This paper introduces a new method for performing actuator fault detection and diagnostics (FDD) in heating ventilation and air conditioning (HVAC) systems. The proposed actuator FDD strategy, for testing whether an actuator is stuck in a single position, uses a two-tier approach that includes a dynamic model-based detector and a fast-deciding steady-state detector. The model-based detector is formulated to provide detection performance that asymptotically bounds both the probability of miss and probability of false alarm. To provide a quick confirmation the actuator is working, the steady-state detector utilizes a goodness-of-fit detection strategy to decide if the measurements could be described by an actuator failure. An architecture is introduced that requires multiple steady-state detection experiments to decide that the measurements could be explained by an actuator failure before performing model-based detection. An experimental test bed using a the KTH Royal Institute of Technology campus HVAC system is described and used to evaluate the steady-state and model-based detectors. The experimental test bed is utilized to identify a building dynamics model, that is employed through monte carlo analysis, to characterize the detection performance of both the model-based detector and the steady-state detector.