A Weak Signal Processor Based on an Improved Diffusion Model
Feng Su, Pengcheng Xia, Yixiang Huang, Yue Wang, Chengliang Liu · 2024
To address the issue where signals in fault diagnosis are heavily disturbed by external noise, making feature extraction difficult, this paper proposes a signal denoising method based on an improved diffusion model. The method uses a diffusion model to take pure signals mixed with noise as samples and employs a Bi-LSTM network as the signal denoiser for training. Weak signals subjected to external interference are collected for denoising, and the denoised signals are classified using a one-dimensional convolutional neural network. Results indicate that this approach significantly enhances detection capabilities in fault diagnosis.