An improved decision tree-based fault diagnosis method for practical variable refrigerant flow system using virtual sensor-based fault indicators

Abstract This paper proposes an improved decision tree (DT)-based fault diagnosis method for practical variable refrigerant flow (VRF) system. The proposed method is a three-stage method combining DT model with virtual sensor-based fault indicators (FIs). First, FIs are developed based on the virtual sensor (VS) theory for VRF faults, i.e., condenser air-side fouling (Fouling), refrigerant undercharge (RU) and overcharge (RO). Second, FIs are employed as additional input variables to induct a DT-based classification model classification and regression tree (CART). Third, the FIs-CART classification model is used to diagnose on-line data. Validation is conducted using two different datasets, the experimental testing dataset and the on-line testing dataset. Results indicate that the method correctly isolates the three faults i.e., Fouling, RU and RO. The improved DT method is also compared with three tree-based data-driven methods including CART, random forest (RF) and generalized boosted regression (GBM). Comparative results reveal that the proposed method has better fault diagnosis performance for both the experimental and the on-line testing datasets.