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 unc...