Fault Detection and Diagnosis of Drone rotor faults

Fault Detection and Diagnosis of Drone rotor faults - Simulation

Over the past two years, the research and development team has been developing a method to identify faulty motors on a drone without interrupting a mission (automated or manual). This is of high interest as it will give the pilot more information to ensure the successful recovery of the system in the event of a fault.

The team of engineers has developed a Machine learning (using Artificial Neural Networks) framework that superimposes the dynamics on the controls in order to detect and locate a fault in a rotor without compromising the mission. The graph above shows simulate a rotor fault once the drone reaches 9 m/s and the fault identification system (FIS) detects a fault 1-second later. Once the other rotors are analyzed, the Rotor1 is identified as having a fault and this information is sent back to the pilot.

It's important to know that even though a major fault has occurred, the drone a capable of flying for some time until instability grows and the drone becomes uncontrollable. Without this information, the pilot reaction will be too late (especially if the drone distance to the pilot prevents a visual recovery) and the system will crash.

The next step is to make use of this knowledge to modify the flight controller (Pixhawk, Navio) such that an automated recovery system can be developed in real-time to further assist the pilot, especially during automated missions.

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