基于麻雀搜索算法优化支持向量机的滚动轴承故障诊断

针对支持向量机(support vector machine,SVM)的分类性能受自身参数选择影响较大的问题,提出了基于麻雀搜索算法(sparrow search algorithm,SSA)优化SVM的故障诊断方法.利用麻雀搜索算法(SSA)对支持向量机的惩罚参数(C)与核参数(g)进行优化,并构建SSA-SVM滚动轴承故障诊断模型.结果表明:对于滚动轴承的常见故障,SSA-SVM诊断模型的测试正确率为96.67％,比传统的遗传算法(genetic algorithm,GA)-SVM和粒子群算法(particle swarm optimization,PSO)-SVM诊断模型分别提高3.34％和1.67％,且收敛速度更快,可有效应用于故障诊断.

Fault Diagnosis of Rolling Bearing Based on Sparrow Search Algorithm Optimized Support Vector Machine

Aiming at the problem that the classification performance of support vector machine (SVM) is greatly affected by its own parameter selection,A method to optimize SVM based on Sparrow Search Algorithm (SSA) was proposed .The sparrow search algorithm (SSA) was used to optimize the penalty factor C and the kernel function g of the support vector machine, and the SSA-SVM rolling bearing fault diagnosis model was constructed.The results show that for several common faults of rolling bearings, the test accuracy of the SSA-SVM diagnostic model is 96.67%, which is 3.34% and 1.67% higher than the traditional GA-SVM and PSO-SVM diagnostic models, and the convergence speed is faster,It can be effectively applied to fault diagnosis.