噪声与振动控制2024，Vol.44Issue(3)：117-124,8.DOI:10.3969/j.issn.1006-1355.2024.03.018
基于ELDA降维与MPA-SVM的滚动轴承故障诊断方法
Rolling Bearing Fault Diagnosis Method Based on ELDA Dimension Reduction and MPA-SVM
摘要
Abstract
In order to raise the fault diagnosis accuracy of rolling bearings,a new rolling bearing fault classification method based on Eccentric Linear Discriminant Analysis(ELDA)dimension reduction algorithm and Marine Predators Algorithm(MPA)optimized support vector machine was proposed.Firstly,the time domain and frequency domain analysis methods were used to construct the high dimension eigenmatrix of bearing signals.Then,the Adaptive Maximum Likelihood Estimation(AMLE)method was used to estimate the intrinsic dimension.The ELDA algorithm was used to extract the secondary features,so as to fully explore the sensitive features and reduce the impact of redundant features on fault diagnosis.Finally,the low-dimensional sensitive separable matrix was input into the MPA-SVM classifier to identify the fault type.Experimental analysis shows that the proposed method can effectively shorten the training time and improve the accuracy of diagnosis.关键词
故障诊断/滚动轴承/特征降维/海洋捕食者算法/支持向量机Key words
fault diagnosis/rolling bearing/feature dimension reduction/marine predator algorithm/support vector machine分类
机械制造引用本文复制引用
刘运航,宋宇博,朱大鹏..基于ELDA降维与MPA-SVM的滚动轴承故障诊断方法[J].噪声与振动控制,2024,44(3):117-124,8.基金项目
甘肃省教育厅青年博士基金资助项目(2021QB-053) （2021QB-053）
国家自然科学基金资助项目(51765028) （51765028）
