Fault Detection and Diagnosis Strategies for Reversible Heat Pumps: A Comprehensive Review
Résumé
Modern eco-friendly buildings widely adopt reversible heat pumps for their dual heating and cooling needs, reducing both energy consumption and carbon emissions. However, ensuring optimal performance and avoiding system failure requires robust fault detection and diagnosis (FDD) methods. This paper reviews FDD strategies, categorizing them into physics-based white-box models, data-driven black-box techniques, and hybrid gray-box approaches. It examines key faults—including refrigerant charge imbalances, non-condensable gas intrusion, fouling in heat exchangers, liquid line restrictions, compressor anomalies, and valve leakages—as well as the impact of frosting on system efficiency. By integrating diverse modeling and diagnosis approaches, the study emphasizes how advanced FDD systems can boost energy efficiency, lower maintenance costs, and support sustainable building operations.