基于Self-Weight与t-SNE的滚动轴承故障诊断  

Rolling bearing fault diagnosis based on Self-Weight and t-SNE

作　　者：倪伟 蒋占四[1] NI Wei;JIANG Zhansi(School of Mechanical and Electrical Engineering,Guilin University of Electronic Technology,Guilin 541004)

机构地区：[1]桂林电子科技大学机电工程学院,广西桂林541004

出　　处：《桂林电子科技大学学报》2022年第6期463-467,共5页Journal of Guilin University of Electronic Technology

基　　金：国家自然科学基金(51565008);广西自然科学基金(2019JJB160062);广西科技基地和人才专项(2019AC20266);广西区研究生创新项目(YCSW2020149)。

摘　　要：针对滚动轴承故障信号非线性、故障特征种类繁多难以准确分类的问题,提出了一种Self-Weigh与t-SNE相结合的解决方法。先用WPT完成对原始故障信号的处理及特征的提取,然后采用Self-Weigh评估每个特征的敏感程度,获取最优特征;再对这些最优特征通过t-SNE进行降维可视化处理,获取低维敏感特征,并将其作为AP传播聚类的输入,从而实现故障类型100%正确识别。采用机械综合模拟实验平台的轴承数据加以验证,并与采用t-SNE、Self-Weigh+PCA方法进行对比,结果体现了所提方法的优势。In order to solve the problem that the fault signal of rolling bearing is nonlinear and the fault features are various,and it is difficult to classify accurately,a method combining Self-Weight feature selection with t-SNE algorithm is proposed.Firstly,WPT is used to process the original fault signal and extract the features.Then Self-Weight is used to evaluate the sensitivity of each feature to obtain the optimal feature.Then,these optimal features are visualized by t-SNE to obtain low dimensional sensitive features,which are used as the input of affine propagation clustering(AP)to achieve 100%accuracy of fault type identification.The results are verified by the bearing data of the MFS-MG,Compared with t-SNE without feature selection and Self-Weight+PCA,the results show the advantages of the proposed method.

关 键 词：自权重 t分布随机近邻嵌入 滚动轴承 故障诊断 特征提取 

分 类 号：TH133.33[机械工程—机械制造及自动化]