Protocol and Implementation Sensitivity in Raw-Index-Audited Few-Shot Bearing Fault Diagnosis Benchmarking: Evidence from CWRU and HUSTbearing

Sliding-window few-shot evaluations can place support and query windows over shared raw samples. We audited episode-internal overlap, source-noise policy, normalization, and fixed-feature construction using Case Western Reserve University (CWRU) data and a HUSTbearing cross-speed task reconstructed from raw files. In the primary CWRU 0 hp to 3 hp (0 to approximately 2.24 kW), 10-way, 5-shot evaluation at a signal-to-noise ratio (SNR) of −5 dB across 20 seeds, common-pool construction increased log-compressed fast Fourier transform prototype (Log-FFT) accuracy by 3.83 percentage points (95% confidence interval (CI): [+3.63, +4.02]). The corresponding increase for the clean source-supervised cross-entropy prototype encoder (Source-CE-Proto) was 0.95 percentage points and was not statistically distinguishable from zero (95% CI: [−0.004, +1.90]; exact p = 0.05084). Under raw-index-separated evaluation, leave-one-SNR-out Source-CE-Proto achieved 93.23% without direct −5 dB source exposure, compared with 82.43% for Log-FFT. A No-log FFT control achieved 99.09% in the primary task and also exceeded the learned encoders in a second CWRU load pair. HUSTbearing likewise showed channel-dependent fixed-feature accuracy and overlap effects. These results do not support a general method ranking; they support reporting raw-index provenance, source-noise distributions, normalization, and exact feature construction before interpreting method rankings in few-shot bearing fault diagnosis benchmarks.