DDPM-based Dynamic Feature Embedding Digital Twin Fault Diagnosis of Rolling Bearing

Abstract

Fault diagnosis of rolling bearings used with imbalance data has always been a particularly challenging problem. The proposed digital twin technique, which complements the unbalanced dataset, provided a completely new approach that will greatly improve the effectiveness of the diagnosis. In this paper, A digital twin method based on feature embedding in a diffusion model to generate fault data with more accurate feature representation was proposed and applied to fault diagnosis. Firstly, A bearing mechanism model is constructed from the dynamics to enable the generation of raw one-dimensional signals with fault characteristics. Secondly, constructing diffusion models based on variational Autoencoders(VAE), aiming at the application of diffusion models to low-dimensional signals to form a stable signal generative approach. Finally, A feature embedding method in a diffusion model is proposed to enable the model to be generated to include more accurate feature representations, thus enhancing the datasets and improving fault diagnosis performance. In addition, comparison experiments with digital twins based on Generative Adversarial Networks (GANs) show that the method is more accurate and robust.