IEEE

Prototype Space Boundary Alignment Network for Bearing Continuous Fault Diagnosis Under Class-Incremental Scenarios

Abstract: In recent years, the fault diagnosis method based on continuous learning has been widely used in the field of health monitoring of key components of rotating machinery. Most existing ...
IEEE

DGNSCA: A Domain Generalized Network Leveraging Similarity and Class-Conditional Contrast for Robust Fault Diagnosis of Rotating Machinery under Unseen Operating Conditions

Abstract: Timely and accurate detection of bearing faults is critical to the reliability of rotating machinery, yet models trained under fixed conditions often fail on unseen domains owing to ...