Detecting the faults at the incipient stage is important for keeping chiller systems healthy and saving energy and maintenance cost. Traditional principle component analysis (PCA) and support vector data description (SVDD) methods are insensitive to two common faults, condenser fouling (CdF) and refrigerant leakage (RfL). To improve the fault detection performance, this study proposed a PCA-R-SVDD based method. Instead of principle component subspace (PCs), it develops a SVDD model in the residual subspace (Rs) using the PCA modeling residual data. The SVDD based distance based monitoring statistic was used for fault detection. The proposed method shows significant improvement comparing with the traditional methods due to the better fault data distribution and tighter monitoring statistic. It is sensitive to six common faults. At least 50% of the fault data can be correctly detected even at the least severe fault level. Centrifugal chiller experimental data from the ASHRAE Research Project 1043 (RP-1043) was used to evaluate the methods. (C) 2015 Elsevier B.V. All rights reserved.