Class-aware temporal and contextual contrastive framework for semi-supervised automated fault detection and diagnosis in air handling units

Automated Fault Detection and Diagnosis (AFDD) for Air Handling Units (AHUs) has largely relied on supervised learning, which is difficult to deploy when labeled data are scarce and fault classes are imbalanced. Existing label-efficient AFDD studies often evaluate self-supervised schemes on simulated or laboratory datasets, frequently in tabular form, and therefore do not fully capture the temporal structure and noise characteristics of real operational logs. This study proposes Class-Aware Temporal and Contextual Contrasting (CA-TCC) for label-efficient AHU AFDD on real buildings. CA-TCC is a semi-supervised framework that first performs self-supervised temporal/contextual contrastive pretraining on unlabeled…