【作者】 王刚；

【导师】 陈长征；

【作者基本信息】 沈阳工业大学 ， 机械工程， 2013， 硕士

【摘要】 滚动轴承是机械设备中最重要的零件之一，同时它的故障发生率很高，轴承工作状态的好坏影响着整台设备甚至整条生产线的运行情况。因此，对滚动轴承进行故障诊断研究有很大的必要性和重要现实的意义。本文以滚动轴承为研究对象,分析了滚动轴承的基本结构和失效形式，然后系统的研究了轴承的故障机理和振动特征。根据滚动轴承的故障信号的特点,本文利用加速度传感器采集振动信号，对滚动轴承的四种工作状态：正常运行、内圈故障、外圈故障和滚动体故障分别进行了振动信号的采集。由于传统故障诊断需要大量的数据样本，但是现实测试中数据样本不容易获取，这给故障诊断带来很大的难度。所以本文针对有限数据样本情况下的诊断特点，把支持向量机引入到故障诊断中,为故障智能诊断提供了一种新的研究方法。本文采用理论研究和计算机仿真相结合的研究方法，首先用传感器采集轴承的振动信号，然后利用小波阀值法对滚动轴承振动信号进行小波降噪处理，去除信号中的干扰信号。接着利用小波包技术提取降噪后信号的频带能量，最后将小波包分析得到的频带能量构成的特征集作为支持向量机的输入向量，利用支持向量机智能分类判断轴承工作状态。本文的计算机分析都是采用MATLAB软件进行仿真，最终的实验仿真结果表明该方法对滚动轴承的小样本故障诊断有很高的分类识别能力。更多还原

【Abstract】 Rolling bearing is one of the most ordinary parts in mechanical machines, and thefrequency of fault is high. Its working condition influences on performance of the wholemachine, even the whole production line. Therefore, it is very essential and important tostudy the fault diagnosis of rolling bearing.Rolling bearing is regarded as the research object, and the basic structure and failuremodes of the bearing are analyzed in this paper. Then the fault mechanism and vibrationfeature are studied systematically. Based on the characteristics of rolling bearing faultsignal, acceleration sensor is used to collect vibration signal from four different workingcondition of bearing: the normal, inner fault, outer fault, rolling body fault.Because the traditional fault diagnosis requires a lot of data samples, while the realtest data sample is not easy to obtain, it brings great difficulty to fault diagnosis.Therefore，according to the feature of the smaller number samples, the method of SVM isapplied to the fault diagnosis of rolling bearing, which offers a new research method forintelligent fault diagnosis. The research method based on theoretical research andcomputer simulation is proposed. First the vibration signal is collected by the sensor, thenthe noise of vibration signal is removed by the method of wavelet threshold. Theexperiment data is transformed by wavelet packet, the vibration signal is decomposedinto the individual frequency bands. The energy spectrum feature vectors are extractedfrom the individual frequency bands, and they are set as the input vectors of SVM.Finally, the running condition of the rolling bearing is diagnosed intelligently by theanalysis method of SVM.The computer analysis of this paper is all based on the software of MATLAB. Atthe end, the experimental results show that, the proposed method can diagnose the faultof rolling bearing with smaller number samples more accurately.更多还原