The application of Fault Detection and Diagnosis (FDD) could play a key role in operating chillers at optimum efficiency. As air-conditioning consumes a significant proportion (approximately 30% to 50%) of the total power consumption of a commercial building, an effective FDD tool is very much required for a reliable and economic chiller operation. In this paper, the renowned Simple Thermodynamic Model, STM (also known as Gordon and Ng Universal Chiller Model) was utilized to detect and diagnose some of the common faults in centrifugal chillers. Based on the readily available, nonintrusive, steady state measurements extracted from the on-site chillers, the STM model was regressed to obtain two physically meaningful coefficients, namely, Thermal Resistance and Internal Entropy Generation. These information-rich coefficients were then analyzed to evaluate the performances and the conditions of the chillers. Despite the large and yet complex on-site chiller operations, equipped with built-in heat recovery, such as economizer and intricate control mechanisms, the predictive and diagnostic capabilities of STM in detecting chiller faults has been successfully tested and evaluated based on the data collected from on-site centrifugal chillers. Faults like condenser fouling and reduction in condenser cooling water flow rate were succinctly captured by the STM model.
Overview on hybrid approaches to fault detection and diagnosis: Combining data-driven, physics-based and knowledge-based models
