Rotor Fault Detection and Identification on a Hexacopter Based on Statistical Time Series Methods

This work introduces the use of statistical time series methods to detect rotor failures in multicopters. A concise
overview of the development of various time series models using scalar or vector signals, statistics, and fault detection
methods is provided. The fault detection methods employed in this study are based on parametric time series representations
and response-only signals of the aircraft state, as the external excitation is non-observable. The comparative
assessment of the effectiveness of scalar and vector statistical models and several residual-based fault detection
methods are presented in the presence of external disturbances, such as various levels of turbulence and uncertainty,
and for different rotor failure scenarios. The results of this study demonstrate the effectiveness of all the proposed
residual-based time series methods in terms of prompt rotor fault detection, although the methods based on Vector
AutoRegressive (VAR) models exhibit improved performance compared to their scalar counterparts with respect to
their robustness and effectiveness for different turbulence levels and ability to distinguish between healthy and fault
compensated condition after rotor failure.