关键词: 齿轮, 故障诊断, 不均衡数据集, 集成学习

Abstract: Defect is one of the important factors resulting in gear fault，so it is significant to study the technology of defect diagnosis for gear.Class imbalance problem is encountered in the fault diagnosis，which causes seriously negative effect on the performance of classifiers that assume a balanced distribution of classes.Though it is critical，few previous works paid attention to this class imbalance problem in the fault diagnosis of gear.In imbalanced problems，some features are redundant and even irrelevant.These features will hurt the generalization performance of learning machines.This paper proposes REE（Relief based feature selection for EasyEnsemble） to solve the class imbalanced problem in the fault diagnosis of gear.Experimental results on UCI data sets and gear data set show that RIEE improves the classification performance and prediction ability on the imbalanced dataset.

Key words: gear, fault diagnosis, imbalanced data sets, ensemble learning