Although Energy Management and Control Systems have contributed significantly to the reduction (20-40%) of energy use in buildings without sacrificing occupants' comfort, their full capabilities have not been completely realized. This is primarily due to their inability to quickly detect and compensate for failures in the Heating Ventilating and Air Conditioning (HVAC) system. In fact, no matter how good the control scheme for the HVAC system might be, the presence of undetected faults can completely wipe out any expected savings. This research focuses on the problem of fault detection and diagnosis of HVAC systems. The primary goal of this project is to develop an efficient methodology based on extended Kalman filtering and Maximum Likelihood system identification for the detection and diagnosis of faults in HVAC systems and to demonstrate its effectiveness through application to an air handler system. The methodology is to be powerful enough to permit rapid on-line fault detection and diagnosis and yet be implementable on a personal computer. Also, color graphic display capability is to be incorporated to aid the operator in monitoring the state of the system. To enhance the Phase III commercialization effort that will follow, a prototype system will be developed and tested.
