An ensemble-based faults detection and diagnosis approach for determining faults severities at whole-building level

Data-driven fault detection and diagnosis (FDD) approaches rely on operational data even when in-depth knowledge of the studied system is lacking. Simulated data is affordable and can cover numerous fault types and severities. Developing FDD models with a single fault severity has limitations on the model’s generalisability owing to evolution of faults with severities. Thus, machine learning models must be individually trained on diverse scenarios. Current studies on building FDD are often at system levels, thereby leaving significant knowledge gaps when there is interest in energy performance of an entire building. This research presents a data-driven FDD approach for classifying several building faults using different ensemble multi-class classification approaches. Additionally, the impact of noise on training datasets is investigated. The XGBoost classifier achieved the highest classification accuracy for the considered faults during validation and testing stages, with low sensitivity to noise, thereby demonstrating a promising potential for wider scale deployment.