Anti-aliasing nonstationary signals detecion algorithm based on interpolation in the frequency domain using the short time Fourier transform

To eliminate the aliasing that appeared during the measurement of multi-components nonstationary signals, a novel kind of anti-aliasing algorithm based on the short time Fourier transform （STFT） is brought forward. First the physical essence of aliasing that occurs is analyzed; second the interpolation algorithm model is setup based on the Hamming window; then the fast implementation of the algorithm using the Newton iteration method is given. Using the numerical simulation the feasibility of algorithm is validated. Finally, the electrical circuit experiment shows the practicality of the algorithm in the electrical engineering.

Suppression to the cross-channel interference based on the short time Fourier transform

A new cross-channel interference suppression method is proposed to decrease the cross-channel interference in beat signals based on the short time Fourier transform(STFT)and the inverse short time Fourier transform(ISTFT)when the dual-orthogonal polarimetric frequency-modulated continuous wave(FMCW)radar adopts the opposite-slope linear frequency modulation signal pair in the simultaneous measurement mode.The STFT is applied only on the signals in the cross-interference intervals in the four polarimetric channels to decrease the computation complexity.A mask matrix for suppressing the interference is constructed using the constant false alarm ratio(CFAR)detection on the spectrograms by the STFT.The simulative results show that the cross-channel interference is efficiently suppressed by the proposed method.The comparison between the proposed method and the rejection method verifies the improved performance of the proposed method.

Application of short-time Fourier transform to high-rise frame structural-health monitoring based on change of inherent frequency over time

The high-rise frame structure has become more and more widespread, like its damage from the complication of the environment. The traditional method of damage detection, which is only suitable for the stationary signal, does not apply to a high-rise frame structure because its damage signal is non-stationary. Thus, this paper presents an application of the short-time Fourier transform(STFT) to damage detection of high-rise frame structures. Compared with the fast Fourier transform, STFT is found to be able to express the frequency spectrum property of the time interval using the signal within this interval. Application of STFT to analyzing a Matlab model and the shaking table test with a twelve-story frame-structure model reveals that there is a positive correlation between the slope of the frequency versus time and the damage level. If the slope is equal to or greater than zero, the structure is not damaged. If the slope is smaller than zero, the structure is damaged, and the less the slope is, the more serious the damage is. The damage results from calculation based on the Matlab model are consistent with those from the shaking table test, demonstrating that STFT can be a reliable tool for the damage detection of high-rise frame structures.

Short-Term Sinusoidal Modeling of an Oriental Music Signal by Using CQT Transform

In this paper, we propose a method for characterizing a musical signal by extracting a set of harmonic descriptors reflecting the maximum information contained in this signal. We focus our study on a signal of oriental music characterized by its richness in tone that can be extended to 1/4 tone, taking into account the frequency and time characteristics of this type of music. To do so, the original signal is slotted and analyzed on a window of short duration. This signal is viewed as the result of a combined modulation of amplitude and frequency. For this result, we apply short-term the non-stationary sinusoidal modeling technique. In each segment, the signal is represented by a set of sinusoids characterized by their intrinsic parameters: amplitudes, frequencies and phases. The modeling approach adopted is closely related to the slot window;therefore great importance is devoted to the study and the choice of the kind of the window and its width. It must be of variable length in order to get better results in the practical implementation of our method. For this purpose, evaluation tests were carried out by synthesizing the signal from the estimated parameters. Interesting results have been identified concerning the comparison of the synthesized signal with the original signal.

基于短时傅里叶变换和深度网络的模块化多电平换流器子模块IGBT开路故障诊断

针对现有模块化多电平换流器(MMC)子模块故障诊断过程中所需传感器较多、测量干扰较大等问题,提出一种基于深度学习的MMC子模块IGBT开路故障诊断方法。在对MMC子模块开路故障特征进行分析的基础上,利用短时傅里叶变换(STFT)提取桥臂电压信号的谐波分量信息作为故障诊断所需的特征参数。将所得到的特征参数进行处理后构建故障诊断样本,在通过深度置信网络实现故障类型快速检测的基础上,依据不同故障类型,构建多个基于卷积神经网络的故障定位网络,进而实现开路故障的检测与定位。通过129电平的MMC系统仿真模型和降功率的MMC实验系统搭建,对该文所提方法进行了验证。仿真和实验结果表明,所提故障诊断方法可以在减少传感器数量的基础上实现子模块开路故障的诊断,提高系统的可靠性。

基于STFT图像和迁移学习的次同步振荡源定位方法

直驱风机与电网交互引发次同步振荡,严重威胁电网的安全稳定运行。为快速定位诱发机组,提出了一种基于短时傅里叶变换(STFT)图像和迁移学习的次同步振荡源定位方法。首先,采用压缩感知技术将出口数据转化为观测信号,再对观测信号进行STFT得到具备振荡特征的映射图,构建映射图与振荡源机组之间的联系;然后,采用对抗式迁移学习架构,结合电力系统,实现对目标域无标签振荡数据的快速泛化;最后,与传统迁移学习方法进行比较,结果表明所提方法在定位准确率和效率方面表现更优,且具备较强的抗噪能力。

基于深度学习的电机故障诊断

故障诊断在保证电机的稳定运行中占据着非常重要的地位,因此,故障诊断在当前的研究中是一个热点。该研究利用短时傅里叶变换把一维的振动信号转换成二维的时频图,进而解决电机轴承的振动信号的非线性和不稳定性问题,并且作为卷积神经网络的输入,通过对故障特征信号的直接提取,来形成样本数据集,通过卷积神经网络与softmax多分类器来建立故障诊断模型,在Python中验证该算法优化的准确性,证明了该算法可以提高电机故障诊断的准确率。

融合CNN和ViT的声信号轴承故障诊断方法

针对轴承故障诊断任务数据量少、故障信号非平稳等特点,提出一种短时傅里叶变换、卷积神经网络和视觉转换器相结合的轴承故障诊断方法。首先,利用短时傅里叶变换将原始声信号转换为包含时序信息和频率信息的时频图像。其次,将时频图像作为卷积神经网络的输入,用于隐式提取图像的深层特征,其输出作为视觉转换器的输入。视觉转换器用于提取信号的时间序列信息。并在输出层利用Softmax函数实现故障模式的识别。试验结果表明,该方法对于轴承故障诊断准确率较高。为了更好解释和优化提出的轴承故障诊断方法,利用t-分布领域嵌入算法对分类特征进行了可视化展示。

基于STFT-ECA-ResNet18网络模型的滚动轴承变负载故障诊断

针对传统方法处理变负载轴承故障诊断时存在的自适应能力弱,模型泛化性差的问题,提出了一种改进的基于深度残差网络的故障诊断方法。首先,将采集到的一维时间序列信号进行短时傅里叶变换得到二维时频数据,再利用二维卷积神经网络从变换后的数据中提取特征。然后,通过高效通道注意力机制获取通道全局信息并对其权值进行调整,以增强改进网络模型的泛化能力,使其在变负载工况下分类效果得到提高。最后,通过仿真对所提方法进行了验证,结果表明相比传统方法诊断效果改进明显。

基于光纤镀膜探针的固体介质中应力波粒子速度测量技术

粒子速度是分析固体介质中应力波传播规律的一个重要参数。结合激光多普勒效应和全光纤干涉测速系统,提出了一种基于光纤镀膜探针的固体介质中应力波粒子速度的测量方法。将光纤镀膜探针嵌入有机玻璃(PMMA)中,距爆心同一半径处,采用0.125 g TNT当量的微型炸药球作为爆炸源,进行填实爆炸产生应力波,通过采集光纤探针端面的运动信息,基于短时傅里叶变换的时频分析方法,解调出端面运动速度,进而反推出粒子速度。实验结果表明:不同光纤镀膜探针测得的速度分别为22.648 m/s、23.505 m/s,将反推的粒子速度与传统的圆环型电磁粒子速度计方法获取到的数据进行对比,两者的相对偏差低于5.00%,验证了光纤镀膜探针测量固体介质中应力波粒子速度的可行性。

自适应窗口旋转优化短时傅里叶变换的变转速滚动轴承故障诊断

针对短时傅里叶变换(STFT)中固定窗效应所导致的能量集中度不高的问题,提出了一种自适应窗口旋转优化短时傅里叶变换(AWROSTFT)的变转速滚动轴承故障诊断方法。通过变分模态分解(VMD)对原始振动信号进行降噪,并利用粒子群优化算法(PSO)解决了VMD参数选择困难的问题;利用切线思想对STFT中水平窗口自适应匹配一系列的旋转算子,使得窗口旋转方向接近甚至等于瞬时调频率,提高了时频表示的能量集中度;计算出谱峰检测法提取到的瞬时频率与转频的平均比值,将得到的结果与轴承的故障特征系数进行匹配,以此实现变转速工况下滚动轴承的故障诊断。仿真和实验的结果都表明,本文所提方法能够兼顾PSO-VMD和AWROST-FT的优势,通过切线思想自适应的旋转窗口使得信号与窗函数在全局上的夹角都为零,从而达到提高能量集中度和锐化时频脊线的目的,实现了变转速工况下滚动轴承的故障诊断。

基于弧触头接触振动特征分析的高压SF6断路器电寿命在线监测方法

高压SF6断路器电寿命评估对保障电力系统安全稳定运行具有重要意义。然而,现有的电寿命评估仍旧依赖累计开断电流法与动态回路电阻法,缺乏有效的在线监测手段。该文针对真实126 kV高压SF6断路器设备,研究了基于弧触头接触点分析的电寿命评估方法。结合变分模态分解(VMD)与短时傅里叶变换(STFT)分析,发现弧触头接触时产生了10 kHz以上的高频振动特征信号。通过调整机构拉杆的位置以改变弧触头刚合点,发现了高频振动特征频率出现时刻与弧触头刚合点之间的相关性,进而提出了一种基于弧触头接触振动特征分析的高压SF6断路器电寿命在线监测方法。现场瓷柱式断路器的实验结果表明,弧触头刚合点测量误差约为0.905 mm,因此该文提出的方法有望在高压SF6断路器电寿命在线监测领域实现推广应用。

海上柔性直流输电系统的宽频带振荡非同时性研究

随着“双高”电力电子设备的大量使用,宽频带振荡对于电力系统稳定性的影响亟待解决。目前分析振荡波形的“频谱”特性主要是采用快速傅里叶变换提取波形的频谱图进行分析。文章分析了快速傅里叶变换的不足之处,提出了利用快速傅里叶变换和短时傅里叶变换提取振荡波形“时频谱”图的方法,对振荡后的波形进行研究分析。根据海上柔性直流输电系统的结构在PSCAD/EMTDC搭建其仿真模型,研究系统控制参数突变失稳以及系统故障时出现的宽频带振荡波形的时频谱特性,利用短时傅里叶变换发现不同的振荡频率出现的时刻不同,发现了海上柔性直流输电系统宽频带振荡非同时性的存在,为研究宽频带振荡的传播以及振荡的抑制提供了一种思路。

一种雷达辐射源智能个体识别的方法

针对使用传统的卷积神经网络及低信噪比环境下雷达辐射源智能个体识别研究中识别性能不够的问题,提出了一种基于短时傅里叶变换(STFT)和EfficientNet的雷达辐射源个体识别方法。首先对雷达信号进行短时傅里叶变换,提取时频特征,然后利用EfficientNet中多个MBconv模块对不同时频特征图像的叠加,挖掘出信号图像隐含的更加复杂和抽象的深层次时频特征,包括信号强度的分布、时频模式、周期性变化等,从而完成个体分类识别。EfficientNet可以同时改变网络深度、宽度、图像分辨率3个参数,解决了梯度消失、梯度爆炸等问题。实验结果表明,基于STFT和EfficientNet的雷达辐射源智能个体识别的方法,相比于传统卷积神经网络在低信噪比环境下具有更好的识别性能。

基于高斯短时分数阶Fourier变换的海面微动目标检测方法

海上目标随海面颠簸导致姿态变化,引起回波功率调制效应,导致回波多普勒体现时变和非平稳特性.为此,本文将微多普勒理论应用于海杂波中弱目标检测,提出一种基于高斯短时分数阶Fourier变换(GSTFRFT)的海面微动目标检测方法.首先,建立海面目标的平动和三维转动回波模型;然后,基于海尖峰判别方法对回波信号进行数据筛选,改善信杂比,并采用GSTFRFT对微动信号进行增强处理,利用海面目标与海杂波的微动特征差异设计恒虚警检测方法;最后,通过GSTFRFT域滤波,提取信号的微动特征并得到瞬时频率.实测雷达数据仿真结果验证了算法的有效性,具有在强海杂波中检测微弱目标的能力.

基于自适应短时Chirp-Fourier变换的瞬时转速估计及应用

回转设备的瞬时转速是实现故障诊断的重要参数之一,也是提取表征设备运行状态其他关键参数的基本前提。针对传统短时傅里叶变换方法存在瞬时转速估计精度不足的问题,提出一种基于自适应短时Chirp-Fourier变换的瞬时转速估计方法。该方法建立调频参数的自适应选取策略,可根据信号的时频分布特性对信号进行自适应匹配分解,不但提高了计算效率,而且避免了谱图模糊问题,实现瞬时转速的精确估计。在此基础上结合信号解调技术,形成无键相幅值相位解调方法,并成功应用于变工况下的齿轮箱故障诊断。

一级气体炮发射冲击响应测试分析

为了获得一级气体炮冲击响应分布特征的时变规律,基于动高压理论构建了一级气体炮的内弹道方程,研制了发射口径为50 mm弹丸质量大于1 kg的一级气体炮,并对不同出膛速度和不同质量弹丸发射响应试验测试,分析了冲击响应时域、频域和时频特征规律。试验测试结果分析表明:构建的内弹道方程能准确描述弹丸质量、压力等参数对出膛速度的关系;频谱密度主要分布在400~460 Hz、732~742 Hz、1 015~1 035 Hz和1 083~1 416 Hz四个频段,冲击响应谱线幅值随弹丸出膛速度呈线性增大,频谱区域分布宽度则线性变窄;弹丸出膛速度对频谱能量密度分布高频时变集中特性更加显著。该研究为气动发射在相关领域应用中的设计、冲击评估提供最基本的响应谱线。

Output only modal identification and structural damage detection using time frequency & wavelet techniques

The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification of multi-degree of freedom （MDOF） linear time invariant （LTI） and linear time variant （LTV--due to damage） systems based on Time-frequency （TF） techniques--such as short-time Fourier transform （STFT）, empirical mode decomposition （EMD）, and wavelets--is proposed. STFT, EMD, and wavelet methods developed to date are reviewed in detail. In addition a Hilbert transform （HT） approach to determine frequency and damping is also presented. In this paper, STFT, EMD, HT and wavelet techniques are developed for decomposition of free vibration response of MDOF systems into their modal components. Once the modal components are obtained, each one is processed using Hilbert transform to obtain the modal frequency and damping ratios. In addition, the ratio of modal components at different degrees of freedom facilitate determination of mode shape. In cases with output only modal identification using ambient/random response, the random decrement technique is used to obtain free vibration response. The advantage of TF techniques is that they arc signal based; hence, can be used for output only modal identification. A three degree of freedom 1：10 scale model test structure is used to validate the proposed output only modal identification techniques based on STFT, EMD, HT, wavelets. Both measured free vibration and forced vibration （white noise） response are considered. The secondary objective of this paper is to show the relative ease with which the TF techniques can be used for modal identification and their potential for real world applications where output only identification is essential. Recorded ambient vibration data processed using techniques such as the random decrement technique can be used to obtain the free vibration response, so that further processing using TF based modal identification can be performed.

基于叩诊法的单排多螺栓钢梁柱节点松动检测方法

叩诊法是指利用力锤敲击待测结构振动发声,通过声音特征诊断结构有无异常的一种检测方法。基于叩诊法的螺栓松动检测是近年来的研究热点之一。现有研究多关注单螺栓松动问题,而工程中常采用多螺栓连接方式,因此现有的研究方法无法满足工程多螺栓松动检测需求。以一单排多螺栓钢结构梁柱节点为研究对象,提出了一种基于叩诊法的多螺栓松动快速检测方法。首先通过短时傅里叶变换和二值化处理提取节点中各螺栓的叩诊声音特征向量,再计算各螺栓松动工况与基准工况特征向量间的欧式距离以量化松动特征。结合所有螺栓松动特征值构造标准化松动指标衡量节点松动程度后,对比各螺栓松动特征值与其基准值的差异性大小识别松动螺栓。研究结果表明,研究方法可快速准确地识别多螺栓节点中的松动螺栓,进一步提升叩诊检测方法在工程中的应用潜力。