<ul>
	<li>DOI:10.1109/ICUAS57906.2023.10156355</li>
	<li>
	Conference: International Conference on Unmanned Aircraft Systems ICUAS 2023
	 
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</ul>

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Link

Link - https://www.researchgate.net/profile/Gabriel-Torre/publication/371742681

This work presents a method for detecting and identificating possible damages to propeller blades in multirotor vehicles, for a particular case study of a quadrotor. The detection method is based on a neural network, which takes as input the energy of several spectral bands of the inertial measurements and control variables, and outputs a measure of how damaged a propeller is. The ability of the network to correctly generalize from a limited dataset will be shown by training it using data gathered from an indoor, controlled environment, and testing it using data from outdoor flights.

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Neural network-based propeller damage detection for multirotors

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