不平衡数据集下齿轮装配的故障诊断方法  

Fault Diagnosis Method of Gear Assembly under Imbalanced Data Set

作　　者：王喆 徐曦 张毕生[3] 黄晓玮 胡万里 WANG Zhe;XU Xi;ZHANG Bisheng;HUANG Xiaowei;HU Wanli(School of Computer Science,Hunan University of Technology,Zhuzhou 412007,China;Key Laboratory of Intelligent Information Perception and Processing Technology of Hunan Province,Hunan University of Technology,Zhuzhou 412007,China;Bosch Automotive Products(Changsha)Co.,Ltd,Changsha 410100,China;Changsha Robot Technology Co.,Ltd.,Changsha 410100,China)

机构地区：[1]湖南工业大学计算机学院,湖南株洲412007 [2]湖南工业大学智能信息感知及处理技术湖南省重点实验室,湖南株洲412007 [3]博世汽车部件(长沙)有限公司,湖南长沙410100 [4]长沙锐博特科技有限公司,湖南长沙410100

出　　处：《现代信息科技》2023年第6期139-142,148,共5页Modern Information Technology

基　　金：湖南省教委科研基金(19K026);湖南省重点实验室建设项目(2020KF02)。

摘　　要：汽车零部件的齿轮装配过程中往往伴随着多种类型的故障,快速且精准地判断故障类型,对保证齿轮装配工位稳定运行具有重要意义。因此,提出一种基于SMOTE采样方法和随机森林(RF)分类方法的故障诊断模型——SMOTE-RF。首先,在实际齿轮装配过程中,故障数据是不平衡的,可以使用SMOTE算法生成平衡的故障数据;其次,将平衡后的数据作为随机森林算法的输入实现故障分类;最后,对模型进行性能评估。实验结果表明,SMOTE-RF模型的分类效果优于SVM和XGBoost。The gear assembly process of automobile parts is often accompanied by various types of faults.It is of great significance to quickly and accurately determine the fault type to ensure the stable operation of the gear assembly station.Therefore,a fault diagnosis model based on SMOTE sampling method and Random Forest(RF)classification method,SMOTE-RF,is proposed.Firstly,in the actual gear assembly process,the fault data is unbalanced,and the SMOTE algorithm can be used to generate balanced fault data.Secondly,the balanced data is used as the input of Random Forest algorithm to realize fault classification.Finally,the performance of the model is evaluated.The experimental results show that the classification effect of SMOTE-RF model is better than that of SVM and XGBoost.

关 键 词：故障诊断 不平衡数据 SMOTE算法 随机森林 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]