EMD与ICA在故障诊断中的应用研究

由于经验模式分解(EMD)的自身分解能力的不足和外部信号的影响,在处理固有模态函数的精确分离和消除调制信号的干扰成分时存在困难,不能得到合适的IMF分量。提出了一种经验模式分解和独立分量分析联合的方法。该方法将振动信号进行经验模式分解(EMD)形成本征模式函数IMF,再基于互相关准则对分解后的本征模函数进行重新组合,组织虚构的采集信号通道,与源信号一起,最终形成Fast ICA的输入矩阵。此方法不但解决盲源分离算法中观测信号数目需大于等于源信号数目的问题,而且能分离出单独IMF分量的固有特性,保证每个IMF分量丢失的信息特征得到复原后,解决了各个分量之间的信息失真和模态混叠现象,充分发挥出经验模式分解和独立分量分析的特征提取优点。进行数据仿真和实验,验证了该方法在滚动轴承故障特征提取中具有明显效果。     

基于DEMD的高压隔膜泵单向阀早期故障诊断

针对高压隔膜泵单向阀的早期故障特征提取困难的问题,提出基于微分经验模态分解(differential empirical mode decomposition,简称DEMD)的高压隔膜泵单向阀早期故障诊断方法。首先,对振动信号进行微分运算,提高高频成分的振幅比,使微弱高频成分在后续分解中更易提取;其次,对得到的新信号进行经验模态分解(empirical mode decomposition,简称EMD),并将分解后的本征模函数(intrinsic mode function,简称IMF)分量信号进行积分还原;最后,计算分量信号与原振动信号的Kullback-Leibler散度(Kullback-Leibler divergence,简称K-L散度)值,选取K-L散度值较小的分量信号进行重构,并利用Hilbert边际谱对重构信号进行瞬时频谱分析,以提取故障振动信号的特征。仿真与工程实验分析表明,该方法能够较好地提取出单向阀早期故障特征信息。     

基于EEMD与奇异熵增量谱的齿轮故障特征提取

针对齿轮振动信号非线性、非平稳的特点,提出一种基于集合经验模态分解(EEMD)与奇异熵增量谱的齿轮故障特征提取方法。首先,利用EEMD方法将齿轮振动信号分解为若干个平稳的本征模态函数(IMF)分量。EEMD方法利用正态分布白噪声的二进尺度分解特性,能够有效抑制经验模态分解(EMD)中的模态混叠现象。但由于背景噪声和残余辅助白噪声的影响,EEMD分解得到的IMF分量难以准确提取齿轮故障特征。利用奇异值分解(SVD)对IMF分量进行消噪和重构,根据奇异熵增量谱确定重构阶次,准确地提取齿轮的故障特征频率。仿真信号分析和齿轮箱齿轮故障实验验证了该方法的准确性和有效性。     

EMD的LabVIEW实现及其在滚动轴承故障信号分析中的应用

通过对图形化编程软件LabVIEW的二次开发实现了经验模态分解(EMD)算法,为利用LabVIEW构建振动信号分析系统提供了有利分析工具。根据滚动轴承故障产生机理和故障信号的振动特点,将此方法运用到对轴承故障信号的分解上,对分解出的高频本征模函数(IMF)做包络解调从而提取出故障信息,并通过对实际故障轴承数据的分析验证了此方法的有效性。     

大跨径桥梁实时动态挠度信号的分离

由于经验模式分解(empirical mode decomposition,简称EMD)将非线性非平稳信号分解成为一系列线性、平稳的本征模函数(intrinsic mode function,简称IMF)信号,针对单通道大跨径桥梁挠度信号分离问题,结合盲源分离和经验模式分解各自优点,提出基于经验模式分解的盲源分离方法。利用奇异值分解(singular value decomposition,简称SVD)估计信号源数目,根据源信号数目将单通道挠度信号和其本征模函数重组为多通道输入信号,应用独立分量分析(independent component analysis,简称ICA)理论中的快速独立分量分析(fast independent component analysis,简称FastICA)算法对输入信号进行分解,实现桥梁挠度信号各分量的分离。仿真研究表明,该方法能较好地解决ICA模型源数估计和单通道挠度信号盲源分离难题。     

VMD-模平方阈值与PNN相结合的齿轮故障诊断

针对故障齿轮振动信号的非平稳和调制特性,提出了在变分模态分解(VMD)-模平方阈值降噪的基础上利用概率神经网络(PNN)进行齿轮故障诊断的方法。首先,利用VMD将原始振动信号分解为若干个本征模态函数分量,采用模平方阈值方法对各分量处理后并重构;然后,提取重构信号的峭度和均方根作为特征值组成特征向量;最后,将特征向量输入PNN实现故障类型识别。通过齿轮故障试验分析,将其与基于EMD-模平方阈值、LMD-模平方阈值和EEMD-模平方阈值的BP神经网络故障诊断方法相比较。结果表明,该方法能有效的提取特征信息,故障诊断准确率高达96.875%,证明了所提方法的可行性和有效性。     

大跨径桥梁实时动态挠度信号的分离

由于经验模式分解(empirical mode decomposition,简称EMD)将非线性非平稳信号分解成为一系列线性、平稳的本征模函数(intrinsic mode function,简称IMF)信号,针对单通道大跨径桥梁挠度信号分离问题,结合盲源分离和经验模式分解各自优点,提出基于经验模式分解的盲源分离方法。利用奇异值分解(singular value decomposition,简称SVD)估计信号源数目,根据源信号数目将单通道挠度信号和其本征模函数重组为多通道输入信号,应用独立分量分析（independent component analysis,简称ICA）理论中的快速独立分量分析（fast independent component analysis,简称FastICA）算法对输入信号进行分解,实现桥梁挠度信号各分量的分离。仿真研究表明,该方法能较好地解决ICA模型源数估计和单通道挠度信号盲源分离难题。     

基于EMD与同态滤波解调的矿用齿轮箱故障诊断

针对矿用齿轮箱振动信号的特点,提出了一种基于经验模态分解(empirical mode decomposition,简称EMD)与同态滤波相结合的故障解调方法.利用EMD对某矿用皮带机齿轮箱故障信号进行分解,得到若干个本征模态函数(intrinsic mode functions,简称IMFs)分量,然后对其中较突出的IMFs进行同态滤波解调分析,提取出了频率为7.0Hz的调制故障信号.研究表明,EMD与同态滤波解调相结合是一种有效的齿轮箱故障诊断方法.     

改进WMRA在滚动轴承故障诊断研究中的应用

针对滚动轴承初期故障诊断时故障特征信号微弱,且传统的包络谱分析方法需要预先依靠经验确定出分析频段的问题,提出了基于经验模态分解(EMD)和改进的小波多分辨率分析(WMRA)的诊断方法。首先通过对滚动轴承故障振动信号进行EMD分解,利用峭度系数和振动固有频率特征参数对分解后的本征模态函数(IMF)分量进行了分类,筛选出了最佳IMF分量,然后通过希尔伯特变换(HT)计算得到了所选IMF分量的包络信号,最后利用改进后的WMRA对包络信号进行了重构,所得到的包络谱明显地突出了故障特征频率。实验结果表明:相比单独的EMD或传统的WMRA,该方法有效地提高了信号分析的准确性。     

EMD与cICA方法在多级齿轮传动微弱故障特征提取中的应用

为提取多级齿轮传动单通道测量信号中隐含的微弱低频故障特征信息,提出了一种基于经验模态分解(Empirical mode decomposition,EMD)与约束独立分量分析(Constrained independent component analysis,cICA)相结合的故障特征提取方法。首先对实测的齿轮箱单通道测量信号进行EMD分解;然后计算各个本征模态函数(Intrinsic mode function,IMF)的峭度及其与原信号的互相关系数,并选择合适的IMFs分量与原信号组成新的虚拟观测向量;最后,通过构建合适的参考信号进行cICA分析,提取出了理想的微弱低频故障特征。通过多级齿轮传动中的低速级断齿故障特征提取试验分析,验证了该方法的有效性和适用性。     

A Feature Extraction Method for the Wear of Milling Tools Based on the Hilbert Marginal Spectrum

Abstract

The Hilbert–Huang transform (HHT) can adaptively delineate complex non-linear, non-stationary signals when used as the Hilbert–Huang marginal spectrum through empirical mode decomposition (EMD) and the Hilbert transform, to highlight local features of signals. Characterized by high resolution, the Hilbert marginal spectrum has been widely applied in mechanical signal processing and fault diagnosis. In the research, an HHT based on the improved EMD was proposed to analyze the cutting force, vibration acceleration (AC), and acoustic emission (AE) signals during tool wear in the milling process. At first, the collected signals were subjected to range analysis, which revealed that tool wear was closely related to the signals collected during the cutting process. Then, EMD was applied to the signals, followed by variance analysis after calculating the energies of each intrinsic mode function (IMF) component. Afterwards, the IMF components significantly influenced by wear degree, while slightly influenced by the three cutting factors (cutting velocity, feed per tooth, and cutting depth), were selected as IMF sensitive to the degree of wear. The HHT was finally applied to the sensitive IMF components of signals containing major tool wear information, thus obtaining the Hilbert marginal spectra of the signals, which were able to reflect the changes in signal amplitude with frequency. On the basis of the Hilbert marginal spectrum, the method defined the feature energy function which was then used as the eigenvector for predicting tool wear in milling processes. The analysis of signals in four tool wear states indicated that the method can extract salient tool wear features.     

Prediction of tool breakage in face milling using support vector machine

A new approach is proposed using a support vector machine (SVM) to classify the feature of the cutting force signal for the prediction of tool breakage in face milling. The cutting force signal is compressed by averaging the cutting force signals per tooth to extract the feature of the cutting force signal due to tool breakage. With the SVM learning process, the output of SVM’s decision function can be utilized to identify a milling cutter with or without tool breakage. Experimental results are presented to verify the feasibility of this tool breakage prediction system in milling operations.     

异形螺杆铣削过程刀具磨损建模研究

介绍了一种螺杆铣削过程刀具磨损建模的方法。该方法针对螺杆加工中变切削参数的工况，提取了振动信号和功率信号的刀具磨损特征值，并建立了信号特征值与刀具磨损量之间的映射关系，从而得到刀具磨损模型。实验证明，由此建立的刀具磨损模型。能够排除切削参数变化的干扰，可以较好地反映加工中刀具磨损状态。同时也为具有时变切削参数特性的加工过程刀具磨损状态监控提供了新的研究方法。     

基于振动法的铣刀破损特征量提取

建立了铣削加工中振信号的检测系统，并介绍了利用振动信号进行铣刀破损试验的整个试验过程。根据试验数据，对切削过程中产生的振动信号进行了分析与处理，提出了能过反映刀具破损的特征量。为后续的刀具破损系统辩识帮好了充分准备。     

基于EMD与功率谱分析的滚动轴承故障诊断方法研究

针对西部油田大型设备故障信号的非线性、非平稳特征，提出一种基于经验模态分解方法EMD（empirical mode decomposition）和功率谱的分析方法。首先对滚动轴承振动信号进行经验模态分解，然后对分解后包含轴承故障特征信息的固有模态函数分量作功率谱分析，得到各分量的功率谱图，清晰直观显示出故障特征信号的功率谱，从混有背景信号和噪声的振动信号中提取轴承故障信息。由于EMD方法具有自适应特性，适宜于非线性、非平稳信号的分解，该方法应用于滚动轴承的故障振动信号分析中，结果表明，该方法能够突出滚动轴承振动信号的故障特征，从而提高滚动轴承故障诊断的准确性。     

利用改进的EMD方法进行高速转子振动信号分析

弹用涡扇发动机整机振动信号中经常包含着非线性和非平稳的高速转子振动信号成份。近年来采用一些新的信号处理方法来分析这些振动信号是国内外研究的热点,其中经验模态分解(EMD)方法颇具研究和应用前景。但是EMD方法往往在第一阶内在模函数(IMF)中包含着宽频信号,因而得不出单一成份信号。本文利用小波分析方法对待分析的振动信号进行预处理,把信号分解成一系列的窄带信号,然后应用EMD方法使得各阶IMF均为单一成份信号。该改进的EMD方法用来分析某型弹用涡扇发动机整机振动信号,从中得出了一些有益结论。     

Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis

Gear is a vital transmission element, finding numerous applications in small, medium and large machinery. Excessive loads, speeds and improper operating conditions may cause defects on their bearing surfaces, thereby triggering abnormal vibrations in whole machine structures. This paper describes the implementation of empirical mode decomposition (EMD) method for monitoring simulated faults using vibration and acoustic signals in a two stage helical gearbox. By using EMD method, a complicated signal can be decomposed into a number of intrinsic mode functions (IMF) based on the local characteristic time scale of the signal. Vibration and acoustic signals are decomposed to extract higher order statistical parameters. Results demonstrate the effectiveness of EMD based statistical parameters to diagnose severity of local faults on helical gear tooth. Kurtosis values from EMD and that obtained from vibration and acoustic signals are compared to demonstrate the superiority of EMD based technique.     

基于改进全息希尔伯特谱分析的旋转机械故障诊断方法

时频分析方法能够有效同时提取故障设备振动信号的时间和频率信息,但在全面反映非线性振动信号幅值调制与频率调制特征之间的跨尺度耦合关系方面仍存在局限,且容易受到噪声干扰。对此,创新性地将全息希尔伯特谱分析(Holo-Hilbert spectral analysis,HHSA)方法引入到机械故障诊断中。HHSA通过双层经验模态分解(EMD)结构可完整地描述振动信号的内部调制特性,非常适合机械局部故障的检测。同时,为了进一步提升HHSA的诊断精度、抑制EMD模态混叠和噪声干扰,提出一种基于改进再生相移正弦辅助经验模式分解(Improved regenerated phase-shifted sinusoid-assisted EMD,IRPSEMD)的改进HHSA方法(IHHSA)。通过仿真信号验证IHHSA方法用于局部故障检测和诊断的有效性。最后,将IHHSA应用于齿轮裂纹故障和滚动轴承局部故障诊断中,结果表明,提出的IHHSA方法能够更全面地反映和呈现非线性故障振动信号的内部调制关系,且具有更好的故障识别能力。     

GEARBOX FAULTDIAGNOSIS BASED ON EMPIRICAL MODE DECOMPOSITION

Time synchronous averaging of vibration data is a fundament technique for gearbox diagnosis. Currently, this technique relies on hardware tachometer to give phase synchronous information. Empirical mode decomposition (HMD) is introduced to replace time synchronous averaging of gearbox vibration signal. With it, any complicated dataset can be decomposed into a finite and often small number of intrinsic mode functions (IMF). The key problem is how to assure that vibration signals deduced by gear defects could be sifted out by HMD. The characteristic vibration signals of gear defects are proved IMFs, which makes it possible to utilize EMD for the diagnosis of gearbox faults. The method is validated by data from recordings of the vibration of a single-stage spiral bevel gearbox with fatigue pitting. The results show EMD is powerful to extract characteristic information from noisy vibration signals.