基于经验模态分解和分类器集的滚动轴承故障诊断方法  

The EMD and classifier ensemble-based ball bearing fault diagnosis method

作　　者：景治 张纯龙 Jing Zhi;Zhang Chunlong(Coal Preparation Center,Ningxia Coal Industry Co.Ltd.,National Energy Group,Yinchuan 750409,China;School of Chemical Engineering and Technology,China University of Mining and Technology,Xuzhou 221116,China)

机构地区：[1]国家能源集团宁夏煤业有限责任公司洗选中心,宁夏银川750409 [2]中国矿业大学化工学院,江苏徐州221116

出　　处：《选煤技术》2023年第5期94-98,共5页Coal Preparation Technology

基　　金：宁夏煤业科技创新计划项目(NXMY-20-12)。

摘　　要：为解决滚动轴承故障诊断过程不透明的问题,使用经验模态分解提取6个时域特征和5个频域特征,并模仿技术人员思考推理过程,构建了基于规则的分类器集,通过多样性指标筛选出最佳基分类器,采用遗传算法对构成候选基分类器的约简和诊断规律进行了研究,采用加权投票策略形成分类器集。结果表明:在不同转速下,对正常轴承、内环、外环和滚珠故障识别正确率为90%,诊断过程类似于技术人员推理过程,解释性好,不像其他黑箱模型,更容易被现场技术人员接受。To address the problem regarding the nontranparency of ball bearing fault diagnosis process,a study is made by taking the following steps:construction of the rule-based classifier ensembles based on the six time-domain features and five frequency-domain features extracted through empirical modal decomposition and imitation of the thinking and inference process of technical personnel;singling out the optimum base classifiers through screening according to diversity indices;determination of the reduction and diagnosis rules of the candidate base classifiers using genetic algorithm;and formation of the classifier ensembles by using the weighted voting strategy.Practice shows with the use of the method for identifying the failure of the inner ring,outer ring and balls of a normal ball bearing running at different speeds,the correct identification rate is up to 90%.Unlike the black-box models,the diagnosis process can be proceeded in a way similar to the reasoning process of a technician,featuring a good interpretability-a method which is more likely to be accepted by technical personnel working on site.

关 键 词：轴承故障诊断 经验模态分解 分类器集 轴承试验 故障识别正确率 

分 类 号：TD452[矿业工程—矿山机电] TH133.3[机械工程—机械制造及自动化]