|dc.description.abstract||Heating, ventilating and air conditioning (HVAC) systems consume 43 % of the
energy used by buildings. This percentage grows when the HVAC system operates
with malfunctions. Fault detection and diagnosis (FDD) methods are developed to
reduce abnormal events and down-times and to promote energy saving use of
equipment. Most FDD methodologies for HVAC systems found in the literature revolve around first principle models and mathematical models. This dissertation describes a FDD solution based on process history data and artificial neural network (ANN) models. ANN models, of HVAC components, are built from fault-free operation data. Faulty data are then used with the ANN models to build various residuals and statistical residual transformations. From these residuals, unique residual patterns are assigned to discern between a variety of malfunctions.
This FDD strategy is, firstly, applied to a static pressure control loop and secondly, applied to the overall power consumption of an HVAC system. In both studies, the FDD system successfully detected and classified unwanted anomalies - some deviating as little as 5% from normal operational standards. Finally, the FDD system is rated according to a common set of criteria reviewed in the literature study. This criterion shows the FDD strategy to be robust and adaptable, with low modelling and computational requirements.||