From Simple Auditory Inspection to Acoustic Feature Extraction of Drone Brushless Motors

Abstract

As the application of Unmanned Aerial Vehicles (UAVs) continues to expand globally, the operational health of propulsion components such as brushless motors is critical for ensuring flight safety. Traditional inspection typically relies on manual auditory diagnostics; however, this method is inherently subjective. Because human hearing sensitivity fluctuates across different frequencies, significant discrepancies often exist between objective sound pressure levels and human perception—especially at frequency extremes. Consequently, the reliability and consistency of such auditory-based fault detection are frequently scrutinized. This research establishes an objective, multi-dimensional acoustic feature extraction framework to provide scientific, quantified data that supports human judgment, thereby enhancing diagnostic accuracy. The methodology integrates time-domain, Envelope Analysis, and Time Synchronous Averaging (TSA) techniques to extract key signal features. Analysis of a specific audio sample revealed a signal dominated by intense high-frequency noise peaking at 5,871.09 Hz, exhibiting sharp impulsive characteristics with a Crest Factor (CF) of 4.23. Following TSA processing, asynchronous noise was attenuated by approximately 84.3%, successfully isolating a periodic impact signal at 40.28 Hz, which is precisely synchronous with the shaft rotation speed. The resulting CF of 3.31 confirms the presence of regular, persistent impacts, suggesting potential bearing looseness. The proposed framework effectively isolates weak, fault-related signals from high-intensity noise environments. These objective, quantified results provide a robust scientific basis to assist operators in making more consistent and precise assessments of UAV health. Future research will focus on expanding the experimental dataset and integrating machine learning models to develop a fully automated diagnostic system.