Temporal–contextual self-supervised time-series learning for automated fault detection and diagnosis of air handling units in buildings

• Temporal–contextual self-supervised backbone is proposed for AHU AFDD • Proposed method achieves ∼80% F1 score with 5% labels and >92% F1 score with 15% labels • Proposed method reaches ≈99% F1 score and 99.5% accuracy with 30% labels across three datasets • Benchmarking is conducted against supervised and self-supervised baselines • Semi-supervised learning baselines are included under limited-label settings 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. To reduce the labeling burden, recent work has explored self-supervised learning to leverage unlabeled operational logs;…