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.

最近，数据驱动的故障检测和诊断 (FDD) 技术已被广泛研究，以及早检测故障状态并维护楼宇自动化系统 (BAS) 的健康。在构建 FDD 系统的各种算法中，自动编码器 (AE) 被广泛用作无监督的深度学习方法。传统的基于 AE 的 FDD 方法可以使用从 AE 的新结构生成的两种类型的信息：(1) 残差矩阵 (REM) 和 (2) 潜在空间矩阵 (LSM)。然而，关于 AE 结构的基本讨论很少，并且很少比较 REM 和 LSM 用于构建 FDD 模型的用途。在本研究中，建议使用基于 AE 的 FDD 方法。在设计的故障条件和实际操作故障（狩猎）下进行了定量比较。使用 AE 潜在空间维度设计了基于 AE 的故障检测模型。对于故障诊断模型，使用了基于 REM 和 LSM 的模型。然后将每个模型细分为 AE 潜在空间维度。根据设计的案例，检测模型的性能没有显示出有意义的差异。然而，对于诊断模型，基于 LSM 的模型的性能比基于 REM 的模型好 14.4%。此外，潜在空间的维度导致模型性能变化高达 21.5%。发现并研究了两个主要问题——训练数据依赖性和潜在空间维度，以提高基于 AE 的 FDD 的性能。根据研究结果建议建模指南。这些对于在实际 BAS 中工作传感器和数据集有限的 FDD 应用成功很有价值。