Effective Generative Data Augmentation in Condition Monitoring

In this paper, we investigate the effectiveness of a diffusion model to generate an augmented labeled dataset of synthetic images. The creation of synthetic images becomes indispensable in condition monitoring applications where capturing a diverse range of damages for training a supervised deep-learning model is impracticable or infeasible. To evaluate the potential of this technique, we explore the case of damage classification in synthetic fiber ropes. The approach involves training and fine- tuning a stable diffusion model to create synthetic photorealistic images, representing three distinct types of damages commonly found in fiber ropes. We assessed the efficacy of our method by training and testing the Resnet50 classifier on the synthetic dataset. Our results show that the classifier’s performance remains stable when up to 75% of the images used for training are synthetically generated. This outcome suggests that synthetically generated images produced by a finely-tuned diffusion model possess sufficient quality to serve as a viable addition to real images in data augmentation.