摘要
针对电机轴承故障诊断模型构建时间长、准确率不高的问题,提出一种基于改进贝叶斯分类的故障诊断方法。首先通过小波包变化、粗糙集及主成分分析方法分别构造原始故障特征集、降维后的故障特征集,再将原始故障特征集和降维后的故障特征集输入到改进贝叶斯分类模型中实现故障诊断,以此为基础设计一套交流发电机轴承故障诊断系统。最后以国内车辆车载电机轴承振动数据为依据,将改进贝叶斯分类方法和神经网络及最小二乘支持向量机方法作对比分析,结果表明:改进贝叶斯分类方法建模时间更短,故障诊断准确率更高。
The problem of long time and low accuracy of the generator bearing fault diagnosis model was considered,and a fault diagnosis method based on improved naive Bayesian classification was proposed.Taking a subway axial flow fan bearing vibration data as the foundation,the feature set of the original fault and the fault feature set after reducing the dimension were constructed separately by using the method of wavelet packet transform,rough set and principal component analysis.The original fault feature set and the dimensionality reduction feature set were input into the simple Bias classification model.On this basis,the fault diagnosis system of alternator bearing was designed.Finally,the improved Bayesian classification method,the neural network and least squares support vector machine method were compared and analyzed based on the vibration data of bearing of a metro vehicle.The results show the modeling time is shorter and the accuracy of fault diagnosis is higher based on the improved Bayesian classification method.
作者
杨晓珍
周继续
邓举明
YANG Xiaozhen;ZHOU Jixu;DENG Juming(College of Intelligent Manufacturing and Information Engineering,Yaan Vocational College,Yaan Sichuan 625100,China;Institute of Oceanography,Chinese Academy of Sciences,Qingdao Shandong 266071,China;Operation Branch,Qingdao Metro Group Co.,Ltd.,Qingdao Shandong 266041,China)
出处
《机床与液压》
北大核心
2020年第20期172-175,共4页
Machine Tool & Hydraulics
关键词
电机轴承
故障诊断
小波包
贝叶斯
Alternator bearing
Fault diagnosis
Wavelet packet
Bayesian
