Kai Chen

TrustMamba: calibrated uncertainty quantification for reliable and label-efficient bearing fault diagnosis

Vibration-based bearing fault diagnosis informs maintenance decisions, but a predicted fault label is useful in practice only when the uncertainty of that prediction is quantified reliably. Existing deep-learning diagnostic models are often overconfident, poorly calibrated, and dependent on large labeled datasets, wh...