A data-driven approach for fault diagnosis in HVAC chiller systems

Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller systems can lead to discomfort for the user, energy wastage, system unreliability and shorter equipment life. Faults need to be diagnosed early to prevent further deterioration of the system behaviour and energy losses. Since it is not a common practice to collect historical data regarding unforeseen phenomena and abnormal behaviours for HVAC installations, in this paper a semi-supervised, data-driven approach is employed for fault detection and isolation that makes no use of a priori knowledge. The proposed method exploits Principal Component Analysis to distinguish between anomalies and normal operation variability and a reconstruction-based contribution approach to isolate variables related to faults. The diagnosis task is then tackled by means of a decision table. The fault diagnosis algorithm performance is assessed by exploiting an experimental dataset from a frictionless centrifugal chiller system.