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Fault Detection and Diagnosis of HVAC System Based on Federated Learning

Automation and accurate fault detection and diagnosis of heating, ventilation, and air conditioning (HVAC) systems are among the most important technologies for reducing time, energy, and financial costs in the field of intelligent industrial facility maintenance. Convolutional neural networks have been widely used in various fields for prediction and classification problems because of their excellent feature extraction ability and potent portability. In this study, we develop a Federated Learning-based CNN model (Fed_CNN), which utilizes multiparty data for multi-scale joint modeling to deal with the fault detection and diagnosis of HVAC systems. Our proposed Fed_CNN model can simultaneously detect and diagnose multilevel faults of chillers, and the fault detection and diagnosis effect of each level exceed 0.9. The Fed_CNN model can also perform cross-domain fault detection and diagnosis for chiller and AHU. A series of comparative experimental results show that the proposed custom federated learning framework outperforms most existing fault detection and diagnosis methods for HVAC systems.