The focus of this thesis is on the use of analytical redundancy to improve the reliability of low-cost unmanned aerial vehicles (UAVs). Specifically, a model-based fault detection algorithm is designed and tested for one critical UAV component: a servo-actuator. As the name suggests, a key requirement to developing this type of fault detection algorithm on actuators is the availability of an accurate actuator model. This is accomplished by developing a dedicated Arduino based experimental test-bed to analyze servos. Using input-output data from these experiments, a second/third-order dynamic model is identified for healthy actuators using system identification methods in MATLAB software. Using the identified model, a fault detection filter is designed based on polynomial basis vectors to generate a residual proportional to fault. The performance of the fault detection algorithm is experimentally tested on both healthy and faulty actuators and the detection thresholds are set. Finally, the actuator model and the fault detection filter are validated using actuator commands from recent flight tests conducted at the University of Minnesota.