A hybrid ICA-BPNN-based FDD strategy for refrigerant charge faults in variable refrigerant flow system

Abstract

Refrigerant charge fault is inevitable in air conditioning systems causing serious negative influences on system performance. This study presented a hybrid ICA-BPNN-based fault detection and diagnosis (FDD) strategy for refrigerant charge faults in variable refrigerant flow (VRF) system. It consists of two steps. Firstly, the independent component analysis (ICA) method is employed to detect the faults, and the normal-charge operating data set is used to train the ICA model. Secondly, a fault diagnosis model is established using the back-propagation neural network (BPNN) method, and the BPNN model is trained by the faulty operating data set with labels. The results show that the original data dimensions are reduced from 12 to 4 by the ICA algorithm. ICA-based method can detect the faults using both I2-statistic and I2-SPE-statistic. The accuracy rates are 93.6% and 95.9% respectively. Combined with the BPNN model, the hybrid ICA-BPNN model shows good fault diagnosis performance. Compared with single BPNN method, the hybrid model improves the accuracy rates from 82.7% to 93.8% for overcharge fault data using I2-SPE-statistic.