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为了实现超声检测对缺陷的智能识别,引入小波包分析与人工神经网络技术。该方法利用超声信号进行三层小波包分解,提取各频率成分能量为特征值。建立并训练了一种BP缺陷识别的神经网络,该网络使用Levenberg—Marquardt算法。实验分析表明,小波包分析和人工神经网络的引用能为缺陷类型提供有效的智能识别。 相似文献
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粗晶材料超声检测中的非线性信号处理 总被引:3,自引:1,他引:3
针对粗晶材料超声检测时严重的结构噪声使信噪比很低的问题。使用非线性时频分布对超声信号进行处理,充分考虑信号的时域、频域和相位的信息,根据超声信号在缺陷和噪声处瞬时频率的不同,结合超声信号的空间投影特点。提出了一种基于信号瞬时频率的超声信号处理方法。首先使用Choi—Willianms分布将信号变换到时频域。估计出信号瞬时频率,然后通过瞬时频率的有序度对超声信号进行加权处理。该算法充分利用了超声信号时域、频域和相位的信息。不仅消噪性能好,而且缺陷定位准确。 相似文献
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《机械工程学报》2018,(18)
超声相控阵是核电站组件无损检测中广泛采用的手段之一。针对低压汽轮机叶轮轮缘缺陷检测中存在的数据量大的问题,提出一种基于贪婪算法的超声相控阵信号压缩感知方法。利用CIVA平台建立了超声相控阵缺陷检测仿真模型,使用四种贪婪算法对仿真信号进行压缩感知并重构,计算不同采样率和不同压缩率下的百分比均方误差,根据结果选取最优算法;使用上述算法对汽轮机叶轮模型电火花加工缺陷回波信号进行压缩重构;通过与小波压缩重构精度的对比,验证该算法在超声相控阵汽轮机叶轮缺陷检测中的适用性。结果表明,使用仿真数据可以用低于奈奎斯特极限的测量点数精确重构信号;在压缩率为60%时,使用试验信号的平均重构误差仅为4.815 2%,与小波压缩的重构精度相当。 相似文献
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为便于缺陷可视化和提高缺陷定量分析精度,必须降低超声检测过程中多种未知噪声源对超声A波信号的干扰。首先将超声A波信号进行相空间重构,得到一个相空间重构矩阵;然后进行基于FastICA算法的独立分量分析;最后从独立分量中提取出所需的超声信号。为了验证降噪效果,将该降噪方法与小波去噪方法进行对比。实验结果表明:该方法与小波去噪方法效果接近,极大地提高了超声信号的信噪比。同时,该方法与小波去噪方法相比具有自适应能力强、简单且易于实现等优点。 相似文献
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超声漏表面波可用于检测表面或近表面缺陷,其非接触检测的优点易于实现自动化检测.但由于波型转换与传播衰减,漏表面波的回波幅值较小,不利于缺陷检测和成像.仿真分析了漏表面波的传播特性及缺陷回波特征,应用主成分分析分离回波信号中的干扰波,再利用小波域隐马尔可夫模型算法分离整段信号的系统噪声,联合两种方法提取漏表面波信号中的缺陷信息,最后通过频域合成孔径算法对漏表面波扫查数据进行了高分辨率图像重建.结果 表明,相比于传统B扫成像,基于PCA-WHIMM的超声漏表面波F-SAFT方法在回波信号平均信噪比上提高了10.05 dB,平均成像误差降低了26.3%,为金属表面及近表面缺陷检测提供了一种有效方法. 相似文献
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基于支持矢量机的小波域超声信号消噪技术 总被引:1,自引:0,他引:1
为了提高超声无损检测与无损评价基础数据的信噪比,提出一种基于支持矢量机模式识别理论的小波域超声信号消噪技术.该技术在研究材料内部散射体引起的结构噪声产生机理,以及分析传统裂谱分析算法局限性的基础上,利用小波变换方法将原始超声检测信号分解到小波空间,并通过采用以高斯函数为核函数的支持矢量机所构成的信噪分离器对信号和噪声进行识别、分离来消除噪声,得到高信噪比的超声回波信号.试验结果表明,与传统裂谱分析算法相比,该技术提高了消噪性能的稳定性,增强了湮没材料内部各种散射体散射中的缺陷回波信号能力. 相似文献
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非线性超声信号具有非平稳性、非线性和高次谐波信噪比低的特点,为提高非线性超声无损检测技术对缺陷的表征能力,提出一种基于双树复小波系数层间相关性结合软阈值滤波算法的超声谐波提取方法。首先采用双树复小波将信号分解为基频和二倍频等不同频带的分量;由于各分量存在一定程度的频率混叠,利用小波系数层间相关性对各分量信号滤波,消除频率混叠并得到修正后的细节子波;然后结合软阈值算法对修正后的小波系数进一步降噪;最后将滤波降噪后的各分量系数重构,即可实现对非线性超声信号中基波和二次谐波信号的提取。实验结果表明,该算法滤波效果良好,有效地提取了二次谐波信号,提高了非线性超声检测结果的准确性和鲁棒性。 相似文献
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钢轨踏面上的疲劳裂纹严重影响着列车行车安全。 针对如何快速有效地检测出踏面斜裂纹的问题,本文提出了一种快
速检测钢轨踏面裂纹的方法。 首先分别建立了含高斯白噪声、正弦信号加高斯白噪声干扰的数学模型,分析了编码脉冲压缩、
同步挤压小波变换和先同步挤压小波变换后脉冲压缩共 3 种信号处理方法的噪音抑制效果。 其次,为了验证上述方法对噪音
的抑制能力,使用激励频率为 1 MHz 的表面波电磁超声换能器对含裂纹的钢轨踏面进行检测。 最后,以检测得到裂纹的超声
回波为研究对象,比较了希尔伯特黄方法处理单一频率脉冲对应的超声回波信号和先同步挤压后脉冲压缩方法对应的降噪能
力和超声成像效果。 实验结果表明:本文所提方法可以获得钢轨踏面裂纹的位置信息及其数量。 希尔伯特黄变换在处理无同
步平均的原始超声回波时,由于回波信噪比低,经验模态分解(EMD)失效。 在以巴克码为激励信号且无同步平均采集的条件
下,先进行同步挤压小波变换后脉冲压缩处理,得到的超声回波信噪比相较于只采用相位编码脉冲压缩提高了 6. 82 dB,相比于
只做同步挤压小波变换提高了 11. 02 dB,能明显提升检测速度和 B 扫图像分辨率。 相似文献
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This paper examines the technique of denoising and signal extraction using wavelet transform scale space decomposition. The noisy signal is decomposed into multiple scales by the dyadic wavelet transform. At a given position, the level of correlation is used to deduce the presence or absence of significant feature of signals or images, which is then passed through the filter. By comparing the correlation information of the data at that scale with those at larger scales, noise is preferentially removed from the data. In the wavelet transform domain, the desired features are identified and retained because they are strongly correlated across scales compared to noise. This denoising algorithm can be used to reduce noise to a high degree of accuracy, while preserving most of the important features of the signal. In this paper, this technique of scale space filtering is applied to sample signals and images. Representative results are presented which shows that considerable noise content in signals and images can be reduced while preserving the value of the desired signal. 相似文献
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条纹图存在噪声干扰时,将二维小波变换系数模的最大值作为小波脊,会产生较大误差。针对这一问题,提出了基于价值函数的二维小波变换小波脊提取算法。首先,提取二维小波变换系数模的最大值点,并将最大值90%的局部极值点提取出来,共同作为小波脊候选点;其次,在模上引入尺度因子的梯度,建立价值函数进而评估所有候选点的价值,利用对数Logistic模型进行权值调整改进,从而得到更加合理的价值估计;最后,使用动态规划思想准确找出最优的小波脊线,提取脊线处的相位即可得到包裹相位。其优势在于能准确解调信噪比较低的条纹图案,抗噪性能优于直接最大模的小波脊提取;并且只需投影一幅条纹图案即可重建物体形貌,可用于恶劣环境下的动态三维测量。计算机仿真和实验结果表明,对于含有噪声污染的条纹图,所提算法相较于最大模的小波脊提取算法,三维形貌恢复精度明显提高;而相较于全部局部极值点提取,其运算时间缩短了46.9%。同时,应用不同母小波于所提方法,仿真结果表明二维Cauchy小波具有更好的方向性和更高的精度。 相似文献
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The noise suppression techniques with wavelet transform (WT) are widely used in nondestructive testing and evaluation (NDT&E),
especially in ultrasonics. But the wavelet based filter has the property of equal Q-factor, so, it is impossible to choose the central frequency and the bandwidth arbitrarily at the same time. This paper develops
a new technique using WT to eliminate this drawback. In this paper, a weak ultrasonic signals identification method by using
the optimal parameter Gabor wavelet transform is proposed. We address the choice of the optimal central frequency and bandwidth
of the Gabor wavelet using the kurtosis maximization algorithm. The central frequency and bandwidth of the optimal parameter
Gabor wavelet matched that of the ultrasonic signal very well. Numerical and experimental results have been presented to evaluate
the effectiveness of the optimal parameter Gabor wavelet transform on ultrasonic flaw detection. This technique is a simpler
and effective technique for processing heavy noised ultrasonic signals. 相似文献
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Qingkun Liu Peiwen Que Huawei Guo Shoupeng Song 《Russian Journal of Nondestructive Testing》2006,42(1):63-68
This paper proposes a new denoising method for ultrasonic NDE (nondestructive evaluation) signals using blind separation (BSS)
technology. The proposed denoising method consists of four steps. First, a reconstructed phase space (RPS) is constructed
from observed ultrasonic NDE signals. The information about the underlying sources (e.g., ultrasonic signal, noise, etc.)
acting on this system is contained in this RPS. Second, independent component analysis (ICA) is performed on the RPS to recover
all sources underlying the RPS. Next, the ultrasonic signal component is selected by a decision criterion related to the denoising
application and, finally, is reconstructed to obtain the denoised ultrasonic signal. To validate the proposed method, it has
been applied to the experimental ultrasonic NDE signals of the test sample and is compared with the wavelet denoising method
in SNR (signal-to-noise ratio) enhancement. The experimental results show that the SNR of the ultrasonic NDE signals can be
enhanced greatly using the proposed denoising method and the proposed method has almost the same denoising performance as
the wavelet denoising method in SNR enhancement. A trait of the proposed denoising method is the ability to denoise ultrasonic
NDE signals by separating the ultrasonic signal and noise using blind source separation technology.
The text was submitted by the authors in English. 相似文献
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针对远距离超声波测距系统中回波信号信噪比低的问题,采用小波变换对超声波的回波信号进行去噪处理。为取得较好的去噪效果,对小波变换的参数选取进行了研究。根据小波基的特性,通过能量与能量熵选取最优小波基;基于回波信号噪声的白噪声特征,采用白噪声检验自适应确定分解层数;引入参考噪声信号,确定小波系数处理阈值,并选用一种结合软、硬阈值函数的改进阈值函数进行小波系数处理。为验证方法的有效性,搭建基于NI数据采集卡和LabVIEW的超声回波信号采集平台,利用MATLAB小波工具包完成回波信号的去噪处理,并通过信噪比、均方根误差等指标对去噪效果进行综合评判。实验表明小波去噪可以达到很好的去噪效果,为大量程超声测距提供理论基础。 相似文献
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A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to realize single channel compound fault diagnosis of bearings and improve the diagnosis accuracy, an improved CICA algorithm named constrained independent component analysis based on the energy method (E-CICA) is proposed. With the approach, the single channel vibration signal is firstly decomposed into several wavelet coefficients by discrete wavelet transform(DWT) method for the purpose of obtaining multichannel signals. Then the envelope signals of the reconstructed wavelet coefficients are selected as the input of E-CICA algorithm, which fulfills the requirements that the number of sensors is greater than or equal to that of the source signals and makes it more suitable to be processed by CICA strategy. The frequency energy ratio(ER) of each wavelet reconstructed signal to the total energy of the given synchronous signal is calculated, and then the synchronous signal with maximum ER value is set as the reference signal accordingly. By this way, the reference signal contains a priori knowledge of fault source signal and the influence on fault signal extraction accuracy which is caused by the initial phase angle and the duty ratio of the reference signal in the traditional CICA algorithm is avoided. Experimental results show that E-CICA algorithm can effectively separate out the outer-race defect and the rollers defect from the single channel compound fault and fulfill the needs of compound fault diagnosis of rolling bearings, and the running time is 0.12% of that of the traditional CICA algorithm and the extraction accuracy is 1.4 times of that of CICA as well. The proposed research provides a new method to separate single channel compound fault signals. 相似文献