基于SPWVD-CNN的滚动轴承故障诊断

针对传统的滚动轴承故障诊断方法难以提取轴承振动数据有效特征的缺陷,提出一种基于平滑伪Wigner-Vill分布(smooth and pseudo Wigner-Ville distribution,SPWVD)和卷积神经网络(convolutional neural network,CNN)的网络模型SPWVD-CNN。对振动数据进行平滑伪Wigner-Vill分布变换,将获得的时频图进行压缩,作为CNN的输入,利用迁移学习的思想进行网络训练,使得模型对于不同负载的数据具有良好的诊断性能,提高了网络的泛化能力。实验结果表明:SPWVD-CNN对轴承故障数据的平均分类准确率提升至99.27%,总体性能优于使用单一的CNN和其他传统的故障诊断方法。

Fault diagnosis for rolling bearings based on SPWVD-CNN

Aiming at the shortcoming that the traditional fault diagnosis method of rolling bearings is difficult to extract the effective characteristics of bearing vibration data, a network model SPWVD-CNN based on smooth and pseudo Wigner-Vill distribution (SPWVD) and convolutional neural network(CNN) was proposed. It used smooth and pseudo Wigner-Vill distribution to analyze vibration signals of rolling bearings and Compressed the obtained time frequency representations as input to CNN. Using the idea of migration learning to train the network, the model had good diagnostic performance for the data of different loads and improved the generalization ability of the network. The experimental results indicate that the average classification accuracy of SPWVD-CNN to bearing fault data is increased to 99.27%, and the overall performance is better than using a single CNN and other traditional fault diagnosis methods.