Model-based fault detection and diagnosis for centrifugal chillers

Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller systems can lead to discomfort for the users, energy wastage, system unreliability and shorter equipment life. Faults need to be early diagnosed to prevent further deterioration of the system behaviour and energy losses. In this paper a model-based approach is used in order to detect important chiller systems faults. First, a linear dynamic black-box model is identified for each of the relevant characteristic features of the system during the normal functioning of the chiller. Then, an on-line correlogram method verifies the whiteness property of the residuals in order to distinguish anomalies from normal operations. A decision table, that matches the influence of anomalies with the characteristic features, allows to identify chiller faults. The proposed fault detection and diagnosis approach is assessed by using real chiller data provided by the ASHRAE research project RP-1043.