An Attention-Based TabNet Framework for Fault Detection and Diagnosis in HVAC Systems with Limited Labeled Data
TL;DR
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.
Abstract
Heating, Ventilation, and Air Conditioning (HVAC) systems play a critical role in ensuring energy efficiency, occupant comfort, and sustainability in modern buildings. Their complex operational dynamics,however, make them prone to faults that, if undetected, can result in higher energy consumption, reduced comfort, and costly maintenance. Existing approaches to fault detection and diagnosis (FDD) often face challenges related to scalability and interpretability, with many relying heavily on Transformer-or GNN-based models. This study introduces 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. Validated on the 2022 LBNL FDD FCU dataset, the model achieves an accuracy of 82.1%, along with high ROC-AUC and F1-scores values of 96.1% and 82.2% respectively, while its attention-based feature importance provided insights into the most influential operational variables. These results demonstrate that the TabNet framework effectively balances predictive power and interpretability, making it a practical solution for semi-supervised FDD in HVAC systems. The proposed approach contributes to building automation by offering a transparent and reliable pathway for fault detection.