POVMD与包络阶次谱的变工况滚动轴承故障诊断

针对变转速滚动轴承故障特征提取较难的问题,提出一种基于参数优化变分模态分解(parameter optimized variational mode decomposition,简称POVMD)与包络阶次谱的变工况滚动轴承故障诊断方法。首先,采用POVMD对变转速滚动轴承振动信号进行分解,得到若干个本征模态函数之和;其次,对各个分量的时域信号进行角域重采样,将时变信号转化为平稳信号处理,再利用Hilbert变换估计重采样后的平稳信号的包络;最后,对得到的包络信号进行阶比分析,从谱图中读取故障特征信息。将POVMD方法与经验模态分解进行了对比,仿真信号分析结果表明了POVMD方法的优越性。将提出的变转速滚动轴承故障诊断方法应用于试验数据分析,分析结果表明,所提出的方法能够实现变转速滚动轴承的故障诊断,而且诊断效果优于现有方法。     

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Based on Multi-Masking Empirical Mode Decomposition(MMEMD) and fuzzy c-means(FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into di erent clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into di erent frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition(EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition(MEMD). By adding multi-masking signals to the signals to be decomposed in di erent levels, it can restrain low-frequency components from mixing in highfrequency components e ectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.     

基于参数自适应特征模态分解的滚动轴承故障诊断方法

针对强背景噪声下轴承故障信息难以有效提取的问题,提出一种基于参数自适应特征模态分解的滚动轴承故障诊断方法。首先,为了克服原始特征模态分解(FMD)需要依赖人为经验设定关键参数而不具有自适应性的缺点,提出基于平方包络谱特征能量比(FER-SES)的网格搜索方法自动地确定FMD的模态个数n和滤波器长度L;随后,采用参数优化的FMD将原轴承振动信号划分为n个模态分量,并选取具有最大FER-SES的模态分量为敏感模态分量;最后,通过计算敏感模态分量的平方包络谱来提取故障特征频率,从而判别轴承故障类型。通过仿真信号和工程案例分析验证了提出方法的有效性。与变分模态分解(VMD)和谱峭度方法(SK)相比,提出方法具有更好的故障特征提取性能。     

改进的经验模式分解在机械故障诊断中的应用

针对经验模式分解(empirical mode decomposition,简称EMD)在工程应用中存在的端点效应和模式混叠问题,提出了一种改进的EMD方法。首先,利用遗传支持向量回归对短信号进行延拓;然后,采用改进的包络拟合方法并结合总体经验模式分解(ensemble empirical mode decomposition,简称EEMD)处理信号,数值仿真结果验证了该方法能够有效抑制端点效应和模式混叠;最后,利用该方法并结合包络解调对滚动轴承内圈故障信号进行实验与分析。结果表明,与EMD相比,该方法可以更有效地提取故障特征,满足机械设备故障诊断工程实际需求。     

FAULT DIAGNOSIS APPROACH FOR ROLLER BEARINGS BASED ON EMPIRICAL MODE DECOMPOSITION METHOD AND HILBERT TRANSFORM

Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.     

基于经验模态分解包络谱的滚动轴承故障诊断方法

针对滚动轴承故障振动信号的非平稳特征和传统包络分析法的缺陷,提出了一种基于经验模态分解包络谱的滚动轴承故障诊断方法.该方法首先采用经验模态分解将原始信号分解为若干个平稳的固有模态函数之和,然后求出包含主要故障信息的若干个固有模态函数分量的包络谱,再定义包络谱中故障特征频率处的幅值比为特征幅值比,最后以特征幅值比作为故障特征向量,输入神经网络,以神经网络的输出来判断滚动轴承的工作状态和故障类型.对滚动轴承内圈、外圈故障振动信号的分析结果表明,基于经验模态分解包络谱的故障诊断方法能有效地提取滚动轴承的故障特征.     

OEEMD与Teager能量算子结合的轴承故障诊断

针对滚动轴承发生局部故障时振动信号中微弱周期性冲击的特征提取问题,提出参数优化集合经验模式分解(optimal ensemble empirical mode decomposition,简称OEEMD)与Teager能量算子解调结合的滚动轴承故障诊断方法。首先,针对集合经验模式分解(ensemble empirical mode decomposition,简称EEMD)过程中两个关键参数k(加入白噪声的幅值系数)和m(集合平均次数)的准确选取问题,通过引入相关系数、相关均方根误差和信噪比分析,给出一种可自适应确定这两个参数取值的OEEMD方法,通过OEEMD将冲击从滚动轴承振动信号中分离出来;其次,采用Teager能量算子对其进行包络解调,计算出瞬时幅值后再对瞬时幅值进行包络谱分析,以获取冲击的特征频率,从而对滚动轴承故障进行准确诊断。仿真信号分析和应用实例验证了该方法的有效性。     

基于EMD和峭度的Hilbert包络解调在滚动轴承故障诊断中的应用分析

针对直接运用快速傅里叶变换（FFT）无法有效提取具有非线性非平稳特性的滚动轴承振动信号故障特征频率的问题,提出了一种基于经验模式分解和峭度指标的Hilbert包络解调方法.首先对滚动轴承的振动信号进行了经验模式分解（EMD）,得到了包含轴承故障特征信息的各阶本征模态函数（IMF）,再计算各阶IMF的峭度值,选取了峭度值较大的几阶IMF分量重构信号,并对重构信号进行了Hilbert包络解调分析,从而获得了滚动轴承的准确故障特征信息.分别对仿真模拟信号和实际滚动轴承发生内圈故障的振动信号进行了分析,清晰地得到了故障特征频率.研究结果表明,利用融合EMD、峭度系数和Hilbert包络解调的诊断方法能够快速、准确地提取滚动轴承的故障特征频率,从而可以对滚动轴承进行有效地故障诊断.     

基于EMD的轴承故障包络谱分析

首先对滚动轴承振动信号进行经验模态分解;然后对分解后包含故障特征信息的本征模函数做Hilbert包络谱分析,在得到的包络谱中,清晰显示出故障特征信号的包络谱.试验结果表明,通过联合经验模态分解和Hilbert包络谱分析,能有效地提取出滚动轴承信号的故障信息,进而判定出轴承的损伤部位.     

基于本征模态函数包络谱的滚动轴承故障诊断

针对滚动轴承故障振动信号非平稳的特征,以及传统傅里叶变换不能反映信号细节的缺陷,引入了一种基于本征模态函数包络谱的方法。首先,采用经验模态分解（empirical mode decomposition,EMD）将滚动轴承故障振动信号分解成若干个本征模态函数（intrinsic mode function,IMF）之和;然后,求出包含主要信息成分的IMF分量的Hilbert包络谱;最后,对照滚动轴承故障特征频率,进而判定故障类型。通过对滚动轴承内圈、外圈故障振动信号的分析处理,表明该方法能有效地提取滚动轴承的故障特征。     

基于小波包和细化包络分析的滚动轴承故障诊断方法研究

滚动轴承故障是旋转机械常见的故障之一,针对传统包络解调分析方法需要人为选定共振频带的缺陷,首先采用小波包变换滤波的方法提取滚动轴承固有频率共振频带的信号,并对提取的信号进行重构,滤除了其他信号的干扰．然后用Hilbert变换检波的方法对提取的重构信号实现包络解调,去除高频固有振动成分,诊断轴承的缺陷信息．为了进一步提高包络谱的分辨率,最后采用快速傅立叶变换-傅立叶级数（FFT—FS）方法细化频谱．并在ADBE-56-N4型交流电机上实测了6350型滚动轴承故障模拟信号,与理论分析基本吻合．     

基于改进EMD和谱峭度法滚动轴承故障特征提取

针对滚动轴承故障信号的强背景噪声特点,提出一种基于改进经验模态分解(empirical mode decomposition,简称EMD)与谱峭度法的滚动轴承故障特征提取方法.首先,利用EMD方法对原故障信号进行分解,得到若干平稳固有模态分量(intrinsic mode function,简称IMF);然后,采用灰色关联度与互信息相结合方法剔除传统EMD分解结果中存在的虚假分量;最后,运用谱峭度法和包络解调方法对真实IMF分量进行分析,提取故障特征频率.通过对实际滚动轴承故障信号的应用表明,该方法可有效地提取滚动轴承故障特征,且能够取得比传统包络解调分析更好的效果.     

Compound fault diagnosis of bearings using improved fast spectral kurtosis with VMD

The fast spectrum kurtosis (FSK) algorithm can adaptively identify resonance bands of a signal, and fault characteristics can be extracted by analyzing the selected frequency bands. However, in practical applications, the bearing failure may be composed of various faults (inner ring/outer ring/rolling element) and the faults may be located in different resonant bands. Due to the interference between different fault components and noise, the weak components may be submerged when FSK is used to deal with compound fault signals. To improve the accuracy of an FSK processing compound fault located in different resonance bands, an improved FSK method combined with the variational mode decomposition (VMD) is proposed. First, the parameters (number of components K / penalty factor α ) in the VMD decomposition are selected, and the original compound fault signal is preprocessed by VMD decomposition, so that the original signal is decomposed into K variational intrinsic mode function (VIMF) components. The resonance center bands of these signals are different from each other, so the different fault information is located in different VIMF. Finally, each VIMF component is calculated by FSK. Through the simulated and experimental analysis, the method can accurately identify the resonance bands, and identify the weak fault characteristics of compound bearing fault.     

CEEMD和小波半软阈值相结合的滚动轴承降噪

针对滚动轴承振动信号降噪处理时如何保证信号边缘信息完整性的问题,提出将互补集合经验模态分解(complementary ensemble empirical mode decomposition,简称CEEMD)与小波半软阈值相结合的信号降噪方法,对滚动轴承故障高频振动信号进行降噪处理。首先,采用CEEMD方法对故障振动信号进行分解,针对信号特点自适应获取不同频段模态分量;其次,将对包含噪声污染的高频信号模态分量进行相关性分析,得到含噪成分较高的高频模态分量,进一步采用小波半软阈值进行降噪处理;最后,将降噪后的模态分量同残余分量进行信号重构,完成降噪过程。分析结果表明,相对于传统小波阈值降噪和CEEMD强制降噪方法,提出的方法能够有效去除高频信号的噪声,且最大程度地保证了原始信号的完整性,降噪效果更好。     

基于阶次跟踪和经验模态分解的滚动轴承包络解调分析

针对齿轮箱升降速过程中振动信号非平稳的特点,将计算阶次跟踪方法与经验模态分解技术相结合,提出一种研究旋转机械瞬态信号故障诊断的分析方法。首先对齿轮箱启动时测得的振动信号进行时域采样,再对时域信号进行等角度重采样,将其转化为角域准平稳信号,然后对角域里的信号进行经验模态分解得到多个固有模态函数分量,最后对包含轴承故障信息的高频固有模态分量进行包络解调分析。结果显示：阶次跟踪技术能够有效地避免传统频谱方法所无法解决的“频率模糊”现象,将非平稳信号转化为准平稳信号;经验模态分解方法能够提取包含故障信息的固有模态分量,将两种方法相结合是对传统频谱分析法的有力补充,具有很广阔的应用前景。     

A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM

Targeting the modulation characteristics of roller bearing fault vibration signals, a method of fault feature extraction based on intrinsic mode function (IMF) envelope spectrum is proposed to overcome the limitations of conventional envelope analysis method. By utilizing the proposed feature extraction method, the disadvantages of conventional envelope analysis method such as the chosen of central frequency of filter with experience in advance, looking for spectral line of fault characteristic frequencies in envelope spectrum and so on could be overcome. Firstly, the original modulation signals are decomposed into a number of IMFs by empirical mode decomposition (EMD) method. Secondly, the ratios of amplitudes at the different fault characteristic frequencies in the envelope spectra of some IMFs that include dominant fault information are defined as the characteristic amplitude ratios. Finally, the characteristic amplitude ratios serve as the fault characteristic vectors to be input to the support vector machine (SVM) classifiers and the work condition and fault patterns of the roller bearings are identified. Since the recognition results are available directly from the output of the SVM classifiers, the proposed diagnosis method provides the possibility to fulfill the automatic recognition to machinery faults.     

基于时序分析的经验模式分解法及其应用

经验模式分解方法可以将非线性非平稳信号分解为有限的固有模式函数,在故障诊断中这个固有模式函数常常就是故障信号。但当两侧端点不为极值点时,会造成三次样条拟合的极值包络线大大偏离实际值,并且随着分解的不断进行向内“污染”。提出采用时间序列建模与预测方法,对原信号两端点进行预测,有效地消除了端点效应。指出经验模式分解具有分解的自适应性特点。最后,给出了齿轮箱振动信号的应用实例。     

基于改进双树复小波变换的轴承多故障诊断

针对双树复小波变换产生频率混叠的缺陷,提出了改进双树复小波变换的轴承多故障诊断方法,该方法综合利用了双树复小波包变换和经验模态分解技术。首先,利用双树复小波变换将振动信号分解成不同频带的分量;然后,将各小波分量进行经验模态分解,获得各小波分量的主频率分量信号;最后,计算各小波分量的主频率分量信号的包络谱,根据包络谱识别齿轮箱轴承的故障部位和类型。通过仿真信号和齿轮箱轴承多故障振动实验信号的研究结果表明,该方法不仅消除了频率混叠现象,提高了信噪比和频带选择的正确性,而且提高了从强噪声环境中提取瞬态冲击特征的能力,能有效识别轴承的故障类型。     

Application of EEMD and improved frequency band entropy in bearing fault feature extraction

Ensemble empirical mode decomposition (EEMD) is widely used in condition monitoring of modern machine for its unique advantages. However, when the signal-to-noise ratio is low, the de-noising function of it is often not ideal. Thus, a new fault feature extraction method for rolling bearing combining EEMD and improved frequency band entropy (IFBE) is proposed, i.e., EEMD–IFBE. According to the problem of multiple intrinsic mode functions (IMFs) generated by EEMD, how to select the sensitive IMF(s) that can better reflect fault characteristics, a novel method based on FBE for sensitive IMF is proposed. In addition, since the bandwidth parameter is set empirically when the band-pass filter is designed based on the original FBE, a novel bandwidth parameter optimization method based on the principle of maximum envelope kurtosis is proposed. First, the original vibration signal is subjected to EEMD to obtain a series of IMFs; Then, the FBE values are obtained for the original signal and each IMF component, and the bandwidth of the band-pass filter (empirically) is designed as the characteristic frequency band at the minimum entropy value, and the affiliation between the characteristic frequency band of each IMF and the characteristic frequency band of the original signal is compared, and then selecting the sensitive IMF(s) that reflects the characteristics of the fault; Third, due to the influence of background noise, it is difficult to accurately obtain the fault frequency from the selected IMF(s). Therefore, the band-pass filter designed based on FBE is used, and the bandwidth parameter is optimized based on the principle of envelope kurtosis maximum, and then the selected sensitive IMF is band-pass filtered. Finally, the envelope power spectrum analysis is performed on the filtered signal to extract the fault characteristic frequency, and then the fault diagnosis of the bearing is realized. The method is successfully applied to simulated data and actual data of rolling bearing, which can accurately diagnose fault characteristics of bearing and prove the effectiveness and advantages of the method.     

基于经验模式分解和谱峭度的滚动轴承故障诊断

滚动轴承的振动信号是强背景噪声下的非平稳非线性信号,其特征提取是滚动轴承故障诊断的难点。为了提高滚动轴承的故障诊断效果,提出了基于经验模式分解(Empirical Mode Decomposi-tion,EMD)和谱峭度(Spectrum Kurtosis,SK)的滚动轴承故障诊断方法。首先利用EMD方法对轴承故障信号进行分解,剔除趋势项,利用归一化白噪声分量的统计特性来滤除信号中的噪声分量,然后利用谱峭度方法估计带通滤波器的中心频率和带宽,最后对剩余的信号执行带通滤波和包络解调进行故障诊断。对滚动轴承故障诊断的结果表明,本文提出的方法能够有效地提高轴承故障诊断的效果。