Optimized neural network-based fault diagnosis strategy for VRF system in heating mode using data mining

Abstract

This paper presents the optimized back propagation neural network (BPNN) method for fault diagnosis of the variable refrigerant flow air conditioning (VRF) system in the heating mode. A feature variable set optimization approach of diagnosis models is proposed based on data mining method. First, the correlation analysis method is used to eliminate redundant variables. Then, the association rule mining method is used to optimize the feature set (FS) selection. Five FSs (FS1-FS5) are obtained by optimized feature variable selection. FS1 is the original set. FS2 is the set obtained by correlation analysis and FS3-FS5 are the sets obtained by the association rule mining. The fault diagnosis models with different FSs are evaluated using four fault experiments which include outdoor unit heat exchanger air-side fouling, four-way reversing valve fault, refrigerant undercharge and refrigerant overcharge faults. The results show that the correlation analysis method can effectively eliminate redundant variables and the association rule mining method is feasible to optimize the FSs for fault diagnosis. The BPNN-FS5 model shows the best fault diagnosis performance of the VRF system in the heating mode, whose fault diagnosis correct rate has increased from 88.71% to 96.40% and hit rates of four faults are higher than 90%.