Application of Self-tuning Models to Air Handling Units for Fault Detection

Building management control systems (BMCS) are widely employed in modern buildings. The huge amount of data available on central stations and outstations provide rich information for fault diagnosis of HVAC systems. An online fault diagnosis method for variable air volume air handling units was presented using self-tuning HVAC component models. The model parameters are tuned online by using a genetic algorithm (GA) which minimizes the error between measured and estimated performance data, so high modeling accuracy is assured. If the error between measured and estimated performance data exceeds preset thresholds, it means the occurrence of faults or abnormalities in the air handling unit system. The statistical method of selecting thresholds also is presented. The fault detection method was tested and validated using data collected from real HVAC systems. The results of validation show that the fault detection method can be integrated in BMCS systems to detect faults in air handling unit systems efficiently.