In the U.S., buildings consume 39% of primary energy, of which, 13.5% is attributed to HVAC systems. Faults, arising from sensors, equipment, and control systems in building HVAC systems, are major contribution to the energy wastage and equipment failures in buildings. Among all HVAC systems, the focus of this study is on air handling units (AHU) which greatly affect building energy consumption and indoor environment quality. The first stage of this study is to develop and validate an AHU and building zone simulation model to produce fault free and faulty data for a large variety of faults with a range of fault severities that can be used to assess the performance of AHU automated fault detection and diagnosis (AFDD) methods. Experiments for three different seasons are designed and implemented in a full scale test facility to collect AHU operation data with known faults. The second stage of this study is to develop a new data-driven AFDD methodology using Principal Components Analysis (PCA) method. Two methods, namely, Wavelet-PCA and Pattern Matching-PCA are developed in this study. The feasibility of using these two methods for AHU AFDD is examined using both experimental and simulation data.;Keyword. Air handling units (AHU), automated fault detection and diagnosis (AFDD), model validation, Principal Components Analysis (PCA)
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