The Delfi-n3Xt nanosatellite is the second Dutch university satellite currently being developed at the Delft University of Technology. In its design, the Attitude Determination System (ADS) will be pivotal for optimal power point tracking to adequately provide the energy needed for normal operation and charging of the batteries. In this paper we explore a fault detection mechanism for the ADS based on the Unscented Kalman Filter (UKF) state estimator which has been successfully integrated into the simulation and modelling environment. The UKF provides a more computationally efficient estimator than traditional Kalman Filter variants. Faults introduced in the system include changes in the noise model and stuck-at-0 faults, resulting in disturbances in the output of the filter. Parameters of the filter are varied and the behaviour of the outcoming residuals is analyzed to evaluate its effectiveness in the detection of these errors.