基于SK等指标和SVM的滚动轴承性能退化评估研究  

Research on Performance Evaluation of Rolling Bearing Performance Based on SK and Other Indicators and SVM

作　　者：李超[1] 郭瑜[1] LI Chao;GUO Yu(School of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学机电工程学院

出　　处：《电子科技》2020年第1期6-12,共7页Electronic Science and Technology

基　　金：国家自然科学基金(51675251);云南省重点项目(2017FA028)~~

摘　　要：文中提出一种基于谱峭度等指标和支持向量机的滚动轴承性能退化评估的新方法。针对滚动轴承全寿命过程中各个时期故障损伤程度的不同,将故障监测分为4个阶段:正常、初期、中期、末期。通过与传统指标,例如均方根值、峭度值、峰峰值指标等对比,验证了谱峭度作为初期故障特征指标的优势。选取谱峭度等指标作为特征输入,构建多分类支持向量机预测模型来预测轴承性能退化阶段。使用轴承全寿命试验数据对预测模型进行检验,证明了该方法的有效性和可行性。A new method for performance degradation evaluation of rolling bearings based on multiple support vector machine and indicator selection was proposed in this paper.The degree of fault at different stage in rolling bearing full life is different.In the study,fault monitoring was divided into 4 parts including normal stage,incipient stage,mid-term stage and final stage.The advantages of spectral kurtosis which was sensitive to incipient fault was demonstrated by comparing with traditional indicators such as RMS,kurtosis value and peak to peak value etc.Then,the spectral kurtosis and other indicators were selected as inputs,and the multi-class support vector machine prediction model was constructed to predict the degradation stage of rolling bearing.The prediction model was finally tested by bearing full-life test data,which verified the effectiveness and feasibility of the method and had practical engineering significance.

关 键 词：谱峭度 多分类支持向量机 故障监测 滚动轴承 性能退化评估 机器学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]