A comparison study of basic data-driven fault diagnosis methods for variable refrigerant flow system

Abstract

HVAC systems occupy a large part of the building’s energy consumption. The development of fault detection and diagnosis (FDD) techniques for HVAC system is becoming increasingly essential for building energy saving. The present paper proposes a comparison study on the basic data-driven methods for variable refrigerant flow (VRF) system fault diagnosis. And five widely used data-driven methods were analyzed, which were decision tree (DT), support vector machines (SVM), clustering (CL), shallow neural networks (SNN), and deep neural networks (DNN). The six common types of fault data in VRF system and three evaluation indexes were used to compare the performance of the proposed five FDD methods in single fault and multiple faults. Results indicate that the single fault diagnosis performance of all methods is better than multiple fault diagnosis. The performances of DNN, SNN, and SVM are better than CART and CL no matter single fault or multiple fault diagnosis. Among them, we believe that the SVM method is preferred for single fault diagnosis and the DNN method is preferred for multiple fault diagnosis. And the performance of method CL is the worst, especially in the case of multiple fault classification, the accuracy is only about 48%. The study results are dedicated to providing a reference for subsequent research in FDD of VRF system. Some important remarks are finally concluded in this paper.