The liquid floodback in a compressor has an adverse impact on the variable refrigerant volume (VRF) air-conditioning system

which will cause energy loss. Nowadays

Big Data is being broadly utilized in fault detection and diagnostic (FDD). Thus

the PCA-Clustering method

which is combined with Big Data

was proposed to diagnose compressor liquid refrigerant floodback fault. First

data quality was improved by data preprocessing; secondly

the principal component analysis (PCA) method was employed to obtain the new dimensional variable data; finally

the new principal variables were clustered to get the classification label of liquid refrigerant floodback fault data. The results show that the model can preferably diagnose the liquid refrigerant floodback problem in the absence of real label data

with the diagnostic rate of the compressor liquid refrigerant floodback reaching 94.29%.