振动信号模型和散度在诊断滚动轴承故障中的应用  

The Application of Vibration Signal Model and Divergence in Fault Diagnosis of Rolling Bearing

作　　者：郭艳平[1] 龙涛元[1] GUO Yan-ping;LONG Tao-yuan(Zhongshan Torch Polytechnic,Guangdong Zhongshan 528436,China)

机构地区：[1]中山火炬职业技术学院,广东中山528436

出　　处：《机械设计与制造》2024年第9期311-315,319,共6页Machinery Design & Manufacture

基　　金：广东省教育厅科技项目(2018GkQNCX007);广东省教育厅科技项目(2019KZDZX2036)。

摘　　要：在分析旋转机械滚动轴承振动信号特点的基础上,首先建立轴承振动信号理论模型,并对其进行实验验证,然后计算待诊断样本和各种典型状态下振动信号模型之间的散度值,通过散度值的大小对比可知轴承故障部位和故障程度变化,最后通过对试验台数据和风电场实验样机数据的分析验证了此方法的有效性和实用价值,该诊断方法采用具有一定稳定性的轴承各部位故障特征频率为特征参数,且不需要对原始振动信号进行处理,也不需要大量具有典型故障的信号样本作为基础支撑,这三个特点决定了此诊断方法在实时性和鲁棒性方面的优越性,因此非常适合用于旋转机械滚动轴承的在线监测和自动故障诊断。On the basis of analyzing the vibration signal characteristics of rolling machinery rolling bearings,the theoretical model of the bearing vibration signal is first established and verified by experiments.Then the divergence value between the sample to be diagnosed and the vibration signal model under various typical conditions is calculated.The comparison of the degree value shows the bearing fault location and the change of the fault degree.Finally,the validity and practical value of this method are verified by analyzing the data of the test bench and the data of the experimental prototype of the wind farm.The diagnosis method adopts the bearing with certain stability.The characteristic frequency of the local fault is a characteristic parameter,and it does not need to process the original vibration signal,nor does it require a large number of signal samples with typical faults as basic support.These three characteristics determine the real-time and robustness of this diagnostic method.Superiority,so it is very suit⁃able for on-line monitoring and automatic fault diagnosis of rolling machinery rolling bearings.

关 键 词：滚动轴承 振动信号模型 散度 故障特征频率 

分 类 号：TH16[机械工程—机械制造及自动化] TH133.33