Unsupervised Learning for Fault Detection and Diagnosis of Air Handling Units

Abstract Supervised learning techniques have witnessed significant successes in fault detection and diagnosis (FDD) for heating ventilation and air-conditioning (HVAC) systems. Despite the good performance, these techniques heavily rely on balanced datasets that contain a large amount of both faulty and normal data points. In real-world scenarios, however, it is often very challenging to collect a sufficient amount of faulty training samples that are necessary for building a balanced training dataset. In this paper, we introduce a framework that utilizes the generative adversarial network (GAN) to address the imbalanced data problem in FDD for air handling units (AHUs). To this end, we first show the necessary procedures of applying GAN to increase the number of faulty training samples in the training pool and re-balance the training dataset. The proposed framework then uses supervised classifiers to train the re-balanced datasets. Finally, we present a comparative study that illustrates the advantages of the proposed method for FDD of AHU with various evaluation metrics. Our work demonstrates the promising prospects of performing robust FDD of AHU with a limited number of faulty training samples.