Detection of echoes using time-frequency analysis techniques

The following is a presentation of echo detection techniques based on time-frequency signal analysis for the measuring of thickness in thin multilayer structures. These techniques are shown to provide high-resolution signal characterization in a time-frequency space, and good noise rejection performance. In particular, the short-time Fourier transform, the Gabor expansion, the cross-ambiguity function and the Wigner-Ville distribution are analyzed and compared with techniques such as the logarithmic power spectrum, cepstrum and the segmented chirp Z-Transform. A suitable operating procedure was set up, based on an initial emulation phase in which simulated signals were considered, followed by a second phase in which real signals were processed. The results show the optimum performances of these new techniques compared with the traditional ones and, in particular, that the accurate measurement of thickness can be obtained also when waveform transients partially overlap     

水声信号多参量估计的PWVD改进方法

Pseudo Winger-Ville分布（PWVD）是处理非平稳信号的有力工具,因其具有较高的时频聚集性更适合于实时处理,且选择合适的窗函数可降低旁瓣大小,提高分辨率。传统的PWVD可以对信号瞬时频率进行无偏估计,但其频率分辨率和时间分辨率不能同时兼顾;提出了一种插值PWVD法可克服此不足,极大地减小了频谱泄漏的负面影响,有效提高了频率估计与时间估计的精度。仿真和实测数据的分析结果验证了插值PWVD法的可行性与有效性。     

几种超声NDE信号降噪方法的性能比较

为提高超声无损检测的准确性,需要对超声NDE信号中因随机分布于媒质中的大量散射微粒所引起的结构噪声进行降噪。由于信号和噪声的频谱范围基本重叠,传统的线性滤波方法不能提供理想的降噪结果。介绍了几种对超声NDE信号进行降噪的新方法:Wigner-Ville分布法、小波变换法和基于时间延迟的神经网络法,并从信噪比(SNR)、检测概率(PD)和估测深度(ED)等三个重要参数对它们的降噪性能进行计算机仿真实验的比较。结果表明:小波变换法和神经网络法的降噪效果较Wigner-Ville分布法要好。对实际信号的测试还表明,小波变换由于不像神经网络那样需要训练,是一种更为理想的超声NDE信号降噪方法。     

爆破振动信号的时频分析

爆破振动信号的研究方法已由单纯的频域分析过渡到时频联合分布分析。在讨论FOURIER变换(FT)和短时 FOURIER变换(STFT)不足的基础上,论述了基于连续小波变换(CWT)和离散小波变换(DWT)在爆破振动信号分析中的应用。作为一种严格的时频分析方法,论文尝试利用二次型时频分布来进行爆破振动信号的时频联合分析;通过几种二次型时频分布的对比分析,认为CWD在具有较强的时频聚集性的同时又较好的对交叉项进行了抑制,适合于进行爆破振动信号的时频分析。     

基于现代时频分析技术的地震波时变谱估计

利用短时傅里叶变换（STFT）、Wigner-Ville分布、连续小波变换、S变换以及变复窗口的S变换等现代时频分析技术对地震波进行时变谱估计。推导了基于连续小波变换的小波能量谱的具体表达式,并把近年来提出的变复窗口S变换应用到地震波的时频分析。通过研究表明对于均匀调制非平稳地震波,通过适当选择窗口的长度,利用STFT估计时变谱可得到较好的效果,小波变换与S变换以及变复窗口S变换的估计值在高频段存在能量泄漏现象;各方法估计的时变谱时间边缘函数与目标函数均符合较好;而频率边缘函数,Wigner-Ville分布与目标函数符合得最好,STFT次之,小波变换、S变换以及复窗S变换在高频段存在一定误差。对于频率非平稳性较强的地震波,通过适当地调节窗口参数,利用变复窗口S变换可得到地震波在时频域内有较好分辨率的时变谱;连续小波变换虽然在高频段存在能量泄漏现象,但其在时间域内有较好的分辨率。     

旋转机械非平稳振动信号的时频分析比较

信号分析与处理是机械故障监测与诊断中故障提取的常用方法,传统的振动故障分析方法难以满足频率随时间变化的非平稳信号的要求,联合时频分析是非平稳信号比较有力的分析工具。以转子实验台的典型振动故障信号为研究对象,分析研究了几种时频分析方法如STFT、Wigner-Ville分布、小波变换和Hilbert-Huang变换。对比结果表明:STFT和Wigner-Ville分布的时频分辨率是矛盾的,易出交叉项或使信号变得微弱;小波分解会出现多余信号;Hilbert-Huang变换的时频分析能够直观检测信号中的微弱奇异成分,清楚给出时频分布情况,为旋转机械状态监测和故障诊断提供了新的手段。     

Short-time Fourier transform and wavelet transform with Fourier-domain processing

We discuss the semicontinuous short-time Fourier transform (STFT) and the semicontinual wavelet transform (WT) with Fourier-domain processing, which is suitable for optical implementation. We also systematically analyze the selection of the window functions, especially those based on the biorthogonality and the orthogonality constraints for perfect signal reconstruction. We show that one of the best substitutions for the Gaussian function in the Fourier domain is a squared sinusoid function that can form a biorthogonal window function in the time domain. The merit of a biorthogonal window is that it could simplify the inverse STFT and the inverse WT. A couple of optical architectures based on Fourier-domain processing for the STFT and the WT, by which real-time signal processing can be realized, are proposed.     

时频分布的弱信号检测技术的研究及其应用

双线性时间-频率变换在时间域与频率域都具有较高的分辨率,有利于复杂背景条件下微弱瞬时信号的探测。研究发现,频谱图的分辨率较低,W igner-V ille分布存在很强的交叉项,不适合瞬时信号的检测。Cho i-W illiam s分布存在频率混叠及信息丢失现象,检测效果也不理想。提出了一种无混叠时频分布,能够避免CW D中的信息丢失,有效抑制交叉项,而且具有较高的分辨率。通过数字仿真与齿轮箱故障检测实例,证明新的时频分布能够有效检测复杂信号中的瞬时分量。     

时频分布在爆破震动信号处理中的应用

爆破震动信号是典型的非平稳信号,其处理方法已由单纯的频域分析过渡到时频联合分布分析。在讨论Fourier变换(FT)和短时Fourier变换(STFT)不足的基础上,论述了基于连续小波变换(CWT)和离散小波变换(DWT)在爆破震动信号分析中的应用。作为一种严格的时频分析方法,论文尝试利用二次型时频分布来进行爆破震动信号的时频联合分析;通过几种二次型时频分布的对比分析,认为CWD在具有较强的时频聚集性的同时又较好地对交叉项进行了抑制,适合于进行爆破震动信号的时频分析。     

锦屏二级水电站2~#引水隧洞爆破治理岩爆试验研究

岩爆是地下工程的一大地质灾害,目前还没有成熟的预报和治理方法。利用爆破手段释放高地应力来减少岩爆,一直是地下工程施工研究的重要课题。在锦屏二级水电站2#引水隧洞,进行了爆破释放应力试验,分别试验了掌子面倾斜辐射孔和围岩内倾斜辐射孔两种方案。试验结果证明了爆破是高应力释放的有效手段,通过合理的爆破设计与施工,可以达到控制或缓解岩爆的目的。     

Identification of the dynamical properties of structures using free vibration data and distributed genetic algorithms

The dynamical properties of structures, such as natural frequencies, damping ratios and mode shapes, can be obtained by several identification methods. Some are based on the direct signal processing in a time domain; others transform response data to the frequency domain. The development of these techniques is useful in the production of more accurate structural models; they can be also used to test the level of damage in structures (or verify their strength to support new load actions) by using experimental data. There are situations where frequency domain algorithms and conventional system identification techniques fail, do not allow adequate solution of the identification problems or become trapped in a local optimum. It is in these cases where evolutionary optimization techniques are important tools for evaluating the dynamical properties of structural systems in practical applications. This article presents a methodology to determine the dynamic properties of structures knowing their response in terms of displacement, velocities or accelerations in the time domain when they are subjected to a free vibration excitation. In order to do that, a specialized evolutionary algorithm capable of adapting its parameters to the different types of registers obtained from the dynamic time response of a structure is implemented in a robust way, making this approach useful in practical situations. A distributed real genetic algorithm (DRGA) based on an island model of different subpopulations is used to adjust a simulated response signal of a building to the real response signal. Initially, computer-generated synthetic response signals are used but, in future, the approach will be validated with signals obtained from free vibration experimental tests and will be extended to other types of dynamical excitation signals. Finally, the method will be tested with data obtained from earthquake events.     

基于EEMD和低秩稀疏分解的超声缺陷回波检测方法

针对应用超声对金属材料微小缺陷检测时缺陷回波容易被噪声干扰的问题,提出了一种基于集合经验模态分解(EEMD)和低秩稀疏分解相结合的检测方法,以避免传统基于经验模态分解(EMD)的去噪方法难以消除结构噪声的问题.首先对缺陷检测信号进行EEMD得到一系列本征模态函数(IMF),采用基于概率密度函数的相似性测量方法选取相关模...     

超宽带信号的时频分析

超宽带(UWB)信号的特性更多表现为非平稳信号的特性,而对非平稳信号采用时频域的变换更能描述信号频谱的分布情况.首先引入了一种基于布莱克曼窗的短时傅立叶变换,然后给出两种典型的UWB信号,利用这种时频分析方法对两种UWB信号进行了时频分析.分析结果表明,无论是PAM-DS-UWB信号还是OFDM-UWB信号都符合FCC对UWB信号的功率限制.     

The application of time-frequency analysis to the air-coupled ultrasonic testing of concrete

Air-coupled ultrasound has been used for the nondestructive evaluation of concrete, using broad bandwidth electrostatic transducers and chirp excitation. This paper investigates the benefits of using time-frequency analysis in such situations, for both waveform retrieval and imaging in the presence of low signal levels. The use of the short-term Fourier transform, the Wavelet transform, and the Wigner-Ville distribution all are considered, in which accurate tracking of the ultrasonic chirp signals is demonstrated. The Hough transform then is applied as a filter. An image of a steel reinforcement bar in concrete has been produced to illustrate this approach.     

调频初相位补偿的广义Warblet变换时频分析

信号时频分析的长时间窗时频分析法通常可提高输出信噪比和频率分辨率,但对于调频信号,会降低线谱时频能量聚集度并影响瞬时频率估计。对于调频信号广义Warblet变换(Generalized Warblet Transform,GWT),具有较短时傅里叶变换(Short Time Fourier Transform,STFT)更优的时频分析性能,但在长时间窗分析时,调频初相位估计误差会使算法性能下降甚至失效。针对该问题,提出调频初相位补偿的GWT(Frequency Modulation Initial Phase CompensationGWT,FMIPC-GWT)时频分析方法。在调频参数估计时将一半时间窗长所经过的相位补偿到调频初相位中,提高调频参数估计的准确性以增加瞬时频率估计精度。仿真和实验数据验证了,相比STFT法和GWT法,FMIPC-GWT法对于非线性调频信号时频分析性能更优。FMIPC-GWT法在调频信号线谱检测与瞬时频率估计等方面具有应用前景。     

Cluster analysis of acoustic emission signals in pitting corrosion of low carbon steel

The pitting corrosion characteristics of low carbon steel specimens are studied by acoustic emission (AE) and electrochemical techniques, in a 3.0 wt.% NaCl solution acidified to pH 2.0. The acoustic emission signals generated by pitting corrosion are classified based on multiple acoustic emission parameters using K‐means clustering algorithm, then each classified signals are analyzed by acoustic emission parameters correlation plot and distribution with time. Furthermore, each acoustic source characteristics is extracted using Gabor wavelet transform (WT) in the time and frequency domain. An error back propagation (BP) artificial neural network (ANN) is trained according to the classified signals, so as to successfully identify the acoustic emission signals from parallel experiments. Experimental results show that the hydrogen bubble activation, oxidized film rupture and pit growth are typical acoustic emission sources in pitting corrosion process, which can be effectively classified by cluster analysis and recognized by back propagation neural network. The data gathered from laboratory tests combined with the real data from acoustic emission on‐line storage tank floor inspection can help to evaluate the bottom corrosion severity and interpreter the corrosion source, further to make the on‐site testing more reliable and reduce the risk.     

Classification of washing machines vibration signals using discretewavelet analysis for feature extraction

Wavelets provide a powerful tool for nonstationary signal analysis. In vibration monitoring, the occurrence of occasional transient disturbances makes the recorded signal nonstationary, especially during the start-up of an engine. Through the wavelet analysis, transients can be decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave frequency band. Disturbances of small extent (duration) are amplified relative to the rest of the signal when projected to similar size wavelet bases and, thus, they can be easily detected in the corresponding frequency band. This paper presents a new method for extracting features in the wavelet domain and uses them for classification of washing machines vibration transient signals. The discrete wavelet transform (DWT), in conjunction with statistical digital signal processing techniques, is used for feature extraction. The Karhunen Loeve transform (KLT) is used for feature reduction and decorrelation of the feature vectors. The Euclidean, Mahalanobis, and Bayesian distance classifiers, the learning vector quantization (LVQ) classifier, and the fuzzy gradient classifier are used for classification of the resulting feature space. Classification results are illustrated and compared for the rising part of vibration velocity signals of a variety of real washing machines with various defects     

Application of wavelet transform to the analysis of signals with pulsed components

A wavelet transform is compared with a short-term Fourier transform (STFT) spectrogram for analysis of signals having pulsed components. The technique can be applied to the analysis and recognition of various diagnostic signals with pulsed components. Translated from Izmeritel'naya Tekhnika, No. 8, pp. 43–45, August, 2000.     

基于卡尔曼滤波的STFT域回声抵消算法

提出了一种低复杂度的短时傅里叶变换域卡尔曼滤波算法来解决声学回声抵消问题。首先在短时傅里叶变换域建立了基于频域卷积传递函数的观测方程,并利用一阶马尔科夫模型对频域回声路径进行建模,给出了精确的卡尔曼滤波方程,并讨论了过程噪声和观测噪声的估计问题。为降低算法计算复杂度,提出了低复杂度卡尔曼滤波算法。另外,在更新滤波器时加入远端信号邻近频点的信息来进一步提高回声抵消性能。实验结果表明,所提算法对近端干扰不敏感,不需要额外的双端对讲检测算法,且比传统的频域自适应滤波算法具有更快的收敛速度。     

An improved computation of the pseudo Wigner‐Ville distribution and its applications

Abstract

The Wigner‐Ville distribution (WVD), generally calculated by the fast Fourier transform (FFT), is a useful tool for time‐frequency signal analysis. However, for nonstationary multicomponent signals, the inherent bilinear structure of the WVD causes undesirable interfering cross terms, and becomes troublesome to many applications. The FFT requires complex arithmetic computations, but the fast Hartley transform (FHT) only requires real arithmetic computations. Therefore, the FHT performs much faster than the FFT. An improved WVD computation using the FHT and running windowed exponential distribution is proposed in this paper. The cross‐terms of nonstationary multicomponent signals can be completely eliminated, and the result is favorable for pattern recognition and signal classification. The derived algorithm is also applied to building up a real‐time processing scheme for conducting experiments in an anechoic chamber.