Contrastive self-supervised learning for lightweight and automated fault detection and diagnosis in HVAC systems

Heating, ventilation, and air conditioning (HVAC) systems often operate with scarce fault labels and limited computational resources, posing challenges for reliable fault detection and diagnosis (FDD). Existing FDD studies largely rely on fully supervised data or post-hoc alarm aggregation, treat FDD as static classification without considering temporal dependencies, and employ complex backbones without evaluating deployment efficiency. Moreover, common contrastive learning (CL) augmentations such as scaling or permutation violate HVAC physical constraints, erasing magnitude anomalies critical for diagnosis. To address these limitations, this study reframes HVAC FDD as a multivariate time-series representation learning problem and proposes a contrastive self-supervised framework coupling a lightweight temporal encoder with a compact classifier. A physics-consistent strategy—combining timestamp masking and partially overlapping cropping—constructs positive pairs without destroying magnitude or channel semantics, while a hierarchical dual contrastive loss aligns same-timestamp embeddings and separates cross-sequence states across multiple resolutions. The resulting encoder–SVM architecture explicitly targets deployability, achieving high diagnostic accuracy with up to 90–97% less memory and 20–25% faster training than Transformer baselines. Experiments on the MZVAV AHU dataset with rigorous day-level splits show consistent superiority over recurrent, linear, and Transformer-based models, improving diagnostic accuracy by 20–30% and macro-F1 by 40–50%. This work delivers a label-efficient, physics-consistent, and deployment-ready framework for automated FDD in real-time building management systems.