基于小波包分析的激光超声缺陷信号处理方法研究

发布时间：2018-01-25 07:48

本文关键词： 激光超声检测小波包去噪频域分析小波包能量分解能量特征　出处：《中北大学》2017年硕士论文　论文类型：学位论文

【摘要】：激光超声无损检测技术是目前的研究热点之一,它具有其自身的优势,是一种适用于检测恶劣环境下的无损评估技术,目前已经广泛应用到各种工业领域。为了更好的对被测物体进行缺陷评估,对于激光超声信号的后期处理与分析逐渐成为激光超声表面缺陷检测技术中的重要研究内容。本文主要是基于小波包分析的方法,对激光超声缺陷信号进行一系列的处理研究。首先,对激光超声波的激发机理和声表面波进行了概述,介绍了激光超声检测系统的组成与工作原理,并对实验进行了详细说明;同时叙述了几种用于处理激光超声表面缺陷信号的处理方法。其次,介绍了小波去噪和小波包去噪的原理与步骤,对小波参数的选取进行了讨论。通过对比两种去噪方法对模拟信号的去噪效果,结果证明小波包去噪方法具有较好的自适应性,能够很好的识别并分离信号与噪声成分,信噪改善比平均达到5.0042dB,相对于小波去噪方法信噪比平均提升了0.6683dB,相对性能增强了13.37%。同时在小波包降噪的基础上,对反射回波信号进行了频域分析,提取出信号的频域特征量,并与仿真信号作对比,结果表明去噪后的实验信号频域特征与有限元仿真信号的频域特征相吻合,验证了实验检测缺陷的可靠性与小波包降噪方法的有效性,为后面的研究奠定了理论基础。最后,简述了小波分析的基本理论、多分辨分析、小波包分析等相关理论内容,利用小波分析理论中的多分辨分析和小波包分解,研究了激光超声缺陷信号不同频段内的能量特征提取算法,从而提取出不同缺陷裂纹深度下反射回波信号的能量分布特征。分析结果表明,以缺陷深度0.5mm为分界线,两侧的信号能量分布有差异,为激光超声表面缺陷检测提供了参考依据。
[Abstract]:Laser ultrasonic nondestructive testing (NDT) technology is one of the research hotspots at present. It has its own advantages and is a kind of nondestructive assessment technology which is suitable for the detection of harsh environment. At present, it has been widely used in a variety of industrial fields. In order to better evaluate the defects of the object under test. The post-processing and analysis of laser ultrasonic signal has gradually become an important research content in laser ultrasonic surface defect detection technology. This paper is mainly based on wavelet packet analysis method. A series of research on laser ultrasonic defect signal processing. Firstly, the excitation mechanism of laser ultrasonic wave and surface acoustic wave are summarized, and the composition and working principle of laser ultrasonic detection system are introduced. The experiment is described in detail. At the same time, several processing methods of laser ultrasonic surface defect signal are described. Secondly, the principle and steps of wavelet denoising and wavelet packet de-noising are introduced. The selection of wavelet parameters is discussed. The results show that the wavelet packet denoising method has good adaptability by comparing the two denoising methods to the effect of analog signal de-noising. It can identify and separate the signal and noise components very well. The improvement ratio of signal to noise is 5.0042 dB on average, which is 0.6683dB higher than that of wavelet denoising method. At the same time, on the basis of wavelet packet noise reduction, the reflected echo signal is analyzed in frequency domain, and the frequency domain characteristic quantity of the signal is extracted and compared with the simulation signal. The results show that the frequency domain characteristics of the experimental signals after denoising are consistent with those of the finite element simulation signals, and the reliability of the experimental detection defects and the effectiveness of the wavelet packet denoising method are verified. Finally, the basic theory of wavelet analysis, multi-resolution analysis, wavelet packet analysis and other related theoretical content is briefly described. Based on the wavelet analysis theory and wavelet packet decomposition, the energy feature extraction algorithm in different frequency bands of laser ultrasonic defect signal is studied. The energy distribution characteristics of reflected echo signals with different depth of defect crack are extracted. The results show that the energy distribution of the two sides is different with the 0.5 mm depth of defect as the dividing line. It provides a reference for laser ultrasonic surface defect detection.
【学位授予单位】：中北大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN249;TN911.7

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