箱型图与特征融合模型在轮对轴承标签混淆数据分类中的应用  

The application of box graph and feature fusion model in the classification of wheel set bearing label confusion data

作　　者：张雄 李嘉禄[2] 董帆 武文博 万书亭 顾晓辉 ZHANG Xiong;LI Jialu;DONG Fan;WU Wenbo;WAN Shuting;GU Xiaohui(Hebei Key Laboratory of Electric Machinery Health Maintenance&Failure Prevention,Baoding 071003,China;Department of Mechanical Engineering,North China Electric Power University,Baoding 071003,China;State Key Laboratory of Mechan-ics Behavior and System Safety of Traffic Engineering Structures,Shijiazhuang Tiedao University,Shijiazhuang 050043,China)

机构地区：[1]河北省电力机械装备健康维护与失效预防重点实验室,河北保定071003 [2]华北电力大学机械工程系,河北保定071003 [3]石家庄铁道大学省部共建交通工程结构力学行为与系统安全国家重点实验室,河北石家庄050043

出　　处：《振动工程学报》2025年第1期88-95,共8页Journal of Vibration Engineering

基　　金：国家自然科学基金资助项目(52105098);河北省自然科学基金资助项目(E2021502038)。

摘　　要：深度学习方法在列车轮对轴承故障诊断领域表现出了巨大的潜力,但其可以有效实现的前提是各类数据与类别标签之间能够正确匹配,对于含有少量标签错误样本的数据,传统深度学习方法难以实现预期的诊断效果。针对此问题,提出了一种箱型图法与特征融合模型相结合的故障诊断方法。利用列车轮对轴承实验数据对所提方法进行验证,结果表明,相比于直接利用传统神经网络模型进行故障诊断,本文所提方法的诊断准确率更高,说明本文方法对于含有少量标签错误样本的轴承数据具有更好的处理效果。Deep learning methods have shown great potential in the field of fault diagnosis of train wheelset bearings,but their effective implementation is based on the correct matching between various types of data and category labels.For data with a small number of label error samples,traditional deep learning methods are difficult to achieve the expected diagnostic effect.To address this issue,this paper proposes a fault diagnosis method combining box graph method and feature fusion model is proposed to address this issue.In this method,the outlier in each group of bearing signals is removed by box graph method,and the remaining data is expanded by the SMOTE method to restore to the original data size;Input the processed sample data into the improved feature fusion model for fault identification and classification.The experimental data of train wheel bearings was used for validation.The results showed that compared to directly using traditional neural network models for fault diagnosis,the diagnostic accuracy of the method proposed in this paper is higher,indicating that the method has better processing performance for bearing data with a small number of label error samples.

关 键 词：故障诊断 轮对轴承 标签错误 特征融合 箱型图 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH133.3