.含不同缺陷的金属材料声发射特性[J].吉林大学学报(工学版), 2002, 33(4): 21-25
[18]夏国旺.声发射技术在直升机疲劳实验裂纹检测中的运用[J].直升机技术, 2000, 123(3): 37-41
[19]戴光,徐彦廷等译.无损检测与评价手册[M].北京:中国石化出版社, 2006年.300-316
[20]耿荣生,沈功田,刘时风.模态声发射基本理论[J].无损检测, 2002, 24(7): 302-306.
[21] M. Matsumoto, A. Iida, T. Hamaguchi. Measurement of slider/disk collision forces using acoustic emission source wave analysis [J]. Tribology Transactions, 1993, 36(4): 736 - 740
[22]施克仁.无损检测新技术[M].北京:清华大学出版社. 2007年. 54-62
[23] Qi Gang. Attenuation of acoustic emission body waves in acrylic bone cement and synthetic bone using wavelet time-scale analysis [J]. Journal of Biomedical Materials Research, 2000, 52(1): 148~156
[24]徐春广,王信义,杨大勇,袁洪芳.磨削过程声发射信号传播特性研究[J].北京理工大学学报, 1998, 18(4): 415-419
[25]李建功,邹银辉,刘红,董国伟. AE声发射在波导器中传播规律的数值模拟[J].地下空间与工程学报, 2008, 4(6): 1148-1151
[26]邹银辉,文光才,胡千庭,许进鹏.岩体声发射传播衰减理论分析与实验研究[J].煤炭学报, 2004, 29(6): 663-667
[27]孙立瑛,李一博,靳世久,刘铁根,等.充液管道中声发射波的传播及衰减特性研究[J].压电与声光, 2008, 30(4): 401-403
[28]邹银辉,董国伟,张庆华,吕贵春.波导器中声发射信号传播规律研究[J].矿业安全与环保, 2007, 34(6): 13-14
[29] Hyunje Jeong, Jang Young-Su. Wavelet analysis of plate wave propagation in composite laminates [J]. Composite structure, 2000, 49(4): 443-450
[30] Jeong H. Analysis of plate wave propagation in anisotropic laminates using a wavelet transform [J]. NDT & E International, 2001, 34(3): 185-190
[31]焦敬品,吴斌,何存富,费仁元,李涌.基于模态声发射和小波变换的薄板中导波传播特性的实验研究[J].中国机械工程. 2004, 15(13):1179-1182
[32]毛汉领,王向红,黄振峰.基于小波包分析的声发射信号界面的衰减研究[J].振动与冲击, 2008, 27(9): 139-141
[33] Donoho D. L., Johnstone J. M.. Ideal spatial adaptation by wavelet shrinkage [J]. Biometrika, 1994, 81(3): 425-455
[34] P. J. de Groot, P. A. M. Wijnen, R. B. F. Janssen. Real-time frequency determination of acoustic emission for different fracture mechanisms in carbon/epoxy composites [J]. Composites Science and Technology, 1995, 55(4): 405-412
[35]张闯,刘素贞,杨庆新,金亮,等.基于FFT和小波包变换的电磁声发射信号处理[J].电工技术学报, 2010, 25(4): 24-28
[36]李宝玉,冯进来,戴光,李伟.应用声发射评定金属点蚀过程的研究进展[J].声学技术, 2005, 24(3): 170-172
[37]李录平,邹新元,唐月清.小波变换在声发射信号特征参数检测中的应用[J].振动与冲击, 2001, 20(2) : 67-68
[38]李光海,刘时风.基于小波分析的声发射源定位技术[J].机械工程学报, 2004, 40(7) : 136-140
[39]欧燕,王永宏.声发射检测中的噪声信号及其分析处理[J].陕西省第九届无损检测年会论文集, 2004年. 21-29
[40]姜长泓,王龙山,尤文,翟宁,等.基于平移不变小波的声发射信号去噪研究[J].仪器仪表学报, 2006, 27(6) : 607-610
[41]龚斌,金文,李兆南,金志浩.不同小波基在碳钢材料声发射信号分析中的应用[J].仪器仪表学报, 2008, 29(3) : 506-511
[42]廖传军,李学军,刘德顺.小波再分配尺度谱在声发射信号特征提取中的应用[J].机械工程学报, 2009, 45(2) : 273-279
[43] Terchi A., Au Y. H. J.. Acoustic emission signal processing[J]. Measurement and control, 2001, 34(8): 240-244
[44] Pitton, J.W., Kuansan Wang, Biing-Hwang Juang. Time-frequency analysis and auditory modeling for automatic recognition of speech[J]. Proceedings of the IEEE, 1996, 84(9): 1199-1215
[45]程道来,仪垂杰,郭健翔,等.基于W igner-Ville分布和Wavelet时间尺度的飞机非平稳抖杆背景声分析[J].机械工程学报, 2007, 43(5): 150-154.
[46] Gaunaurd G.C., Strifors H.C.. Signal analysis by means of time-frequency distribution-applications to sonar and radar echoes[J]. Proceedings of the IEEE, 1996, 84(9): 1231-1284
[47] Daponte P., Fazio G., Molinaro, A.. Detection of echoes using time-frequency analysis techniques[J]. IEEE Transactions on Instrumentation and Measurement, 1996, 45(1): 30-40
[48] Andria G., Application of wigner-ville distribution to measurements on transient signals[J]. IEEE Transactions on Instrumentation and Measurement, 1994, 43(2): 187-193
[49] Shie Qian, Dapang Chen. Joint time-frequency analysis[J]. IEEE signal processing magazine, 1999, 16(2): 52-67
[50]姜鸣,陈进,汪慰军.几种Cohen类时频分布的比较及应用[J].机械工程学报, 2003, 19(8): 129-134
[51] K. B. Kim, D. J. Yoon, J. C. Jeong and S. S. Lee. Determining the stress intensity factor of a material with an artificial neural network from acoustic emission measurements[J]. NDT & E International, 2004, 37(6): 423–429
[52] T. Sasikumar, S. RajendraBoopathy, K.M. Usha, E.S. Vasudev. Failure strength prediction of unidirectional tensile coupons using acoustic emission peak amplitude and energy parameter with artificial neural networks [J]. Composites Science and Technology, 2009, 69(7): 1151–1155.
[53] C. C. Kuo. Artificial recognition system for defective types of transformers by acoustic emission[J]. Expert Systems with Applications, 2009, 36(7): 10304–10311
[54] S. Rajendraboopathy, T. Sasikumar, K. M. Usha and E. S. Vasudev. Neural network prediction of failure strength of composite tensile specimens using acoustic emission counts [J]. Journal of Non destructive Testing & Evaluation, 2008, 7(2): 21–26
[55]侯素霞,罗积军,徐军,王汉功.神经网络在声发射信号模式识别中的应用研究[J].应用声学, 2003, 22(1): 44-47
[56]纪洪广,张天森,张志勇,蔡美峰.无损检测中常用声发射参数的分析与评价[J].无损检测, 2001, 23(7): 289-291
[57]王健,金周庚,刘哲军. C/E复合材料声发射信号小波分析及人工神经网络模式识别[J].宇航材料工艺, 2001, 31(5): 49-57
[58]谢秀娴,付攀,曹伟青.声发射和小波包分解技术在刀具磨损状态中的应用[J].中国测试技术, 2006, 32(2): 40-42
[59]戴光,余永增,张颖,于江林.基于小波包分析和BP神经网络的滚动轴承非接触声发射诊断方法[J].化工机械, 2008, 36(5): 274-277.
[60]杨永.数控机床刀具磨损的振动监测法[J].机械, 2009, 36(7): 58-60
[61] Mikael Johnson. Classification of AE transients based on numerical simulation of composite laminates[J]. NDT & E International, 2003, 36(5): 319-329
[62]殷冬萌,王军,刘云飞.木塑复合材料缺陷及损伤的声发射信号特征分析及神经网络模式识别[J].应用声学, 2007, 26(6): 352-356
[63] Achmad Widodo, Bo-Suk Yang. Support vector machine in machine condition monitoring and faultdiagnosis [J]. Mechanical Systems and Signal Processing, 2007, 21 (6): 2560–2574
[64] Achmad Widodo, Eric Y. Kim, Jong-Duk Son. Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine [J]. Expert Systems with Application, 2009, 36 (3): 7252–7261
[65] Siril Yella, Narendra K. Gupta and Mark S. Dougherty. Comparison of pattern recognition techniques for the classification of impact acoustic emissions [J]. Transportation Research Part C: Emerging Technologies, 2007, 15(6): 345-360
[66] B. Samanta, K.R. Al-Balushi, S.A. Al-Araimi. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection [J]. Engineering Applications of Artificial Intelligence , 2003, 16 (7) : 657–665
[67] J. Sun, M. Rahman, Y. S. Wong and G. S. Hong. Multiclassification of tool wear with support vector machine by manufacturing loss consideration [J]. International Journal of Machine Tools and Manufacture, 2004, 44(11): 1179-1187
[68] TONG F., XU X.M., B.L. Luk, K.P. Liu. Evaluation of tile-wall bonding integrity based on impact acoustics and support vector machine [J]. Sensors and Actuators A: Physical, 2008, 144(1): 97-104
[69] Zhang Q. H., Benveniate A.. Wavelet networks [J]. IEEE Trans. Neural networks, 1992, 3(6): 889-898
[70]尹熙鹏,樊养余,段哲民,程伟.基于自适应小波神经网络实现鱼雷超声引信信号消噪[J].鱼雷技术, 2009, 17(5): 19-22
[71]张臻,于海勋.基于小波包及小波神经网络的光纤熔接缺陷模式识别[J].测控技术, 2008, 27(10): 16-19
[72]芮执元,徐龙云,冯瑞成,任丽娜.基于小波神经网络(WNN)的齿轮故障诊断[J].机械传动, 2008, 32(1): 80-83
[73] Mendz A., More T.F., Mendz F. Applications of embedded optical fiber sensors in reinforced concrete buildings and structures [J]. Proc. SPIE, 1989, 1170: 60-69
[74] Barton, J., Reuben, B. Tool wear monitoring by optical techniques [J]. Materials World, 1996, 4(3): 131-132.
[75] McBride, R., Barolan, T, A., Barton, J. S., Wilcox, S. J., Borthwick, et al. Detection of acoustic emission in cutting processes by fibre optic interferometry [J]. Measurement Science and Technology, 1993, 4(10): 1122-1128.
[76]王信义,邢济收,徐春广.空气声发射传感器.中国,实用新型专利, ZL96200752[P],1997年2月19日
[77]邢济收,王信义.空气传导新型声发射传感器的研究[J].北京理工大学学报, 1996, 16(5): 517-522
[78] Albanese, R. A., Banks, H. T., Raye, J. K.. Nondestructive evaluation of materials using pulsed microwave interrogating signals and acoustic wave induced reflections[J]. Inverse Problems, 2002, 18(6): 1935-1958
[79]Smith, G. C., Lee, S. S.. A method for detecting tool wear on a CNC lathe using a Doppler Radar Detector[J]. International Journal of Advanced Manufacturing Technology, 2005, 25: 270-280
[80]Smith, G. C. A Noncontact Method for Detecting Acoustic Emission Using a Microwave Doppler Radar Motion Detector[J]. IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2005, 52(9): 1613-1617
[81]刘贵民.无损检测技术[M].北京:国防工业出版社, 2007年. 8-10
[82] http://www.pacndt.com.cn/ap-plane.htm
[83]艾春安,刘瑜,蔡堃.固体火箭发动机壳体/绝热层界面缺陷的声-超声检测[J].固体火箭技术. 2009, 32(1): 114-118
[84]张移山,张蕾,华庆祥,潘冬冬.声-超声技术用于铝板损伤监控的实验研究[J].机械科学与技术, 2009, 28(10): 1292-1296
[85]袁方,胡斌梁,周知进.基于声-超声技术的钢丝绳强度检测方法的研究[J].机械设计与制造, 2010, 7: 104-105
[86] Finkel P, Godinez V, Miller R. Electromagnetically induced acoustic emission—novel NDT technique for damage evaluation[J]. American Institute of Physics Conference Proceedings, 2001, 557: 1747-1754.
[87] Finkel P, Godinez V. Electromagnetic simulation of the ultrasonic signal for nondestructive detection of ferromagnetic inclusions and flaws[J]. IEEE Transactions on Magnetics, 2004, 40(4): 2179-2181.
[88]刘素贞,杨庆新,金亮,张闯.电磁声发射技术在无损检测中的应用研究[J].电工技术学报, 2009, 24(1): 23-27.
[89]陈中剑.电磁声发射技术研究:[硕士学位论文].天津:河北工业大学, 2006年
[90]陈中剑,杨庆新.电磁声发射技术研究[J].大众科技, 2006, 6: 23-24.
[91]郑惠强.金属结构[M].上海:同济大学出版社,1996年. 86-94
[92]陈海英.常用无损检测方法的特点及应用选择[J].无损探伤. 2009, 33(5): 23-24
[93]金亮.电磁声发射的实验研究:[硕士学位论文].天津:河北工业大学, 2008年
[94]颜威利,杨庆新,汪友华,等.电气工程电磁场数值分析[M].北京:机械工业出版社, 2005年. 214–215
[95]杨明纬.声发射检测[M].北京:机械工业出版社, 2005年. 7-12
[96]陈明祥.弹塑性力学[M].北京:科学出版社, 2007年. 143–152
[97] P. F. Ribeiro, B. K. Johnson, M. L. Crow, et al. Energy storage systems for advanced power applications [J]. Proceedings of the IEEE, 2001, 89(12): 1744-1756
[98] E. L. Neau. Environmental and industrial applications of pulsed power systems [J]. IEEE Transactions on Plasma Science, 1994, 22(1): 2-4
[99] T. Heeren, T. Ueno, Wang Douyan, et al. Novel dual marx generator for microplasma applications [J]. IEEE Transactions on Plasma Science, 2005, 33(4): 1205-1209
[100]张慧妍,韦统振,齐智平.超级电容器储能装置研究[J].电网技术, 2006, 30(8): 1-6
[101]林福昌,代新,徐智安,李劲,等.高储能密度电容器[J].强激光与粒子束, 2003, 15(1): 94-96
[102]李建辉,刘秀成,王春明,王赞基.电感储能型线圈炮系统的建模和参数优化[J].电工电能新技术, 2007, 26(2): 54-58
[103]戴兴建,李奕良,于涵.高储能密度飞轮结构设计方法[J].清华大学学报:自然科学版, 2008, 48(3): 378-381
[104]李维波,毛承雄,李启炎,等.神光III强激光能源模块测量线圈研究[J].中国电机工程学报, 2003, 23(5):53-57
[105] Jack S, Bernardes, Michael F, Stumborg, and Thomas E. Jean. Analysis of a capacitor-based pulsed-power system for driving long-range electromagnetic guns[J]. IEEE Transactions on Magnetics, 2003, 39(1): 486-487
[106] J. Biebach, P. Ehrhart, A. Muller. Compact modular power supplies for superconducting inductivestorage and for capacitor charging[J]. IEEE Transactions on Magnetics, 2001, 37(1): 353-354
[107] Chuang Zhang, Suzhen Liu, Qingxin Yang, Liang Jin. Experimental study on high-current pulse generator for electromagnetically induced acoustic emission[J]. The 11th International Conference on Electrical Machines and Systems(ICEMS2008), 2008. 2205-2207
[108]曹树谦,张文德,萧龙翔.振动结构模态分析:理论、实验与应用[M].天津:天津大学出版社, 2001年. 80-87
[109] Gong Z., Nyborg E. O., Oommen G.. Acoustic emission monitoring of steel railroad bridges [J]. Materials Evaluation, 1992, 50(6): 883-887.
[110]彭振明.声发射技术及其应用[M].北京:机械工业出版社, 1985年. 30-36
[111]贾慧明,张广纯,徐可北,张建卫,等. GB/T 8651-2002,金属板材超声板波探伤方法.北京:中国标准出版社, 2002年
[112]张冠生.电器理论基础[M].北京:机械工业出版社,1989年.
[113]万建伟.信号处理仿真技术[M].长沙:国防科技大学出版社,2008年. 88-89
[114]马世伟.非平稳信号的参数自适应时频表示及其应用的研究[M].上海:上海大学出版社,2000年. 16
[115]张晔.信号时频分析及应用[M].哈尔滨:哈尔滨工业大学出版社,2006年. 104-108
[116]葛哲学,陈仲生. MATLAB时频分析技术及其应用[M].北京:人民邮电出版社,2006年. 14-18
[117] Auger F., Flandrin P.. Improving the readability of time-frequency and time-scale representations by the reassignment method [J]. IEEE Transactions on Signal Processing, 1995, 43(5): 1068-1089
[118]飞思科技产品研发中心.小波分析理论与MATLAB7实现[M].北京:电子工业出版社, 2005年. 14-18
[119] Velayudham A., Krishnamurthy R., Soundarapandian T.. Acoustic emission based drill condition monitoring during drilling of glass/phenolic polymeric composite using wavelet packet transform [J]. Materials Science and Engineering A, 2005, 412(1): 141-145
[120]耿荣生.全尺寸飞机疲劳实验过程中的无损检测及声发射监测技术研究[J].南昌航空大学学报(自然科学版), 2007, 21(3): 1-7.
[121] S. G. Chang, B. Yu, M. Vetterli. Adaptive wavelet thresholding for image denoising and compression [J]. IEEE Transactions on Image Processing, 2000, 9(9): 1532-1543
[122] Y. Niu, L. Shen. Wavelet denoising using the pareto optimal threshold [J]. International Journal of Computer Science and Network Security, 2007, 7(1): 30-34
[123]丛爽.面向MATLAB工具箱的神经网络理论与应用[M].(第三版).合肥:中国科学技术大学出版社, 2009年. 64-86
[124]刘志刚,王晓茹,何正友,钱清泉.小波变换、神经网络和小波网络的函数逼近能力分析与比较[J].电力系统自动化, 2002, 26(20): 39-44
[2]陈国华.结构完整性评估[M].北京:科学出版社, 2002年. 96-97
[3]冉启芳.无损检测方法的分类及其特征简介[J].无损检测, 1999, 21(2): 75-80
[4] Baby Sony, Balasubramanian T., Pardikar R. J., et al. Time-of-flight diffraction (TOFD) technique for accurate sizing of cracks embedded in sub-cladding[J]. Insight Non-Destructive Testing and Condition Monitoring, 2003, 45(9): 600-604
[5]迟大钊,刚铁,姚英学,袁媛.基于超声波法的紫铜焊接结构缺陷检测[J].焊接学报, 2010, 31(5): 21-24
[6]陈国华,张新梅,谢常欢,何湘柏.超声检测中裂纹型缺陷深度的智能识别[J].华南理工大学学报(自然科学版),2005, 33(8): 1-5
[7]陈明,马跃洲,陈光. X射线线阵实时成像焊缝缺陷检测方法[J].焊接学报, 2007, 28(6): 81-84
[8]王百荣,张国华,蒋以山. X射线无损检测在炮弹识别中的应用[J].核电子学与探测技术, 2006, 26(1):14-15
[9]侯润石,都东,邵家鑫,朱新杰.用于焊缝识别的X射线实时图像序列时空域滤波[J].清华大学学报(自然科学版), 2009, 49(5): 643-646
[10]杨涛,王太勇,李清,冷永刚.油气管道缺陷漏磁检测实验[J].天津大学学报, 2004, 37(8): 686-689
[11]郭付吉,武修明.油管漏磁无损检测中的磁化、剩磁及退磁技术[J].石油天然气学报, 2005, 27(4): 723-724
[12] Mustafa G?ktepe. Non-destructive crack detection by capturing local flux leakage field[J]. Sensors and Actuators A: Physical, 2001, 91(1): 70-72
[13] Guiyun Tian, Ali Sophian. Defect classification using a new feature for pulsed eddy current sensors[J]. NDT & E International, 1997, 30(3): 163-170
[14]薛光辉,赵国瑞,朱世钢,苏明辉等.基于金属磁记忆检测和涡流检测技术的大型振动筛下横梁裂纹检测方法[J].煤炭学报, 2010, 35(7): 1215-1218
[15]姚运萍,韩捷,廖述圣.核电站管道缺陷涡流定量检测的可靠性分析[J].核动力工程, 2009, 30(4): 17-20
[16]成建国,毛汉领,黄振峰,黄云奇.金属材料声发射信号特征提取方法[J].声学技术, 2008, 27(3): 309-314
[17]何舒,马羽宽,杨建波.含不同缺陷的金属材料声发射特性[J].吉林大学学报(工学版), 2002, 33(4): 21-25
[18]夏国旺.声发射技术在直升机疲劳实验裂纹检测中的运用[J].直升机技术, 2000, 123(3): 37-41
[19]戴光,徐彦廷等译.无损检测与评价手册[M].北京:中国石化出版社, 2006年.300-316
[20]耿荣生,沈功田,刘时风.模态声发射基本理论[J].无损检测, 2002, 24(7): 302-306.
[21] M. Matsumoto, A. Iida, T. Hamaguchi. Measurement of slider/disk collision forces using acoustic emission source wave analysis [J]. Tribology Transactions, 1993, 36(4): 736 - 740
[22]施克仁.无损检测新技术[M].北京:清华大学出版社. 2007年. 54-62
[23] Qi Gang. Attenuation of acoustic emission body waves in acrylic bone cement and synthetic bone using wavelet time-scale analysis [J]. Journal of Biomedical Materials Research, 2000, 52(1): 148~156
[24]徐春广,王信义,杨大勇,袁洪芳.磨削过程声发射信号传播特性研究[J].北京理工大学学报, 1998, 18(4): 415-419
[25]李建功,邹银辉,刘红,董国伟. AE声发射在波导器中传播规律的数值模拟[J].地下空间与工程学报, 2008, 4(6): 1148-1151
[26]邹银辉,文光才,胡千庭,许进鹏.岩体声发射传播衰减理论分析与实验研究[J].煤炭学报, 2004, 29(6): 663-667
[27]孙立瑛,李一博,靳世久,刘铁根,等.充液管道中声发射波的传播及衰减特性研究[J].压电与声光, 2008, 30(4): 401-403
[28]邹银辉,董国伟,张庆华,吕贵春.波导器中声发射信号传播规律研究[J].矿业安全与环保, 2007, 34(6): 13-14
[29] Hyunje Jeong, Jang Young-Su. Wavelet analysis of plate wave propagation in composite laminates [J]. Composite structure, 2000, 49(4): 443-450
[30] Jeong H. Analysis of plate wave propagation in anisotropic laminates using a wavelet transform [J]. NDT & E International, 2001, 34(3): 185-190
[31]焦敬品,吴斌,何存富,费仁元,李涌.基于模态声发射和小波变换的薄板中导波传播特性的实验研究[J].中国机械工程. 2004, 15(13):1179-1182
[32]毛汉领,王向红,黄振峰.基于小波包分析的声发射信号界面的衰减研究[J].振动与冲击, 2008, 27(9): 139-141
[33] Donoho D. L., Johnstone J. M.. Ideal spatial adaptation by wavelet shrinkage [J]. Biometrika, 1994, 81(3): 425-455
[34] P. J. de Groot, P. A. M. Wijnen, R. B. F. Janssen. Real-time frequency determination of acoustic emission for different fracture mechanisms in carbon/epoxy composites [J]. Composites Science and Technology, 1995, 55(4): 405-412
[35]张闯,刘素贞,杨庆新,金亮,等.基于FFT和小波包变换的电磁声发射信号处理[J].电工技术学报, 2010, 25(4): 24-28
[36]李宝玉,冯进来,戴光,李伟.应用声发射评定金属点蚀过程的研究进展[J].声学技术, 2005, 24(3): 170-172
[37]李录平,邹新元,唐月清.小波变换在声发射信号特征参数检测中的应用[J].振动与冲击, 2001, 20(2) : 67-68
[38]李光海,刘时风.基于小波分析的声发射源定位技术[J].机械工程学报, 2004, 40(7) : 136-140
[39]欧燕,王永宏.声发射检测中的噪声信号及其分析处理[J].陕西省第九届无损检测年会论文集, 2004年. 21-29
[40]姜长泓,王龙山,尤文,翟宁,等.基于平移不变小波的声发射信号去噪研究[J].仪器仪表学报, 2006, 27(6) : 607-610
[41]龚斌,金文,李兆南,金志浩.不同小波基在碳钢材料声发射信号分析中的应用[J].仪器仪表学报, 2008, 29(3) : 506-511
[42]廖传军,李学军,刘德顺.小波再分配尺度谱在声发射信号特征提取中的应用[J].机械工程学报, 2009, 45(2) : 273-279
[43] Terchi A., Au Y. H. J.. Acoustic emission signal processing[J]. Measurement and control, 2001, 34(8): 240-244
[44] Pitton, J.W., Kuansan Wang, Biing-Hwang Juang. Time-frequency analysis and auditory modeling for automatic recognition of speech[J]. Proceedings of the IEEE, 1996, 84(9): 1199-1215
[45]程道来,仪垂杰,郭健翔,等.基于W igner-Ville分布和Wavelet时间尺度的飞机非平稳抖杆背景声分析[J].机械工程学报, 2007, 43(5): 150-154.
[46] Gaunaurd G.C., Strifors H.C.. Signal analysis by means of time-frequency distribution-applications to sonar and radar echoes[J]. Proceedings of the IEEE, 1996, 84(9): 1231-1284
[47] Daponte P., Fazio G., Molinaro, A.. Detection of echoes using time-frequency analysis techniques[J]. IEEE Transactions on Instrumentation and Measurement, 1996, 45(1): 30-40
[48] Andria G., Application of wigner-ville distribution to measurements on transient signals[J]. IEEE Transactions on Instrumentation and Measurement, 1994, 43(2): 187-193
[49] Shie Qian, Dapang Chen. Joint time-frequency analysis[J]. IEEE signal processing magazine, 1999, 16(2): 52-67
[50]姜鸣,陈进,汪慰军.几种Cohen类时频分布的比较及应用[J].机械工程学报, 2003, 19(8): 129-134
[51] K. B. Kim, D. J. Yoon, J. C. Jeong and S. S. Lee. Determining the stress intensity factor of a material with an artificial neural network from acoustic emission measurements[J]. NDT & E International, 2004, 37(6): 423–429
[52] T. Sasikumar, S. RajendraBoopathy, K.M. Usha, E.S. Vasudev. Failure strength prediction of unidirectional tensile coupons using acoustic emission peak amplitude and energy parameter with artificial neural networks [J]. Composites Science and Technology, 2009, 69(7): 1151–1155.
[53] C. C. Kuo. Artificial recognition system for defective types of transformers by acoustic emission[J]. Expert Systems with Applications, 2009, 36(7): 10304–10311
[54] S. Rajendraboopathy, T. Sasikumar, K. M. Usha and E. S. Vasudev. Neural network prediction of failure strength of composite tensile specimens using acoustic emission counts [J]. Journal of Non destructive Testing & Evaluation, 2008, 7(2): 21–26
[55]侯素霞,罗积军,徐军,王汉功.神经网络在声发射信号模式识别中的应用研究[J].应用声学, 2003, 22(1): 44-47
[56]纪洪广,张天森,张志勇,蔡美峰.无损检测中常用声发射参数的分析与评价[J].无损检测, 2001, 23(7): 289-291
[57]王健,金周庚,刘哲军. C/E复合材料声发射信号小波分析及人工神经网络模式识别[J].宇航材料工艺, 2001, 31(5): 49-57
[58]谢秀娴,付攀,曹伟青.声发射和小波包分解技术在刀具磨损状态中的应用[J].中国测试技术, 2006, 32(2): 40-42
[59]戴光,余永增,张颖,于江林.基于小波包分析和BP神经网络的滚动轴承非接触声发射诊断方法[J].化工机械, 2008, 36(5): 274-277.
[60]杨永.数控机床刀具磨损的振动监测法[J].机械, 2009, 36(7): 58-60
[61] Mikael Johnson. Classification of AE transients based on numerical simulation of composite laminates[J]. NDT & E International, 2003, 36(5): 319-329
[62]殷冬萌,王军,刘云飞.木塑复合材料缺陷及损伤的声发射信号特征分析及神经网络模式识别[J].应用声学, 2007, 26(6): 352-356
[63] Achmad Widodo, Bo-Suk Yang. Support vector machine in machine condition monitoring and faultdiagnosis [J]. Mechanical Systems and Signal Processing, 2007, 21 (6): 2560–2574
[64] Achmad Widodo, Eric Y. Kim, Jong-Duk Son. Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine [J]. Expert Systems with Application, 2009, 36 (3): 7252–7261
[65] Siril Yella, Narendra K. Gupta and Mark S. Dougherty. Comparison of pattern recognition techniques for the classification of impact acoustic emissions [J]. Transportation Research Part C: Emerging Technologies, 2007, 15(6): 345-360
[66] B. Samanta, K.R. Al-Balushi, S.A. Al-Araimi. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection [J]. Engineering Applications of Artificial Intelligence , 2003, 16 (7) : 657–665
[67] J. Sun, M. Rahman, Y. S. Wong and G. S. Hong. Multiclassification of tool wear with support vector machine by manufacturing loss consideration [J]. International Journal of Machine Tools and Manufacture, 2004, 44(11): 1179-1187
[68] TONG F., XU X.M., B.L. Luk, K.P. Liu. Evaluation of tile-wall bonding integrity based on impact acoustics and support vector machine [J]. Sensors and Actuators A: Physical, 2008, 144(1): 97-104
[69] Zhang Q. H., Benveniate A.. Wavelet networks [J]. IEEE Trans. Neural networks, 1992, 3(6): 889-898
[70]尹熙鹏,樊养余,段哲民,程伟.基于自适应小波神经网络实现鱼雷超声引信信号消噪[J].鱼雷技术, 2009, 17(5): 19-22
[71]张臻,于海勋.基于小波包及小波神经网络的光纤熔接缺陷模式识别[J].测控技术, 2008, 27(10): 16-19
[72]芮执元,徐龙云,冯瑞成,任丽娜.基于小波神经网络(WNN)的齿轮故障诊断[J].机械传动, 2008, 32(1): 80-83
[73] Mendz A., More T.F., Mendz F. Applications of embedded optical fiber sensors in reinforced concrete buildings and structures [J]. Proc. SPIE, 1989, 1170: 60-69
[74] Barton, J., Reuben, B. Tool wear monitoring by optical techniques [J]. Materials World, 1996, 4(3): 131-132.
[75] McBride, R., Barolan, T, A., Barton, J. S., Wilcox, S. J., Borthwick, et al. Detection of acoustic emission in cutting processes by fibre optic interferometry [J]. Measurement Science and Technology, 1993, 4(10): 1122-1128.
[76]王信义,邢济收,徐春广.空气声发射传感器.中国,实用新型专利, ZL96200752[P],1997年2月19日
[77]邢济收,王信义.空气传导新型声发射传感器的研究[J].北京理工大学学报, 1996, 16(5): 517-522
[78] Albanese, R. A., Banks, H. T., Raye, J. K.. Nondestructive evaluation of materials using pulsed microwave interrogating signals and acoustic wave induced reflections[J]. Inverse Problems, 2002, 18(6): 1935-1958
[79]Smith, G. C., Lee, S. S.. A method for detecting tool wear on a CNC lathe using a Doppler Radar Detector[J]. International Journal of Advanced Manufacturing Technology, 2005, 25: 270-280
[80]Smith, G. C. A Noncontact Method for Detecting Acoustic Emission Using a Microwave Doppler Radar Motion Detector[J]. IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2005, 52(9): 1613-1617
[81]刘贵民.无损检测技术[M].北京:国防工业出版社, 2007年. 8-10
[82] http://www.pacndt.com.cn/ap-plane.htm
[83]艾春安,刘瑜,蔡堃.固体火箭发动机壳体/绝热层界面缺陷的声-超声检测[J].固体火箭技术. 2009, 32(1): 114-118
[84]张移山,张蕾,华庆祥,潘冬冬.声-超声技术用于铝板损伤监控的实验研究[J].机械科学与技术, 2009, 28(10): 1292-1296
[85]袁方,胡斌梁,周知进.基于声-超声技术的钢丝绳强度检测方法的研究[J].机械设计与制造, 2010, 7: 104-105
[86] Finkel P, Godinez V, Miller R. Electromagnetically induced acoustic emission—novel NDT technique for damage evaluation[J]. American Institute of Physics Conference Proceedings, 2001, 557: 1747-1754.
[87] Finkel P, Godinez V. Electromagnetic simulation of the ultrasonic signal for nondestructive detection of ferromagnetic inclusions and flaws[J]. IEEE Transactions on Magnetics, 2004, 40(4): 2179-2181.
[88]刘素贞,杨庆新,金亮,张闯.电磁声发射技术在无损检测中的应用研究[J].电工技术学报, 2009, 24(1): 23-27.
[89]陈中剑.电磁声发射技术研究:[硕士学位论文].天津:河北工业大学, 2006年
[90]陈中剑,杨庆新.电磁声发射技术研究[J].大众科技, 2006, 6: 23-24.
[91]郑惠强.金属结构[M].上海:同济大学出版社,1996年. 86-94
[92]陈海英.常用无损检测方法的特点及应用选择[J].无损探伤. 2009, 33(5): 23-24
[93]金亮.电磁声发射的实验研究:[硕士学位论文].天津:河北工业大学, 2008年
[94]颜威利,杨庆新,汪友华,等.电气工程电磁场数值分析[M].北京:机械工业出版社, 2005年. 214–215
[95]杨明纬.声发射检测[M].北京:机械工业出版社, 2005年. 7-12
[96]陈明祥.弹塑性力学[M].北京:科学出版社, 2007年. 143–152
[97] P. F. Ribeiro, B. K. Johnson, M. L. Crow, et al. Energy storage systems for advanced power applications [J]. Proceedings of the IEEE, 2001, 89(12): 1744-1756
[98] E. L. Neau. Environmental and industrial applications of pulsed power systems [J]. IEEE Transactions on Plasma Science, 1994, 22(1): 2-4
[99] T. Heeren, T. Ueno, Wang Douyan, et al. Novel dual marx generator for microplasma applications [J]. IEEE Transactions on Plasma Science, 2005, 33(4): 1205-1209
[100]张慧妍,韦统振,齐智平.超级电容器储能装置研究[J].电网技术, 2006, 30(8): 1-6
[101]林福昌,代新,徐智安,李劲,等.高储能密度电容器[J].强激光与粒子束, 2003, 15(1): 94-96
[102]李建辉,刘秀成,王春明,王赞基.电感储能型线圈炮系统的建模和参数优化[J].电工电能新技术, 2007, 26(2): 54-58
[103]戴兴建,李奕良,于涵.高储能密度飞轮结构设计方法[J].清华大学学报:自然科学版, 2008, 48(3): 378-381
[104]李维波,毛承雄,李启炎,等.神光III强激光能源模块测量线圈研究[J].中国电机工程学报, 2003, 23(5):53-57
[105] Jack S, Bernardes, Michael F, Stumborg, and Thomas E. Jean. Analysis of a capacitor-based pulsed-power system for driving long-range electromagnetic guns[J]. IEEE Transactions on Magnetics, 2003, 39(1): 486-487
[106] J. Biebach, P. Ehrhart, A. Muller. Compact modular power supplies for superconducting inductivestorage and for capacitor charging[J]. IEEE Transactions on Magnetics, 2001, 37(1): 353-354
[107] Chuang Zhang, Suzhen Liu, Qingxin Yang, Liang Jin. Experimental study on high-current pulse generator for electromagnetically induced acoustic emission[J]. The 11th International Conference on Electrical Machines and Systems(ICEMS2008), 2008. 2205-2207
[108]曹树谦,张文德,萧龙翔.振动结构模态分析:理论、实验与应用[M].天津:天津大学出版社, 2001年. 80-87
[109] Gong Z., Nyborg E. O., Oommen G.. Acoustic emission monitoring of steel railroad bridges [J]. Materials Evaluation, 1992, 50(6): 883-887.
[110]彭振明.声发射技术及其应用[M].北京:机械工业出版社, 1985年. 30-36
[111]贾慧明,张广纯,徐可北,张建卫,等. GB/T 8651-2002,金属板材超声板波探伤方法.北京:中国标准出版社, 2002年
[112]张冠生.电器理论基础[M].北京:机械工业出版社,1989年.
[113]万建伟.信号处理仿真技术[M].长沙:国防科技大学出版社,2008年. 88-89
[114]马世伟.非平稳信号的参数自适应时频表示及其应用的研究[M].上海:上海大学出版社,2000年. 16
[115]张晔.信号时频分析及应用[M].哈尔滨:哈尔滨工业大学出版社,2006年. 104-108
[116]葛哲学,陈仲生. MATLAB时频分析技术及其应用[M].北京:人民邮电出版社,2006年. 14-18
[117] Auger F., Flandrin P.. Improving the readability of time-frequency and time-scale representations by the reassignment method [J]. IEEE Transactions on Signal Processing, 1995, 43(5): 1068-1089
[118]飞思科技产品研发中心.小波分析理论与MATLAB7实现[M].北京:电子工业出版社, 2005年. 14-18
[119] Velayudham A., Krishnamurthy R., Soundarapandian T.. Acoustic emission based drill condition monitoring during drilling of glass/phenolic polymeric composite using wavelet packet transform [J]. Materials Science and Engineering A, 2005, 412(1): 141-145
[120]耿荣生.全尺寸飞机疲劳实验过程中的无损检测及声发射监测技术研究[J].南昌航空大学学报(自然科学版), 2007, 21(3): 1-7.
[121] S. G. Chang, B. Yu, M. Vetterli. Adaptive wavelet thresholding for image denoising and compression [J]. IEEE Transactions on Image Processing, 2000, 9(9): 1532-1543
[122] Y. Niu, L. Shen. Wavelet denoising using the pareto optimal threshold [J]. International Journal of Computer Science and Network Security, 2007, 7(1): 30-34
[123]丛爽.面向MATLAB工具箱的神经网络理论与应用[M].(第三版).合肥:中国科学技术大学出版社, 2009年. 64-86
[124]刘志刚,王晓茹,何正友,钱清泉.小波变换、神经网络和小波网络的函数逼近能力分析与比较[J].电力系统自动化, 2002, 26(20): 39-44
© 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 |