Shivaan Sehgal1 Aakash Maniar1 Harikumar Kandath1 Deepak Gangadharan1
1 Robotics Research Center,KCIS, IIIT Hyderabad, India
This paper introduces a novel approach for fault detection and localization in a motor of a Hexacopter UAV. The proposed two-stage architecture leverages Long Short-Term Memory (LSTM) [1] networks for latent temporal feature extrac-tion and Random Forest [2] for localization. By combining them, we see improved fault detection and isolation performance. Our evaluations show the robustness of this approach in varying noise levels and real-world-like environments. Analysis of computa-tional efficiency in rapid detection shows that the model identified faults within 2-5 time steps of the flight. Finally, we show that the proposed method surpasses classical statistical models and deep learning techniques in terms of overall accuracy (96.78%).