Rotor fault detection and identification for a hexacopter based on control and state signals via statistical learning methods

A robust framework for fault detection and identification of rotor degradation in multicopters while effectively rejecting the effects of gusts is introduced. The rotor fault detection and identification methods employed in this study are based on excitation-response signals of the aircraft under ambient turbulence to distinguish between an aircraft response to gusts and rotor faults. A concise overview of the development of statistical time series model for healthy aircraft using the aircraft attitudes as the output and controller commands as the input is presented. This model is utilized to extract quality features for training a simple neural network to perform effective online rotor fault detection and identification in a hexacopter exceptional speed of making a decision and accuracy of fault classification. It is shown that using a statistical time series model assisted neural network employed for online monitoring is capable of rejecting gusts, sensitive to even 20% rotor degradation and achieves fault detection and identification in less than 2 s after the fault with an accuracy over 99%.