Handling Domain Drift and Unknown Fault Detection in Rotating Machinery Using Few-Shot Learning With Data Scaling

This article presents a novel approach for fault diagnosis in rotating machinery using the few-shot learning-based unknown recognition and classification (FSLB-UR&C) model. The core contribution of this research lies in addressing the challenges of domain drift, unknown fault detection, and uneven sampling intervals. The model effectively incorporates data scaling using Min–Max scaling to manage vibration data drift without altering the source data distribution, significantly extending the classification range. Principal component analysis visualizations substantiate the superior performance of Min–Max scaling over standard methods in processing drifted data, thus enhancing model accuracy. We validated the model on an in-house bearing simulator and a coal conveyor motor, demonstrating its enhanced ability to classify drifted data and identify previously unknown faults. Additionally, time interpolation was applied to handle irregular sampling intervals, further optimizing the framework's robustness. Compared to other deep learning techniques, the FSLB-UR&C model with data scaling exhibits markedly improved performance.