Identification of bearing faults using time domain zero-crossings

In this paper, zero-crossing characteristic features are employed for early detection and identification of single point bearing defects in rotating machinery. As a result of bearing defects, characteristic defect frequencies appear in the machine vibration signal, normally requiring spectral analysis or envelope analysis to identify the defect type. Zero-crossing features are extracted directly from the time domain vibration signal using only the duration between successive zero-crossing intervals and do not require estimation of the rotational frequency. The features are a time domain representation of the composite vibration signature in the spectral domain. Features are normalized by the length of the observation window and classification is performed using a multilayer feedforward neural network. The model was evaluated on vibration data recorded using an accelerometer mounted on an induction motor housing subjected to a number of single point defects with different severity levels.     

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LMD和阶次跟踪分析的滚动轴承故障诊断

变转速工况下的滚动轴承故障振动信号具有多分量调制以及故障特征频率受到转频调制的特点,从而导致故障特征提取困难。对此,将局部均值分解(local mean decomposition,简称LMD)与阶次跟踪分析相结合,提出了一种变转速工况下的滚动轴承故障诊断方法。首先,采用阶次跟踪采样将时域滚动轴承故障振动信号转换到角域;然后,对角域信号进行LMD分解得到若干个乘积函数(product function,简称PF)分量;最后,对各个PF分量的瞬时幅值进行频谱分析,判断滚动轴承的故障部位和类型。通过对滚动轴承实验故障振动信号的分析,结果表明该方法能有效地应用于变转速工况下的滚动轴承故障诊断。     

无转速计下变工况滚动轴承故障特征量化表征提取

无转速计下变工况滚动轴承振动信号中各信号分量来源难以确定以及瞬时转频准确估计困难,而现有大多数研究依赖于已知转速并关注于时变冲击带来的频谱畸变,鲜有在无转速计变工况下开展轴承故障特征提取探究。提出无转速计下变工况滚动轴承故障特征量化表征提取方法,从振动信号希尔伯特包络中提取轴承故障特征,为定量描述各振动包络分量间关系,提出基于来源假设的特征模型与量化表征方法,利用同步压缩小波变换的时频重排与可重构特性,基于最大能量与最小曲率准则依次估计多时频脊瞬时频率,为降低广义解调后振动包络中干扰分量对量化结果的影响,提出基于选择性重构与广义解调的变工况下干扰抑制与平稳化重置方法。将所提方法用于仿真信号以及轴承振动数据分析,10 k长度信号包络分量在不同来源假设下的特征提取用时约为3 s,同时在无转速计下实现了对2 s内转速变化分别约为300 r/min和200 r/min的内圈故障轴承以及复合故障轴承的特征提取。     

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.     

Nonlinear Dynamic Response of Cylindrical Roller Bearing–Rotor System with 9 Degree of Freedom Model Having a Combined Localized Defect at Inner–Outer Races of Bearing

This article presents a nonlinear dynamic model for a cylindrical roller bearing–rotor system with interaction forces between the inner race, outer race, and roller. Roller–race contacts are modeled predicting nonlinear stiffness (Hertz contact theory) and nonlinear damping for a rotor–cylindrical roller bearing system. Here a shaft–rotor bearing system is modeled with 9 degrees of freedom with one defect on the inner race and one defect on the outer race for a case of combined localized defects. In the mathematical formulation, contacts between rolling elements and inner and outer races are considered as nonlinear springs and nonlinear damping is taken into consideration. Contact force calculations with nonlinearity are solved using the Newton-Raphson method for n unknown nonlinear simultaneous equation. The Newmark-β implicit integration technique coupled with the Newton-Raphson method is used to solve the differential equations. The results are obtained in the form of a time domain plot, frequency domain plot, and phase plot/Poincare map. The validity of the proposed model is compared with experimental results. A bifurcation graph of speed versus peak amplitude predicts the behavior of the system.     

Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time–frequency ridge enhancement

Healthy rolling element bearings are vital guarantees for safe operation of the rotating machinery. Time–frequency (TF) signal analysis is an effective tool to detect bearing defects under time-varying shaft speed condition. However, it is a challenging work dealing with defective characteristic frequency and rotation frequency simultaneously without a tachometer. For this reason, a technique using the generalized synchrosqueezing transform (GST) guided by enhanced TF ridge extraction is suggested to detect the existence of the bearing defects. The low frequency band and the resonance band are first chopped from the Fourier spectrum of the bearing vibration measurements. The TF information of the lower band component and the resonance band envelope are represented using short-time Fourier transform, where the TF ridge are extracted by harmonic summation search and ridge candidate fusion operations. The inverse of the extracted TF ridge is subsequently used to guide the GST mapping the chirped TF representation to the constant one. The rectified TF pictures are then synchrosqueezed as sharper spectra where the rotation frequency and the defective characteristic frequency can be identified, respectively. Both simulated and experimental signals were used to evaluate the present technique. The results validate the effectiveness of the suggested technique for the bearing defect detection.     

基于RSGWPT-LCD的轴承信号故障特征提取及模式识别

为了有效提取滚动轴承振动信号的故障特征和提高分类识别精度,提出了一种基于冗余二代小波包变换-局部特征尺度分解(redundant second generation wavelet packet transform-local characteristic scale decomposition,简称RSGWPT-LCD)和极限学习机(extreme learning machine,简称ELM)相结合的故障特征提取和分类识别方法。首先,利用希尔伯特变换对原始振动信号进行处理,得到包络信号;其次,基于双层筛选机制,结合冗余二代小波包变换(redundant second generation wavelet packet transform,简称RSGWPT)和局部特征尺度分解(local characteristic-scale decomposition,简称LCD)方法对包络信号进行分解,筛选出包含主要信息的内禀尺度分量(intrinsic scale components,简称ISCs);然后,对提取的各ISCs分量构建初始特征矩阵并进行奇异值分解(singular value decomposition,简称SVD),将得到的奇异值作为表征各损伤信号的特征向量;最后,以提取的特征向量为输入样本,建立ELM模式分类器对滚动轴承损伤信号进行识别。信号仿真和实测数据表明,该方法可有效提取振动信号故障特征,提高分类识别精度,实现滚动轴承故障诊断。     

基于多项式Chirplet变换和变分模态分解的变转速工况轴向柱塞泵故障诊断

针对变转速工况轴向柱塞泵故障诊断时故障特征提取困难的问题,提出了基于多项式Chirplet变换和变分模态分解的诊断方法。首先使用多项式Chirplet变换估计瞬时频率;然后基于估计的瞬时频率重采样,将时域非平稳信号转化为角域平稳信号;最后对角域信号进行变分模态分解。根据峭度对所得的本征模态函数分量进行重构并作包络阶次谱分析,判断轴向柱塞泵中轴承的故障类型。实验结果表明,该方法有效提取了变转速工况轴向柱塞泵轴承的故障特征。     

基于倒谱预白化和随机共振的轴承故障增强检测

轴承损伤引起的冲击受到离散频率分量和噪声干扰,使轴承故障检测面临困难。结合基于倒谱编辑(Cepstrum editing procedure, CEP)的信号预白化和随机共振(Stochastic resonance, SR)微弱信号检测技术,提出一种轴承故障增强检测的新方法。信号预白化能够提升轴承振动信号的冲击特性,产生包含白噪声和轴承局部故障信号的白化信号。在未知最优共振频带的情况下,对白化后的轴承振动信号进行包络分析,增强故障特征分量的同时引入了较多噪声。通过随机共振的归一化尺度变换,将轴承包络信号作为检测模型的输入,增强轴承故障特征频率分量。提出将轴承故障特征频率处的局部谱峭度和局部信噪比作为对照指标。实测正常和外环植入故障轴承的诊断结果表明,提出的方法优于基于谱峭度优化的包络分析和单纯的信号预白化方法。     

Application of acoustic emission technique for the detection of defects in rolling element bearings

The usefulness of acoustic emission (AE) measurements for the detection of defects in roller bearings has been investigated in the present study. Defects were simulated in the roller and inner race of the bearings by the spark erosion method. AE of bearings without defect and with defects of different sizes has been measured. For small defect sizes, ringdown counts of AE signal has been found to be a very good parameter for the detection of defects both in the inner race and roller of the bearings tested. However, the counts stopped increasing after a certain defect size. Distributions of events by ringdown counts and peak amplitudes are also found to be good indicators of bearing defect detection. With a defect on a bearing element, the distributions of events tend to be over a wider range of peak amplitudes and counts.     

Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition

There have been extensive studies on vibration based condition monitoring, prognosis of rotating element bearings; and reviews of the methods on how to identify bearing fault and predict the final failure reported widely in literature. The investigated bearings commonly discussed in the literatures were run in moderate and high rotating speed, and damages were artificially introduced e.g. with artificial crack or seeded defect. This paper deals with very low rotational-speed slewing bearing (1–4.5 rpm) without artificial fault. Two real vibration data were utilized, namely data collected from lab slewing bearing subject to accelerated life test and from a sheet metal company. Empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) were applied in both lab slewing bearing data and real case data. Outer race fault (BPFO) and rolling element fault (BSF) frequencies of slewing bearing can be identified. However, these fault frequencies could not be identified using fast Fourier transform (FFT).     

基于LMD和HMM的旋转机械故障诊断

提出了基于局部均值分解（LMD）和隐马尔科夫模型(HMM)的旋转机械故障诊断方法。首先,对故障信号进行局部均值分解,提取瞬时能量作为故障特征向量;然后将故障特征向量输入HMM分类器进行模式识别,输出各状态的似然概率;以最大似然概率所对应的故障状态为诊断结果。通过滚动轴承点蚀故障诊断试验验证了该方法的有效性,并将其与基于EMD-HMM的故障诊断方法进行了比较。结果表明,基于LMD-HMM的故障诊断方法更适用于旋转机械的故障诊断。     

SINGULARITY ANALYSIS USING CONTINUOUS WAVELET TRANSFORM FOR BEARING FAULT DIAGNOSIS

In this paper, wavelet transform is applied to detect abrupt changes in the vibration signals obtained from operating bearings being monitored. In particular, singularity analysis across all scales of the continuous wavelet transform is performed to identify the location (in time) of defect-induced bursts in the vibration signals. Through modifying the intensity of the wavelet transform modulus maxima, defect-related vibration signature is highlighted and can be easily associated with the bearing defect characteristic frequencies for diagnosis. Due to the fact that vibration characteristics of faulty bearings are complex and defect-related vibration signature is normally buried in the wideband noise and high frequency structural resonance, simple signal processing cannot be used to detect bearing fault. We show, through experimental results, that the proposed method has the ability to discriminate noise from the signal significantly and is robust to bearing operating conditions, such as load and speed, and severity of the bearing damage. These properties are desirable for automatic detection of machine faults.     

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。     

基于经验模态分解的滚动轴承故障诊断方法

提出了一种基于经验模态分解的滚动轴承故障诊断方法，并定义了能量熵的概念。从不同状态的滚动轴承振动信号的能量熵值中发现，当滚动轴承发生故障时，各频带的能量会发生变化。为了进一步对滚动轴承的状态和故障类型进行分类，再从若干个包含主要故障信息的IMF分量中提取能量特征参数作为神经网络的输入参数来识别滚动轴承的故障类型。对滚动轴承的正常状态、内圈故障和外圈故障振动信号的分析结果表明，以经验模态分解为预处理器提取各频带能量作为特征参数的神经网络诊断方法比以小波包分析为预处理器的神经网络诊断方法有更高的故障识别率，可以准确、有效地识别滚动轴承的工作状态和故障类别。     

变速非平稳条件下滚动轴承故障特征提取方法研究

旋转机械设备启停、电压波动及载荷变化等因素使滚动轴承通常是在变速非平稳条件下运行,利用传统共振解调方法获得的谱图将变得模糊,无法识别故障特征.根据该条件下滚动轴承损伤点冲击引起的固有谐振信号频率不变的特点,利用连续小波分析方法提取固有谐振信号,通过瞬时频率估计、阶比跟踪手段进行阶比谱分析,提出滚动轴承故障特征阶比系数的概念和计算方法.将故障特征阶比系数与阶比谱进行比较,可以很好地识别滚动轴承损伤类故障.     

Enhancing Weak Defect Features Using Undecimated and Adaptive Wavelet Transform for Estimation of Roller Defect Size in a Bearing

Defects in bearings affect the vibration level, resulting in and increase in temperature and decomposition of lubricant. Estimation of roller defect size is a complex task because it revolves as well as rotates during the motion. Signals from a defective roller of a bearing are superimposed by the signal from races, cage, and background noise. In this communication, a signal processing scheme is proposed that makes the signal suitable for estimating the size of the defect in the rolling element of a tapered roller bearing. To achieve this, in the first stage of processing, shift-invariant soft thresholding is applied to denoise the signal. It suppresses the noise without affecting defect-related features. Further, in the second stage of processing, continuous wavelet transform (CWT) using adaptive wavelet is applied. The adaptive wavelet is designed from the impulse extracted from the signal using the least squares fitting method. It results in higher coefficients in the region of impulse produced due to the defect. Finally, time marginal integration (TMI) of CWT coefficients is carried out for estimation of defect width. A study was performed for six different cases in which the size of the defect and orientation varies. Results of measurements of roller defect widths estimated using the proposed scheme were compared with defect widths calculated using image examination. For the nonoverlapping signature of defects (such as defects at 0° and 90° orientations), the maximum deviation in the width measurement using the proposed scheme is 6.52%. The error may increase when signature of two defects are overlapped.     

Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum

In vibration analysis, weak fault feature extraction under strong background noise is of great importance. A method based on cyclic Wiener filter and envelope spectrum analysis is proposed. Cyclic Wiener filter exploits the spectral coherence theory induced by the second-order cyclostationary signal. The original signal is duplicated and shifted in the frequency domain by amounts corresponding to the cyclic frequencies. The noise component is optimally filtered by a filter-bank. The filtered signal is analyzed by performing envelope spectrum. In the envelope spectrum, characteristic frequencies are quite clear. Then the most impactive part is effectively extracted for further fault diagnosis. The effectiveness of the method is demonstrated on both simulated signal and actual data from rolling bearing accelerated life test.