Vibration signals reconstruction with diffusion model for bearing fault diagnosis

Abstract

Rotating bearing plays an important role in many mechanical equipment operation, such as satellite in orbit. There are in urgent need of constructing effective prognosis and diagnosis system oriented rotating bearing. Traditional bearing fault diagnosis methods enhance fault feature with sparsity-assisted prior, which are not suitable for complex vibration signals. Meanwhile, it is difficult to extract fault feature from under-sampled vibration signals. In this paper, we firstly utilize generation diffusion model to learn probability distribution of bearing fault data. As a deep generation prior, we combine the diffusion model and reconstruction problem with regularization term. The unsupervised learning method is not restricted to specific measurement matrix. The experiment in Machinery Failure Prevention Technology (MFPT) Dataset verifies the effectiveness of our algorithm. Compared with the sparse model (L1-norm) and hierarchical hyper-Laplacian prior induced model (HHLP), our method achieves better reconstruction signal to noise ratio (SNR) performance and maintains the fault frequency information from different under-sampled data.