EMAT noise suppression using information fusion in stationary wavelet packets

An important issue in ultrasonic nondestructive testing is the detection of flaw echoes in the presence of background noise created by instrumentation and by clutter noise. Signal averaging, autoregressive analysis, spectrum analysis, matched filtering, and the wavelet transform have all been used to filter noise in ultrasonic signals. Widely-used wavelet threshold estimation algorithms are not designed for electromagnetic acoustic transducer (EMAT) pulse-echo signals, and therefore do not exploit their unique impulse nature. The approach to ultrasonic signal filtering proposed in this paper is based on stationary wavelet packet denoising with a threshold influenced by several information sources: a statistical echo detection, the amplitude distribution of the wavelet transform coefficients, and a priori known system frequency characteristics. The proposed method was evaluated on signals measured with EMAT probes and under various SNR conditions; it outperforms the wavelet transform with the Stein unbiased risk estimate (SURE) threshold estimation method and split-spectrum processing (SSP). The results indicate SNR enhancement of 19 dB with real EMAT data.     

基于小波变换的裂谱分析法

 为了提高超声无损检测（UNDT）和无损评价（UNDE）中基础数据的信噪比（SNR）,提出了一种基于小波变换多分辨率分析的裂谱分析新方法．该方法在分析传统裂谱分析（SSP）方法原理及其局限性的基础上,通过采用小波变换多分辨率分析能力将原始超声回波信号进行等Q子带分解,然后按照一定的信噪分离规则来消除噪声,达到提高信噪比的目的．实验结果表明,与传统裂谱分析方法相比,该方法提高了消噪性能的稳定性,增强了湮没晶粒(或其他散射体)散射中的缺陷回波信号能力．     

舰船辐射噪声的小波域HMM分析

基于小波的统计信息处理技术，一般都上波系数建模为独立或联合高斯的，但这些模型对许多实际信号来说并不理想，在本文中，我们使用这一种基于小波域洋可夫模型（ＨＭＭ）的统计信号处理结构，对实际领事涉及的统计独立性和非高斯统计进行建模。小波域ＨＭＭ使用小波变换的基本性质来设计，提供了强有力并处理的随机信号模型。我们使用一种有效的期望值最模型的参数可以区分不同类型的信号。     

基于约束独立成分分析的轴承复合故障特征提取方法

为从复合故障信号中提取各故障特征,提出一种离散小波变换(DWT)和约束独立成分分析(CICA)相结合的单通道复合故障诊断方法。首先通过DWT方法将单通道振动信号进行小波分解后,利用小波重构函数重构各层分解信号。然后取重构信号的包络信号作为CICA算法的输入矩阵,基于滚动轴承先验知识建立参考信号,从而分离出轴承各故障信号,提取故障特征。最后,在滚动轴承故障模拟实验台上进行了方法验证。结果表明:该方法可有效分离滚动轴承外圈和滚动体故障,实现了轴承复合故障的诊断。     

Wavelet Transform–Based Denoising Method for Processing Eddy Current Signals

Wavelet transform (WT)–based denoising method is proposed for processing eddy current signals of thin-walled stainless steel fuel tubes with periodic wall thickness variations formed due to fluctuation in tube drawing process parameters. In this method, discrete wavelet transform with level-based threshold has been applied to selectively eliminate the noise due to periodic wall thickness variations towards meeting the quality assurance requirement of detection of holes larger than 0.3 mm diameter and linear defects deeper than 0.075 mm (20% wall thickness). The method has been applied to tubes having machined holes, longitudinal notches, and circumferential notches, and an overall improvement of 20 dB in signal-to-noise ratio has been observed. The method has been able to detect defects present anywhere in the wall thickness variation regions and also in tubes without any wall thickness variations.     

Automatic Defect Classification in Ultrasonic NDT Using Artificial Intelligence

A methodology is developed to detect defects in NDT of materials using an Artificial Neural Network and signal processing technique. This technique is proposed to improve the sensibility of flaw detection and to classify defects in Ultrasonic testing. Wavelet transform is used to derive a feature vector which contains two-dimensional information on various types of defects. These vectors are then classified using an ANN trained with the back propagation algorithm. The inputs of the ANN are the features extracted from each ultrasonic oscillogram. Four different types of defect are considered namely porosity, lack of fusion, and tungsten inclusion and non defect. The training of the ANN uses supervised learning mechanism and therefore each input has the respective desired output. The available dataset is randomly split into a training subset (to update the weight values) and a validation subset. With the wavelet features and ANN, good classification at the rate of 94% is obtained. According to the results, the algorithms developed and applied to ultrasonic signals are highly reliable and precise for online quality monitoring.     

Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection

The utilization of signal processing techniques in nondestructive testing, especially in ultrasonics, is widespread. Signal averaging, matched filtering, frequency spectrum analysis, neural nets, and autoregressive analysis have all been used to analyze ultrasonic signals. The Wavelet Transform (WT) is the most recent technique for processing signals with time-varying spectra. Interest in wavelets and their potential applications has resulted in an explosion of papers; some have called the wavelets the most significant mathematical event of the past decade. In this work, the Wavelet Transform is utilized to improve ultrasonic flaw detection in noisy signals as an alternative to the Split-Spectrum Processing (SSP) technique. In SSP, the frequency spectrum of the signal is split using overlapping Gaussian passband filters with different central frequencies and fixed absolute bandwidth. A similar approach is utilized in the WT, but in this case the relative bandwidth is constant, resulting in a filter bank with a self-adjusting window structure that can display the temporal variation of the signal's spectral components with varying resolutions. This property of the WT is extremely useful for detecting flaw echoes embedded in background noise. The detection of ultrasonic pulses using the wavelet transform is described and numerical results show good detection even for signal-to-noise ratios (SNR) of -15 dB. The improvement in detection was experimentally verified using steel samples with simulated flaws.     

转子系统动静件碰摩故障信号处理方法的研究

由于在转子系统动静件碰故障测试信号中背景信号、噪场信号较强,使得在利用小波变换进行故障诊断时,难以得到准确的结果。本文提出了一种信号处理方法,即首先利用测试信号的时间重构吸引子的轨迹矩阵,并且对该矩阵进行奇异值分解以剔除背景信号、提取残余信号,然后再对残余信号进行小波变换。利用该方法分另对仿真信号、实测信号进行了处理和分析,结果表明本方法是非常有效的,可对转子系统动静件碰摩故障进行准确地诊断。     

基于小波变换的反射波法基桩检测

鉴于反射波法在低应变检测中主要受较大长径比、桩侧土剪切模量及微小缺陷、桩顶非裸露自由等条件限制存在的局限性,而小波变换时频特性优良,适用于基桩检测信号中探测奇异成分,故通过建立三维轴对称有限元模型,对模拟信号进行小波变换后发现：对顶部裸露自由桩,小波变换可获取微小桩底或缺陷反射,可减小长径比、桩侧土剪切模量、缺陷面积的限制;通过平台激振,使平台在有效尺寸范围内实现对桩底的深度检测。     

Characterization of Structural Noise Patterns and Echo Separation in the Time-Frequency Plane for Austenitic Stainless Steels

The aim of this study is to characterize the structural noise for a better flaw detection in heterogeneous materials (steels, weld, composites...) using ultrasonic waves. For this purpose, the continuous wavelet transform is applied to ultrasonic A-scan signals acquired using an ultrasonic non destructive testing (NDT) device. The time-scale representation provided, which highlights the temporal evolution of the spectral content of the A-scan signals, is relevant but can lead to misinterpretation. The problem is to identify if each pattern from the wavelet representation is due to the structural noise or the flaw. To solve this problem, a detection technique based on statistical significance testing in the time-scale plane is used. Information about the structural noise signals is injected into the decision process using an autoregressive model, which seems relevant according to the spectral content of the signal. The approach is tested on experimental signals, obtained by ultrasonic NDT of metallic materials (austenitic stainless steel) then on a weld in this steel and indeed enables to distinguish the components of the signal as flaw echoes, which differ from the structural noise.     

汽轮机轮缘超声相控阵检测中缺陷方向识别

利用超声相控阵检测系统对含有裂纹缺陷的外包菌型低压汽轮机叶轮圆盘实验试块进行检测.针对超声波脉冲反射法中缺陷方向难以确定,尺寸容易误判等问题,提出一种频谱分析方法,研究线状缺陷方向变化对于超声回波的影响.对于3种倾斜角度的裂纹缺陷回波信号,分析其功率谱并进行小波包的分解和重构,用"频率-能量"的方法提取各方向缺陷回波信号的能量特征.实验结果表明,各方向缺陷回波信号的能量特征差别明显,并绘制出声束轴线与缺陷的夹角和高频带所占能量之间关系曲线.该方法实现了缺陷方向的识别,并为后续线状缺陷的准确定量提供依据.     

基于提升小波变换的超声信号消噪技术

为了提高超声无损检测（UNDT）和无损评价（UNDE）中基础数据的信噪比（SNR）,提出了一种基于提升小波变换多分辨率分析的超声信号消噪新技术.在分析传统裂谱分析（SSP）方法原理及其局限性的基础上,通过采用提升小波变换多分辨率分析能力将原始超声回波信号进行子带分解,然后按照一定的信噪分离规则来消除噪声,达到提高信噪比的目的.实验结果表明,与传统裂谱分析方法相比,该方法增强了消噪性能的稳定性,提高了超声回波信号的信噪比.     

小波变换在履带车辆振动信号处理中的应用

为解决履带车辆振动信号受强噪声信号干扰的影响,运用小波变换原理,根据重构因子比较几种小波基,确定适合履带车辆振动信号的小波基和阈值,描述小波分层方法,对履带车辆振动信号进行去噪。仿真计算结果表明小波变换去噪方法能有效地抑制背景噪声,提取有用状态信息,提高信噪比和减小均方误差,为履带车辆振动信号预处理提供一种可行的方法。     

基于小波包和LMS自适应降噪的柴油机振动诊断

由于柴油机振动信号的特征频带和噪声频带存在重叠现象,利用小波阈值消噪时难以选取合适的小波阈值,针对该问题提出一种基于小波包的LMS自适应滤波降噪方法。该方法将小波包与LMS自适应滤波相结合,首先利用小波包变换对信号进行多层分解,然后以噪声干扰对应尺度上的第一层“细节”分量及最大分解尺度上的逼近分量重构信号,将重构后的信号作为LMS自适应滤波器原始输入信号,再以小波包最大分解尺度上的高频细节信号作为自适应抵消器的参考输入信号,进行LMS自适应滤波降噪处理。仿真计算和工程应用表明,该方法参数设置较少,易于控制,不涉及小波阈值降噪中阈值的选取问题,对比试验信号的分析验证了方法的有效性,将该法应用在柴油机振动诊断中提高了故障识别率。     

滚动轴承振动信号的小波奇异性故障检测研究

该文以滚动轴承振动信号为分析对象 ,基于小波奇异性分析原理进行滚动轴承故障检测新方法的研究。通过求解待测信号的小波变换极大模来检测和识别信号中奇异点位置和奇异性大小 ,以及对噪声极大模的抑制处理 ,达到抑制或消除噪声的目的 ;最后 ,在剩余小波极大模的基础上进行信号重构 ,展现原待测信号中的故障信号模式。通过对铁路货车车轮用滚柱轴承振动信号的分析表明 ,此方法在大幅度地提高信噪比的同时 ,对由轴承损伤冲击造成的信号突变仍保持了较高的灵敏度和分辨率。为滚动轴承故障检测打下了良好的基础。     

基于连续小波变换的玻璃纤维增强树脂复合材料太赫兹特征增强及缺陷成像

玻璃纤维增强树脂复合材料(Glass fiber reinforced polymer,GFRP)因其耐腐蚀、强度高等优点被广泛应用于航空航天、运输等领域,但在其制作过程中存在分层、气泡等缺陷,故需对其进行无损检测。本文针对不同位置的GFRP脱粘缺陷太赫兹无损检测信号特征微弱的问题进行分析与研究,提出了利用连续小波变换(Continue wavelet transform,CWT)对太赫兹特征进行增强的方法,并通过计算图像对比度客观评价连续小波变换后得到的太赫兹图像。最终选择gaus2小波基函数,对变换后的信号进行缺陷成像,其峰值较原来增强了4.5倍,连续小波变换处理后的太赫兹缺陷成像的图像对比度提升了1.3倍,最终实现了6 mm GFRP 5 mm位置处50 μm脱粘缺陷的识别。      

自适应神经网络模糊小波语音消噪算法

针对有色噪声,采用自适应神经网络模糊系统模糊(Auto Neural Fuzzy Inference System,ANFIS)逼近有色噪声,利用自适应神经模糊推理系统ANFIS对噪声的非线性动态特性进行建模,提出了语音自适应神经网络模糊小波消噪算法,建立并训练了消噪系统。对被有色噪声污染的测量信号经模糊消噪后,根据信号和噪声的小波系数在不同分解尺度上的传递性,进行中值滤波和小波重构,得到了干净的语音。对算法进行了仿真实验,结果表明,消噪效果明显。     

油气管道缺陷漏磁图像的小波压缩技术研究

针对漏磁信号的特点,提出了基于小波分析的缺陷漏磁图像压缩方案。该方案通过调整正交函数零极点位置来设计小波基函数,利用该函数对图像作小波变换,对获得的各级小波系数用更新的JPEG图像压缩表阈值量化,再用算术编码方案处理量化结果,从而得到压缩的图像。试验结果表明,当压缩比小于30%时,图像压缩引起的失真不会对缺陷分析产生影响。     

基于小波变换的低碳钢连铸坯超声探伤

利用离散小波变换对粗晶钢材超声检测信号进行处理,比较了使用不同的小波基,并对不同尺度中突出体现的信息分别进行处理后重构信号的去噪效果。结果表明,对不同尺度系数作不同的阈值处理有利于缺陷的识别,这种方法对粗晶钢材料的检测有积极的意义。     

小波变换在轿车车内噪声源识别中的应用研究

阐述小波变换及小波分解的基本原理,对车内噪声及相关噪声、振动信号进行测量,利用小波分解原理对噪声及振动信号进行分解,得到各信号的特征向量,根据各信号的特征向量确定车内噪声与其它振动、噪声信号的相关系数,根据车内噪声与各振动、噪声信号的相关程度确定车内噪声的主要噪声源,并试验验证小波变换对噪声源识别的有效性和正确性。