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

摘要： 齿轮是传动机械中的重要部件，也是在运行过程中产生故障的主要原因之一，因此对齿轮进行故障诊断研究就具有十分重要的意义。但是在齿轮故障诊断数据集中，故障样本数通常比非故障样本数要少很多，由此引发了数据不均衡问题下故障诊断的问题。以往的研究很少关注这种数据不均衡问题对故障诊断的影响。此外，在故障数据集中有一些冗余甚至是不相关的特征，这些特征降低了学习器的泛化能力。为解决这类问题，提出了一种基于Relief的EasyEnsemble算法来解决故障诊断中的数据不均衡问题。在UCI数据集和齿轮数据集上的实验结果表明新算法提高了分类器在不均衡数据集上的分类性能和预报能力。

关键词: 齿轮, 故障诊断, 不均衡数据集, 集成学习