The scope of this thesis is to develop an automated building fault detection, diagnostic, and evaluation (FDDE) framework for buildings. This framework aims to provide a holistic approach to facilitate information delivery and decision-making on building faults. It is adaptable to different building systems as well as flexible to both distributed and centralised implementations. The first part of the framework, fault detection, uses a novel technique called constrained dual Extended Kalman Filter (EKF) to estimate system parameters, which then generates symptom descriptions in terms of probability and severity. For the fault diagnostic step, Dynamic Bayesian Network (DBN) with leaky Noisy-Max model simplification is chosen to accommodate probabilistic descriptions of faults and symptoms. The fault evaluation aspect of the system employs existing building performance simulation (BPS) tools to estimate quantitative metrics of fault impacts. A model reduction process called "model-cluster-reduce" is also developed to speed up information delivery. Each component of the framework is created with the intention to be generalized to other related areas of research such as model predictive control and BPS optimization. Four case studies of both zone-level and air handling unit (AHU)-level are adopted to demonstrate the functionalities of the proposed FDDE framework. Overall, the framework shows promising results with short fault diagnosis time, low false positive and false negative rates, albeit the tendency of overestimating fault impacts. Still, many fundamental research questions arise from this thesis in addition to the future work to further expand the FDDE framework.