An Efficient Approach for Synthetic Data Generation and Fault Diagnosis for Rotating Machinery
Synthetic Data Generation Pipeline
This study explores the use of an Auxiliary Classifier Wasserstein GAN with Gradient Penalty (ACWGAN-GP) for synthetic data generation and fault diagnosis in rotating machinery, addressing data scarcity and class imbalance challenges.
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.