【作者】 王玉梅；

【导师】 杨理践；

【作者基本信息】 沈阳工业大学 ， 检测技术与自动化装置， 2002， 硕士

【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。更多还原

【Abstract】 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 defectMFL signal’s c character and its distributing rules. And it can establish a foundation for pipeline MFL signal processing and intelligent defect discriminating.更多还原