基于三维谱峭图算法的共振解调技术研究及应用

针对滚动轴承出现故障时振动信号表现出的周期冲击性特征,以寻求表征故障信号特征的最优频带为目的,提出了一种利用二分法思想从带宽和中心频率两个角度进行优化搜索的三维谱峭图算法。将该方法应用于共振解调技术带通滤波器参数的确定,形成基于三维谱峭图算法的共振解调技术。为了验证该技术的有效性,在铁路货车轮对跑合实验台上进行了轮对故障轴承的振动测试。采用基于三维谱峭图算法的共振解调技术进行故障诊断分析,并与基于快速谱峭图的共振解调技术进行了对比。结果表明,基于三维谱峭图算法的共振解调技术能够更好地诊断轴承故障。最后,通过对时间复杂度的求解,证明了三维谱峭图算法具有较高的执行效率,在工程应用方面具有一定的参考价值。     

小波变换在滚动轴承故障分析中的应用

采用离散小波变换对滚动轴承进行故障诊断,通过对测试到的故障轴承的振动速度信号进行分析,分别绘出故障信号的频谱图,对结果进行分析比较表明,离散小波分析技术在滚动轴承内圈及滚动体故障特征频率提取方面有较大的优越性。     

低速滚动轴承故障诊断方法研究

低速滚动轴承结构和工作条件特殊,故障机理复杂,诊断难度较大。本文根据低速滚动轴承的故障特性,提出了利用应力波与小波分析进行低速滚动轴承故障诊断的方法。首先以低速运转Cooper轴承系列01B65 EX滚子轴承为例,建立了完好和故障低速滚动轴承的三维整体接触计算模型,运用有限元软件对其进行了比较全面、精确的分析,计算出外圈故障模型的最大应力和应变及各元件之间的接触应力,将发生故障前后的外圈外表面应力应变分布规律以及接触应力分布规律进行比较。然后在应力波实验分析的基础上,选择db6母小波、尺度j=4对实验所采集的数据信号进行小波变换,成功提取了外圈模拟故障的应力波信号特征频率。     

基于WAEEMD和MSB的滚动轴承故障特征提取

针对调制信号双谱（MSB）方法仅能处理平稳信号的不足,提出了一种基于加权平均集成经验模态分解（WAEEMD）和MSB的滚动轴承故障特征提取方法。首先,利用WAEEMD将滚动轴承的非平稳振动信号分解成一系列具有平稳特性的固有模态函数（IMF）;然后,开发了一种基于Teager能量峭度（TEK）的加权平均方法以强调敏感IMF的重要性,并将加权后的IMF重构为WAEEMD滤波信号;最后,应用MSB分解WAEEMD滤波信号中的调制分量并提取故障特征频率。仿真和实验结果表明,相对于快速谱峭度（FK）和EEMD-MSB方法,WAEEMD-MSB方法能更准确地获取故障特征,从而验证了WAEEMD-MSB方法的有效性。     

基于LMD能量特征的滚动轴承故障诊断方法

针对滚动轴承故障振动信号的多载波多调制特性,提出一种基于局域均值分解(local mean decomposition,简称LMD)能量特征的特征向量提取方法,并与支持向量机相结合用于滚动轴承的故障诊断。首先,采用LMD方法将复杂调制振动信号分解为若干单分量信号乘积函数(production function,简称PF);然后,对反映信号主要特征的PF基于时间轴积分,得到各PF分量能量矩并构造特征向量;最后,将其输入多分类支持向量机中,用于区分滚动轴承的故障类型与故障程度。对滚动轴承内圈故障、外圈故障及滚动体故障振动信号的分析结果表明,该方法能有效提取滚动轴承各工作状态信号的故障特征,能准确识别故障类型,同时对故障程度的判断表现出较高的识别率。     

基于时变零相位滤波的变转速滚动轴承故障诊断

针对变转速下齿轮箱中滚动轴承故障调制特征的提取与分离,提出了基于时变零相位滤波的变转速滚动轴承故障诊断方法。该方法先用线调频小波路径追踪(CPP)算法从齿轮箱滚动轴承故障振动信号中估计出齿轮啮合频率,由啮合频率除以齿数得到齿轮箱的转速,同时,采用Hilbert包络解调方法获取轴承故障振动信号的包络信号;然后根据获取的转速信息设计各阶时变零相位滤波器;再采用各时变零相位滤波器对包络信号进行分析,获取各调制信号;最后,利用转速信号对求取的各调制信号进行阶次分析,并根据各阶次谱来诊断滚动轴承故障。算法仿真和应用实例分析表明,该方法可有效提取和分离变速齿轮箱中滚动轴承的各阶故障调制特征。     

Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier

The fault diagnosis of rotating machinery has attracted considerable research attention in recent years because such components as bearings and gears frequently suffer failure, resulting in unexpected machine breakdowns. Signal processing-based condition monitoring and fault diagnosis methods have proved effective in fault identification, but the revelation of faults from the resulting signals requires a high degree of expertise. In addition, it is difficult to extract the fault-induced signatures in complex machinery via signal processing-based methods. In this paper, a new intelligent fault diagnosis scheme based on the extraction of statistical parameters from the paving of a wavelet packet transform (WPT), a distance evaluation technique (DET) and a support vector regression (SVR)-based generic multi-class solver is proposed. The collected signals are first pre-processed by the WPT at different decomposition depths. In this paper, the wavelet packet coefficients at different decomposition depths are referred to as WPT paving. Statistical parameters are then extracted from the signals obtained via the WPT at different decomposition depths. In selecting the sensitive fault features for fault pattern expression, a DET is employed to reduce the dimensionality of the feature space. Finally, a SVR-based generic multi-class solver is proposed to identify the different fault patterns of rotating machinery. The effectiveness of the proposed intelligent fault diagnosis scheme is validated separately using datasets from bearing and gearbox test rigs. In addition, the effects of different wavelet basis functions on the performance of the proposed scheme are investigated experimentally. The results demonstrate that the proposed intelligent fault diagnosis scheme is highly accurate in differentiating the fault patterns of both bearings and gears.     

Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution

This paper proposes a new diagnosis method based on Adaptive maximum correlated kurtosis deconvolution (AMCKD) for accurate identification of compound faults of rolling bearings. The AMCKD method combines the powerful capability of cuckoo search algorithm for global optimization with the advantage of Maximum correlated kurtosis deconvolution (MCKD) for impact signal extraction. In contrast to traditional methods, such as direct envelop spectrum, Discrete wavelet transform (DWT), and empirical mode decomposition, the proposed method extracts each fault signal related to the single failed part from the compound fault signals and effectively separates the coupled fault features. First, the original signal is processed using AMCKD method. Demodulation operation is then performed on the obtained single fault signal, and the envelope spectrum is calculated to identify the characteristic frequency information. Verification is performed on simulated and experimental signals. Results show that the proposed method is more suitable for detecting compound faults in rolling bearings compared with traditional methods. This research provides a basis for improving the monitoring and diagnosis precision of rolling bearings.     

An improved simplex-based adaptive evolutionary digital filter and its application for fault detection of rolling element bearings

The de-noising performance and convergence behavior of the adaptive evolutionary digital filter (EDF) are restricted by the factors of constant evolutionary coefficients and taking the reciprocal of average energy of residual signal as the fitness function. In this paper, an improved adaptive evolutionary digital filter based on the simplex method (EDF-SM) is proposed to overcome the shortcomings of the original EDF. A new evolutionary rule was constructed by introducing the simplex-based mutating method and by then combining this with the original cloning and mating methods. The reciprocal of sample entropy was taken as the fitness function and variable evolutionary coefficients were employed. Numerical examples show that the proposed EDF-SM exhibits a higher convergence rate and a better de-noising behavior than the other EDFs. The effectiveness of the proposed method in discovering fault characteristics and detecting faults of rolling element bearings is supported using an experimental test.     

APLCD-WPT在滚动轴承特征提取算法中的应用

针对滚动轴承特征频率提取问题,提出自适应部分集成局部特征尺度分解(adaptive partly-ensemble local charact-eristic-scale decomposition,简称APLCD)与小波包变换(wavelet package transform,简称WPT)结合的APLCD-WPT方法。首先,利用APLCD对滚动轴承振动信号进行处理,通过添加幅值随频率变化的噪声改善信号极值点分布,再提取内禀尺度分量(intrinsic mode component,简称ISC);其次,对ISC分量中模态混淆部分使用WPT进行修正,提取滚动轴承特征频率信号。应用提出方法对实测的卧式螺旋离心机振动信号进行研究,结果表明,基于APLCD-WPT的算法能够有效地解决模态混淆问题,实现特征频率信号的精确提取。     

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。     

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。     

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。     

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.     

Hilbert-Huang变换在滚动轴承故障诊断中的应用

提出了一种新的滚动轴承故障诊断方法——基于小波系数包络信号的局部Hilbert边际谱方法，在Hilbert—Huang变换的基础上介绍了局部Hilbert谱和局部Hilbert边际谱，并将它应用于滚动轴承的故障诊断中。用小波基将滚动轴承故障振动信号分解，对高频段的小波系数用Hilbert进行包络分析得到包络信号，再对包络信号进行Hilbert—Huang变换求出局部Hilbert边际谱，从局部Hilbert边际谱中就可以判断滚动轴承的故障部位和类型。通过对滚动轴承具有外圈缺陷、内圈缺陷的情况下的振动信号的分析，说明该方法比传统的包络分析方法更能有效地提取滚动轴承故障特征。     

基于自适应自相关谱峭度图的滚动轴承故障诊断方法

自相关谱峭度图通过最大重叠离散小波包变换对信号频谱进行分割,并选取最大峭度值所对应频带内的信号进行诊断分析。针对自相关谱峭度图方法在分割频带时因遵循二叉树结构而导致的频带划分区域固定问题,提出一种基于自适应自相关谱峭度图方法的滚动轴承故障诊断方法。自适应自相关谱峭度图方法以改进的经验小波变换为基础,对原始信号傅里叶谱进行包络与平滑处理后再分割,实现了自相关谱峭度图方法自适应分割频带的目的。通过仿真信号与实验数据分析,并将所提方法与快速谱峭度及自相关谱峭度图方法进行对比,结果表明,所提出方法能够准确地检测到合适的解调频带,同时其故障特征更加明显。     

基于改进奇异值分解滤波和谱峭度的滚动轴承故障诊断

针对含噪信号的有效奇异值个数难以确定的问题,提出了一种改进的奇异值分解降噪方法--奇异值累积法。该方法通过计算奇异值的实际下降值与奇异值平均下降速度累积量的差值,并取该差值最大值点的位置作为有效奇异值的分界点来确定有效奇异值的个数。在此基础上,提出了一种基于奇异值累积法与快速谱峭度的滚动轴承故障诊断方法。采用奇异值累积法对原信号进行降噪处理,然后利用快速谱峭度确定滤波器中心频率及带宽,通过分析频段包络谱中明显的频率成分来诊断故障。该方法可以有效去除信号中的噪声,使得到的峭度值所反映的故障冲击更接近实际情况。对含内圈、外圈故障的滚动轴承实验数据进行分析,实验结果表明,相比快速谱峭度的故障诊断方法,该方法具有更好的故障识别效果。     

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.     

Weak fault detection method of rolling bearing based on testing signal far away from fault source

In some cases, because of the complex internal structure of the machines, the positions of the vibration sensors are far away from the rolling bearings, such as in an aeroengine, causing the fault features to become extremely weak, which brings great challenge to the detection of rolling bearings. To address this problem, an integrated detection method is proposed. First, a method named MEDL is proposed to determine the optimal filter length in minimum entropy deconvolution (MED) to enhance the periodic fault impulse component in the weak signal, which accuracy is 1. After that, the MEDL is combined with variational mode decomposition (VMD) and autocorrelation to extract fault features from strong background noise. A series of fault simulation experiments for rolling bearings were conducted by using an aeroengine rotor experimental rig with casing. The results verify that the accuracy of the integrated detection method is 100 % in different measuring points, speeds and fault types. At the same time, it compared with spectral kurtosis (SK) and empirical wavelet transform (EWT). It proves that the integrated detection method is more robust in extracting the weak fault characteristic of rolling bearings from the casing signals effectively.     

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。