Augmentation Framework for HVAC Fault Diagnosis Based on Denoising Diffusion Models

Abstract

Fault detection and diagnosis in HVAC systems are essential for maintaining energy efficiency and indoor comfort. However, the scarcity of fault samples, particularly for rare faults, leads to severe data imbalance, degrading model performance and increasing false alarms. While deep learning methods have improved diagnostic accuracy, they often struggle to capture the complex spatiotemporal interactions of HVAC faults using one-dimensional data. To address this challenge, we propose a novel data augmentation framework based on Denoising Diffusion Probabilistic Models, integrating diffusion models with Gramian Angular Field transformation. This approach effectively captures intricate dynamic patterns and generates high-quality synthetic fault samples, helping to mitigate data imbalance. Experimental results on the ASHRAE dataset demonstrate that our method outperforms existing approaches in sample quality, data distribution alignment, and diagnostic accuracy, achieving a 3.78% improvement over CVAE-GAN while significantly reducing false positives and missed detections for rare faults. Additionally, we introduce a comprehensive evaluation framework to ensure that generated samples meet high application standards. By providing a more robust and generalizable solution for HVAC fault detection, this study contributes to the advancement of intelligent building management and energy-efficient operation.