Latent Code Description for Unsupervised AHU Fault Detection Using Adaptive Adversarial Autoencoder

Timely fault detection in HVAC systems is crucial for preventing energy waste and maintaining thermal comfort in commercial buildings. This paper introduces the adaptive adversarial autoencoder (AdaAAE), an innovative anomaly detection approach designed for unsupervised fault detection within air handling units (AHUs) of HVAC systems. By combining the adversarial autoencoder (AAE) with deep support vector data description (DSVDD), AdaAAE efficiently trains the reconstruction, regularization, and compactness phases to produce a spherical and compact latent representation of the training data, enabling effective fault detection within the latent space. A key feature of AdaAAE is its ability to accurately determine the anomaly threshold at the end of the training phase, which is particularly beneficial in scenarios where there is no prior knowledge of the anomaly ratio for test instances—a common challenge in real-world applications. AdaAAE’s performance was evaluated on a real-world AHU system developed as part of the ASHRAE research project 1312 (RP-1312). Experimental results, especially with regard to AUC-ROC and AUC-PR, highlight AdaAAE’s superior fault detection capabilities across AHU datasets with varying complexities. Notably, AdaAAE achieved outstanding performance, with an average AUC-ROC of 97.5% and AUC-PR of 94.4%, significantly outperforming the baseline methods in this study. Additionally, the experimental findings demonstrate AdaAAE’s proficiency in accurately estimating the anomaly threshold without prior knowledge, with minimal performance degradation when shifting from a scenario with a known anomaly ratio to one requiring estimation.Note to Practitioners—The concept of anomaly detection through subspace learning has attracted significant interest, with a focus on achieving a compact latent distribution. In this study, we introduce a new anomaly detection technique called AdaAAE, which is designed to produce a spherical and compact latent representation of training data. This feature enhances the method’s ability to identify anomalies within the latent space. Moreover, AdaAAE offers a distinct advantage in accurately determining the anomaly threshold at the end of the training phase, which is especially useful in practical scenarios where prior information about anomaly ratios for test instances is unavailable. To ensure reproducibility and allow for future enhancements by other researchers, the entire source code for this study is available in the following repository: https://github.com/viettra-xai/Ada-AAE.

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