Heating

ventilation

air-conditioning

and refrigeration (HVAC&R) systems operating under faulty condition often result in extra energy consumption (up to 30% for commercial buildings) and cost

less comfort control and bad indoor/outdoor air quality

especially when multiple faults happened simultaneously. Because of the diversity of individual faults in reality and the possible synergistic effect

detection and diagnosis of multiple-simultaneous faults (MSF) is one of the puzzles encountered by fault detection and diagnosis (FDD) experts. This study presents a novel hybrid strategy that combines support vector machine (SVM) and multi-label (ML) technique for the automated detection and diagnosis of multiple-simultaneous faults (MSF)

and elaborates its application to a building chiller. One of the great advantages ML has against the mono-label (mL) technique is that no MSF data are needed for model training while good FDD performance for MSF could be obtained. Two individual chiller faults and one of their combinations (an MSF) were investigated. Detailed studies on the use of 8 fault indicative features from the previous study and the training of the model with/without normal or/and MSF data were conducted and compared with the mL-SVM model. The results showed that the ML-SVM model performed well

even without MSF samples for model training

and it could still detect and diagnose MSF effectively with a correct rate (CR) as high as 99. 902%. The performance of the detection and identification of MSF was even better than that trained with MSF data. That demonstrated a promising effort in searching for the solution to the MSF puzzle.