滚动轴承支撑人字齿轮传动系统动力传递过程分析研究

为能更有效地分析滚动轴承支撑人字齿轮传动系统的振动传递特性,提出基于轮齿承载接触分析、同时考虑齿轮轴扭转变形及安装误差的人字齿轮左右轮齿啮合刚度计算方法,建立综合考虑时变啮合刚度、啮入冲击激励的人字齿轮啮合型弯-扭-轴耦合振动模型。在提出的考虑轴承内部载荷分布的滚动轴承支撑系统载荷分配计算方法以及包含承载滚子、内外圈的滚动轴承动力学模型基础上,较完整地分析人字齿轮传动系统的齿对啮合振动经由齿轮轴分配到支撑滚动轴承,最后再由轴承内圈传递到外圈的传递过程。以某滚动轴承支撑人字齿轮传动系统为实例进行的仿真计算结果表明:该振动传递计算方法较科学合理地计算出人字齿轮系统的动载荷传递过程,更精确地得到箱体轴承孔内壁的振动载荷,为下一步有效地分析人字齿轮箱体振动特性提供了保障。同时滚动轴承在传递载荷的过程中起到类似浮筏隔振系统的隔振降噪作用。     

基于RLMD和WSET的变转速下滚动轴承故障诊断

在变转速工况下,对起重机、风机等重载设备中滚动轴承的故障诊断不够准确。针对这一问题,提出了一种基于鲁棒局部均值分解(RLMD)和同步提取小波变换(WSET)的故障诊断方法。首先,以振动信号分析为基础,研究了变转速下的滚动轴承的故障特征频率,对RLMD和WSET公式进行了推导;利用RLMD对滚动轴承振动信号进行了模式分解,以最大互相关系数原则选取了最佳分量;然后,对选取的分量进行了WSET,获得了能量集中的时频表示,从时频平面中提取了故障特征曲线,并将其与理论故障特征频率曲线进行了比较,从而进行了变转速下的故障诊断;最后,研究设计了多分量信号的数值模拟仿真实验,进行了轴承-齿轮故障综合试验台变转速滚动轴承的故障诊断。研究结果表明：所提出的方法能够很好地消除噪声的影响,获得准确的故障特征频率曲线,在变转速滚动轴承故障诊断中具有有效性。     

面齿轮副的典型故障振动分析

目前国内外对齿轮以及轴承的故障动力学研究已经很完善,但两者复合的故障动力学研究还不够深入。将面齿轮副与滚动轴承的动力学耦合到一起,建立齿轮-轴承耦合动力学模型,通过实验研究获得了模型在单一以及复合故障下的时域以及频域响应规律,为齿轮传动故障诊断提供理论依据。     

新书介绍

1．《齿轮及齿轮箱故障诊断实用技术》一书系统论述了齿轮、滚动轴承和轴的常见失效形式，振动噪声产生的机理和故障的主要形式，调制现象和边频带分布特点，齿轮及齿轮箱振动信号的时域、频域和现代解调分析的各种处理方法，提取了十种典型故障振动信号特征。从现场诊断技术需要的角度出发介绍了齿轮箱故障诊断常用的振动噪声故障诊断方法、扭振故障诊断方法和润滑油分析技术，以及使用这屿方法和技术进行齿轮箱故障诊断的常用仪器和系统，判别标准建立方法和一些典型标准，最后分类介绍了齿轮箱故障诊断的工业应用实例。定价31元（含邮资）。     

基于LabVIEW的滚动轴承故障诊断系统

为实现对滚动轴承工作状态的监测,提出了一种基于LabVIEW的滚动轴承故障诊断系统的设计方案,给出了滚动轴承振动信号的采集与故障诊断方法,在LabVIEW的诊断平台下进行信号处理与分析,然后结合滚动轴承故障诊断理论与信号分析结果来对该轴承运行状态进行判断。最后利用旋转机械振动及故障模拟试验平台对该系统进行验证,验证结果体现了该系统具有可行性和适用性。     

新书介绍

1．《齿轮及齿轮箱故障诊断实用技术》一书系统论述了齿轮箱中的三大零部件——齿轮、滚动轴承和轴的常见失效形式，振动噪声产生的机理和故障的主要形式，调制现象和边频带分布特点，齿轮及齿轮箱振动信号的时域、频域和现代解调分析的各种处理方法，提取了十种典型故障振动信号特征。从现场诊断技术需要的角度出发介绍了齿轮箱故障诊断常用的振动噪声故障诊断方法、     

基于包络解调分析的滚动轴承故障诊断研究

利用包络解调分析方法对滚动轴承的各种故障进行了模拟和诊断研究。总结了滚动轴承的故障诊断机理,介绍了包络解调分析方法的基本原理,论述了将包络解调分析用于滚动轴承故障诊断的基本思想,通过对QPZZ—II．旋转机械故障试验系统进行滚动轴承各种故障的实例验证分析,验证了将包络解调分析方法运用于滚动轴承的故障诊断的实际效果。     

小波分析-模糊聚类法在滚动轴承故障诊断

为了对减速器内轴承的运行状态进行故障监测和诊断,在对振动加速度信号进行小波分析的基础上,提出了基于尺度-能量谱的特征提取和模糊聚类相结合的滚动轴承故障诊断方法.该方法应用于齿轮减速器JZQ250的故障诊断中, 经对大量实测数据的处理和分析,能够比较准确地识别和诊断出减速器的正常运行状态、内圈故障和外圈故障运行状态,具有一定的工程实用价值.     

基于振动分析的滚动轴承故障诊断系统设计

滚动轴承故障诊断意义重大,振动分析是诊断滚动轴承故障最有效的手段。文中根据滚动轴承振动信号的特点,以包络检波技术为基础,利用LabVIEW设计出相应的滚动轴承故障诊断系统。提出带通滤波和包络谱细化这两个系统设计中的关键技术问题并给出了解决方法。试验结果证明:系统能够有效地诊断出轴承存在故障且能判定发生故障的元件。     

基于LabVIEW的虚拟轴承故障诊断系统的研究

介绍了滚动轴承故障特点及采用共振解调技术对滚动轴承进行故障诊断的原理和特点，并在此基础之上，虚拟出了以LabVIEW为界面的故障诊断程序系统。     

信息融合技术在托辊轴承故障诊断中的应用

将信息融合技术引入矿用输送机托辊轴承故障诊断中,通过对托辊轴承故障特征信息的提取,利用神经网络技术和Dempster-Shafer证据理论对其进行数据融合,实现对托辊轴承故障的融合诊断,并结合示例进行了详细论述。     

基于小波包和AR谱分析的滚动轴承故障诊断

针对滚动轴承故障振动信号的非平稳性,提出了一种基于小波包和AR谱分析的滚动轴承故障诊断方法.该方法对系统输出信号进行小波包分解,然后进行重构,再对重构信号进行AR谱分析,从而提取出故障特征频率.试验结果表明,这种方法能有效地提取滚动轴承的故障特征,诊断其故障.     

基于小波包近似熵和SVM的圆柱滚子轴承诊断

为了解决特种车辆变速箱圆柱滚子轴承由于振动信号的非线性、非平稳特征较为微弱,提取的特征量数值不明显且现实中难以获得大量含丰富特征的典型故障样本而难以对其进行准确诊断的问题,应用小波包近似熵和支持向量机对特种车辆变速箱圆柱滚子轴承进行诊断。首先,在自行搭建的模拟实验台上采集某型特种车辆变速箱圆柱滚子轴承正常、外圈磨损、滚动体故障、点蚀和压痕4种典型状态的振动信号;然后,分别提取4种典型状态振动信号的小波包近似熵值作为支持向量机的输入,根据支持向量机的输出结果来确定圆柱滚子轴承是否发生故障和故障类型。结果表明,该方法能有效对某型特种车辆变速箱圆柱滚子轴承的典型状态进行诊断,为其他相似变速箱圆柱滚子轴承的故障诊断提供一种参考途径,具有一定的工程实用价值。     

A fault diagnosis approach for roller bearing based on VPMCD under variable speed condition

Essentially the fault diagnosis of roller bearing is a process of pattern recognition. However, existing pattern recognition method failed to capitalize on the nature of multivariate associations between the extracted fault features. Targeting such limitation, a new pattern recognition method – variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification. The VPMCD consider that all or part of the feature values will exhibit interactions in nature and these associations will have different performances between different classes, which is always true in practice when faults occur in roller bearings. Target to the characteristics of non-stationary and amplitude-modulated and frequency-modulated (AM–FM) of vibration signal picked up under variable speed condition, a fault diagnosis method based upon the VPMCD, order tracking technique and local mean decomposition (LMD) is put forward and applied to the roller bearing fault identification. Firstly, LMD and order tracking analysis method are combined to extract the fault features of roller bearing vibration signals under variable speed condition; Secondly, the feature values are regard as the input of VPMCD classifier; finally, the working condition and fault patterns of the roller bearings are identified automatically by the output of VPMCD classifier. The analysis results from experimental signals with normal and defective roller bearings indicate that the proposed fault diagnosis approach can distinguish the roller bearing status-with or without fault and fault patterns under variable speed condition accurately and effectively.     

基于倒频谱理论的滚动轴承故障检测

探讨了倒频谱技术在滚动轴承故障诊断中的应用。针对滚动轴承振动信号的特征,利用倒频谱技术可简化谱图的特征分析,提高滚动轴承故障诊断的准确性。     

基于多特征参数和概率神经网络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性,提出了一种基于多特征参数和概率神经网络的滚动轴承故障诊断方法。首先利用经验模态分解（EMD）方法将采集到的滚动轴承原始振动信号分解为有限个固有模式函数（IMF）之和,然后提取表征故障信息的若干个IMF的能量、峭度和偏度作为概率神经网络的输入参数来进行故障分类。试验结果表明,该方法可以准确、有效地识别滚动轴承的工作状态和故障类型,是一种可行的滚动轴承故障诊断方法。     

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。     

An Experimental Investigation of Micropitting Using a Roller Disk Machine

Micropitting is a microscopic form of progressive fatigue wear. It is most often associated with case hardened gears although such failures have also been reported for rolling element bearings. This paper reviews the micropitting phenomenon and reports the results of an experimental study conducted using a roller disk machine. Analysis of the micropitted test specimens confirmed that the roller disk machine experiments faithfully reproduce the failure mode as found on full size gears. The effect of various operating parameters was investigated. The results show that increasing load, decreasing specific film thickness and maintaining negative relative sliding all increased the rate of micropitting wear. The authors also found that micropitting is almost completely eliminated at a very low, but non-zero, slide-to-roll ratio.     

Intelligent condition monitoring of a gearbox using artificial neural network

This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a multi-layer perceptron neural network. Feature vector which is one of the most significant parameters to design an appropriate neural network was innovated by standard deviation of wavelet packet coefficients. The gear conditions were considered to be normal gearbox and slight- and medium-worn and broken-teeth gears faults and a general bearing fault which were five neurons of output layer with the aim of fault detection and identification. A downscaled 2-layer multi-layer perceptron neural-network-based system with great accuracy was designed to carry out the task. In this research, vibration signals were recognised as the most reliable source to extract the feature vector which were synchronised by piecewise cubic hermite interpolation (PCHI) and pre-processed using the standard deviation of wavelet packet coefficients.     

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.