A Hybrid RBF_GPR_EWMA Model for Data-driven Fault Detection in Centrifugal Chiller Systems

Abstract

— Fault Detection and Diagnosis (FDD) are vital for maintaining the energy efficiency and reliability of centrifugal chillers. This study proposes a hybrid data-driven approach that combines Radial Basis Function (RBF) regression and Gaussian Process Regression (GPR) to develop an accurate residual-based reference model. Deviations in thermodynamic parameters are continuously tracked using an Exponentially Weighted Moving Average (EWMA) control chart to detect condenser fouling and refrigerant leakage at multiple severity levels. The proposed RBF_GPR_EWMA framework was validated using the ASHRAE RP-1043 dataset and real chiller data obtained from a hospital in Ho Chi Minh City. The results demonstrate high prediction accuracy (R 2 > 0.99) and reliable detection of early-stage performance degradation. The proposed framework does not require fault labels or complex feature design, offering robustness and interpretability. The simplicity and adaptability of the framework make it suitable for integration into building management systems to support condition-based maintenance and energy-efficient operation of heating, ventilation, air conditioning, and refrigeration equipment. 