基于CPW和SCD的行星轴承内圈故障特征提取

行星齿轮箱振动信号传递路径具有时变性,各振动分量间相互耦合和调制,拾取的信号往往比较复杂。此外,行星轴承早期故障对应的振动信号微弱,常湮没于背景噪声和较强的齿轮啮合振动信号中,使得行星轴承故障特征提取较为困难。为此,笔者提出一种基于倒谱预白化（cepstral pre?whitening,简称CPW）和谱相关密度（spectral correlation density,简称SCD）的行星轴承内圈故障特征提取方法。首先,采用CPW削弱具有严格周期特性振动分量的能量幅值,增强轴承故障分量的冲击幅值;其次,基于谱峭度算法获取与轴承故障冲击相关的谱峭度最大值时对应的解调频带参数,并获得带通滤波后复包络信号,进而消除解调频带外成分的干扰;最后,基于轴承故障的随机滑动特性,结合SCD提取行星轴承故障振动分量,进而包络谱分析提取出行星轴承故障特征。利用行星轴承内圈故障实测数据验证了方法的有效性。     

基于LMD和增强包络谱的滚动轴承故障分析

针对滚动轴承发生故障时振动信号幅值分布的峭度和歪度都会发生变化的特点,提出基于峭度-歪度的局部均值分解分量筛选准则,将峭度值和歪度绝对值最大的分量筛选出来并重构故障信号,以达到降噪的目的。对降噪后的信号进行增强包络谱分析,得到故障的特征频率。应用提出的新方法对实测的滚动轴承外圈、滚动体和内圈发生故障时的振动信号分别进行了分析。结果表明,基于峭度-歪度的局部均值分解分量筛选准则有效地降低了信号中的噪声,在此基础上应用增强包络谱有效地减少带内噪声影响,从而使故障特征信息凸现出来,有利于对滚动轴承的各种故障进行诊断。     

基于形态分量分析和包络谱的轴承故障诊断

滚动轴承出现局部损伤时,其振动信号往往由包含轴承自身振动的谐振分量、包含轴承故障信息的冲击分量及随机噪声分量构成。提出了基于形态分量分析和包络谱的滚动轴承故障诊断方法。该方法根据轴承振动信号中各组成成分的形态差异,利用改进的形态分量分析对滚动轴承故障振动信号中的谐振分量、冲击分量和噪声分量进行分离,然后对冲击分量进行Hilbert包络解调分析,根据包络谱诊断滚动轴承故障。算法仿真和应用实例表明,该方法能有效提取滚动轴承故障特征。     

基于ELMD-MCKD在滚动轴承故障诊断中的应用

针对在强噪声环境下,滚动轴承故障特征信息微弱、特征频率难以识别的问题,提出基于总体局部均值分解(Ensemble Local Mean Decomposition,ELMD)与最大相关峭度卷积(Maximum Correlated Kurtosis Deconvolution,MCKD)的轴承故障诊断方法,用于处理轴承故障振动信号。首先,使用ELMD将原始数据分解为1组乘积函数(PF);然后,利用MCKD对每一个PF分量进行降噪处理;最后,对各降噪的PF分量求取包络谱,从而在包络谱中寻找轴承的故障特征频率。为了验证ELMD-MCKD在检测故障中的有效性,进行了一系列轴承故障模拟实验分析。结果表明,提出的ELMD-MCKD方法提高了轴承故障识别的准确性,可用于实际应用中的故障诊断。     

一种基于时频峭度谱的滚动轴承损伤诊断方法

为了准确地提取滚动轴承损伤特征频率,提出一种基于频率切片小波变换的时频峭度谱分析方法。采用频率切片小波变换对振动信号进行时频分解,求取与各个频率分量对应的幅值峭度,由幅值峭度序列构造信号的时频峭度谱。以时频峭度谱的若干个较大谱峰对应的频率作为中心频率,确定相应的共振频带,并在时频空间选择时频切片,然后采用重构分离出这些信号分量,并用包络解调获取重构信号的包络。在此基础上,通过包络信号的等效功率谱确定滚动轴承的损伤特征频率。试验证明,这种方法可以有效地提取滚动轴承的特征频率,由于采用了多个频带保证了足够多的信号能量可用于包络分析,当轴承存在多种损伤时,也可以有效地鉴别不同损伤特征频率。     

基于MCKD降噪和改进EWT信号分解的轴承故障识别

为了提高在传动系统振动信号识别过程中经验小波变换(Empirical wavelet transform, EWT)微弱故障识别能力,设计了一种通过MCKD降噪与IEWT相结合得到的新算法。先以MCKD算法完成轴承故障信号的消噪过程;接着通过IEWT算法完成降噪数据的频谱分类,生成多个分量信号的情况下再对信号进行平方包络谱处理;最后再对故障特征开展识别确定故障特征。研究结果表明：轴承外圈和内圈信号冲击特征获得显著增强的效果。根据平方包络谱确定外圈故障特征频率与倍频,由此准确检测轴承的外圈和内圈故障。以MCKD-IEWT算法处理包含强噪声的信号时,可以实现Fourier频谱的准确分段,也可根据峭度指标从中确定最佳信号分量,满足强噪声条件下的故障识别要求。该研究适用于其它的机械传动系统,具有很好的理论支撑价值。     

基于小波包能量与峭度谱的滚动轴承故障诊断

针对故障轴承的振动信号中包含冲击成分,导致信号的能量集中的问题,提出了一种基于小波包能量与峭度谱相结合的方法用以提取轴承故障信号特征.首先应用小波包对测量信号进行分解、能量归一化处理和信号重构,然后将重构信号采用峭度谱确定带通滤波器的最佳中心频率和带宽,最后将滤波信号进行包络解调并提取故障特征频率.分别对仿真信号和试验...     

基于MODWPT和MCKD的滚动轴承早期故障诊断

针对齿轮啮合强振动干扰下滚动轴承微弱故障特征提取难的问题，提出一种最大重叠离散小波包变换(MODWPT)和最大相关峭度解卷积(MCKD)相结合的滚动轴承早期故障诊断方法。首先采用MODWPT方法将复杂的轴承故障振动信号分解为若干分量，然后依据峭度准则，选取峭度较大的分量进行MCKD滤波，最后对滤波后所得信号做Hilbert包络分析，将包络谱呈现的频率特征与理论故障特征频率相比较，识别故障特征，实现故障诊断。通过轴承故障的仿真及实验研究，并对比单一MCKD方法和EMD-MED方法的提取效果，说明该方法可以在一定程度上抑制齿轮啮合强振动及噪声的干扰，增强并有效提取出滚动轴承早期低频微弱故障特征。     

基于离散随机分离的齿轮箱复合故障分析法

齿轮箱复合故障中,较弱的故障特征往往被较强的故障信号所淹没,传统方法较难实现对较弱故障特征的提取。为解决上述问题,提出一种基于离散随机分离的齿轮箱复合故障振动分析法。该方法首先使用快速谱峭度算法获取对齿轮箱振动信号的共振带参数,依据该共振带参数设计带通滤波器及结合Hilbert变换实现对振动信号包络提取;之后应用角域重采样将时域包络信号转换到角域以消除转速波动影响;再应用离散随机分离对角域包络信号进行分离,分别得到齿轮故障和轴承故障对应的角域包络信号;最后,分别对角域包络信号进行包络谱分析获得齿轮、轴承故障的特征频率信息。试验结果表明,该方法可实现齿轮箱齿轮及轴承复合故障特征的有效提取。     

基于峭度准则VMD及平稳小波的轴承故障诊断

为了从强噪声背景下的轴承振动信号中准确稳定地提取滚动轴承故障特征,提出了基于峭度准则VMD及平稳小波的轴承故障诊断方法。使用变分模态分解对同一负荷下的故障信号进行预处理,通过峭度准则筛选出最佳和次佳信号分量进行重构并使用平稳小波进行去噪处理,最后分析信号的包络谱来对轴承的故障类型进行判断。通过对仿真滚动轴承内圈故障信号进行分析,该方法可成功提取出微弱特征频率信息,噪声抑制效果优于EMD。由此表明,基于峭度准则VMD及平稳小波的轴承故障诊断可有效提取强声背景下的滚动轴承早期故障信息,具有一定的可靠性和应用价值。     

Enhanced detection of rolling element bearing fault based on stochastic resonance

Early bearing faults can generate a series of weak impacts. All the influence factors in measurement may degrade the vibration signal. Currently, bearing fault enhanced detection method based on stochastic resonance(SR) is implemented by expensive computation and demands high sampling rate, which requires high quality software and hardware for fault diagnosis. In order to extract bearing characteristic frequencies component, SR normalized scale transform procedures are presented and a circuit module is designed based on parameter-tuning bistable SR. In the simulation test, discrete and analog sinusoidal signals under heavy noise are enhanced by SR normalized scale transform and circuit module respectively. Two bearing fault enhanced detection strategies are proposed. One is realized by pure computation with normalized scale transform for sampled vibration signal, and the other is carried out by designed SR hardware with circuit module for analog vibration signal directly. The first strategy is flexible for discrete signal processing, and the second strategy demands much lower sampling frequency and less computational cost. The application results of the two strategies on bearing inner race fault detection of a test rig show that the local signal to noise ratio of the characteristic components obtained by the proposed methods are enhanced by about 50% compared with the band pass envelope analysis for the bearing with weaker fault. In addition, helicopter transmission bearing fault detection validates the effectiveness of the enhanced detection strategy with hardware. The combination of SR normalized scale transform and circuit module can meet the need of different application fields or conditions, thus providing a practical scheme for enhanced detection of bearing fault.     

基于IEWT和MCKD的滚动轴承故障诊断方法

针对经验小波变换（Empirical wavelet transform,EWT）对强噪声环境中滚动轴承微弱故障诊断的不足,主要是傅里叶频谱分段不当的问题。提出一种基于最大相关峭度解卷积（Maximum correlated kurtosis deconvolution,MCKD）降噪与改进EWT相结合的滚动轴承早期故障识别方法。首先采用最大相关峭度解卷积算法以包络谱的相关峭度最大化为目标对原信号进行降噪处理、检测信号中的周期性冲击成分,然后根据信号Fourier频谱的包络极大值进行分段,通过分析各频段平方包络谱中明显的频率成分来诊断故障。新方法能有效降噪、增强信号中周期性冲击特征、降低单次偶然冲击的影响、抑制非冲击成分。通过对含外圈、内圈故障的滚动轴承进行试验分析,结果表明,相比于快速谱峭度图和小波包络分析方法,该方法提取出的特征更加明显,能有效实现滚动轴承早期微弱故障的识别。     

基于PPCA-EWT的滚动轴承轻微故障诊断

针对经验小波变换(empirical wavelet transform,简称EWT)在强背景噪声下对轴承的轻微故障特征提取不足的问题,提出了概率主成分分析(probabilistic principal component analysis,简称PPCA)结合EWT的滚动轴承轻微故障诊断方法。首先,对信号做PPCA预处理,提取信号主要故障特征成分,去除强背景噪声干扰;然后,采用EWT方法分解轴承故障信号,按相关系数-峭度准则选出故障特征较为明显的分量,并将所选分量重构故障信号;最后,对信号采取包络分析,提取出轴承故障特征。仿真和实验结果表明,该方法能够有效地诊断出轴承故障且效果优于对信号进行EWT包络分析。     

Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement

The fault diagnosis of rolling element bearing is important for improving mechanical system reliability and performance. When localized fault occurs in a bearing, the periodic impulsive feature of the vibration signal appears in time domain, and the corresponding bearing characteristic frequencies (BCFs) emerge in frequency domain. However, in the early stage of bearing failures, the BCFs contain very little energy and are often overwhelmed by noise and higher-level macro-structural vibrations, an effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are optimized by genetic algorithm. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. In the enhanced autocorrelation envelope power spectrum, only several single spectrum lines would be left, which is very simple for operator to identify the bearing fault type. Moreover, the proposed method can be conducted in an almost automatic way. The results obtained from simulated and practical experiments prove that the proposed method is very effective for bearing faults diagnosis.     

Fault diagnosis of rolling element bearing using a new optimal scale morphology analysis method

Periodic transient impulses are key indicators of rolling element bearing defects. Efficient acquisition of impact impulses concerned with the defects is of much concern to the precise detection of bearing defects. However, transient features of rolling element bearing are generally immersed in stochastic noise and harmonic interference. Therefore, in this paper, a new optimal scale morphology analysis method, named adaptive multiscale combination morphological filter-hat transform (AMCMFH), is proposed for rolling element bearing fault diagnosis, which can both reduce stochastic noise and reserve signal details. In this method, firstly, an adaptive selection strategy based on the feature energy factor (FEF) is introduced to determine the optimal structuring element (SE) scale of multiscale combination morphological filter-hat transform (MCMFH). Subsequently, MCMFH containing the optimal SE scale is applied to obtain the impulse components from the bearing vibration signal. Finally, fault types of bearing are confirmed by extracting the defective frequency from envelope spectrum of the impulse components. The validity of the proposed method is verified through the simulated analysis and bearing vibration data derived from the laboratory bench. Results indicate that the proposed method has a good capability to recognize localized faults appeared on rolling element bearing from vibration signal. The study supplies a novel technique for the detection of faulty bearing.     

滚动轴承自适应特征提取的包络谱多点峭度多级降噪方法

滚动轴承故障信号主要包含高品质因子振动分量和低品质因子瞬态冲击分量。采用多点最优最小熵解卷积方法初步削弱传输路径等干扰影响,使微弱瞬态冲击成分得到初步增强,然后针对共振稀疏分解(RSSD)方法存在的品质因子选择困难问题,同时考虑包络谱中故障频率成分的严格周期性,提出包络谱多点峭度(ESMK)概念并将其作为优化指标,采用粒子群优化算法(PSO)对品质因子进行选择,得到一种自适应稀疏分解方法（PSO-RSSD）用于瞬态冲击信号的提取,以消除信号中高幅值干扰冲击和背景噪声的影响。轴承仿真与实测信号分析结果表明,与最小熵解卷积信号共振稀疏分解方法相比,在强冲击干扰下ESMK能够有效度量周期性瞬态冲击,PSO-RSSD方法能自适应分离最优低品质共振分量,验证了该方法的有效性和优越性。     

基于谱峭度和CEEMD的滚动轴承声信号故障诊断研究

使用声信号来诊断轴承故障越来越受到重视.针对滚动轴承故障信号的强背景噪声特点,提出一种基于谱峭度和互补集合经验模态分解(CEEMD)的故障特征提取方法.该方法首先对滚动轴承声信号进行快速谱峭度计算并进行带通滤波预处理,使滚动轴承声信号变得简单且噪声小,故障冲击成分明显;然后利用CEEMD将滤波信号进行分解运算,得到一系...     

Fault feature extraction of rolling bearing based on an improved cyclical spectrum density method

The traditional cyclical spectrum density(CSD) method is widely used to analyze the fault signals of rolling bearing. All modulation frequencies are demodulated in the cyclic frequency spectrum. Consequently, recognizing bearing fault type is difficult. Therefore, a new CSD method based on kurtosis(CSDK) is proposed. The kurtosis value of each cyclic frequency is used to measure the modulation capability of cyclic frequency. When the kurtosis value is large, the modulation capability is strong. Thus, the kurtosis value is regarded as the weight coefficient to accumulate all cyclic frequencies to extract fault features. Compared with the traditional method, CSDK can reduce the interference of harmonic frequency in fault frequency, which makes fault characteristics distinct from background noise. To validate the effectiveness of the method, experiments are performed on the simulation signal, the fault signal of the bearing outer race in the test bed, and the signal gathered from the bearing of the blast furnace belt cylinder. Experimental results show that the CSDK is better than the resonance demodulation method and the CSD in extracting fault features and recognizing degradation trends. The proposed method provides a new solution to fault diagnosis in bearings.     

基于OSF的SET信号重构方法及故障诊断应用

同步提取变换(synchroextracting transform, 简称SET)通过提取短时傅里叶变换(short-time Fourier transform, 简称STFT)在瞬时频率位置的时频系数可获得较理想的时频谱,该方法提高了时频分辨率,减少了交叉项的影响,一定程度上抑制了噪声对STFT时频谱的干扰。针对在SET时频谱的基础上进行信号分量的重构与故障诊断拓展方面的应用,提出了一种基于顺序统计滤波器(order statistics filter, 简称OSF)的SET信号分量重构方法。首先,利用边际谱表征SET时频谱中信号的幅值在整个频率范围内随频率变化的情况;其次,采用顺序统计滤波器分割边际谱,将分割所得边界映射至SET时频谱后,利用SET逆变换重构信号分量;最后,利用峭度指标筛选包含丰富故障信息的分量并进行包络分析,提取故障特征。仿真信号及滚动轴承内圈故障信号的处理结果证明了该方法的有效性。