A VRF charge fault diagnosis method based on expert modification C5.0 decision tree
Abstract VRF (Variable Refrigerant Flow) system is an efficient and energy-saving air-conditioning system. The VRF system performance is significantly affected by the quantity of refrigerant (charge). Both insufficient and excessive refrigerant charge will lead a dramatic decline of air conditioning system performance. Accordingly, it is necessary to forecast refrigerant charge in existing systems. This study proposed a charge fault diagnosis method based on expert modification C5.0 decision tree. Initially, C5.0 decision tree is used to diagnosis VRF system charge faults in nine different conditions. Furthermore, in order to reduce error rate of the decision tree, expert experience is used to modify the decision tree. And temperature differential variables are used to improve the diagnosis result. The result s of the C5.0 decision tree using temperature differential variables shows fault diagnosis error rates under expert experience guidance are 10% lower than without expert experience. Besides, this method an increased accuracy in classifies between excessive and normal charge states.