用声音检测无人机螺旋桨裂纹，无需拆机。
Acoustic Anomaly Detection on UAM Propeller Defect with Acoustic dataset for Crack of drone Propeller (ADCP)
- 结合FFT与STFT分析声音频谱，捕捉全局与局部特征
- 通过调节麦克风角度和油门功率生成正常/断裂/撕裂声数据
- 构建了首个无人机螺旋桨裂纹声学数据集ADC P，适合航空维护研究
UAM的商业化迫在眉睫，亟需基于AI的稳定维护系统以保障乘客与行人的安全。本文提出一种非破坏性检测无人机螺旋桨裂纹的方法，利用无人机螺旋桨声音数据集进行分析。正常运行声音被录制，通过改变麦克风与螺旋桨夹角及油门功率，生成不同异常声音（分为撕裂与断裂两类）。新方法融合傅里叶变换（FFT）与短时傅里叶变换（STFT）预处理技术，同时捕捉全局频率模式与局部时频变化，显著提升异常检测性能。所构建的无人机螺旋桨裂纹声学数据集（ADCP）验证了裂纹检测的可行性，为未来UAM维护应用奠定了基础。
原文摘要 · Abstract (English)
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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