Comparing experimental results.

Accurate diagnosis of rolling bearing faults is vital for the safe operation of rotating machinery. However, real-world fault datasets often suffer from severe class imbalance, which hinders the performance of deep learning models. To address this challenge, we propose PFRNet, a novel diagnostic framework integrating a Poisson Flow-based generative model with a lightweight residual network. Raw vibration signals are transformed into time-frequency representations via CWT to capture non-stationary fault features. The Poisson generative mechanism models sample evolution in high-dimensional latent space to synthesize realistic minority-class samples by learning statistical distributions of real data, mitigating imbalance. These augmented datasets are subsequently classified using an efficient residual network designed for robust feature extraction with minimal complexity. Experiments on the CWRU benchmark demonstrate that PFRNet outperforms state-of-the-art methods in diagnostic accuracy, robustness, and generalization across various imbalance scenarios. Quantitative evaluations further confirm that the generated samples closely resemble real data in both quality and diversity, supporting the effectiveness of the proposed method. The proposed approach offers a promising solution for reliable fault diagnosis under practical, imbalance-prone industrial conditions.