小波变换在管道漏磁检测信号分析技术中的应用
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摘要
缺陷鉴别是管道漏磁检测的重要环节，而实现计算机智能化缺陷鉴别是漏磁检测的最终目的。本文叙述了油气管道漏磁检测及其缺陷鉴别技术的研究意义和国内外发展概况。在对各种信号分析方法进行比较之后，选择了小波变换作为本课题的主要研究工具。
论文首先叙述了漏磁无损检测的基本原理，介绍了管道漏磁检测装置的工作原理和基本结构，对ANSYS有限元分析软件在本课题中的应用作了简要介绍。
其后，本文详细介绍了小波变换的基本理论，包括多分辨率分析(MRA)思想和Mallat分解—重构算法。随后，利用小波变换在分析信号时的聚焦特性，介绍了信号奇异性检测理论以及用小波变换分析管道漏磁信号的途径，并与传统的傅立叶分析作比较，从而验证了小波变换用于管道漏磁信号分析的优越性和有效性。
最后，论文介绍了小波包变换的相关理论及其应用。小波包变换是在小波变换的基础上发展起来的，它的一个最大的优点是能够根据信号自适应地产生一组最佳基来表征信号。按照这一理论，我们对一些模拟信号和管道漏磁信号作了小波包分解，然后从中选出最优小波包基，再把被选中的小波包基的相应砌块表示在时—频相平面上的相应位置上，并用灰度级别表示这个分量权重的强弱，结果较形象地把被分析信号的时—频结构表现出来，达到了预期的目的。
本课题的成功是管道漏磁检测技术发展的重要步骤，通过对此项技术的研究，可以掌握大量缺陷漏磁信号的时一频特征和分布规律，对于管道漏磁信号处理和计算机智能化缺陷鉴别奠定了基础。
Defect discriminating is an important part in pipeline magnetic flux leakage (MFL) detecting, and to realize intelligent defect discriminating is the final intent of MFL detecting. The paper presents the significance of pipeline magnetic flux leakage detecting technology developing and defect discriminating technology developing. By comparing the different methods of signal analyzing, wavelet transform is selected as the main studying tool for the project.
Firstly, the basic theory of MFL detecting together with the structure and the working procedure of pipeline MFL detecting instrument are introduced. Then it briefly describes the finite element analysis software ANSYS on it's application in this task.
Secondly, it gives out the basic theory of wavelet transform in detail, concluding multi-resolution analysis (MRA) and decompose-reconstruct arithmetic of Mallat. Subsequently, using the focusing character of wavelet transform in analyzing signal, it recommends the theory of testing signal singularity and the route of analyzing pipeline MFL signal by wavelet transform. Then, compared the result with Fourier transform, wavelet transform's superiority and validity are validated in analyzing pipeline MFL signal.
Lastly, the paper introduces wavelet package transform and its application. Wavelet package transform grows on the basis of wavelet transform. Its most excellence is to adjustably produce a best base as a token to signal according to different signal. In term of this theory, we decompose some simulate signal and pipeline MFL signal by wavelet package transform. Then select the best wavelet package base out of the decomposed coefficient and figure the brickwork of the best base on the relevant position in time-frequency plane showing the strong or weak of every coefficient. As a result, the time-frequency structure of signal analyzed is relatively visually represented; that is to say, an anticipative result achieves.
The success of this project plays an important role to the developing of pipeline MFL detecting technology. Through it, we can master lots of defect
MFL signal's c character and its distributing rules. And it can establish a foundation for pipeline MFL signal processing and intelligent defect discriminating.
论文首先叙述了漏磁无损检测的基本原理，介绍了管道漏磁检测装置的工作原理和基本结构，对ANSYS有限元分析软件在本课题中的应用作了简要介绍。
其后，本文详细介绍了小波变换的基本理论，包括多分辨率分析(MRA)思想和Mallat分解—重构算法。随后，利用小波变换在分析信号时的聚焦特性，介绍了信号奇异性检测理论以及用小波变换分析管道漏磁信号的途径，并与传统的傅立叶分析作比较，从而验证了小波变换用于管道漏磁信号分析的优越性和有效性。
最后，论文介绍了小波包变换的相关理论及其应用。小波包变换是在小波变换的基础上发展起来的，它的一个最大的优点是能够根据信号自适应地产生一组最佳基来表征信号。按照这一理论，我们对一些模拟信号和管道漏磁信号作了小波包分解，然后从中选出最优小波包基，再把被选中的小波包基的相应砌块表示在时—频相平面上的相应位置上，并用灰度级别表示这个分量权重的强弱，结果较形象地把被分析信号的时—频结构表现出来，达到了预期的目的。
本课题的成功是管道漏磁检测技术发展的重要步骤，通过对此项技术的研究，可以掌握大量缺陷漏磁信号的时一频特征和分布规律，对于管道漏磁信号处理和计算机智能化缺陷鉴别奠定了基础。
Defect discriminating is an important part in pipeline magnetic flux leakage (MFL) detecting, and to realize intelligent defect discriminating is the final intent of MFL detecting. The paper presents the significance of pipeline magnetic flux leakage detecting technology developing and defect discriminating technology developing. By comparing the different methods of signal analyzing, wavelet transform is selected as the main studying tool for the project.
Firstly, the basic theory of MFL detecting together with the structure and the working procedure of pipeline MFL detecting instrument are introduced. Then it briefly describes the finite element analysis software ANSYS on it's application in this task.
Secondly, it gives out the basic theory of wavelet transform in detail, concluding multi-resolution analysis (MRA) and decompose-reconstruct arithmetic of Mallat. Subsequently, using the focusing character of wavelet transform in analyzing signal, it recommends the theory of testing signal singularity and the route of analyzing pipeline MFL signal by wavelet transform. Then, compared the result with Fourier transform, wavelet transform's superiority and validity are validated in analyzing pipeline MFL signal.
Lastly, the paper introduces wavelet package transform and its application. Wavelet package transform grows on the basis of wavelet transform. Its most excellence is to adjustably produce a best base as a token to signal according to different signal. In term of this theory, we decompose some simulate signal and pipeline MFL signal by wavelet package transform. Then select the best wavelet package base out of the decomposed coefficient and figure the brickwork of the best base on the relevant position in time-frequency plane showing the strong or weak of every coefficient. As a result, the time-frequency structure of signal analyzed is relatively visually represented; that is to say, an anticipative result achieves.
The success of this project plays an important role to the developing of pipeline MFL detecting technology. Through it, we can master lots of defect
MFL signal's c character and its distributing rules. And it can establish a foundation for pipeline MFL signal processing and intelligent defect discriminating.
引文
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[5] 何辅云，石油管道的高速检测与缺陷识别，无损检测，2000，22(5)206-208
[6] 吴先梅、钱梦騄，有限元法在管道漏磁检测中的应用，无损检测，2000．Vol．22 No．4 147-150
[7] 余浩然、吴斌、陈丽萍，漏磁通法油气管道在役检测技术，实用测试技术，1997，No．5 1-9
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[23] 何岭松等，小波分析及其在设备故障诊断中的应用，华中理工大学学报，1993，Vol．21 No．1 82-87
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[28] 胡春生，激光对潜通信中信号预处理及图像传输的研究，华中理工大学硕士学位论文，1999 8-35
[29] 蔡志强等，故障诊断与切削颤震的小波分析，华中理工大学学报，1993．Vol．21 No．1 88-94
[30] 彭玉华等，应用小波变换于电磁场目标后向散射信号的时频图分析，电子学报，1995，Vol．23 No．9 109-11
[31] 刘东辉等，一种基于小波分析的除噪方法，仪器仪表学报，2000，Vol．21 No．6 636-637，656
[32] 韩雷、许静，小波分析用于机械系统突发信号在线检测，仪器仪表学报，2001，Vol．22 No．1 5-9
[33] 刘刚、屈梁生，应用连续小波变换提取机械故障的特征，西安交通大学学报，2000，Vol．34 No．11 74-77
[34] Tsutomu Hoshimiya, Nondestructive evaluation of surface defects under dry/wet environment by the use of photacoustic and photothermal electrochemical imaging, NDT&E International 32(1999) 133-137
[35] S.Mandayam, L.Udpa, S.S.Udpa and W.Lord Wavelet-based permeability compensation technique for characterizing magnetic flux leakage images, NDT&E International 33(2000) No.5 297-303
[36] F.Lingvall, T.Stepinski Automatic detecting and classifying defects suring eddy current inspection of riveted lap-joints, NDT&E International
33(2000) 47-55
[37] Jorge J.Perdomo, Robinson J.Medina Risk-based analysis suggests expanding maintenance interval, PIPELINE & GAS INDUSTRY/MARCH 2000 33-38
[38] Harvey Haines, Patrick C.Porter, Lisa Barksull, Muhammad Afzal and Jun-Youl Lee Advanced MFL signal analysis sids pipe corrosion detection, PIPILINE & GAS INDUSTRY/MARCH 1999 49-63
[39] R.R.Coifman, M.V.Wickerhauser, Entropy-based Algorithms for Best Basis Selection, IEEE Trans On Information Theory, Vol.38 No.2, March,1992
[40] James S.Walker, A Primer on Wavelets and their Scientific Application, CHAPMAN & HALL/CRC, 1999 95-129
[41] I.Daubechies, Where do wavelets come from? —A personal point of view, Proc, IEEE, 1984(5) 510-513
[42] I.Daubechies, The wavelet transform, time-frequency localization and signal analysis, IEEE Trans, PAMI-11(7), 674-693, 1989
[43] A.K.Louis P.MaaB A.Rieder, Wavelets Theory and Applications, Wiley inter-science, 1997 1-22, 73-203,231-239
[44] Howard L.Resnikoff Raymond O.Wells, Jr., Wavelets Analysis, Sprinter, 1998 364-365
[45] Ali N. Akansu Michael J.Medley, Wavelet, Sub-band and Block Transforms in Communications and Multimedia, Kluwer Academic Publishers, 1999 183-205
[46] AK Peters Wellesley, Massachusetts, The World According to Wavelets, Barbara Burke Hubbard, 1998 149-222
[47] I.Daubechies, The lectures on wavelets, Philadelphia: SIAM Publ, 1992
[48] Antonini M, Barlaud M, Mathieu P, et al., Image coding using wavelet transforms, IEEE Trans on Image Processing, 1992,1(2) 205-220
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[2] 陈积懋，无损检测新技术20年回顾，无损检测，1998，Vol．20 No．7 181-185
[3] 王玉忠、陈建兰，漏磁检测技术在我国管道中的应用，化学清洗，1998．Vol．14 NO．5 24-27
[4] 黄松岭、李路明等，在用管道漏磁检测装置的研制，无损检测，1999．Vol．21 No．8 344-346
[5] 何辅云，石油管道的高速检测与缺陷识别，无损检测，2000，22(5)206-208
[6] 吴先梅、钱梦騄，有限元法在管道漏磁检测中的应用，无损检测，2000．Vol．22 No．4 147-150
[7] 余浩然、吴斌、陈丽萍，漏磁通法油气管道在役检测技术，实用测试技术，1997，No．5 1-9
[8] ANSYS高级技术分析指南，美国ANSYS公司北京办事处，2000
[9] ANSYS电磁场分析指南，美国ANSYS公司北京办事处，1998
[10] ANSYS非线性分析指南，美国ANSYS公司北京办事处，2000
[11] 杨福生，小波变换的工程分析与应用，科学出版社，2000
[12] 胡昌华等，基于MATLAB的系统分析与设计，西安电子科技大学出版社，2000
[13] (美)崔锦泰，小波分析导论，西安交通大学出版社，1997
[14] 彭玉华，小波变换与工程应用，科学出版社，2000 8-10，13-20，24-54，63-102，115-121
[15] 何光明等，小波变换在地震高分辨率处理中的应用，石油物探，1996，Vol．35 No．2
[16] 杨福生，带通信号的采样定理，信号处理，1986，2(1)58-61
[17] 李世雄，小波变换及其应用，高教出版社，1997 25-108，183-217
[18] 张贤达、保铮，非平稳信号分析与处理，国防工业出版社，1999 12-31，105-109，224-306
[19] 李建平，小波分析与信号处理-理论、应用及软件实现，重庆出版社，2001 3-15，46-95，125-137，193-218
[20] 胡军，基于小波变换的信号综合处理，中国科学技术大学硕士学位论文11-17
[21] 刘国华，时频分析技术在核设备无损检测中的应用研究，西安交通大学博士学位论文，1998 51-72
[22] 张亮，毫米波导弹头目标识别与实时处理研究，国防科技大学博士论文，1997 36-48
[23] 何岭松等，小波分析及其在设备故障诊断中的应用，华中理工大学学报，1993，Vol．21 No．1 82-87
[24] 吴波等，基于小波变换的波形特征抽取与识别，华中理工大学学报，1993．Vol．21 No．1 77-81
[25] 盛硕，小波变换和小波包在特征提取和去噪中的应用，清华大学硕士学位论文，1997 1-14
[26] 吴爱弟，小波变换理论及其在地震资料处理中的应用研究，石油大学
博士学位论文，1995
[27] 赵众等，基于小波包变换的生产过程低信噪比突变故障检测，浙江大学学报(增刊)，1998，6 431-439
[28] 胡春生，激光对潜通信中信号预处理及图像传输的研究，华中理工大学硕士学位论文，1999 8-35
[29] 蔡志强等，故障诊断与切削颤震的小波分析，华中理工大学学报，1993．Vol．21 No．1 88-94
[30] 彭玉华等，应用小波变换于电磁场目标后向散射信号的时频图分析，电子学报，1995，Vol．23 No．9 109-11
[31] 刘东辉等，一种基于小波分析的除噪方法，仪器仪表学报，2000，Vol．21 No．6 636-637，656
[32] 韩雷、许静，小波分析用于机械系统突发信号在线检测，仪器仪表学报，2001，Vol．22 No．1 5-9
[33] 刘刚、屈梁生，应用连续小波变换提取机械故障的特征，西安交通大学学报，2000，Vol．34 No．11 74-77
[34] Tsutomu Hoshimiya, Nondestructive evaluation of surface defects under dry/wet environment by the use of photacoustic and photothermal electrochemical imaging, NDT&E International 32(1999) 133-137
[35] S.Mandayam, L.Udpa, S.S.Udpa and W.Lord Wavelet-based permeability compensation technique for characterizing magnetic flux leakage images, NDT&E International 33(2000) No.5 297-303
[36] F.Lingvall, T.Stepinski Automatic detecting and classifying defects suring eddy current inspection of riveted lap-joints, NDT&E International
33(2000) 47-55
[37] Jorge J.Perdomo, Robinson J.Medina Risk-based analysis suggests expanding maintenance interval, PIPELINE & GAS INDUSTRY/MARCH 2000 33-38
[38] Harvey Haines, Patrick C.Porter, Lisa Barksull, Muhammad Afzal and Jun-Youl Lee Advanced MFL signal analysis sids pipe corrosion detection, PIPILINE & GAS INDUSTRY/MARCH 1999 49-63
[39] R.R.Coifman, M.V.Wickerhauser, Entropy-based Algorithms for Best Basis Selection, IEEE Trans On Information Theory, Vol.38 No.2, March,1992
[40] James S.Walker, A Primer on Wavelets and their Scientific Application, CHAPMAN & HALL/CRC, 1999 95-129
[41] I.Daubechies, Where do wavelets come from? —A personal point of view, Proc, IEEE, 1984(5) 510-513
[42] I.Daubechies, The wavelet transform, time-frequency localization and signal analysis, IEEE Trans, PAMI-11(7), 674-693, 1989
[43] A.K.Louis P.MaaB A.Rieder, Wavelets Theory and Applications, Wiley inter-science, 1997 1-22, 73-203,231-239
[44] Howard L.Resnikoff Raymond O.Wells, Jr., Wavelets Analysis, Sprinter, 1998 364-365
[45] Ali N. Akansu Michael J.Medley, Wavelet, Sub-band and Block Transforms in Communications and Multimedia, Kluwer Academic Publishers, 1999 183-205
[46] AK Peters Wellesley, Massachusetts, The World According to Wavelets, Barbara Burke Hubbard, 1998 149-222
[47] I.Daubechies, The lectures on wavelets, Philadelphia: SIAM Publ, 1992
[48] Antonini M, Barlaud M, Mathieu P, et al., Image coding using wavelet transforms, IEEE Trans on Image Processing, 1992,1(2) 205-220
[49] J.Combes ed., Wavelet, time-frequency methods and phase space, Proc. First Intern. Conf. on Wavelet, Springer-Verlag, 1989
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