一种滚动轴承故障诊断方法

A fault diagnosis method of rolling bearing

针对基于支持向量机的滚动轴承故障诊断方法中支持向量机的参数优化问题,提出一种改进的果蝇优化算法,即以模式分类准确率作为果蝇味道浓度函数,并采用该算法来优化支持向量机模型的惩罚因子和核函数参数;基于改进果蝇优化算法和支持向量机对滚动轴承的故障模式进行分类诊断,结果表明改进的果蝇优化算法具有较高的收敛速度和寻优效率,基于该算法和支持向量机的滚动轴承故障诊断方法具有较高的分类准确率。

For parameter optimization of support vector machine in fault diagnosis method of rolling bearing based on support vector machine,an improved fruit fly optimization algorithm was proposed which took accuracy rate of pattern classification as taste concentration function of fruit fly.The improved algorithm was used to optimize penalty factor and kernel function parameter of support vector machine model.Classified diagnosis of fault patterns of rolling bearing was maken based on the improved fruit fly optimization algorithm and support vector machine.The experimental results show that the improved fruit fly optimization algorithm has higher convergence speed and the optimization efficiency,and fault diagnosis method of rolling bearing based on the improved algorithm and support vector machine has higher classification accuracy rate.