석사수료 윤태휘(지도교수: 정종필)의 연구(Bio–SFDA: Physics–Guided Multimodal Source–Free Domain Adaptation for Reliable Bearing Fault Diagnosis)가 Elsevier - Results in Engineering Jounrnal(Impact Factor: 7.9 (2024), 5-Year JIF: 7.4)에 게재됐다.

https://www.sciencedirect.com/science/article/pii/S259012302602133X / https://doi.org/10.1016/j.rineng.2026.111105

논문요약 - We propose Bio–SFDA, a physics-guided, bio-inspired multimodal framework for strict source-free domain adaptation in bearing fault diagnosis. A dual 1D vibration / 2D spectrogram encoder is adapted via an EMA teacher–student pipeline equipped with three lightweight but crucial modules: WBC, a signal-hygiene guard that filters corrupted or low-quality target windows; EAGLE, a physics-guided reliability functional that exploits BPFO/BPFI/BSF and impulsive-band activity to weight and gate pseudo-labels; and SPIDER, a bounded multi-metric controller that maps online trends in accuracy, macro-F1, mAP, loss, and ECE to conservative updates of learning rate, momentum, augmentation strength, and acceptance thresholds. These bio-inspired operators white-blood-cell filtering, eagle-like spectral hunting, and spider-like tension control—form a closed reliability–control loop that improves pseudo-label quality, mitigates confirmation bias, and preserves fault mechanics under drift.
On strict CWRU → PU, Bio–SFDA attains 99.0% accuracy, 98.5% macro-F1, and 99.0% mAP with markedly lower NLL and ECE than source-only and recent SFDA/TTA baselines, while running in real time on a single commodity GPU. Cross-modal (1D↔2D) experiments confirm robustness to sensing mismatch, and ablations highlight EAGLE as the main driver of performance with SPIDER ensuring stable convergence, yielding reliable and deployment-ready adaptation for industrial condition monitoring.