本文關鍵詞： 激光超聲檢測 小波包去噪 頻域分析 小波包能量分解 能量特征　出處：《中北大學》2017年碩士論文　論文類型：學位論文

【摘要】：激光超聲無損檢測技術是目前的研究熱點之一,它具有其自身的優(yōu)勢,是一種適用于檢測惡劣環(huán)境下的無損評估技術,目前已經廣泛應用到各種工業(yè)領域。為了更好的對被測物體進行缺陷評估,對于激光超聲信號的后期處理與分析逐漸成為激光超聲表面缺陷檢測技術中的重要研究內容。本文主要是基于小波包分析的方法,對激光超聲缺陷信號進行一系列的處理研究。首先,對激光超聲波的激發(fā)機理和聲表面波進行了概述,介紹了激光超聲檢測系統(tǒng)的組成與工作原理,并對實驗進行了詳細說明;同時敘述了幾種用于處理激光超聲表面缺陷信號的處理方法。其次,介紹了小波去噪和小波包去噪的原理與步驟,對小波參數的選取進行了討論。通過對比兩種去噪方法對模擬信號的去噪效果,結果證明小波包去噪方法具有較好的自適應性,能夠很好的識別并分離信號與噪聲成分,信噪改善比平均達到5.0042dB,相對于小波去噪方法信噪比平均提升了0.6683dB,相對性能增強了13.37%。同時在小波包降噪的基礎上,對反射回波信號進行了頻域分析,提取出信號的頻域特征量,并與仿真信號作對比,結果表明去噪后的實驗信號頻域特征與有限元仿真信號的頻域特征相吻合,驗證了實驗檢測缺陷的可靠性與小波包降噪方法的有效性,為后面的研究奠定了理論基礎。最后,簡述了小波分析的基本理論、多分辨分析、小波包分析等相關理論內容,利用小波分析理論中的多分辨分析和小波包分解,研究了激光超聲缺陷信號不同頻段內的能量特征提取算法,從而提取出不同缺陷裂紋深度下反射回波信號的能量分布特征。分析結果表明,以缺陷深度0.5mm為分界線,兩側的信號能量分布有差異,為激光超聲表面缺陷檢測提供了參考依據。
[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.
【學位授予單位】：中北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN249;TN911.7

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