基于移频技术的短时傅里叶变换阶比分析

提出了一种基于移频技术的短时傅里叶变换阶比分析算法.该算法利用傅里叶变换在频域的卷积性质,对原始信号在时域乘以e^-j2πf_it使f_i的频谱能量搬迁到零频处,按一定的频率间隔改变f_i就可以在零频处得到其他频率的频谱能量,以此来提高短时傅里叶变换在时频分析中的频率分辨率.然后在时频面上进行局部阈值降噪,同时跟踪转速的变化,最终应用到变速机械的阶比分析中.与短时傅里叶变换分析结果对比表明,本文方法可以更加准确地跟踪到实际的转速.实际降速过程中轴承信号利用本文方法进行阶比分析,成功提取到轴承的故障特征频率.     

基追踪在齿轮损伤识别中的应用

针对齿轮的振动特点,设计了复合过完备时频字典,利用基追踪方法在匹配信号特征结构、直接提取特征信息方面的优势分析了齿轮箱的现场测试振动信号.根据基追踪分解结果,在时频联合域内提取了齿轮局部损伤的周期性冲击特征,识别了齿轮点蚀缺陷.与短时Fourier变换和小波变换等时频分析方法进行了对比分析,验证了基追踪检测齿轮损伤的有效性.     

基于小波变换的爆破振动信号分解与重构

针对非平稳爆破振动信号采用Fourier变换只能在时域或频域变换的问题,提出将时间和频率结合起来分析非平稳信号的方法--小波分析法.利用小波分析法对同一爆破振动信号采用不同小波基进行分析,说明了差异性.并通过不同的小波基对实测的爆破振动信号的分解与重构,以重构误差的大小确定最优小波基.     

EH4高频大地电磁测深数据的时频分析

介绍了EH4高频大地电磁测深原理,以及希尔伯特-黄变换（HHT）的时频分析原理。应用HHT分析了大地电磁信号的时频分布特征。基于matlab平台,编制了EH4时间序列读取及信号分析程序。结果表明,希尔伯特-黄变换是分析EH4非高斯、非平稳信号的有效方法。     

齿轮局部损伤振动信号的循环平稳性分析

针对齿轮局部损伤故障振动信号的特点,在对其数学模型进行循环平稳性理论分析的基础上,定义并解释了幅值调制能量比系数概念及其算法.通过仿真分析,基于信号累积量的循环相关分析算法不仅能完整地保留信号中的周期成分,还具有较好地抑制加性平稳噪声的优点,利于幅值调制能量比系数指标的提取.实验表明,结合齿轮运转的样本数据,幅值调制能量比系数值随着齿轮局部损伤程度的加大而增大.该方法可以发现齿轮在运转中某轮齿发生的局部损伤故障及损伤程度.     

减小离散误差的时频峰值滤波算法

提出减少时频峰值滤波分段点处阶跃误差的改进方法.经过研究时频峰值滤波在频率调制和时频平面峰值滤波时产生的离散误差以及尺度变换方式,发现分段点处阶跃误差与离散傅里叶变换的长度成反比,且与零点在尺度变换后产生的不确定值有关.提出基于定零点尺度变换的时频峰值滤波,在信号尺度变换时将零点变换到瞬时频率区间上的固定值,使各段时频峰值滤波零点偏移量一致,从而消除分段点处的阶跃误差.仿真实验和实际地震信号时频峰值滤波处理结果表明,改进的时频峰值滤波算法能够有效消减随机噪声,减少分段滤波在分段处的阶跃.     

冲击信号的频谱分析

通过对轧机的冲击信号特征频率进行分析,比较了几种常用频谱(或时频)分析方法(傅立叶变换、短时傅立叶及小波分析)在此类分析中的适用性及各自的特点,得出短时傅立叶在轧机冲击信号特征频率估计中占有一定的优势。     

基于高阶同步压缩变换的行星齿轮箱声音信号共振频带特征提取

建立了非平稳运行工况下行星齿轮箱共振频带内的声音信号解析模型,揭示了齿轮故障特征在声音信号共振频带内的分布规律。根据共振频率不随转速变化的特点定位了齿轮箱共振频率,为在共振频带内提取齿轮故障特征奠定基础。针对传统时频分析方法时频分辨率低的缺陷,研究了基于高阶同步压缩变换的时变故障特征提取方法。通过数值仿真和实验信号分析,验证了所提出的声音信号模型与行星齿轮箱故障特征分布规律的正确性,以及利用高阶同步压缩变换方法提取共振频带内行星齿轮箱故障特征的有效性。      

基于小波变换的结构地震响应分析方法的改进

针对现有线性结构非平稳地震响应分析的小波方法中存在计算效率较低的问题,提出了一种求解时频响应的改进方法,即将原地震信号直接输入结构,求得结构响应,再对该响应进行小波分解和重构,得到结构在各频段的响应,反映出结构响应的时频特性.利用小波变换中多分辨率分析的思想及线性结构响应求解的振型分解法,证明了改进方法与现有方法计算结果的一致性.通过算例,说明了改进方法的正确性.     

不同岩石脆性破坏声发射时频特性及信号识别

针对不同岩石脆性破裂声发射信号的非稳定性等特点,提出了声发射参数、Welch 谱、EMD 和BP 神经网络相结合的声发射信号特征提取及识别方法.通过对3 类脆性岩石进行单轴压缩声发射试验,获取了岩石破裂全过程的力学、声发射参数及波形;对各类岩石的声发射信号的时频特征进行了对比分析;综合声发射参数、峰值频率及EMD 能量熵等特征向量,运用BP 神经网络对岩石声发射及干扰源信号进行模式识别.结果表明,不同岩石在单轴加载下声发射参数随应力或时间的演化特征存在异同;EMD 与Welch 谱可很好体现出不同岩石声发射信号频谱与能量分布的特征差异;不同岩石声发射多种特征的神经网络具有良好的识别效果.      

Simulation of Nonstationary Stochastic Processes by Spectral Representation

This paper presents a rigorous derivation of a previously known formula for simulation of one-dimensional, univariate, nonstationary stochastic processes integrating Priestly’s evolutionary spectral representation theory. Applying this formula, sample functions can be generated with great computational efficiency. The simulated stochastic process is asymptotically Gaussian as the number of terms tends to infinity. This paper shows that (1) these sample functions accurately reflect the prescribed probabilistic characteristics of the stochastic process when the number of terms in the cosine series is large, i.e., the ensemble averaged evolutionary power spectral density function (PSDF) or autocorrelation function approaches the corresponding target function as the sample size increases, and (2) the simulation formula, under certain conditions, can be reduced to that for nonstationary white noise process or Shinozuka’s spectral representation of stationary process. In addition to derivation of simulation formula, three methods are developed in this paper to estimate the evolutionary PSDF of a given time-history data by means of the short-time Fourier transform (STFT), the wavelet transform (WT), and the Hilbert-Huang transform (HHT). A comparison of the PSDF of the well-known El Centro earthquake record estimated by these methods shows that the STFT and the WT give similar results, whereas the HHT gives more concentrated energy at certain frequencies. Effectiveness of the proposed simulation formula for nonstationary sample functions is demonstrated by simulating time histories from the estimated evolutionary PSDFs. Mean acceleration spectrum obtained by averaging the spectra of generated time histories are then presented and compared with the target spectrum to demonstrate the usefulness of this method.     

Measurement of walking endurance and walking velocity with questionnaire: validation of the walking impairment questionnaire in men and women with peripheral arterial disease

This paper presents an application of the continuous wavelet transform (CWT) in the analysis of electrogastrographic (EGG) signals. Due to the nonstationary nature of EGG signals, the CWT method, which uses multiresolution scaled windows, gives a better time-frequency resolution than the short-time Fourier transform, which uses a fixed window. Spike activity due to gastric contraction was investigated through experiments on dogs. During spike activity we observed an increase in magnitude of the slow wave and the appearance of a low frequency component with half the frequency of the slow wave. Studies of the EGG signals from the small intestine are also presented to investigate the hypothesis that its slow wave might be confounded with spike activity in the stomach due to the similarity of their frequency ranges.     

Wavelet Transform Analysis of Open Channel Wake Flows

Wavelet transform analysis offers a new approach to signal processing through its ability to decompose signals in both time and frequency. As such, it is more suited to nonstationary and intermittent signals than traditional Fourier analysis. The first part of this paper provides an introduction to the theory and signal processing properties of both continuous and discrete wavelet transform analysis. An account is then given of the application of wavelet transform analysis to a variety of experimental open channel wake flows. Feature location is undertaken using a continuous wavelet transform, and both turbulent statistical analysis and thresholding of the turbulent signal components are undertaken using a discrete wavelet transform.     

Selective discrete Fourier transform algorithm for time-frequency analysis: method and application on simulated and cardiovascular signals

The Selective Discrete Fourier transform (DFT) Algorithm [SDA] method for the calculation and display of time-frequency distribution has been developed and validated. For each time and frequency, the algorithm selects the shortest required trace length and calculates the corresponding spectral component by means of DFT. This approach can be extended to any cardiovascular related signal and provides time-dependent power spectra which are intuitively easy to consider, due to their close relation to the classical spectral analysis approach. The optimal parameters of the SDA for cardiovascular-like signals were chosen. The SDA perform standard spectral analysis on stationary simulated signals as well as reliably detect abrupt changes in the frequency content of nonstationary signals. The SDA applied during a stimulated respiration experiment, accurately detected the changes in the frequency location and amplitude of the respiratory peak in the heart rate (HR) spectrum. It also detected and quantified the expected increase in vagal tone during vagal stimuli. Furthermore, the HR time-dependent power spectrum displayed the increase in sympathetic activity and the vagal withdrawal on standing. Such transient changes in HR control would have been smeared out by standard heart rate variability (HRV), which requires consideration of long trace lengths. The SDA provides a reliable tool for the evaluation and quantification of the control exerted by the Central Nervous System, during clinical and experimental procedures resulting in nonstationary signals.     

基于小波包分析的AOD炉喷溅预报特征信息提取

研究了用小波包分析方法从炉口音频信号中提取AOD炉喷溅预报特征信息的方法。采用db10（小波基函数）小波对喷溅发生前的特征信号进行4层小波包分解,结合快速傅里叶变换法及小波尺度谱进行时频特征分析,并研究了其各频带分解信号的能量比例特点。结果表明,喷溅前40s信号的主频值较正常信号有明显降低,0~312Hz与312~625Hz频段信号能量值比例变化显著。而且低频重构信号可以极好地滤除多种现场干扰,说明该时频特征可以作为准确预报喷溅的特征向量。最后,通过实验确定了8个特征向量值并分别与喷溅或正常信号的特征向量进行相关性比较,验证得出相关度0.95可作为喷溅预报的判定阈值。从而实现了喷溅预报特征信号的准确提取并可转化为计算机容易识别的数值特征。     

Medication side effects in anxious patients: negative placebo responses?

Heart sounds produce an incessant noise during lung sounds recordings. This noise severely contaminates the breath sounds signal and interferes in the analysis of lung sounds. In this paper, the use of a wavelet transform domain filtering technique as an adaptive de-noising tool, implemented in lung sounds analysis, is presented. The multiresolution representations of the signal, produced by wavelet transform, are used for signal structure extraction. In addition, the use of hard thresholding in the wavelet transform domain results in a separation of the nonstationary part of the input signal (heart sounds) from the stationary one (lung sounds). Thus, the location of the heart sound noise (1st and 2nd heart sound peaks) is automatically detected, without requiring any noise reference signal. Experimental results have shown that the implementation of this wavelet-based filter in lung sound analysis results in an efficient reduction of the superimposed heart sound noise, producing an almost noise-free output signal. Due to its simplicity and its fast implementation the method can easily be used in clinical medicine.