混合特征驱动的滚动轴承智能故障诊断方法  

An intelligent fault diagnosis method for rolling bearings based on hybrid characteristics

作　　者：陆建涛 姚通 李舜酩[1] 崔荣庆 LU Jiantao;YAO Tong;LI Shunming;CUI Rongqing(College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]南京航空航天大学能源与动力学院,南京210016

出　　处：《振动与冲击》2022年第16期79-84,176,共7页Journal of Vibration and Shock

基　　金：国家重点研发项目(2020YFB1709801);预研领域基金课题资助项目(61400040304);国家自然科学基金(51975276);国家科技重大专项资助项目(2017-IV-0008-0045);中央高校基本科研业务费(1002-YAH20008)。

摘　　要：针对采用传统特征指标进行故障诊断准确率较低的问题,提出了一种基于混合标度律特征和改进支持向量机的滚动轴承智能故障诊断方法。首先,利用超阶分析得到指示故障的标度律指标,并将其与常规特征指标相结合构造混合特征指标矩阵,提升特征指标对故障的区分度。其次,采用支持向量机(support vector machines,SVM)对构造的混合特征矩阵进行分类,利用粒子群优化算法对SVM中重要参数进行优化。最后,利用滚动轴承试验台对提出的滚动轴承智能故障诊断方法进行验证。结果表明,与常规特征相比,利用构造混合特征指标得到的训练准确率提高了13%,测试准确率提高了23%。所提方法不仅能识别不同故障类型,而且能对同一故障不同损伤程度进行识别,有望进一步实现滚动轴承故障定量诊断。To address the problem of low accuracy of fault diagnosis using traditional feature indexes,a new intelligent fault diagnosis method for rolling bearings was proposed based on a hybrid scale exponent index and improved support vector machine.First,the scale exponent index for indicating fault was obtained by using the super order analysis,and the hybrid characteristic index matrix was constructed by combining it with the conventional characteristic indexes,so as to improve the discrimination of the characteristic index to the fault.Second,support vector machine(SVM)was used to classify the constructed mixed vectors,and particle swarm optimization algorithm was used to optimize the important parameters of SVM.Finally,the proposed intelligent fault diagnosis method for rolling bearing was verified by using the rolling bearing test bench.Results show that the training accuracy and testing accuracy using the hybrid feature indexes are improved by 13%and 23%,respectively,compared with the conventional feature indexes.The proposed method can not only identify the fault types,but also identify the damage degree of the same fault,which emerges to further realize the quantitative fault diagnosis of rolling bearings.

关 键 词：智能故障诊断 超阶分析 混合特征指标 粒子群优化 支持向量机(SVM) 

分 类 号：TB53[理学—物理] TH113.1[理学—声学]