Research on Small Sample Sault Diagnosis of Rolling Bearing Based on DDPM Diffusion Model
A sufficient number of training samples is the basis for intelligent fault diagnosis to achieve higher accuracy.On the one hand,the traditional solution to the small sample problem is unstable in the ex-pansion process and the quality of the generated samples is low.On the other hand,the denoising diffusion probability model ( DDPM) is widely used in fields such as high-quality image generation.Based on this,this paper proposes a small sample fault diagnosis method based on DDPM.First,the original vibration sig-nal of the bearing is transformed through continuous wavelet transformation to obtain a two-dimensional time-frequency diagram.Then,DDPM is used to expand the small sample,and the expanded sample set is used to train a fault diagnosis model based on convolutional neural network.Experimental results show that this method can effectively improve the accuracy of fault diagnosis and is effective and superior.
万方数据
维普
