基于改进高斯原型网络的小样本轴承故障诊断方法OA

In view of the problem of low fault diagnosis accuracy caused by insufficient fault sample data for ro-tating machinery bearings in practical applications,this study proposes an improved Gaussian prototype network for few-shot fault diagnosis.Vibration signals are converted into time-frequency images using continuous wavelet trans-form,and a convolutional attention module is introduced to optimize the residual network structure,which serves as the feature extraction network of the Gaussian prototype network.The FCM(Fuzzy C-Means Clustering,)algorithm is incorporated to optimize the Gaussian prototype network's ability to distinguish subtle fault categories through a soft classification strategy.Few-shot experiments under varying working conditions are conducted using the bearing data-sets from Case western reserve university and the university of Paderborn.The experimental results show that the ac-curacy of the improved Gaussian prototype network reaches 91.17%and 89.95%,respectively,indicating that the proposed method achieves a significant improvement in fault recognition accuracy when compared with other models.