Acoustic Anomaly Detection on UAM Propeller Defect with Acoustic dataset for Crack of drone Propeller (ADCP)

2025/03/02 by Juho Lee, Lee, Juho, Dong-Hyun Yoon +4
Engineering · #Audio and Speech Processing (eess.AS) #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Electrical engineering #Hydraulic and Pneumatic Systems #Machine Fault Diagnosis Techniques #Sound (cs.SD) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2503.00790

openalex publication_date 2025/03/02 · openalex created_date 2025/10/12 · openalex updated_date 2026/07/28

Abstract

The imminent commercialization of UAM requires stable, AI-based maintenance systems to ensure safety for both passengers and pedestrians. This paper presents a methodology for non-destructively detecting cracks in UAM propellers using drone propeller sound datasets. Normal operating sounds were recorded, and abnormal sounds (categorized as ripped and broken) were differentiated by varying the microphone-propeller angle and throttle power. Our novel approach integrates FFT and STFT preprocessing techniques to capture both global frequency patterns and local time-frequency variations, thereby enhancing anomaly detection performance. The constructed Acoustic Dataset for Crack of Drone Propeller (ADCP) demonstrates the potential for detecting propeller cracks and lays the groundwork for future UAM maintenance applications.

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