SS-CWGAN: A novel fault diagnosis model for building HVAC systems under limited labeled data

Abstract

Fault diagnosis (FD) systems are important to the reliable operations and energy management of the building HVAC systems. However, traditional supervised learning-based FD methods heavily rely on labeled data and underutilize massive unlabeled data. This study proposes a semi-supervised conditional Wasserstein generative adversarial network (SS-CWGAN) for the FD of HVAC systems under limited labeled data. The information contained in a large number of unlabeled data is fully leveraged to improve the fault diagnosis performance of HVAC systems. Besides, a unique semi-supervised learning approach is developed for SS-CWGAN to extract the common distribution information of labeled and unlabeled data, enhancing the model generalization, especially in the practical scenario with limited labeled data. Comparison experiments demonstrate that when the number of labeled data is within 100, the proposed method outperforms the existing supervised and semi-supervised FD methods by more than 14% and 4% in diagnosis accuracy, respectively. Furthermore, the diagnosis performance improvement of the proposed method is more significant at severity level 1, suggesting the promising ability to diagnose the faults at an early stage.