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What is Diffusion-Based Data Augmentation for Fault Diagnosis about?

This document presents a novel data augmentation method for fault diagnosis using a denoising diffusion probabilistic model to address the challenges posed by imbalanced data. The proposed method generates high-quality and diverse samples through physical simulation, avoiding issues like mode collapse associated with generative adversarial networks (GANs). Additionally, a comprehensive evaluation framework is introduced to assess the performance of data augmentation models, demonstrating superior diagnostic

Author

cloudie.may0612

Language

EN