An adaptive augmentation framework for new fault category data in HVAC systems under extremely imbalanced scenarios based on the meta-diffusion model

Fault diagnosis in heating, ventilation, and air-conditioning (HVAC) systems is essential for intelligent energy management. However, extreme class imbalance often degrades diagnostic performance. Existing data augmentation methods typically assume a fixed and known set of fault categories; when new fault categories emerge, these methods may become ineffective. To address this challenge, we propose an adaptive augmentation framework (ADAF) based on a meta-diffusion model. Specifically, we develop a meta-diffusion denoising implicit model (MDDIM) that is meta-trained on known fault categories and can rapidly adapt to imbalanced datasets containing new fault categories, enabling the generation of realistic fault samples. To better capture the distribution of an emerging fault category from scarce data, we further design a prototype-similarity-based strategy to initialize the class condition vector and integrate it with a conditional noise prediction network (CNPN) to improve generation quality. Extensive experiments on three HVAC datasets demonstrate that the proposed method consistently outperforms seven representative augmentation baselines. Across different imbalance settings, MDDIM improves the F1 score by 2.75%–21.75% after augmentation, providing an effective solution for fault diagnosis under extreme imbalance with emerging fault categories.