Rotor Fault Detection and Identification on a Hexacopter under Varying Flight States Based on Global Stochastic Models

This work introduces the use of "global" stochastic models to detect and identify rotor failures in multicopters under different operating conditions, turbulence, and uncertainty. The identification of an extended class of time-series models known as Vector-dependent Functionally Pooled AutoRegressive models, which are characterized by parameters that depend on both forward velocity and gross weight, using scalar or vector aircraft response signals under white noise excitation has been described. A concise overview of the residual based statistical decision making schemes for fault detection and identification of rotor failures is provided. The scalar and vector statistical models, along with residual variance and residual uncorrelatedness methods were validated and their effectiveness was assessed by a proof-of-concept application to aircraft flight for healthy and faulty states under severe turbulence and intermediate operating conditions. 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 models exhibit improved performance compared to their scalar counterparts with respect to their performance in identifying rotor failures in the post-failure controller compensated state.