Propulsion System Diagnostics for Failure Prevention in Unmanned Aerial Vehicles

Abstract

The rapid proliferation of unmanned aerial vehicles across civilian, commercial, and military sectors has necessitated unprecedented levels of reliability in their core subsystems. Among these, the propulsion system remains the most critical point of failure, often leading to catastrophic loss of the vehicle and potential collateral damage. This paper presents a comprehensive investigation into predicting failure prevention through advanced diagnostic techniques and experimental analysis of propulsion systems in unmanned aerial vehicles. By shifting the paradigm from reactive failure detection to proactive failure prevention, this research establishes a robust framework for continuous health monitoring. We analyze the degradation signatures of brushless direct current motors and associated aerodynamic components under various stress conditions. Using a custom experimental testbed, we capture multi-modal diagnostic data, including high-frequency vibrational, acoustic, electrical, and thermal signals. Through rigorous analytical processing, we identify precursor anomalies that precede critical component failure. The results demonstrate that specific harmonic distortions in vibration data and transient spikes in current draw serve as highly reliable indicators of impending mechanical and electrical faults. This methodology not only improves the predictive accuracy of maintenance algorithms but also extends the operational lifespan of unmanned aerial vehicles by enabling timely interventions.