  Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components
of future smart cities. In order to operate efficiently, UAV reliability must
be ensured by constant monitoring for faults and failures. To this end, the
work presented in this paper leverages signal processing and Machine Learning
(ML) methods to analyze the data of a comprehensive vibrational analysis to
determine the presence of rotor blade defects during pre and post-flight
operation. With the help of dimensionality reduction techniques, the Random
Forest algorithm exhibited the best performance and detected defective rotor
blades perfectly. Additionally, a comprehensive analysis of the impact of
various feature subsets is presented to gain insight into the factors affecting
the model's classification decision process.
