Fault Detection and Identification for Multirotor Aircraft by Data-Driven and Statistical Learning Methods

This work compares different data-driven methods for fault detection and identification of
rotor failures in multicopters. The fault detection and identification methods employed in this
study are based on response-only signals of the aircraft state, as the external excitation due
to ambient turbulence is non-observable. Knowledge based methods using the knowledge of
aircraft dynamics in the event of rotor failure is studied. A concise overview of the development
of statistical time series models using the aircraft attitudes and statistical hypothesis testing
to detect and classify rotor failures is presented. These methods are compared with neural
networks trained on different parts of the response signals to achieve online fault detection
identification of rotor failures in a hexacopter with respect to speed and accuracy of fault
classification. It is shown that using a combination of statistical time series model for healthy
aircraft and neural networks employed for online monitoring results in fault detection and
identification in less than 0.3 s with an accuracy of 99.3%.