Explainable AI based Fault Detection and Diagnosis System for Air Handling Units

Fault detection and diagnosis (FDD) methods are designed to determine whether the equipment in buildings is functioning under normal or faulty conditions and aim to identify the type or nature of a fault. Recent years have witnessed an increased interest in the application of machine learning algorithms to FDD problems. Nevertheless, a possible problem is that users may find it difficult to understand the prediction process made by a black-box system that lacks interpretability. This work presents a method that explains the outputs of an XGBoost-based classifier using an eXplainable Artificial Intelligence technique. The proposed approach is validated using real data collected from a commercial facility.