A novel multi-scale dense residual shrinkage GAN for data-limited rotating machinery fault diagnosis

Key Points

• Improved fault diagnosis accuracy with a novel model targeting data-limited scenarios.
• Key results demonstrated a significant enhancement in diagnosing issues in rotating machinery components.
• Analysis employs a generative adversarial network designed to operate effectively under constrained data conditions.
• This method highlights the potential for better predictive maintenance in industrial applications, requiring validation in real-world settings.