Real-time detection of acoustic anomalies in drone servo motors using edge-based machine learning
Tal Kfir, Sahar Tuvyahu, Boaz Ben Moshe, Or Haim Anidjar · Machine Learning with Applications · 2025
The growing demand for Unmanned Aerial Vehicles (UAVs) has led to a significant increase in their variety and usage, emphasizing the need for resilient and autonomous onboard monitoring systems. To address this, we present a lightweight, scalable solution for real-time anomaly detection focused on the mechanical servos that control UAV flight dynamics. While conventional deep learning methods offer high accuracy, they often require substantial computational and memory resources, making them unsuitable for the constrained environments of small aircraft. In this study, we introduce a real-time anomaly detection framework that combines edge computing and Internet of Things (IoT) principles to analyze acoustic signals from UAV servo motors. Our system leverages Tiny Machine Learning (TinyML) techniques to perform local data processing and inference directly on embedded hardware, minimizing latency and energy consumption. The proposed method uses a compact neural network deployed on an ultra-lightweight microcontroller (under 100 grams) to classify servo conditions. Acoustic data collected under multiple fault scenarios were minimally preprocessed and fed into the model. Experimental evaluation shows promising performance with 86% accuracy, 86% recall, and 87% precision. This edge-based AI approach supports distributed deployment across UAV fleets, reduces reliance on external infrastructure, and enhances both safety and maintenance efficiency in diverse operational environments. • Real-time servo anomaly detection using TinyML on a lightweight embedded chip. • Acoustic-based analysis enables fault detection without invasive hardware. • Deployed model achieved 86% accuracy on-chip under realistic noise conditions. • TinyML chip enables onboard detection with no external processing required.