A Hybrid-based Clustering Approach for Fault Detection in HVAC Systems

This paper presents a hybrid model-based fault detection strategy for heating, ventilation, and air conditioning (HVAC) systems, focusing on air handling units (AHUs). Addressing the substantial energy inefficiencies in commercial buildings due to undetected HVAC faults, this research combines first-principles knowledge with data-driven techniques to enhance fault detection accuracy. First-principles based residuals (differences between expected and observed behaviors) are integrated with data (temperature measurements in different locations of AHU) to perform principal component analysis (PCA) (pre-processing step). Pre-processed data (principal component scores) are then utilized to perform clustering analysis using K-means and DBSCAN approaches. The proposed approach is tested against two common faults in AHUs and its performance is evaluated compared to a purely data-driven method. The results indicate that the hybrid method, which synergizes residual knowledge from first-principles models with data, significantly outperforms the purely data-driven approach. This is demonstrated through performance analysis using metrics like the adjusted rand index (ARI) and normalized mutual information (NMI). The research underscores the potential of the hybrid method in improving fault diagnosis of HVAC systems, helping to conserve energy by ensuring efficient and reliable operation.