CLDDGAN for CWT Generation and Fault Classification Using LiteFormer2D

Ritabrata Chakraborty, T. Mian, P. Kundu
September 2025
Project
Type
Preprint
Publication
Manuscript in preparation

This work introduces CLDDGAN for synthetic CWT data generation combined with LiteFormer2D for efficient fault classification in rotating machinery, addressing data scarcity challenges while maintaining computational efficiency.

Fault Diagnosis Synthetic Data Rotating Machinery Diffusion-GAN CWT Data Generation deep-learning Predictive Maintenance Denoising Diffusion
Ritabrata Chakraborty
Ritabrata Chakraborty
CV Research Intern

Research Engineer specializing in robotics, computer vision, and autonomous systems. Currently developing automated data annotation solutions with foundation models for autonomous vehicles at Uber.

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