滚动轴承故障诊断综述

摘要：滚动轴承是旋转机械中使用最多，最为关键，同时也是机械设备中最易损坏的机械零件之一。

滚动轴承质量的好坏对机械设备运行质量影响很大，许多旋转机械设备的运行状况与滚动轴承的质量有很大的关系。

滚动轴承作为旋转机械设备中使用频率较高，同时也是机械设备中较为薄弱的环节，因此对滚动轴承进行故障诊断具有重大意义。

引言：故障诊断技术是一门研究设备运行状况信息，查找故障源，研究故障发展趋势，确定相应决策，与生产实际紧密相结合的实用技术。

故障诊断技术是20世纪中后迅速发展起来的一门新型技术。

国外对滚动轴承故障诊断技术的研究开始于20世纪60年代。

美国是世界上最早研究滚动轴承故障诊断技术的国家，于1967年对滚动轴承故障进行研究，经过几十年的发展，先后研制了基于时域分析，频域分析，和时频分析的滚动轴承故障诊断技术。

目前国外已经研制出先进的滚动轴承故障诊断仪器，并且已经应用于工业生产中，对预防机械事故，减少损失起到了至关重要的作用。

国内对故障诊断技术的研究起步较晚，20世纪80年代我过开始研究滚动轴承故障诊断技术，经过多年的研究，先后出现了基于振动信号的滚动轴承故障诊断，基于声音信号的滚动轴承诊断方法，基于温度的滚动轴承诊断方法，基于油膜电阻的滚动轴承诊断方法和基于光钎的滚动轴承诊断方法。

从实用性方面来看，基于振动信号的滚动轴承诊断方法具有实用性强，效果好，测试和信号处理简单等优点而被广泛采用。

在滚动轴承故障诊断中，比较常用的振动诊断方法有特征参数法，频谱分析法，包络分析法，共振解调技术。

其中共振解调技术是目前公认最有效的方法。

振动检测能检测轴承的剥落、裂纹、磨损、烧伤且适于早期检测和在线检测。

因而，振动诊断法得到一致认可。

包络检测是轴承故障振动诊断的一种有效方法,实际中已广泛使用。

当轴承出现局部损伤类故障后，振动信号中包含了以故障特征频率为周期的周期性冲击成分，虽然这些冲击成分是周期出现的，但单个冲击信号却具有非平稳信号的特性。

Fourier变换在频域上是完全局部化的，但由于其基函数在时域上的全局性使它没有任何的时间分辨率，因此不适合非平稳信号的分析。

短时Fourier 变换虽然在时域和频域上都具有一定的分辨率而由于其基函数只能对信号进行等带宽的分解。

因此基函数一旦确定，其时域和频域分辨率也就不能变化，从而不能自适应地确定信号在不同频段的分辨率。

小波变换基函数的伸缩和平移能形成一系列变化的时频窗，低频处时窗变宽，高频处时窗变窄，而频窗的变化正好相反，这样小波变换就具有了可变分辨率的特征，从而满足了时频分析的要求。

因此，可以用小波变换对滚动轴承进行状态监视和故障诊断。

关键词：轴承监测，故障诊断，共振解调，小波分析Abatact:Roller bearings are one of the most used and important mechanical parts in rotating machines, and vninerable to damage. Many faults of rotating mechanism are related to the state of roller bearings. The performance of roller bearings directly affects the performance of the whole machine. Thus , developing state testing and fault diagnosis of roller bearings is necessary.Introduction:Fault diagnosis technology is a practical skill, who research the operation state information of the equipment ,find the source of the error, research the tendency of the error ,and determine how to do next. Fault diagnosis is a new technology ,who quickly develop in the late of the 20’s century. In the foreign countries ,they research the fault diagnosis of rolling bearing begins at 1960.The United States is the first country who research the fault diagnosis technology in the world .at the year of 1967 ,the United State began researching fault diagnosis technology.After several decades of development, they has developed rolling bearing fault diagnosis technology based on time-domain analysis, frequency domain analysis, and time-frequency analysis .At nowadays, the foreign countries have been developed the advanced equipment of the rolling bearing fault diagnosis , and has been used in industrial, and plays a important role in prevent mechanical accidents ,reduce losses. The research of Fault diagnosis technology in China at the late of 1980. After many years of research, we have development the fault diagnosis of rolling bearing based on the vibration signals , based on sound signals diagnostics, based on temperature diagnosis ,based on the film resistance diagnostics and based on fiber-optic diagnosis . From the view of practical, the rolling bearing diagnosis based on vibration signal method is practical, effective, and signal processing is simpler than others, so it has been used widely.In the fault diagnosis of rolling bearing, the more commonly used methods of vibration diagnostic are characteristic parameter method, spectral analysis, envelope analysis, and resonance demodulation technology. Resonance demodulation technique is the method who currently recognized as the most effective method at nowadays. Vibration test can detect bearing spalling, cracks, wear, burns and is suitable for early detection and on-line testing. Thus, the vibration diagnostics was unanimously approved. Envelope bearing fault detection is an effective method of vibration diagnosis, has been widely used in practice. When the bearing failure, after a partial injury of class, the vibration signal contains the fault characteristic frequency cycle to the cyclical nature of the impact of components, although there is a periodic component of these shocks, but the individual impact of the signal does have non-stationary signal features.. Fourier transform in the frequency domain is fully localized, but because of its b-asis functions in the time domain, the global nature of it there is no time resolution a-nd therefore not suitable fornon-stationary signal analysis. Although the short-time F-ourier transform in time domain and frequency domain have a certain degree of resol-ution which can only be due to its basic function such as the signal bandwidth decom-position. Once the basis functions, therefore, its time-domain and frequency domain r-eolution, it will not change, and thus can not determine the adaptive resolution of the signal at different frequency bands. Wavelet transform basis functions of the scaling and translation can form a series of changes of time-frequency window, low frequ-encies widened time window, high-frequency at time window narrows, while the fr-equency of changes in the opposite window, so that the wavelet transform with a v-ariable resolution rate characteristics, to meet therequirements of time-frequency an-alysis. Therefore, we can use wavelet transform for bearing condition monitoring and fault diagnosis.Keywords：Bearing testing ;Fault Diagnosis ;resonance demodulation ;Wavelet Analysis ;参考文献：[1] Xiaoqin,Zhiyi SUN; "Fault diagnosis approach based on fuzzy neural networks and its applocation". Control and Automation .2002.ICCA. Final Program Book OF Abstracts. The 2002 International Conference on ,June 16-19,2002 Pages:217-218[2] Peng Tao ,Gui Weihua,Wu Min,Xie Yong,Tang Zhaohui;"Fault diagnosis based on intelligent information processing technology".TENCON' 02.Proceedings 2002 IEEE Region 10 Conference on Computers,Communication ,Control and POWER Engineering. Volume:3 ,28-31 Oct .2002 Pages:1708-1712 vol.3[3] J.Shiroishi.Y.Li ,S.Liang,S.DANYLUK ,T。