| Summary: | Automation and accurate fault detection and diagnosis of HVAC systems is one of the most important technologies for reducing time,energy,and financial costs in building performance management.In recent years,data-driven fault detection and diagnosis methods have been heavily studied for fault detection and diagnosis of HVAC systems.However,most existing works deal with single systems and are unable to perform cross-system fault diagnosis.In this paper,a federal learning-based fault detection and diagnosis method is proposed,which uses convolutional neural networks to extract information features,aggregates features using special-designed algorithms,and perform cross-level and cross-system fault detection and diagnosis via federal lear-ning.For multi-fault level fault detection and diagnosis,federal learning is performed using data from four fault levels of chillers.Experimental results show that the average F1-score of the fault detection and diagnosis effect of the four-fault levels is close to 0.97,which is within the practical range.Federal learning uses chiller and air handling unit data for cross-system fault detection and diagnosis.Experimental results show that federal learning using different system data improves the diagnosis results of particular faults,e.g.,14.4% for RefOver faults and 2%~4% for both Refleak and Exoil faults.
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