An Attention-Based TabNet Framework for Fault Detection and Diagnosis in HVAC Systems with Limited Labeled Data
An enhanced TabNet-based semi-supervised framework for multi-class HVAC fault detection, incorporating rigorous pseudo-label generation via Label Propagation, cost-sensitive learning for imbalanced classes, and hybrid feature engineering is introduced.