基于VMD和SVPSO-BP的滚动轴承故障诊断

FAULT DIAGNOSIS OF ROLLING BEARING BASED ON VMD AND SVPSO-BP

为了提高旋转机械滚动轴承故障诊断的准确率,提出一种基于变分模态分解(VMD)和缩放变异粒子群算法(SVPSO)优化BP神经网络的旋转机械滚动轴承故障诊断方法。通过在标准粒子群算法中加入缩放因子以及粒子变异操作提升其局部与全局寻优性能,得到一个改进的粒子群算法——缩放变异粒子群算法(SVPSO),再利用该算法优化BP网络的权值与阈值,提高BP神经网络的故障诊断精度;进一步,为了减少输入特征向量对BP神经网络分类性能的影响,采用VMD分解轴承振动信号,并计算其IMF分量时频熵的方法构建信号特征向量。通过与其他采用相同基准轴承数据集的诊断方法作对比,所提方法的故障诊断精度和算法稳定性均得到有效提升。

In order to improve the accuracy of fault diagnosis of rolling bearings,this paper proposes a fault diagnosis method for rolling bearings of rotating machinery,which based on variational mode decomposition(VMD)and scalable and mutational particle swarm optimization(SVPSO)to optimize BP neural network. By introducing scaling factors and particle mutation operations to improve the local and global optimization performance of the standard particle swarm algorithm,an improved particle swarm algorithm-Scalable and Mutational particle swarm algorithm(SVPSO)is obtained,and then the algorithm is used to optimize the values of the weights and thresholds of the BP network to improve the fault diagnosis accuracy of BP neural network. Furthermore,for reducing the impact of the input feature vector on the classification performance of the BP neural network,VMD is used to decompose the bearing vibration signal and calculate the time-frequency entropy of its IMF component to construct the signal feature vector. By comparing with other diagnostic methods using the same benchmark bearing data set,the fault diagnosis accuracy and algorithm stability of the method proposed in this paper has been effectively improved.