The Time-frequency Characteristic of a Large Volume Airgun Source Wavelet and Its Influencing Factors

Through analyzing the near-field hydrophone records of the airgun experiment in the Jiemian reservoir,Fujian,we study the time-frequency characteristic of airgun source wavelet and the influence of gun depth and firing pressure,and explain the process of bubble oscillation based on the Johnson( 1994) bubble model. The data analysis shows that:( 1) Airgun wavelet is composed of primary pulse and bubble pulse. The primary pulse,which is of large amplitude,short duration and wide frequency band,is usually used in shallow exploration. The bubble pulse,which is concentrated in the low-frequency range,is usually used in deep exploration with deep vertical penetration and far horizontal propagation.( 2) The variation of primary pulse amplitude with gun depth is very small,bubble pulse amplitude and the dominant frequency increase,and peak-bubble ratio and bubble period decrease. When the gun depth is 10 m,primary pulse amplitude and peakbubble ratio are maximum,which is suitable for shallow exploration; when gun depth is25 m,bubble pulse amplitude is large, and peak-bubble ratio is minimum, which is suitable for deep exploration.( 3) The primary pulse amplitude,bubble pulse amplitude,peak-bubble ratio,and bubble period increase and the dominant frequency decreases with increased firing pressure.

Research on Leak Location of Liquid-filled Pipe Based on Frequency Dispersion Characteristics

Due to the material problems and force majeure factors,the leakage will be occurred on the liquid-filled pipe resulting in waste of resources,environmental pollution and even endangering safety.Acoustic wave detection technology is widely used in buried pipeline leak detection,this technology mainly uses the wave(n=0,s=1)in the pipeline acoustic wave to locate the leak.When the leakage acoustic signal propagates along the liquid-filled pipe,the frequency dispersion characteristics can be obtained by wavelet decomposition.And there is a time delay(time difference)value between the leaky acoustic signals collected by the sensors at both ends of the leak.The outputs show that the results obtained by wavelet decomposition are in good agreement with the theoretical calculation results.Based on the obtained dispersion relation,the time delay values at different characteristic frequencies are analyzed by the cross-correlation method,and the leak location accuracy is discussed.This research content provides theoretical support and engineering application guidance for pipe leakage location technology.

Time-Series Characteristics of Wind Power and Its Impact on Jilin Power Grid

With the rapid development of wind power, the large-scale wind power integration brings a new range of issues in dispatching operation. In order to gain a better grasp of the influence caused by wind power combined to the grid, the paper first establishes the impact characteristic indexes, and then analyzes the regularity of wind power time series in different spatial and temporal scales. At last, according to the analysis results, this paper assesses the impact of time-series characteristics of wind power on power grid, such as the frequency regulation, peak load regulation, which can provide the reference for wind power optimal dispatching of Jilin Power Grid.

The Time-Frequency Energy Attenuation Factor and Its Application on the Basis of Gauss Linear Frequency-Modulated Continuous Wavelet Transform

Based on the Gauss linear frequency modulated wavelet transform, a new characteristic index is presented, namely time frequency energy attenuation factor which can reflect the difference features of waveform in earthquake focus mechanism, wave traveling path and its attenuation characteristics in focal area or near field. In order to test its validity, we select the natural earthquakes and explosion or collapse events whose focus mechanisms vary obviously,and some natural earthquakes located at the same site or in a very small area. The study indicates that the time frequency energy attenuation factors of the natural earthquakes are obviously different with that of explosion or collapse events, and the change of the time frequency energy attenuation factors is relatively stable for the earthquakes under the normal seismicity background. Using the above mentioned method, it is expected to offer a useful criterion for strong earthquake prediction by continuous earthquake observation.

A study on characteristic indexesof railway ballast bed underhigh-frequency radar

Purpose–In this paper,a high-frequency radar test system was used to collect the data of clean ballast bed and fouled ballast bed of ballasted tracks,respectively,for a quantitative evaluation of the condition of railway ballast bed.Design/methodology/approach–Based on original radar signals,the time–frequency characteristics of radar signals were analyzed,five ballast bed condition characteristic indexes were proposed,including the frequency domain integral area,scanning area,number of intersections with the time axis,number of timedomain inflection points and amplitude envelope obtained by Hilbert transform,and the effectiveness and sensitivity of the indexes were analyzed.Findings–The thickness of ballast bed tested at the sleep bottom by high-frequency radar is up to 55 cm,which meets the requirements of ballast bed detection.Compared with clean ballast bed,the values of the five indexes of fouled ballast bed are larger,and the five indexes could effectively show the condition of the ballast bed.The computational efficiency of amplitude envelope obtained by Hilbert transform is 140 s$km
1,and the computational efficiency of other indexes is 5 s$km
1.The amplitude envelopes obtained by Hilbert transform in the subgrade sections and tunnel sections are the most sensitive,followed by scanning area.The number of intersections with the time axis in the bridge sections was the most sensitive,followed by the scanning area.The scanning area can adapt to different substructures such as subgrade,bridges and tunnels,with high comprehensive sensitivity.Originality/value–The research can provide appropriate characteristic indexes from the high-frequency radar original signal to quantitatively evaluate ballast bed condition under different substructures.

K-S变换及其电网超谐波时频分析应用

依据FFT→优化窗→IFFT思路,突破线性时频变换的窗函数积分性能桎梏,实现高性能优化窗函数的线性时频变换应用,建立新型时频变换算法——K-S变换.对信号x(t)的FFT频谱向量进行频移处理后,与该频移点下Kaiser优化窗的频谱向量进行Hadamard乘积,再将乘积结果进行FFT逆变换(IFFT),构造出K-S变换复时频矩阵,由此获得x(t)的时间-频率-幅值、时间-频率-相位三维信息;给出逆变换的数学推导与局部性质、线性性质和变分辨率特性;0~150 kHz电网的稳态与时变超谐波信号仿真实验表明,K-S变换的时域、频域分辨能力均优于流行的短时傅里叶变换、S变换,具有优良的变分辨率性能;0~40 kHz超谐波信号的实测证明,基于K-S变换的超谐波电压幅值测量绝对误差均小于0.032 3 V.

断层黏滑失稳过程声-电信号响应特征研究

为深入探索断层黏滑失稳的阶段特性以及声-电多源联合前兆特征,对含不同预制倾角断层的正长花岗岩开展了侧压分别为5、15、25MPa的双向剪切摩擦试验,观察和分析了断层黏滑失稳过程的力学行为以及声发射、电荷感应信号的时频响应特征。结果表明:(1)侧压和断层倾角改变了断层的黏滑特征,进而影响了感应电荷量和声发射事件震级。侧压越大,间黏滑和黏滑失稳阶段的电荷量占比以及电荷累积速度越大。相较于56º断层,45º断层黏滑失稳时基于振幅的统计指标b值更小,大尺度破裂占比更大。(2)声-电信号幅值的分形特征可以反映断层面黏滑错动释放能量的差异。声-电信号频域的阶段性响应特点可有效辨识实验室尺度下断层黏滑失稳的演化过程,可以将声-电信号主频幅值急剧提高作为断层黏滑失稳前兆特征之一。临近断层黏滑失稳时声发射和电荷感应参量(能量、分形维数、频谱幅值)均呈现明显的“突增”特性。(3)声-电信号能量突增与黏滑应力降之间具有较好的对应性,但并不完全同步。断层黏滑失稳时,声发射会先于电荷感应产生高幅值信号。仅采用单一声发射或电荷感应信号来预警有可能造成信息漏报、误报,需结合声发射和电荷感应信号特征以实现声-电二元信号的相互补充与验证。

基于暂态时-频特征差异的配电网高阻接地故障识别方法

高阻接地故障发生时,故障特征微弱,传统故障识别方法存在特征提取困难、阈值选取灵活性较差的技术瓶颈,导致极端故障场景下出现漏判。为此,提出基于暂态时-频特征差异的配电网高阻接地故障识别方法。首先,结合小波包香农熵量化分析高阻接地故障与正常扰动工况暂态信号的时频分布,发现二者存在显著差异:频域上,扰动工况信号的能量集中于低频,而高阻故障信号能量分布相对均匀;时域上,扰动工况信号能量集中于时间窗的前半段,高阻故障信号能量在整个时间窗内均匀分布。在此基础上,以暂态信号时-频域波形作为输入样本,将传统卷积神经网络(convolutional neural networks,CNN)模型中的softmax分类器改进为支持向量机(support vector machine,SVM)分类器,构建适应配电网高阻接地故障识别小样本场景下的CNN-SVM复合分类模型,以卷积层作为特征提取器,以SVM作为分类器,实现高阻接地故障识别。最后,为论证所提方法具有强适应性的内在原因,利用LIME可解释性分析算法可视化展现模型训练过程中的高关注度区域,从模型分类原理层面证明所提方法不受各种故障条件的影响,克服了传统故障识别方法在极端故障场景下出现漏判的缺陷,能准确识别配电线路末端10 kΩ高阻接地故障。

基于EEMD-SVM的光伏阵列直流电弧故障检测

光伏阵列随着运行时间的增长,阵列内数量众多的连接线缆、连接头容易产生破损或连接失效等问题,引发直流电弧故障,严重影响系统的安全运行,因此需要采用合适的检测方法进行故障诊断,以及时发现电弧故障。直流电弧故障的检测方法大致可以分为基于物理特性和时频特性两类。前者成本高,难度大,不适合大型光伏系统;后者随着近几年人工智能技术的兴起,大多数是提取直流电弧故障的时频域特征值形成数据集,运用神经网络或智能算法对其进行识别、训练、归纳等,达到检测目的,目前实际应用的检测方法侧重于后者。选用基于时频域特性的集合经验模态分解和支持向量机结合方法进行检测,在MATLAB/Simulink仿真平台搭建光伏阵列模型和直流电弧故障仿真模型,模拟光伏阵列不同位置的串、并联电弧故障,对电流信号进行采集、分析与处理。实验结果表明,支持向量机模型能够较好地对光伏阵列直流电弧故障进行识别和检测,有效区分光伏阵列正常工作状态与故障工作状态。

基于高阶局部最大值同步挤压变换的浅埋隧道地表高铁振动信号时频与衰减特征分析

为解决浅埋隧道上覆地表采集到的高铁振动信号底噪丰富的问题,提出一种适用于浅埋隧道上覆地表高铁振动信号降噪的高阶局部最大值同步挤压变换方法。首先,引入高阶调制算子推导高阶估算瞬时频率,结合局部最大值同步挤压变换推导出高阶局部最大值同步挤压变换,通过数值模拟多分量加噪信号验证该方法的抗噪性;随后,对高铁振动信号进行相关时频分析、处理,对比验证高阶局部最大值同步挤压变换的降噪效果和适用效果;最后,基于此方法对高铁振动信号的时频及衰减特征进行分析。研究可为隧道场地高铁振动信号提供一种谱精度高、降噪效果好的时频分析方法,并为隧道场地中高铁振动的环境影响研究提供理论分析基础。

Bd-小波用于从强电磁干扰中提取局部放电脉冲信号的研究

研究了 Bd-小波的时频调节特性 ,结合放电脉冲的时频特性 ,得到适合于提取放电脉冲的 Bd-小波。利用该小波研究了局放脉冲在小波域的特征 ,提出了利用小波变换从强电磁干扰环境中提取放电脉冲的方法 ;研究了利用该方法从连续周期性干扰和白噪声干扰中提取放电脉冲的可行性。最后从实际测量到的现场干扰中提取了放电脉冲 。

基于时频特征和PCA-KELM的液压系统故障诊断

为了解决液压系统泄漏、堵塞和气穴等多类型故障下特征提取和模式识别困难的问题,提出基于时频特征和PCA-KELM的液压故障智能诊断新方法。首先利用统计分析和总体平均经验模态分解方法,构造高维混合域初始特征向量,从不同特征指标、不同分析角度对不同种类液压故障进行表征和刻画;然后通过主成分分析对多维初始特征向量进行降维和特征二次提取,将高维相关变量转化为低维独立的主特征向量;最后利用PCA主元构造的主特征向量输入核极限学习机网络中,实现故障类型的识别。实验结果表明,混合域初始特征向量能全面准确地描述故障特征,PCA提取的主特征向量摒弃了冗余信息且简化了分类器结构,KELM网络诊断速度快、分类准确率高。

M-Z干涉仪型光纤分布式扰动传感系统模式识别方法

针对M-Z干涉仪型光纤分布式扰动传感系统输出信号短时频率随外界扰动变化的特征,提出了基于短时频率-时间特性的模式识别算法。采用提取短时过电平率来描述传感信号的短时平均频率-时间特性,并将提取出来的时频特性分段后建立相应的特征元素模型,通过动态规划算法(DTW)筛选出最优特征元素模型,将信号所有最优模型的参数作为信号特征输入到人工神经网络(ANN)进行学习和判决,降低了ANN的训练难度以及对时间的敏感性,提高了系统的环境适应能力。实验结果表明:该方法可以有效区分瞬时作用、长时作用、径向作用和不规则作用等多种不同扰动事件,平均识别速度在0.26 s之内,平均识别准确度在97%以上。

改进希尔伯特-黄变换分析波磨对列车时频特征的影响

本文旨在研究钢轨波浪形磨损对列车时频特征的影响.建立了车辆/轨道空间耦合模型,模拟计算了钢轨短波、长波波磨激励下车辆系统的动力响应.将集成经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)和正交希尔伯特变换(Normalized Hilbert Transform,NHT)应用于车辆/轨道耦合系统的振动信号分析中,分析了不同波深和波长的波磨对转向架动力响应时频特征的影响.结果表明:波磨会引起车辆系统振动响应的频率调制,频率振幅与平均频率之比可作为轮轨冲击的预警指标.

不同参数下封闭矩形声腔的结构-声耦合特性分析

基于模态耦合分析法建立了板-声腔耦合系统的自由振动模型,分析了声腔深度对耦合系统共振频率、模态衰减时间的影响,并详细研究了声腔深度变化时,板模态和声腔模态耦合过程中的能量传递、系统共振频率和模态衰减时间的改变.分析表明,声腔深度改变时,影响声腔模态与板模态传递因子大小的因素有声腔深度和对应的耦合前共振频率差.在声腔模态与板模态出现强耦合的声腔深度附近,对应腔控模态和板控模态的共振频率出现跃变现象,而在耦合最强的声腔深度处耦合系统模态衰减时间趋于相等,声腔声场和板振动之间的能量传递最大.

基于LCD-Hilbert谱奇异值和多级支持向量机的配电网故障识别方法

准确识别故障类型是配电网故障处理的首要任务。提出了一种基于时频矩阵奇异值分解(SVD)和多级支持向量机(SVM)的配电网故障识别方法。利用局部特征尺度分解法(LCD)、Hilbert变换以及带通滤波算法,构造配电网母线电压、主变低压侧进线电流等波形的时频矩阵,对其进行奇异值分解以获取波形奇异谱,提取相应奇异谱的分布参数(如反映奇异值大小的奇异谱均值、反映信号复杂程度的奇异熵等)作为特征向量。将特征向量输入基于多级SVM的分类器以实现故障识别。各类典型工况下的仿真和实验结果表明该识别方法的正确率均>90%,可实现对各类不同故障的有效辨识,且具有很强的适应性和实用性。

不同波形参数冲击电压下油-隔板绝缘放电特性

变电站布置、绕组内部谐振等因素会使得侵入变压器内部的雷电过电压的波形参数异于标准波形;此外,GIS中隔离开关操作造成的特快速暂态过电压(VFTO)带来的危害也与日俱增。为此设计了一种油-隔板绝缘模型,研究了波前时间、振荡频率对击穿特性的影响,以及VFTO下油-隔板绝缘模型击穿特性和隔板几何位置对放电特性的影响。结果表明,油-隔板绝缘模型击穿电压随波前时间的减小而增高;振荡频率增大会提高模型击穿电压,最高提高达30%;油-隔板绝缘模型在VFTO下的击穿电压较标准雷电冲击电压下提高10%;单层隔板时模型击穿电压随纸板与球电极之间距离的增大逐渐降低。

煤岩破裂过程电荷信号时-频域特性及降噪研究

为了拓展电荷信号综合分析手段,进一步提高电荷感应方法的煤岩体冲击地压预测准确性,将室内物理试验和理论分析相结合开展煤岩受载破坏电荷信号监测试验,基于煤岩破裂电荷信号产生机制和傅里叶变换方法获得了不同受载阶段电荷信号时-频域特征及其变化规律,并设计数字滤波器进行电荷信号降噪处理。研究结果表明:电荷时-频域信号幅值与煤岩受载变化具有良好的一致性;非接触式感应电荷信号监测装置工频白噪信号组成频率较固定,白噪时-频域信号幅值随采样频率升高明显提高;煤岩受载过程中时域信号特点为弹性后期小幅值振发型,强化损伤阶段高幅值振发型,峰后破坏阶段高幅值连续型;频域变换得出电荷信号为主频率范围1~100 Hz的甚低频率信号,且该范围内的频域信号幅值随时域信号幅值升高而显著增加,此试验结果是区别电荷信号与高频率电磁辐射信号的主要标志,基于研究结果设计了数字低通滤波器,较好的剔除了工频白噪信号同时又不失真的保留了有益信号;基于电荷信号的时-频域特性可将工频干扰信号与有益信号区分有效提高信号监测准确性,达到预测预警冲击地压发生的目的。

煤体单轴压缩过程电磁辐射时-频特征

为提高电磁辐射监测方法的准确性,采用单轴压缩实验方法,分析煤体受载破坏过程电磁辐射时域、频域及波形变化特征,并对其变化机理进行探究.研究结果表明:煤体单轴压缩过程中电磁辐射脉冲数、能量值、波形主频及幅值均与所受应力近似呈正相关关系,煤体受载破裂过程中,电磁辐射的主频与幅值不断增大,并在煤体失稳时达到最大,电磁辐射脉冲数、能量值、主频或幅值的急剧增加可以作为煤体失稳的前兆特征;煤体电磁辐射的频带为1 Hz^500 k Hz,煤体失稳破裂时主频达到最大值202 k Hz,随着加载的进行,频带内各频率幅值分布逐渐集中于主频附近;煤体破裂过程电磁辐射特征变化与外部载荷输入的机械能有关.

煤岩破裂裂纹演化过程声-电信号时频特征研究

为了探究煤岩破裂裂纹演化过程声-电物理信号规律,开展了单轴压缩条件下煤岩试样失稳破坏全程声-电信号的监测试验,重点分析了煤岩试样裂纹演化过程不同阶段声-电信号时频域特征,并对声-电信号相关参数进行了量化分析。研究结果表明:煤岩破裂裂纹演化过程依次经历了微破裂、次破裂和主破裂阶段,各阶段应力突变和声-电信号的产生及变化具有较好的一致性;随着裂纹逐渐演化,煤岩试样电荷信号峰值均值和声发射能量峰值均值都逐渐增大,高值电荷和声发射信号的同时产生预示着煤岩处于次破裂阶段,可以作为煤岩临近失稳破坏的前兆信息;裂纹的不同演化阶段对煤样电荷幅值的影响要大于对声发射能量幅值的影响;随着煤岩试样裂纹逐渐演化,声-电信号主频逐渐向低频转变,且主频幅值逐渐增大,由此可将声-电信号主频降低以及主频幅值增长的趋势作为煤岩失稳破坏的前兆特征;裂纹的不同演化阶段对岩样声发射能量主频幅值的影响要大于煤样。