基于遗传算法与SOM网络的轴承故障诊断方法  

Bearing Fault Diagnosis Method Based on Genetic Algorithm and SOM Neural Network

作　　者：黄磊 马圣 曹永华 HUANG Lei;MA Sheng;CAO Yong-hua(College of Aeronautical Engineering,Jiangsu Aviation Technical College,Jiangsu Zhenjiang 212134,China;Chengdu CoreMii Technology Co.,Ltd.,Technology Research and Development,Sichuan Chengdu 610213,China)

机构地区：[1]江苏航空职业技术学院航空工程学院,江苏镇江212134 [2]成都芯米科技有限公司技术研发部,四川成都610213

出　　处：《机械设计与制造》2023年第12期97-100,105,共5页Machinery Design & Manufacture

基　　金：2021年度院级课题资助项目(JATC21010104)。

摘　　要：轴承作为旋转机械的核心部件,开展其有关故障诊断方面的研究,有利于对旋转机械运行状态进行监测。针对旋转机械轴承故障的微弱信号容易淹没在其它部件的振动信号中,采用特征提取法,从滚动轴承正常、内环故障、外环故障和滚动体故障四种工况的振动信号中提取时频域统计特征参数;并引入遗传算法消除时频域统计特征间的耦合性与共线性,提取9个时频域最优特征参数作为SOM网络的输入。研究结果表明:不同故障类型下,激活的SOM神经元不呈现明显性的差异性;根据文中神经元激活统计规则,表明SOM具有一定的故障辨识性,且对规则进行调整能够提升SOM网络的诊断效果。Bearing is one of the core component of rotating machinery.The research on fault diagnosis of bearing is helpful to monitor the running state of rotating machinery.In view of the fact that the weak signal of the bearing fault is easily submerged in the vibration signals of other components,the characteristic extraction method is used to extract the time domain and frequency domain statistical characteristic parameters from the vibration signal of the rolling bearing under the four working conditions of normal,inner ring fault,outer ring fault and rolling element fault.And genetic algorithm is introduced to eliminate the coupling and collinearity between statistical characteristic in time domain and frequency domain,and 9 optimal time domain and frequency domain characteristic parameters are extracted as input of SOM neural network.The research shows that under different fault types,the activated SOM neurons do not show obvious difference.According to the statistical rules of neurons activation in this paper,it show that SOM neural network has certain fault identification,and the adjusting the rules of neuron activation statistical rules can improve the diagnosis effect of SOM neural network.

关 键 词：轴承 遗传算法 SOM网络 故障诊断 特征参数 

分 类 号：TH16[机械工程—机械制造及自动化] TH17[航空宇航科学与技术—航空宇航推进理论与工程] V231.1