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This study proposes a dynamic model-driven 1-D Cycle-GAN (CycleGAN-MC) for rolling bearing fault diagnosis. Experimental results demonstrate that the proposed method can address the data imbalance ...

This study introduces innovative deep learning techniques to convert flash images into ambient images, with a particular focus on style transfer methods. A novel approach employing CycleGAN for ...