A few-shot learning framework for HVAC fault diagnosis in data centers with minimal data required

Fault diagnosis in heating, ventilation, and air conditioning (HVAC) systems is crucial for maintaining energy efficiency and reducing carbon emissions in data centers. Most existing data-driven HVAC fault diagnosis approaches depend on a sufficient quantity of labeled or unlabeled operational data. However, newly constructed data centers often lack both labeled and unlabeled HVAC operational data. Moreover, a review of the literature reveals that few-shot learning has received limited attention in the context of HVAC fault diagnosis for data centers with an extremely limited quantity of operational data. In this study, a semi-supervised adaptive weighted prototype network (SSAWPN) incorporating an attentive feature fusion approach is proposed for HVAC fault diagnosis in data centers with minimal data. First, a multi-scale attentive feature fusion network (MSAFN) leverages channel-wise segmentation, residual connections, and an attention mechanism to capture fault signatures across multiple spatial and temporal scales. Then, a semi-supervised adaptive weighted prototype optimization strategy (SAWPS) is employed to incrementally update class prototypes by assigning adaptive weights to unlabeled data. As new data accumulate, the prototypes become increasingly representative of actual fault modes without manual annotation. Lastly, real-world operational data from the chiller and air handling unit (AHU) are used to conduct ablation and comparative experiments. The experimental results show a clear advantage for SSAWPN in HVAC few-shot settings. It achieves mean F1 scores of 73.77 % on the ASHRAE RP1043 chiller fault severity level 1 dataset and 67.22 % on the ASHRAE RP1312 AHU summer dataset, outperforming the second-best approach by 3.21 and 1.35 percentage points, respectively.