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

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.