基于小波变换利用超声估计生物组织散射元平均间距

生物组织散射元平均间距是描述生物组织微观结构和生物组织和超声散射特性和重要参数,文中构建并物理仿真了生物组织散射元一维超声散射模型,用小波变换方法估计了仿一物组织散射元的平均间距。     

超声热疗无创测温中AR模型方法应用研究

研究基于超声功率谱的无创测温技术中AR滤波器对测温精度的影响。模拟了正常肝组织在超声换能器辐照下的散射回波信号,通过对加热前后组织回波信号的功率谱分析,使用AR模型计算频移,并反演温度分布。结果表明,当AR阶次为83～84、窗长为454～460时所反演的组织温度值与在频域求解非线性KZK方程和Pennes方程所得理论结果的偏差较小,约0.08oC。     

浅谈一种基于AR模型的电厂电动机故障诊断新方法

电动机是电厂的重要设备,对发电厂的电动机故障进行诊断有重要意义。本文采用虚拟仪器系统作为开发平台,根据电动机振动信号的特点,对其进行虚拟诊断测试,在实际测试中,提出倒谱分析方法,是基于自回归AR模型的一种电机振动信号检测方法,首先是AR建模,并进行阶次预测和系数预测,得到相关参数;再进行倒谱变换,快速准确地提取出所需要的频谱信息,从而判断电机是否出现故障。     

自回归模型在滚动轴承故障诊断中的研究

本文通过实验室对6203型滚动轴承的时阈振动信号建立自回归模型（AR模型），用模型计算出的功率谱（AR功率谱）及其倒频谱对轴承进行故障诊断。诊断结果和理论值与传统的古典功率谱及其倒频谱的诊断方法作比较。实验表明，该方法的诊断结果和理论值吻合较好，比传统诊断方法的结果准确、可靠，具有良好的实用前景。     

浅海多径信号倒谱分析

介绍了使用倒谱对浅海多径信号进行分析的方法,仿真结果和理论计算的结果一致.仿真表明单个倒谱图很好地反映出多径信号中不同的时延;倒谱瀑布图还反映出了声源位置的变化等一系列信息.因此倒谱法与传统相关法相比在一些情况下更有效.     

Teager能量算子增强倒阶次谱提取轴承微弱故障特征

针对变速器加速过程下轴承故障特征易于暴露难以提取问题,提出一种Teager能量算子增强倒阶次谱方法。计算加速过程等角度重采样信号的Teager能量算子,对Teager能量算子输出进行倒谱分析,获得Teager能量算子增强倒阶次谱。对加速过程滚动轴承外圈、内圈剥落故障信号进行分析,结果表明,Teager能量算子能有效增强冲击成分,抑制非冲击成分;倒阶次谱能从干扰中准确识别被增强的故障冲击特征,提取轴承微弱故障特征。     

基于AR模型和谱峭度法的滚动轴承故障诊断

自回归(AR)模型是平稳信号分析的重要工具,本文利用峭度最大原则确定AR模型最优阶次,然后利用此AR模型对滚动轴承故障信号进行预处理,剔除可线性预测的平稳成分,得到的残余分量中理论上只包含了噪声信号和信号的非平稳部分,从而降低了后期数据分析难度。谱峭度对于非平稳信号非常敏感,它可以将非平稳信号从噪声中检测出来,因此将两者结合起来可以更有效的对滚动轴承故障进行诊断,实验结果验证了此方法的有效性。     

基于AR算法的小波技术在随机场模拟中的应用

采用线性滤波器法中的AR法模拟生成了地震动场和风场的时程信号。由于AR算法具有很高的计算效率,但模拟精度却不理想,故引入小波分析中的小波分解方法和小波包分解方法对AR算法模拟的信号进行调整,并对这两种方法改进的效果进行对比。算例分析表明由改进的AR法得到的样本信号频谱与目标谱吻合良好,且发现小波包分解方法调整信号具有比小波分解方法更好的精度。     

角域AR谱技术在齿轮故障诊断中的应用

利用时频分布平面内信号能量峰脊与瞬时频率之间的对应关系,对信号瞬时频率进行估计;在此基础上利用代数方法求解鉴相时标积分方程,并对经插值重采样得到的角域信号进角域平均处理,提高了角域信号的信噪比;最后对角域信号进行AR建模实现信号的阶次谱分析。实际测试结果表明:采用角域AR谱技术处理齿轮箱非平稳振动信号,能够有效地避免传统频谱方法无法解决的"频率模糊"现象,克服了传统阶次谱分辨率较低,谱线毛糙,易受噪声及轴频调制影响等缺点,对齿轮箱的早期故障有较好的识别能力。     

一种被动拖曳声纳阵列信号模拟器设计

声纳基阵信号模拟器是一种实用的可仿真实际检测目标及使用环境的阵元级信号发生器。设计的被动拖曳声纳阵列信号模拟器,仿真了拖曳阵的拖船干扰时域信号和空间传播特性、目标辐射信号以及环境噪声。通过预设定拖船干扰宽带功率谱,以AR模型拟合该功率谱,利用海底、海面对声场的一次反射作用仿真了拖船干扰的空间多途信道。采用插值滤波器,解决了宽带信号阵元间延时的时延精确控制问题。     

Singular spectrum analysis applied to backscattered ultrasound signals from in vitro human cancellous bone specimens

Mean scatterer spacing (MSS) holds particular promise for the detection of changes in quasiperiodic tissue microstructures such as may occur during development of disease in the liver, spleen, or bones. Many techniques that may be applied for MSS estimation (temporal and spectral autocorrelation, power spectrum and cepstrum, higher order statistics, and quadratic transformation) characterize signals that contain a mixture of periodic and nonperiodic contributions. In contrast, singular spectrum analysis (SSA), a method usually applied in nonlinear dynamics, first identifies components of signals corresponding to periodic structures and, second, identifies dominant periodicity. Thus, SSA may better separate periodic structures from nonperiodic structures and noise. Using an ultrasound echo simulation model, we previously demonstrated SSA's potential to identify MSS of structures in quasiperiodic scattering media. The current work aims to observe the behavior of MSS estimation by SSA using ultrasound measurements in phantom materials (two parallel, nylon-line phantoms and four foam phantoms of different densities). The SSA was able to estimate not only the nylon-line distances but also nylon-line thickness. The method also was sensitive to the average pore-size differences of the four sponges. The algorithms then were applied to characterize human cancellous bone microarchitectures. Using 1-MHz center-frequency, radio-frequency ultrasound signals, MSS was measured in 24 in vitro bone samples and ranged from 1.0 to 1.7 mm. The SSA MSS estimates correlate significantly to MSS measured independently from synchrotron microtomography, r2 = 0.68. Thus, application of SSA to backscattered ultrasound signals seems to be useful for providing information linked to tissue microarchitecture that is not evident from clinical images.     

Simplified inverse filter tracking algorithm for estimating the mean trabecular bone spacing

Ultrasonic backscatter signals provide useful information relevant to bone tissue characterization. Trabecular bone microstructures have been considered as quasi-periodic tissues with a collection of regular and diffuse scatterers. This paper investigates the potential of a novel technique using a simplified inverse filter tracking (SIFT) algorithm to estimate mean trabecular bone spacing (MTBS) from ultrasonic backscatter signals. In contrast to other frequency-based methods, the SIFT algorithm is a time-based method and utilizes the amplitude and phase information of backscatter echoes, thus retaining the advantages of both the autocorrelation and the cepstral analysis techniques. The SIFT algorithm was applied to backscatter signals from simulations, phantoms, and bovine trabeculae in vitro. The estimated MTBS results were compared with those of the autoregressive (AR) cepstrum and quadratic transformation (QT) . The SIFT estimates are better than the AR cepstrum estimates and are comparable with the QT values. The study demonstrates that the SIFT algorithm has the potential to be a reliable and robust method for the estimation of MTBS in the presence of a small signal-to-noise ratio, a large spacing variation between regular scatterers, and a large scattering strength ratio of diffuse scatterers to regular ones.     

Estimating mean scatterer spacing with the frequency-smoothedspectral autocorrelation function

The quasiperiodicity of regularly spaced scatterers results in characteristic patterns in the spectra of backscattered ultrasonic signals from which the mean scatterer spacing can be estimated. The mean spacing has been considered for classifying certain biological tissue. This paper addresses the problem of estimating the mean scatterer spacing from backscattered ultrasound signals using the frequency-smoothed spectral autocorrelation (SAC) function. The SAC function exploits characteristic differences between the phase spectrum of the resolvable quasiperiodic scatterers and the unresolvable uniformly distributed (diffuse) scatterers to improve estimator performance over other estimators that operate directly on the magnitude spectrum. Mean scatterer spacing estimates are compared for the frequency-smoothed SAC function and a cepstral technique using an AR model. Simulation results indicate that SAC-based estimates converge more reliably over smaller amounts of data than cepstrum-based estimates. An example of computing an estimate from liver tissue scans is also presented for the SAC function and the AR cepstrum     

Application of autoregressive spectral analysis to cepstral estimation of mean scatterer spacing

The problem of estimation of mean scatterer spacing in an object containing regularly spaced structures is addressed. An autoregressive (AR) spectral estimation method is compared with a conventional fast Fourier transform (FFT)-based approach for this task. Regularly spaced structures produce a periodicity in the power spectrum of ultrasonic backscatter. This periodicity is manifested as a peak in the cepstrum. A phantom was constructed for comparison of the two methods. It contained regularly spaced nylon filaments. It also contained randomly positioned glass spheres that produced incoherent backscatter. In an experiment in which this target was interrogated using broadband ultrasound, the AR spectral estimate offered considerable improvement over the FFT when the analysis gate length was on the order of the structural dimension. Advantages included improved resolution, reduction in bias and variance of scatterer spacing estimates, and greater resistance to ringing artifacts. Data were also acquired from human liver in vivo. AR spectral estimates on human data exhibited a decreased dependence on gate length. These results offer promise for enhanced spatial resolution and accuracy in ultrasonic tissue characterization and nondestructive evaluation of materials.     

Comparison of cepstrum-based methods for radial blind deconvolutionof ultrasound images

This paper compares the performance of seven different cepstrum-based methods for radial blind deconvolution of medical ultrasound images. The first is the generalized cepstrum method. The second is the spectral root cepstrum method. These methods have received little attention so far. The last five methods are all based on the complex cepstrum, but different computational techniques in the spatial and frequency domain are employed. Using in vivo radio frequency data from a clinical scanner, the generalized cepstrum method gave the best images closely followed by the complex cepstrum using phase unwrapping or polynomial rooting. The complex cepstrum method using higher-order statistics was ranked as low as number five. These results are an important guideline for selecting a specific cepstrum-based radial deconvolution method for implementation in ultrasound scanners     

A robust and computationally efficient algorithm for mean scattererspacing estimation

Mean scatterer spacing (MSS) has been recognized to be a useful tool for tissue characterization. Most of the work in this area either uses the amplitude or the phase information of the spectrum of the backscattered ultrasound echo to estimate the MSS. Simulations have shown that the latter approach is more robust in the presence of irregularities in the scatterer distribution. However, most of the algorithms based on the phase information of the spectrum are computationally demanding and cannot be used in real-time. We present a computationally efficient and robust algorithm which uses the magnitude and phase information of the spectrum to estimate the MSS. This algorithm exploits the spectral redundancy present in the backscattered echo signal by generating spectral lines through a nonlinear (quadratic) transformation of the RF echo signal. Results of simulations comparing the performance of the proposed algorithm and previous approaches from the literature are presented to demonstrate the robustness of the proposed algorithm. Experiments involving phantoms and in vitro tissue samples are also presented. The feasibility of implementing a real-time MSS imaging system based on the proposed method is discussed     

《分数倒谱及其在机械故障诊断中应用研究》

论述了分数倒谱的定义和算法,提出了一种基于分数倒谱的机械故障诊断方法。并与倒谱分析方法进行对比分析。实验研究表明,分数倒谱优于传统的倒频谱分析方法,分数倒谱能有效抑制干扰,提高图谱质量。     

1(1/2)维谱及其倒谱在故障诊断中的研究与应用

为了提高故障诊断正确率,通过将倒谱和1(1/2)维谱相结合定义了倒1(1/2)维谱。1(1/2)维谱能够消除高斯噪声,倒谱能够减少谱图中的虚假谱峰,但噪声对倒谱分析的结果具有较大的影响。倒1(1/2)维谱则可以结合1(1/2)维谱和倒谱的优点。通过将1(1/2)维谱、倒谱和倒1(1/2)维谱分别应用于溢流阀故障诊断实验,结果表明,倒1(1/2)维谱可以取得较好的故障诊断效果。     

基于倒阶次谱分析的齿轮故障诊断研究

针对齿轮箱升速过程中振动信号非平稳的特点，将常规的倒谱分析技术与阶次谱结合，提出了倒阶次谱的齿轮箱故障诊断方法。首先对齿轮箱升降速瞬态信号进行时域同步采样，再对时域信号实行等角度重采样，转化为角域平稳信号，最后对角域重采样信号进行倒谱分析，就可提取齿轮的故障特征。通过对齿轮齿根裂纹和齿面磨损故障实验信号的分析，表明该方法能有效地诊断齿轮的故障。     

Designed strength identification of concrete by ultrasonic signal processing based on artificial intelligence techniques

This paper presents a pattern recognition method to identify the designed strength of concrete by evidence accumulation based on artificial intelligence techniques with multiple feature parameters. Concrete specimens in this experiment, which were designed to have the strengths of 180, 210, 240, 300, and 400 kg/cm2, respectively, have been considered. Variance, zero-crossing, mean frequency, autoregressive (AR) model coefficients, and linear cepstrum coefficients are extracted as feature parameters from ultrasonic signals of concretes. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is introduced to transform the distance for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for concrete pattern identification.