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

作者：Sun, Shaobo; Li, Guannan; Chen, Huanxin^*; Huang, Qianyun; Shi, Shubiao; Hu, Wenju

来源：Applied Thermal Engineering, 2017, 127: 718-728.

DOI：10.1016/j.applthermaleng.2017.08.047

• Independent component analysis
• Back-propagation neural network
• Variable refrigerant flow
• Refrigerant charge fault
• Fault detection and diagnosis

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 12 statistic and l(2)-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 l(2)-SPE-statistic.

• 出版日期2017-12-25
• 单位武汉科技大学; 北京建筑大学; 华中科技大学