Deep Learning Techniques for Acoustic Health Monitoring in AI-Enabled Quadrocopters.pdf

Acoustic health monitoring in AI-enabled quadrocopters is a critical area of research, leveraging deep learning techniques to enhance the reliability and safety of unmanned aerial vehicles (UAVs). This paper explores the application of advanced deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and autoencoders, for real-time anomaly detection and fault diagnosis using acoustic data. By analyzing sound patterns generated by quadrocopter components, these techniques enable the identification of mechanical wear, rotor imbalances, and other potential failures. The integration of AI-driven acoustic monitoring not only improves predictive maintenance but also reduces operational downtime and enhances flight performance. This study highlights the challenges, including noise interference and data scarcity, and proposes solutions such as transfer learning and data augmentation. The results demonstrate the potential of deep learning in transforming acoustic health monitoring for next-generation quadrocopters.