Experimental motor fault detection and identification of a quadrotor UAV using a hybrid deep learning approach

This study presents a novel experimental hybrid sequential deep learning (DL) approach for real-time motor fault detection and magnitude estimation in quadrotor UAVs, addressing critical gaps in current fault-tolerant control systems. The proposed framework integrates long short-term memory (LSTM) networks with 1D convolutional neural networks (1D-CNN) to enhance fault classification and estimation accuracy. The dual capability distinguishes the proposed model from existing methods, which often focus solely on fault detection without addressing magnitude estimation. A novel dataset, generated through Hardware-in-the-Loop (HIL) experiments, incorporates 25,000 unique fault scenarios under diverse configurations and flight conditions. This dataset strengthens the model's robustness and applicability to real-world scenarios. The developed architecture demonstrated superior performance, achieving 99.2% fault detection accuracy, surpassing existing methods in robustness and efficiency. By embedding the model into a dynamic HIL testbed, the study validates the framework's capability to detect faults, estimate magnitudes, and restore stability in quadrotors under challenging conditions. Experimental results highlight the system's effectiveness in reducing motor anomalies, ensuring improved operational safety and reliability. The approach is adaptable to broader UAV systems, offering significant advancements in autonomous fault-tolerant control.