小波与分形在摩擦焊超声检测信号处理中的应用研究

【作者】 郭建平；

【导师】 王玉；

【作者基本信息】 西北工业大学 ， 机械制造及其自动化， 2006， 硕士

【摘要】 作为一种先进的固态连接技术,摩擦焊在航空航天等诸多领域得到了广泛应用。本文以航空发动机常用材料GH4169高温合金摩擦焊接头的超声检测信号为研究对象,进行了小波变换与分形理论在摩擦焊超声检测信号缺陷识别与分类中的应用研究,取得了较好效果。 论文首先对选题以及相关背景进行了探讨,并对相关的小波基础理论进行了阐述,针对摩擦焊超声检测信号及其噪声的特点,主要对小波基函数的选取、分解层数的确定以及阈值方法的选择进行了详细探讨。通过小波变换很好地实现了摩擦焊超声检测信号去噪,为进一步对信号进行分形分析奠定了基础。 采用小波变换研究摩擦焊超声检测信号自相似性,结果表明摩擦焊超声检测信号序列中存在分形结构,可以用分形维数来做定量描述,因此将分形理论应用于摩擦焊超声检测信号分析,把盒维数作为信号复杂程度的判断标准,并通过对正弦信号的盒维数计算验证其计算方法的正确性。在对摩擦焊超声检测信号的盒维数计算过程中,采用网格法确定无标度区,对所有信号的盒维数作统计分析,建立信号类别和盒维数大小间的关系,实现缺陷的准确检测和分类。 最后利用小波变换对摩擦焊超声检测图像进行边缘提取,通过与传统边缘检测算子的比较,突显了小波多尺度边缘检测的优越性,实现了缺陷位置的大致划分,为实际工程应用打下了基础。更多还原

【Abstract】 As a new advanced technology of solid state jointing, fraction welding has been used in aviation, spaceflight and other fields abroad. In the thesis, we investigate the application of wavelet transform and theory of fractal in fraction welding ultrasonic testing signals to identify the defects and classify with the ultrasonic signal detected from friction welding joints which is made from GH4169.Firstly, we discuss the selected topic and its background of the paper, and then go into the basic theory of wavelet transform. Aiming at the characteristic of friction welding ultrasonic testing signals and noise, we mainly focus on the selection of suitable wavelet bases and threshold, and confirming the divided layer number. We extract signals from the noisy signals successfully by wavelet transform, and prepare for the future.Then wavelet transform is used to study the ultrasonic and test signals’ self-similarity characteristic. The result shows that they are fractal and can be characterized quantitatively by fractal dimension. So theory of fractal is introduced to analyze fraction welding ultrasonic testing signals;and box dimension is also introduced as the judgment of signal’s complex degree. The box dimension of sin signal is used to verify the correctness of the method. We use box counting method to identify fractal scaleless band during the computing of fraction welding ultrasonic testing signals, do statistical analysis on box dimension of all signals, and find the relationship between box counting dimension and signal classes so that the defects can be detected and classified exactly.Finally, we use wavelet transform for fraction welding ultrasonic testing image edge detection, and approximately partition defects location, witch indicate wavelet transform is precede than tradition edge detecting methods.更多还原