Bearing fault diagnostic framework under unknown working conditions based on condition-guided diffusion model

Fault diagnosis is of vital importance in ensuring the safety of smart manufacturing. Current diagnostic methodologies require data spanning various working conditions. However, industrial settings offer scarce bearing failure data, leading to failure or degradation of performance of traditional intelligent diagnostic methods. Obtaining a complete sample of industrial environments is intensive and unrealistic. Acquiring a comprehensive sample of industrial environments is both resource-intensive and impractical. To handle this situation, an unknown condition diagnosis framework based on diffusion model (DiffUCD) is proposed, effectively integrating the generation capability of the diffusion model and learning from the condition-guided information (CGI), Specifically, signals under limited working conditions are gradually convert to noise through a forward noising process. Then, CGDiffusion reconstructs signals from the noise by a reverse denoising process. In addition, a condition-guided embedding UNet (CGE-UNet) structure is designed to extract CGI for noise level prediction during the reverse process. Moreover, an Unsupervised Clustering Filter (UCFilter) is constructed to select the qualified signals after generation. Signals under unknown working condition can be generated with specialized CGI. Ultimately, extensive experiments and validations on two public bearing datasets (SDUST and PU) are carried out, which validate the effectiveness of our method compared with the state-of-the-art baselines and the hyperparameter analysis confirms the advances of DiffUCD.