Autoencoder-driven fault detection and diagnosis in building automation systems: Residual-based and latent space-based approaches

Recently, data-driven fault detection and diagnosis (FDD) technologies have been studied extensively to detect the fault status early and maintain the health of building automation systems (BASs). Among the various algorithms for building FDD systems, an autoencoder (AE) is widely used as an unsupervised deep-learning method. Conventional AE-based FDD methods can use two types of information generated from the novel structure of the AE: (1) residual matrix (REM) and (2) latent space matrix (LSM). However, fundamental discussions about AE structures are rare, and the uses of the REM and LSM for building FDD models have seldom been compared. In this study, AE-based FDD methods are suggested. Quantitative comparisons were conducted under the designed fault conditions and real operational faults (hunting). AE-based fault detection models were designed using the AE latent space dimensionality. For fault diagnosis models, REM- and LSM-based models were used. Each model was then subdivided by the AE latent space dimensions. The detection model performances showed no meaningful differences according to the designed cases. However, for the diagnosis models, the performance of the LSM-based models was 14.4% better than that of the REM-based models. Additionally, the dimensions of the latent space caused the model performance to vary as much as 21.5%. Two main issues—training data dependency and latent space dimensionality—were found and investigated to improve the performance of AE-based FDD. Modeling guidelines are suggested based on the findings. These are valuable for successful FDD application with limited working sensors and datasets in real BASs.