基于自适应多尺度自互补Top-Hat变换的轴承故障增强检测

针对实际工程中滚动轴承冲击性故障特征难以提取的问题,提出一种自适应多尺度自互补Top-Hat(Adaptive multi-scale self-complementary Top-Hat, AMSTH)变换方法用于轴承故障的增强检测。自互补Top-Hat变换在消除信号中背景噪声的同时,能有效增强故障振动信号的冲击特性,而构造的多尺度自互补Top-Hat变换方法,可以较有效地兼顾抗噪性能和信号的细节保持。在分析形态学滤波的基础上,提出采用特征幅值能量比(Feature amplitude energy radio, FAER)的方法自适应确定最优结构元素的尺度,并应用于轴承的故障增强检测。通过对仿真信号和实测轴承滚动体、内圈故障信号进行分析,结果表明该方法可有效增强滚动轴承的故障检测,并且在运算效率和提取效果方面优于基于信噪比标准的多尺度形态学开-闭和闭-开组合变换方法。     

最小熵反褶积的数学形态法在滚动轴承故障特征提取中的应用

针对强噪声背景下滚动轴承故障特征提取,提出了基于最小熵反褶积的数学形态法。该方法先应用最小熵反褶积算法加强信号中的冲击特性,再利用数学形态法进行故障特征提取,其中选取具有双向脉冲提取能力的DIF滤波器作为形态算子,并以峭度值作为结构元素长度选取依据。仿真信号和滚动轴承的内外故障实例分析表明该方法具有较好的特征提取效果。通过对比发现:最小熵反褶积算法能够增大信号中峭度值,有效加强信号脉冲特性。     

Application of improved morphological filter to the extraction of impulsive attenuation signals

Rotating machinery response is often characterized by the presence of periodic impulses modulated by high-frequency harmonic components. It can be defined with three parameters, which are natural frequency, fault frequency and decay coefficient. In this paper, we propose an improved morphological filter for feature extraction of the above signals in the time domain. Firstly, an average weighted combination of open-closing and close-opening morphological operator, which eliminates statistical deflection of amplitude, is utilized to extract impulsive component from the original signal. Then, according to the geometric characteristic of impulsive attenuation component, the structure element is constructed with an impulsive attenuation function, and a new criterion is put forward to optimize the structure element. The proposed method is evaluated by simulated impulsive attenuation signals with different natural frequencies and vibration signals measured on defective bearings with outer race fault and inner race fault, respectively. Results show that the background noise can be fully restrained and the entire impulsive attenuation signal is well extracted, which demonstrates that the method is an efficient tool to extract impulsive attenuation component from mechanical signals.     

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.     

Identification of faults through wavelet transform vis-à-vis fast Fourier transform of noisy vibration signals emanated from defective rolling element bearings

Fault diagnosis of rolling element bearings requires efficient signal processing techniques. For this purpose, the performances of envelope detection with fast Fourier transform (FFT) and continuous wavelet transform (CWT) of vibration signals produced from a bearing with defects on inner race and rolling element, have been examined at low signal to noise ratio. Both simulated and experimental signals from identical bearings have been considered for the purpose of analysis. The bearings have been modeled as spring-mass-dashpot systems and the simulated signals have been obtained considering transfer functions for the bearing systems subjected to impulsive loads due to the defects. Frequency B spline wavelets have been applied for CWT and a discussion on wavelet selection has been presented for better effectiveness. Results show that use of CWT with the proposed wavelets overcomes the short coming of FFT while processing a noisy vibration signals for defect detection of bearings.     

基于1 1／2维谱的滚动轴承故障诊断

提出了基于１１／２维谱的滚动轴承故障诊断新方法。１１／２维谱保留了信号的相位信息且能够有效抑制噪声。用１１／２维谱分析滚动轴承振动信号,可以提取由于二次相位耦合产生的非线性特征,识别故障模式。试验结果表明,这种方法能有效地诊断滚动轴承故障,且对初期故障很敏感。     

Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator

Aiming at the problems that the incipient fault of rolling bearings is difficult to recognize and the number of intrinsic mode functions (IMFs) decomposed by variational mode decomposition (VMD) must be set in advance and can not be adaptively selected, taking full advantages of the adaptive segmentation of scale spectrum and Teager energy operator (TEO) demodulation, a new method for early fault feature extraction of rolling bearings based on the modified VMD and Teager energy operator (MVMD-TEO) is proposed. Firstly, the vibration signal of rolling bearings is analyzed by adaptive scale space spectrum segmentation to obtain the spectrum segmentation support boundary, and then the number K of IMFs decomposed by VMD is adaptively determined. Secondly, the original vibration signal is adaptively decomposed into K IMFs, and the effective IMF components are extracted based on the correlation coefficient criterion. Finally, the Teager energy spectrum of the reconstructed signal of the effective IMF components is calculated by the TEO, and then the early fault features of rolling bearings are extracted to realize the fault identification and location. Comparative experiments of the proposed method and the existing fault feature extraction method based on Local Mean Decomposition and Teager energy operator (LMD-TEO) have been implemented using experimental data-sets and a measured data-set. The results of comparative experiments in three application cases show that the presented method can achieve a fairly or slightly better performance than LMD-TEO method, and the validity and feasibility of the proposed method are proved.     

融合量子理论的结构元素尺寸自适应调整策略

受量子理论启发,提出一种针对数学形态学结构元素尺寸自适应调整的新策略,以达到更优的冲击响应信号形态学提取效果。首先,结合量子理论建立起振动信号的量子系统,在此基础上提出了振动信号的量子比特数学表达式,用于刻画振动信号的状态;然后,针对机械振动信号的局部特点,分析1×3邻域的振动信号相关性,提出了机械振动信号在量子概率特征下的结构元素尺寸衡量算子;最后,依据尺寸衡量算子和自适应控制结构元素的长度达到更优的滤波效果。利用该策略对轴承冲击故障信号进行形态滤波,并与传统方法进行了比较,结果表明该方法可以有效提取信号的全局和局部特征。     

Bearing fault diagnosis based on an improved morphological filter

The extraction of repetitive impacts from vibration signals plays an essential role in bearing fault detection. Among different signal processing algorithms, morphological filter (MF) has attracted lots of attention because it could directly extract the geometric structure of the impulsive feature and only needs little computation. However, the conventional MF and some current improvements are based on the local optima of the raw signal to de-noise the noisy signal and its faulty feature extracting capability would be greatly affected by the noise. In this paper, a new improved MF algorithm is proposed to overcome such deficiency. Firstly, morphological gradient (MG) operator is selected in this paper due to its capability of picking up both positive and negative impulses. Then, based on the relationship between the defect induced impulse and a harmonic function with the resonant frequency, the harmonic waveform in a period is adopted to instruct the construction of structuring element (SE). The improved MF can obtain the fault feature from low SNR signals. The processing results of a simulation signal and two sets of experimental signals and a set of comparisons verify the effectiveness and robustness of the proposed method.     

Application of an improved kurtogram method for fault diagnosis of rolling element bearings

Kurtogram, due to the superiority of detecting and characterizing transients in a signal, has been proved to be a very powerful and practical tool in machinery fault diagnosis. Kurtogram, based on the short time Fourier transform (STFT) or FIR filters, however, limits the accuracy improvement of kurtogram in extracting transient characteristics from a noisy signal and identifying machinery fault. Therefore, more precise filters need to be developed and incorporated into the kurtogram method to overcome its shortcomings and to further enhance its accuracy in discovering characteristics and detecting faults. The filter based on wavelet packet transform (WPT) can filter out noise and precisely match the fault characteristics of noisy signals. By introducing WPT into kurtogram, this paper proposes an improved kurtogram method adopting WPT as the filter of kurtogram to overcome the shortcomings of the original kurtogram. The vibration signals collected from rolling element bearings are used to demonstrate the improved performance of the proposed method compared with the original kurtogram. The results verify the effectiveness of the method in extracting fault characteristics and diagnosing faults of rolling element bearings.     

Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings

The detection and recovery of impulsive signature play a vital role in the diagnosis and prognosis of rolling element bearings. Though different approaches have been proposed to deal with this problem so far, challenges still exist when they are applied to the bearings operating under harsh working conditions. The difficulties mainly come from the multi-resonance and multi-modulation characteristics of bearing vibration signals. To overcome this limitation, a new methodology for the detection and recovery of fault impulses is presented in this paper. First, an improved harmonic product spectrum (IHPS) is proposed to detect and identify the multiple modulation sources buried in a vibration signal. With this method, the fault-related impulsive features could be recognized, while the influence caused by non-fault modulation is eliminated. On this basis, a harmonic significance index is further established to quantify the diagnostic information contained in a narrow band signal. By utilizing this index, the optimal resonance band where the fault impulses are most significant could be accurately determined. Finally, IHPS and sideband product spectrum are integrated to reduce the in-band noise and further recover the fault impulses. The performance of this method is evaluated by both simulated data and real vibration data measured from a train wheel bearing with a naturally developed defect. Compared with Kurtogram and Protrugram, the proposed method can detect the resonance band more precisely even in the presence of heavy noise and other impulsive vibration sources. Moreover, with the impulses recovery scheme, the double impact phenomenon caused by a distributed defect is extracted successfully. Benefiting from this, the defect size of a bearing can be estimated from its vibration signal without dismantling, which makes it a promising tool for the bearing diagnosis and prognosis in industrial applications.     

基于小波分析的FastICA联合降噪方法在滚动轴承故障诊断中的应用研究

采用小波分析方法进行振动信号降噪存在选取参数依靠经验的问题,采用独立分量分析（ICA）方法进行振动信号降噪存在欠定问题,为了避免小波降噪以及ICA方法单独使用的缺点,提出了将小波降噪分析和基于负熵的FastICA独立分量分析相结合来处理滚动轴承含噪振动信号的方法。首先对原始信号进行小波降噪处理,然后将处理后的信号与原始信号组成FastICA的输入矩阵,进行FastICA降噪处理,最后利用滚动轴承振动信号对该方法进行有效性验证。实验分析表明：该方法增大了振动信号的峭度值,达到了滚动轴承振动信号降噪的目的。     

基于模型辨识的滚动轴承故障诊断

为了解决小样本环境和早期故障预示问题,研究一种基于物理模型辨识的滚动轴承故障诊断方法,即通过物理模型构建标准模式数据库,进而识别故障。考虑到振动传递路径结合界面动态接触机制,建立了轴承表面缺陷的物理模型,通过仿真获得不同损伤位置的振动信号,求得特征矩阵。由于实际测试信号故障特征比较微弱,提出一种盲反卷积和峭度最优Laplace小波相结合的算法,该算法被用于仿真信号与实际工程中微弱冲击信号的检测中,有效突出了冲击成分。最后,以实测信号特征值作为输入,利用距离函数求出与输入值最近的样本点,进而预测出故障位置。案例分析表明,该方法具有较好的可行性与可靠性。     

连续小波变换在滚动轴承故障诊断中的应用

采用连续小波分析的方法对滚动轴承振动和速度信号进行处理,提取滚动轴承故障特征。通过对滚轴承在正常、内圈剥落、外圈剥落及滚动体落情况下的振动加速度信号进行分析,验证了这种方法的有效性。     

形态滤波在滚动轴承缺陷诊断中的应用

形态滤波为数字信号处理提供了一种新的非线性滤波方式，它可以有效地提取出信号的边缘轮廓以及信号的形状特征。将形态滤波应用到滚动轴承缺陷诊断中，通过形态滤波将滚动轴承原始振动信号的脉冲信号提取出来，从而达到解调的效果。再对滤波后所得的脉冲信号进行频谱分析，容易提取所需的缺陷信息。对形态滤波的解调效果进行了试验验证。     

Detection of Bearing Faults Using a Novel Adaptive Morphological Update Lifting Wavelet

The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper presents a novel signal processing scheme, adaptive morphological update lifting wavelet (AMULW), for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration signals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective rolling element bearings.     

Effect of fluid film wave bearings on attenuation of gear mesh noise and vibration

The attenuation of the gear mesh noise/vibration by fluid film wave bearings relative to rolling element bearings was experimentally investigated. Tests were performed on a gearbox that can accommodate both rolling element bearings and wave bearings. It was found that at specific speeds and torques, the wave bearings could significantly reduce the noise/vibration compared to rolling element bearings. Because the gear noise is accompanied by noise from other sources, a method was developed to extract from the original signal only the mesh harmonic components.The wave bearing dynamic coefficients were also predicted. It was found that adjusting the wave bearing parameters could considerably increase the capacity of the wave bearings to attenuate the gear mesh noise and vibration.     

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。     

A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings

A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings is presented in this paper. Detection of both localized and distributed categories of defect has been considered. An explanation for the vibration and noise generation in bearings is given. Vibration measurement in both time and frequency domains along with signal processing techniques such as the high-frequency resonance technique have been covered. Other acoustic measurement techniques such as sound pressure, sound intensity and acoustic emission have been reviewed. Recent trends in research on the detection of defects in bearings, such as the wavelet transform method and automated data processing, have also been included.     

基于小波变换和ICA的滚动轴承早期故障诊断

滚动轴承早期故障诊断的关键在于如何从低信噪比混合信号中检测出显著的轴承故障特征频率。提出以连续小波变换(CWT)和独立分量分析(ICA)相结合的方法来诊断单通道信号的滚动轴承早期故障,提出按频谱等间隔选取伪中心频率的小波分解尺度,并对ICA处理后的信号进行包络频谱分析以确定故障类型。最后,利用实际的滚动轴承实验数据对该方法进行了验证。