Controlled Neural ODE and GRU Models for Physics-Based Multiclass Fault Detection in Quadrotor Drones
DOI:
https://doi.org/10.46842/ipn.cien.v30n2a04Keywords:
battery fault, controlled Neural ODE, drone fault detection, gated recurrent unit, overheating faultAbstract
This paper presents a fault-diagnosis methodology for detecting progressive faults in quadrotor drones using a controlled Neural ODE network and a GRU recurrent baseline. Training and evaluation data were obtained from a numerical simulation of the physical system, including vehicle dynamics, battery electrical behavior, motor thermal response, and randomly scheduled long-duration degradations. The fault modes considered are battery degradation, loss of motor effectiveness, and overheating. Simulated telemetry was corrupted with measurement noise and slow sensor drift to approximate realistic operating conditions. The analysis compares a continuous latent-space formulation with a discrete recurrent architecture under the same temporal-window representation, class definitions, and evaluation criteria. The proposed framework provides a reproducible basis for studying early incipient fault diagnosis in unmanned aerial vehicles prior to experimental validation using flight logs.
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