Diffusion-assisted framework for fault diagnosis of rotating machinery under highly imbalanced data conditions

Abstract Rotating machinery constitutes a crucial component in modern industrial production, and advanced fault diagnosis technologies are vital for ensuring its safe and reliable operation. Most existing data-driven fault diagnosis frameworks for rotating machinery are designed under conditions of balanced data. However, in practical applications, the amount of data collected under fault conditions is much less than that in normal operating conditions, presenting substantial challenges for accurate fault diagnosis. This paper presents a diffusion-assisted framework to address the challenge of highly imbalanced data, aiming to improve fault diagnosis accuracy and reliability in real-world industrial applications. Firstly, a novel diffusion model-assisted signal generation model is proposed to augment the data in faulty states. This model employs a cooperative modulation strategy and signal filtering techniques to improve the quality of the generated signals. Subsequently, an enhanced pure convolutional network, termed IConvNeXt, incorporates pyramidal feature integration for robust classification based on the generated virtual data. The IConvNeXt employs depthwise separable convolution techniques and introduces an iterative attention-based feature fusion module to fuse features from different stages of the network adaptively. Finally, extensive experiments are conducted using two rotating machinery datasets to validate the performance of the proposed diagnostic framework under highly data-imbalance conditions. The results demonstrate that the proposed method significantly enhances the discriminative capability for minority classes, leading to notable improvements in both diagnostic accuracy and F1-score.