UnderFSL: Boundary-Preserving Undersampling with Few-Shot Relation Networks for Cross-Machine CNC Fault Diagnosis

Fault diagnosis in Computer Numerical Control (CNC) machines remains challenging due to severe class imbalance, scarcity of fault data, and distribution shifts across machines. This paper introduces Undersampling-based Few-shot Learning (UnderFSL), a simple yet effective framework that integrates strategic undersampling using Condensed Nearest Neighbor (U-CNN) with a Relation Network few-shot classifier. The proposed method first transforms raw 1D vibration signals into 2D Continuous Wavelet Transform (CWT) scalograms to capture time–frequency structure and then reduces the majority (normal) class using U-CNN, yielding a compact set of boundary-informative prototypes while alleviating imbalance. Finally, a Relation Network is trained in an episodic FSL regime on the balanced set to support cross-machine generalization. On the Bosch CNC machining benchmark under leave-one-machine-out validation, UnderFSL attains a macro F1-Score of 0.96, an accuracy of 0.96, a recall of 0.92, and a precision of 1.00, surpassing traditional and standard deep baselines. The results suggest that boundary-preserving undersampling combined with metric learning provides a robust and scalable path for industrial fault diagnosis when fault data are extremely limited.