基于补偿距离评估技术与灰色关联分析的滚动轴承故障程度识别  

Fault Severity Recognition of Rolling Bearing based on Compensation Distance Evaluation Technique and Grey Relational Analysis

作　　者：刘鹏[1] 李洪儒[1] 王卫国[1] 许葆华[1] 

机构地区：[1]军械工程学院,河北石家庄050003

出　　处：《机械传动》2015年第10期97-100,共4页Journal of Mechanical Transmission

基　　金：国家自然科学基金(51275524)

摘　　要：针对滚动轴承故障程度识别问题,提出基于补偿距离评估技术与灰色关联分析的滚动轴承故障程度识别方法。利用补偿距离评估技术对全特征集进行特征选择,优选出对故障程度大小更为敏感的特征,然后根据特征的敏感程度对其进行加权。利用敏感特征子集建立标准退化模式矩阵,应用灰色关联分析,根据关联度的大小进行故障程度大小识别。结果表明,该方法能有效识别滚动轴承滚动体的不同故障程度,同时降低了计算的复杂度。Aiming at the problem of rolling bearing fault severity recognition,a fault severity recognition method of rolling bearing based on compensation distance evaluation technique（ CDET） and grey relational analysis（ GRA） is proposed. By utilizing the compensation distance evaluation technique to choose features sensitive to fault severity from the high dimensional feature set,and weights such sensitive features according to their sensitive degree. Then,by using the sensitive feature subset,a standard degradation pattern matrix is built,and the grey relational analysis is applied for fault severity recognition in accordance with correlation degree index. The results show that the proposed method can effectively recognize different rolling bearing fault severity,and at the same time,the calculation complexity is reduced.

关 键 词：补偿距离评估技术 多尺度熵 灰色关联分析 滚动轴承 故障程度识别 

分 类 号：TH133.33[机械工程—机械制造及自动化] TH165.3