LAUSR

Physics theory integrated machine learning models enhance the interpretability and performance of artificial intelligence (AI) techniques to real-world industrial applications, such as the fault detection and diagnosis (FDD) of air handling units (AHU). Traditional machine learning-based automated FDD model demonstrates a high classification accuracy with sufficient training data samples, however, suffers from physical interpretation of the machine learning models. In this article, a physical model integrated Wasserstain generative adversarial network (WGAN) model is presented for AHU FDD with a scenario of insufficient training data samples. The proposed solution tackles the real-world problem of AHU FDD and enhances the model interpretability significantly. A transformer-WGAN model is designed to further improve the proposed FDD framework. Experimental results show that the proposed method outperforms existing AHU FDD methods with imbalanced real-world training data samples.