Few-Shot Bearing Fault Diagnosis under Joint Fault-Severity and Load Shift: A Leak-Free Cross-Domain Benchmark
: Bearing fault diagnosis in industrial deployment must contend with two simultaneous distributional shifts: fault severity increases as damage progresses, and motors operate at loads unseen during training. We define this compound setting as the double domain shift and present a rigorous few-shot benchmark on the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets. Six architectures spanning distinct learning paradigms—a multilayer perceptron (MLP), a capsule network (CapsNet), a residual capsule network (ResCaps), a prototypical network (ProtoNet), a modified residual convolutional network (MRCN), and Deep Correlation Alignment (Deep CORAL)—are evaluated under a strict three-way split (support/validation/held-out test) that prevents the data-leakage patterns prevalent in prior CWRU protocols. Models are adapted using K ∈ { 5, 10, 20 } labelled target samples and assessed on three complementary metrics: accuracy, macro F1-score, and Cohen’s κ . A lightweight 1-D convolutional neural network (CNN) with a capsule routing head (CapsNet) leads on 15 of 18 CWRU conditions and on all PU conditions at K ≥ 10 (with MRCN leading at PU Target-A K = 5), achieving macro F1 of 0.860 and κ = 0.818 at K = 5 on the harder CWRU target—with 18,624 parameters (roughly one-third of the MLP baseline) and without any distribution-alignment objective. The multi-metric evaluation reveals findings invisible to accuracy alone: several baselines fall below moderate agreement ( κ < 0.60) at low K , and MRCN’s accuracy–F1 gap of 5.3 percentage points (pp) at K = 10 exposes class-selective failure that accuracy conceals. On PU, a macro F1 of 0.443 at K = 5 on the real inner-race target quantifies the artificial-to-real fatigue transfer gap, and all models produce κ < 0.06 on the outer-race target at K = 5, establishing a realistic lower bound for future work. Wilcoxon signed-rank tests confirm the CapsNet advantage is statistically significant against the weaker baselines in nearly all conditions. Capsule output norms provide interpretable, per-class confidence-like activation scores without requiring post-hoc attribution methods.