基于网格搜索与支持向量机的轴承故障诊断

针对轴承故障诊断问题,提出一种基于相关度分析与网格搜索算法(GS)优化支持向量机(SVM)的轴承故障诊断方法.采用GS算法对SVM的惩罚参数c和核函数参数g进行寻优,以此建立分类器用于识别轴承故障类型.在模型建立方面巧妙地加入了分层的思想,通过相关度分析之后采用多层GS-SVM模型使轴承的故障诊断准确率相对于近年来的研...

A bearing fault diagnosis method based on GS-SVM

Aiming at the problem of bearing fault diagnosis, a method of bearing fault diagnosis based on correlation analysis and SVM (support vector machine) optimized by GS (grid search optimization) was proposed. GS algorithm was used to optimize SVMs penalty parameter c and kernel function parameter g, so as to establish a classifier for bearing fault identification. In the aspect of model establishment, the idea of stratification was subtly added. After the correlation analysis, the multi-layer GS-SVM was adopted, which significantly improved the accuracy of bearing fault diagnosis compared with recent researches. Finally, the rolling bearing fault samples from case western reserve university were used in the classification and identification experiments. The experimental results show that the proposed bearing fault diagnosis method can not only effectively identify the normal condition of bearing, inner ring fault, outer ring fault and ball fault, but also distinguish the severity of each kind of fault, improve the diagnostic accuracy of fault samples, and has strong practicability.