In this paper, we present a parity space-based fault diagnosability analysis scheme for unmanned aerial vehicles (UAVs) with linear discrete time-varying (LDTV) dynamics. Robust residual generators are first designed through H−/H∞ optimization problems to ensure sensitivity to faults while maintaining robustness against disturbances and noise. Next, an algebraic scheme is employed for effective fault isolation. Furthermore, a comprehensive set of quantitative indices is proposed to evaluate the fault diagnosability of LDTV systems, providing effective references for assessing the performance of parity space-based diagnosis in UAVs. The proposed approach is validated through simulations of a UAV flight dynamics model. The results indicate high detectability indices, with the elevator fault at 99.52, the throttle lever fault at 72.14, the angle of attack sensor fault at 26.49, and the roll gyroscope fault at 3.06. These indices correspond to fault detection rates of 100.00, 100.00, 100.00, and 99.40%, respectively. Furthermore, the scheme achieves strong isolability for the first three faults, with indices exceeding 8.0 and fault isolation rates (FIRs) beyond 99.80%. However, in the case of a roll gyroscope fault, the isolability index of 0.76 results in a lower FIR of 8.38% than the FIRs in the other three faulty cases. These results demonstrate the accuracy and reliability of the proposed indices in evaluating fault diagnosability in UAV systems.