Fault Detection and Diagnosis of HVAC System Based on Federated Learning

Abstract

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.

Keywords

• heating ventilation and air conditioning systems|fault detection and diagnosis|convolutional neural networks|fede-rated learning