Deep-Learning fault detection and classification on a UAV propulsion system

A fault detection and identification method using a Deep-Learning classification method is used to identify several faults that may occur on a UAV propulsion system. Training is performed from a dataset acquired from a simplified multiphysics simulation of the system which allows for the generation of large datasets of modular, interconnected and scalable components of various sizes and performances. We aim to provide a model able to identify faults occurring on a propulsion system using a reduced set of input signals.