基于EWT及互时频的天然气管道泄漏定位

针对天然气管道泄漏定位的问题,提出一种基于经验小波变换(EWT)及互时频的泄漏定位方法。该方法首先采用EWT对泄漏信号进行分解,获得多个分量,进而提出根据互信息的敏感分量自适应选择算法,获取敏感分量;然后采用互时频法对敏感分量进行时频分析,根据互时频图的最大特征峰值计算时延和相关频率;最后根据频散曲线获得相关声波速度,实现对天然气管道泄漏点定位。实验结果表明,该方法能够实现管道泄漏点定位,与基于EMD的互时频法相比,定位精度明显提高。     

空域相关和LMD结合的滚动轴承声发射信号特征提取

为了消除噪声对轴承诊断效果的影响,提出了一种局域均值分解(Local mean decomposition,LMD)和空域相关相结合的轴承声发射信号特征提取方法。首先,利用LMD将轴承故障的声发射信号分解为若干个乘积函数(Production Function,PF)的线性组合;然后,采用峭度准则选取能够反映轴承故障特征的PF分量,对选取的PF分量分别采用空域相关法进行去噪,再重构去噪后的PF分量。最后,对去噪后声发射信号进行Hilbert包络谱分析,并与所选PF分量直接Hilbert包络谱分析结果进行对比。仿真计算和实验分析表明,本文提出的方法能够有效地提取轴承故障声发射信号特征。     

气体管道泄漏模态声发射时频定位方法

针对声发射信号频散特性导致基于时延估计的气体管道泄漏定位误差大的问题,提出一种基于模态声发射时频分析的泄漏定位方法。该方法采用平滑伪Wigner-Ville时频分布对两泄漏信号的互相关函数进行时频分析,利用互相关函数的时频谱可同时提取泄漏信号的时间延迟和与之对应的频率;然后根据泄漏声发射信号的主导模态的频散曲线即可确定该频率对应的声速,利用实时确定的声速和时间延迟并根据两传感器之间的距离即可确定泄漏点的位置。实验结果表明,采用时频分析的气体管道泄漏定位误差与互相关相比减少了6倍。所提出的模态声发射时频定位方法能有效抑制泄漏信号的频散,提高泄漏信号的相关性,从而更适合用于声发射管道泄漏定位。     

基于小波包和HHT变换的声发射信号分析方法

针对声发射管道泄漏检测过程中的噪声干扰问题,对基于小波包和经验模态分解(EMD)的声发射信号处理方法进行了研究.采用小波包分解算法和经验模态分解都可以对管道泄漏声发射信号进行分解,但分解结果却存在一定区别.EMD是近年来非平稳信号分析领域的一个突破,对管道泄漏声发射信号进行EMD分解后,选择包含声发射特征的若干固有模式函数(IMF分量)进行重构,可以提取到管道泄漏声发射信号的本质特征,消除噪声信号的干扰.相对小波包分解方法而言,对根据IMF分量重构的声发射信号进行相关分析计算,得到的管道泄漏点的位置更为精确.     

基于微分局部均值分解的旋转机械故障诊断方法

提出一种基于微分局部均值分解(Differential local mean decomposition,DLMD)的旋转机械故障诊断方法。该方法在局部均值分解(Local mean decomposition,LMD)过程中融入微分和积分运算。对原始信号进行k阶微分,然后对微分后信号进行LMD分解,对分解得到的各乘积函数(Production function,PF)分量循环进行一次积分和一阶LMD分解,直至循环k次,得到m个PF分量和残余分量,将所有PF分量的瞬时幅值和瞬时频率组合,便可以得到原始信号完整时频分布。将该方法应用于旋转机械故障诊断研究中,通过仿真和试验进行分析研究,结果表明,基于微分局部均值分解的旋转机械故障诊断方法能够有效地抑制虚假干扰频率,提高旋转机械故障诊断准确性。     

LMD及马氏距离敏感阈值的滚动轴承故障诊断

针对滚动轴承非平稳性的振动信号,提出了基于局部均值分解(Local Mean Decomposition,LMD)及马氏距离敏感阈值的滚动轴承故障诊断方法。首先,对振动信号进行LMD分解,获得一系列乘积函数(Production Function,PF),有的PF分量包含的故障信息多,有的包含的少,为此采用K-L散度法提取出主要PF分量;计算主要PF分量的时域参数指标,将其组合成特征向量,根据马氏距离提出马氏距离敏感阈值来表征不同的故障状态,取多组正常信号的特征向量均值作为标准特征向量,计算未知特征向量与标准特征向量的马氏距离敏感阈值,从而对其故障状态进行识别。试验结果表明,在不同转速下,该方法能够有效的对滚动轴承故障进行识别,且效果较EMD方法好。     

基于LMD包络谱熵及SVM的天然气管道微小泄漏孔径识别

针对管道泄漏信号的非平稳特征以及管道泄漏孔径大小难以识别的问题,提出一种基于局域均值分解包络谱熵及支持向量机的识别方法。该方法对管道泄漏信号进行局域均值分解,得到若干个瞬时频率具有物理意义的乘积函数(production Function, PF)分量;计算各PF分量的峭度值并据此选出包含主要泄漏信息的分量作为主PF分量,对这些分量进一步采用小波包分解能量法进行分析并重构;再对重构后的主PF分量进行希尔伯特变换求取包络谱,结合信息熵的概念提出包络谱熵并计算熵值;将归一化包络谱熵作为泄漏信号特征输入支持向量机分类器中,用以区分不同的泄漏孔径,完成对泄漏孔径的识别。通过试验采集大量的管道泄漏信号进行处理及分析,试验结果表明该方法能有效识别不同泄漏孔径类别。     

基于总体局域均值分解及稀疏表示分类的天然气管道泄漏孔径识别

针对天然气管道泄漏受孔径、传感器距离、管道内压力等多种因素影响,特征提取及识别算法较为复杂的问题,提出了基于总体局域均值分解-相对熵的特征提取算法并结合稀疏表示分类的泄漏孔径识别新方法。该方法采用总体局域均值分解方法对泄漏信号进行自适应分解,得到不同孔径泄漏信号的特征信息,并根据KL散度选择包含主要泄漏信息的PF分量,在此基础上提取多种时频特征参数,获取全面准确表征泄漏信号的特征向量;针对小样本复杂信号的分类,提出稀疏表示分类器实现泄漏孔径准确分类。该分类器采用过完备字典求得测试信号的最稀疏解,并以此解作为测试信号的稀疏重构系数,以获取测试信号在不同类别中的重构信号,最终通过判断测试信号与重构信号的残差值大小完成泄漏孔径分类。实验结果表明,所提出的算法比传统的SVM及BP分类算法识别准确率高。     

基于LMD近似熵和FCM聚类的机械故障诊断研究

提出一种基于局部均值分解(local mean decomposition,LMD)近似熵和模糊C均值聚类(fuzzy C-means clustering,FCM)相结合的机械故障诊断方法.首先对机械振动信号进行LMD分解,得到若干具有物理意义的乘积函数(product function,PF)分量,再通过相关性分析,筛选出与原始信号相关性最大的3个分量作为数据源,求取其近似熵作为特征向量,最后通过FCM模糊聚类对特征向量进行识别分类.实验表明,基于LMD近似熵和FCM模糊聚类相结合的方法对机械故障信号能够有效准确地进行识别分类,此外,将该方法与基于EMD近似熵和FCM结合的方法进行对比,结果表明该方法具有更好的故障识别效果.     

基于DLMD样本熵和模糊聚类的滚动轴承故障诊断

针对传统的局部均值分解(LMD)方法不能有效提取微弱高频信号成分的问题,提出了一种基于微分的微分局部均值分解(DLMD)方法,在此基础上,将DLMD、样本熵和模糊聚类分析相结合,提出了一种基于DLMD样本熵和模糊聚类的滚动轴承故障诊断方法。该方法首先对滚动轴承振动信号进行微分局部均值分解,得到若干具有物理意义的乘积函数(PF)分量,然后求取各PF分量的样本熵并将其作为特征向量,最后通过模糊聚类对特征向量进行识别分类。实验结果表明,基于DLMD样本熵和模糊聚类相结合的方法能够准确、有效地对滚动轴承故障信号进行识别分类。     

Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM

As it is difficult to identify the scale and aperture of small leaks occurring in a natural gas pipeline, this paper proposes a small leak feature extraction and recognition method based on local mean decomposition (LMD) envelope spectrum entropy and support vector machine (SVM). First, LMD is used to decompose the leakage signals into several FM–AM signals, i.e. into product function (PF) components. Then, based on their kurtosis features, the principal PF components that contain most of the leakage information are selected. Wavelet packet decomposition and energy methods are used to analyze and then reconstruct the principal PF components. The Hilbert transform is applied to these reconstructed principal PF components in order to acquire the envelope spectrum, from which the envelope spectrum entropy is obtained. Finally the normalized envelope spectrum entropy features are input into the SVM as leakage feature vectors in order to enable leak aperture category identification. By analyzing the acquired pipeline leakage signals in field experiments, it shows that this method can effectively identify different leak categories.     

气体管道泄漏声源特性声发射试验研究

对气体管道泄漏孔处声源进行了声发射检测试验,分析了气体泄漏产生声发射的原因,通过对不同泄漏孔直径、不同泄漏内压情况下的声发射信号处理与分析,得出气体管道泄漏声源的频率范围及幅度随管道内部压力、泄漏孔径的变化影响规律,并与管道气体泄漏的数值模拟结果进行了对比分析,试验研究结果为气体管道泄漏声发射检测提供依据。     

Target location method for pipeline pre-warning system based on HHT and time difference of arrival

The increasing serious pipeline leakage accidents are caused by third part damage. The third party damage activity around pipeline can generate seismic wave, and the seismic wave can be used for target identification and damage activity location. This paper presents a novel passive location method based on arrival time difference of specific seismic wave characteristic frequencies. The seismic signals are typically non-stationary and the conventional methods cannot analyze them well. Hilbert–Huang Transform (HHT), including empirical mode decomposition (EMD) and Hilbert transform, is a new time–frequency analysis method and can be used for seismic signals analyzing. Firstly, EMD is applied to process the signals and obtain the intrinsic mode functions (IMFs) features. The kurtosis features are used to identify targets and characteristic frequencies are selected as principal components according to IMFs energy features. These principal components are processed by windowed harmonic wavelet transform and then instantaneous features of seismic signals can be extracted. TDOA can be deduced from the arrival time difference of principal frequency components. Finally, target location can be achieved by the time difference analyzing, sensors layout and the relative position between sensors and targets. The seismic signals acquired from field experiment are analyzed and the results are discussed.     

气体管道泄漏声源产生机理仿真研究

以Lighthill气动声学方程为基础,采用时域差分方法,对不同泄漏孔直径的管道气体泄漏声场进行了数值模拟。数值模拟结果表明,在泄漏孔处,泄漏孔直径、内压对声强影响成线性关系。模拟结果为管道气体泄漏的声发射检测提供依据。     

天然气管道球阀内漏发声机理及检测试验

球阀作为高压天然气输送管道的主要设备,其内漏时的喷流气体会产生声发射信号,通过研究该声发射信号特征规律将有助于阀门内漏流量量化检测。针对这一问题,进行了天然气输送管道球阀内漏发声机理和检测试验研究,分析了阀门内漏声发射现象产生的机理和内漏流量检测评价方法。在此基础上,应用声发射检测系统对3种不同尺寸内漏球阀进行了检测试验,通过试验分析了球阀在不同内漏流量下的声发射信号频谱特征分布规律,并采用小波包分析方法进行信号特征参数(信息熵、均方根、频域峰值)提取。拟合特征参数与内漏流量关系曲线,采用R~2(确定系数)指标对曲线拟合程度进行评价,评价结果表明,采用均方根值(root mean square,简称RMS)的曲线拟合程度最高(R2为0.979),可以用于天然气输送管道球阀内漏流量的量化检测。     

Gas leak locating in steel pipe using wavelet transform and cross-correlation method

Pipelines leakage in power plant, petrochemical complexes, and refineries can lead to explosion, pollution, and severe physical damages, so precise and on time leak locating is very important. There are many techniques for detecting and locating the leakage. In this research, we represent the leak locating principle in pressurized gas pipelines by using acoustic emission theory. An algorithm for finding the location of continuous acoustic waves resulted from leakage is provided by MATLAB software. The used leak locating technique is a combination of wavelet transform, filtering, and cross-correlation methods. The resulted acoustic emission signals were analyzed into high and low frequencies by wavelet transform and available noises on them were omitted completely by filtering. Then de-noised acoustic emission signals were reconstructed. Time differences of de-noised waves were calculated precisely by using cross-correlation function. For studying the accuracy of used method, acoustic emission testing was done by continuous leakage source. The resulted signals of leakage were recorded by two acoustic sensors in two sides of leakage source, and time difference and leak location were calculated by using the algorithm. Several tests were done by changing sensor distance from leakage source and error percent of less than 3 % was resulted in leak locating that indicated high precision of used algorithm.