Few-shot aero-engine bearing fault diagnosis with denoising diffusion based data augmentation

As a core component of aero-engines, the health condition of rolling bearings is crucial to the safety and stability of the aviation power system. In the fault diagnosis of aero-engine rolling bearings, the issue of insufficient fault data has long persisted. The training process of the widely introduced Generative Adversarial Networks (GANs) is difficult to control, and their generalization ability is relatively poor. Therefore, this paper proposes a data augmentation method based on denoising diffusion probabilistic model (DDPM) for aero-engine bearing fault diagnosis under few-shot. The proposed method is grounded in a well-defined probabilistic model and mathematical principles, allowing it to avoid instability and mode collapse during training. Specifically, we first propose using the synchro-squeezed wavelet transform (SST) to convert one-dimensional time-series signals into time-domain images as model input, addressing the insufficient feature extraction of traditional time-frequency analysis methods and clearly illustrating the fault characteristics of frequency variation with time. Next, we design a CRR-UNet based on residual connections to mitigate overfitting caused by insufficient data during the reverse denoising process of DDPM, thereby improving the quality of generated samples. Finally, a deep residual shrinkage network (DRSN) is employed to conduct fault diagnosis on the augmented fault dataset. The research results show that the samples generated by this method are highly similar to the original samples, outperforming existing data augmentation methods, and the fault diagnosis accuracy on the augmented dataset reaches 98%, significantly improving fault diagnosis performance.