A Bayesian approach for sparse flaw detection from noisy signals for ultrasonic NDT

Ultrasonic pulse-echo methods for flaw detection have been widely employed as an effective strategy for nondestructive evaluation, and flaw detection plays an important role due to its ability to detect localized damage in structures. In practice, flaw damage typically occurs in a few areas in the material, resulting in only a few echoes that exist in a received signal, which motivates us to detect flaws using sparse representation methods. In this study, the noisy signal is modelled by a linear combination of modulated Gaussian pulses, which form an over-complete dictionary. The over-complete dictionary is designed such that the sparseness of the representation is expected. A robust sparse Bayesian learning framework is employed with the goal of enforcing model sparseness and reducing the source of ill-conditioning in the inversion problem for flaw detection. Useful information, including the range of frequency and bandwidth parameters of the flaw echoes, is also estimated. Based on this information, we propose a post-processing scheme for structure noise elimination and flaw detection. The capability of the proposed method is quantitatively evaluated by simulation studies and is further validated by the experimental data.     

经验模态分解在电力绝缘气体SF6红外光谱去噪中的应用

经验模态分解(EMD)是以信号极值特征尺度为度量的时空滤波器,它充分保留了信号本身的非线性和非平稳特征,在信号去噪中具有较大的优势。本文以电力绝缘气体SF6为研究对象,在介绍E MD分解方法的基础上,首先对含噪的SF6光谱信号做EMD分解,得到各阶本征模态函数(IMF),然后对高频的IMF分量用阈值法进行处理,把经过阈值处理后的高频I MF分量与低频IMF分量叠加重构得到去噪后的信号。分析了在不同噪声水平上与小波阈值去噪方法的处理效果。实验结果表明EMD阈值去噪法有效地去除了噪声,较好地保留了光谱的细节信息,与小波阈值去噪方法相比较具有自适应的优势。     

Improving the time resolution and signal noise ratio of ultrasonic testing of welds by the wavelet packet

In ultrasonic testing of welds, detection of small flaws is often difficult by the superimposed noise due to the grain structure of the material. The scattering of ultrasonic waves from grain boundaries can interfere and introduce disturbance in the received signal that can sometimes mask indications due to a small but potentially dangerous defect. However, to enhance the flaw characterization, methods based on ‘thresholding’ have given good results only when the signal to noise ratio is high, and since bandwidth of the reflected signal as well as its principal frequency is subject to wide variation, it is impossible to create an appropriate band pass filter. So linear filtering does not provide good results, because both, the structure noise and flaw signal concentrate energy in the same frequency band. Non-linear filtering can be used to reduce or suppress the noise from ultrasonic signals. One way out is to use the time frequency transforms, the method is based on the wavelet packet decomposition. The Debauchee function of order 8 [Daubauchee I. Orthogonal bases of capacity wavelets. Commun Pure Appl Math 1998;41] has been chosen as the analyzing function, and each measured ultrasonic signal is analyzed by a filter bank through only three levels of decomposition. This work demonstrates that the following analysis is very efficient with respect to signal recovery from noisy data. The experimental results have shown that the proposed method has excellent performances on SNR enhancements.     

一种实时小波降噪算法

通过施加滑动数据窗的方式，总剪取实时数据的最新一段，再利用区间小波降噪算法，构造出实时小波降噪算法。实时降噪计算量通过选用正交紧支撑小波和限制小波分解深度降低。区间小波变换的边界干扰通过数据输出的时间延迟避免。引入冗余采样数据消除数据更新的时间滞后。用仿真实验验证了算法的降噪能力。     

一种强噪声背景下的微弱信号检测的新方法

基于自适应滤波算法对小波滤波器的构造方法进行了研究，重点在于解决对强噪声环境下微弱信号提取所需要的小波变换的子带自适应滤波器的构造，把小波变换技术和自适应滤波技术应用于微弱信号的检测。提出基于自适应小波变换的心电信号的检测，利用小波变换的子带编码理论，通过在多个子带权值的自适应匹配，回复后拟合微弱信号。仿真结果表明，该方法可进一步改善信号的检测能力，在检测微弱信号的特征和改善信噪比方面是一种十分有效的方法。     

近红外图像消噪方法的对比实验研究

为了选择一种既能消除近红外图像噪声又能达到保持其图像边缘要求的消噪方法,通过比较各种常见的图像去噪算法,采用信噪比(SNR)、均方根误差(RMSE)和图像灰度曲面图等作为图像去噪效果的评估.对实际拍摄的多幅近红外图像进行消噪效果对比,仿真实验结果表明:在综合考滤图像去噪平滑效果、图像清晰程度和时间复杂度的基础上,分形消噪法较优,可应用于近红外图像的消噪处理.     

经验模态分解在称重检测中的应用研究

针对目前装载车称重系统存在误差较大的现状,设计了基于经验模态分解方法的动态称重检测方法。在分析称重信号采集工作原理的基础上,建立采集信号的数学模型。同时将信号进行经验模态分解,对归一化的本征模态则采用Bayes Shrink法进行有效去噪处理。试验结果表明,当装载车车速较快和较慢时,该方法均能有效抑制噪声,使处理后的信号平稳。     

基于均值漂移的提取羽毛杆的去噪方法研究

为有效去除羽毛片图像中的噪声,便于毛叶与毛杆的分割,提出用均值漂移算法对毛片图像进行平滑滤波,然后对图像进行区域合并,最后利用阈值分割算法提取羽毛杆.实验结果表明:利用均值漂移算法能有效去噪,可以获得更高的图像评价指数和更好的视觉效果,可有效滤除不必要的纹理背景信息,为准确提取毛杆奠定重要基础.     

基于EEMD与DSS-ApEn的脑电信号消噪方法

为了在消除信号中噪声的同时尽可能保留有效信息,提出了一种基于集合经验模态分解(Ensemble Empirical Mode Decomposition, EEMD)和降噪源分离(De-noising Source Separation, DSS)与近似熵(Approximate Entropy, ApEn)相结合的脑电信号消噪方法。利用EEMD分解算法将含噪脑电信号分解为若干个内蕴模态函数(Intrinsic Mode Functions, IMF)分量,滤除最高频分量后的IMF分量应用DSS分离出各独立源信号,再选择频谱近似熵最大的独立源信号作为去噪信号。仿真和真实脑电信号的消噪实验表明,与独立EEMD消噪方法以及基于EEMD与改进提升小波消噪方法相比,本文提出的方法消噪效果更好。     

结构森林边缘检测与渗流模型相结合的混凝土表面裂缝检测

针对现有混凝土表面裂缝检测方法对不同环境下采集的裂缝图像集检测效果鲁棒性不强的问题,引入基于结构森林的学习框架来提取裂缝边缘,并融合改进的快速渗流算法检测裂缝,以保证检测精确率和效率。使用分段函数对彩色图像进行线性变换以增强裂缝,根据包含裂缝块的局部结构特征及彩色图像积分通道特征,利用结构森林边缘检测器快速提取裂缝边缘,同时结合改进的渗流模型快速渗流边缘并去噪。最后,利用形态学方法,连接较小断裂并填充孔洞。在收集的各类裂缝图像集上的实验结果表明,该算法处理速度快、鲁棒性好,且裂缝提取的精确度优于现有算法。