Autoencoder-Based fault detection using building automation system data

This paper explores the application of autoencoder algorithms in Automated Fault Detection (AFD) for Heating, Ventilation, and Air Conditioning (HVAC) systems, specifically focusing on Fan Coil Units (FCUs). The study begins by reviewing the current state of Fault Detection and Diagnostics (FDD), emphasizing the limitations and the potential of unsupervised learning techniques like autoencoders and transfer learning to fill these gaps. Using data from a full-scale building case study featuring five Fan Coil Units (FCUs), the research develops and evaluates autoencoder-based AFD models that models effectively compress multivariate inputs into a reduced latent space, enabling accurate and efficient fault detection. The paper makes two novel contributions: (1) It introduces a methodology to distinguish between equipment-level and system-level faults; and (2) It demonstrates the generalizability of the approach across different types of FCUs through cross-testing and transfer learning. The results indicate that autoencoders outperform other dimensionality reduction algorithms and separate predictors in fault detection accuracy and efficiency. The paper concludes by discussing the implications of these findings for future research and practical applications in building management.