The goal of this multi-disciplinary project is to develop a simple, robust, generic, and scalable model-based and data-driven Fault Detection, Diagnosis and Prognosis (FDDP) process and the associated detection, inference and predictive analytics that are applicable to a variety of buildings. The research is motivated by the observation that buildings account for more than 40% of US energy consumption. Heating, Ventilation and Air Conditioning (HVAC) constitutes 57% of energy used in commercial and residential buildings, valued at $223B in 2009. About 20% of the energy consumed by HVAC is wasted due to abrupt faults (e.g., stuck dampers), performance degradations (e.g., air filter clogging), poor controls (e.g., biases in set points), and improper commissioning (e.g., poorly balanced parallel chillers). This project will develop FDDP methodologies for HVACs to improve equipment availability, lower energy and operating costs, extend equipment life, and enhance occupants' comfort. The FDDP process will be validated and evaluated by applying it to UConn's Tech Park Building; Duncaster, a life-care retirement community, located in Bloomfield, CT; and potentially to others. The project contributes to the vision of green and sustainable buildings equipped with cyber-physical substrata consisting of HVAC modules, networked sensors providing information on spatial and temporal distribution of occupants, smart building management systems providing situation awareness and decision support to human operators, and improved tenant comfort.
