Acoustic Anomaly Detection on UAM Propeller Defect with Acoustic dataset for Crack of drone Propeller (ADCP)
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866909520396025856 |
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| author | Lee, Juho Yoon, Donghyun Jeong, Gumoon Kim, Hyeoncheol |
| author_facet | Lee, Juho Yoon, Donghyun Jeong, Gumoon Kim, Hyeoncheol |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00790 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Acoustic Anomaly Detection on UAM Propeller Defect with Acoustic dataset for Crack of drone Propeller (ADCP) Lee, Juho Yoon, Donghyun Jeong, Gumoon Kim, Hyeoncheol Sound Emerging Technologies Audio and Speech Processing 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. |
| title | Acoustic Anomaly Detection on UAM Propeller Defect with Acoustic dataset for Crack of drone Propeller (ADCP) |
| topic | Sound Emerging Technologies Audio and Speech Processing |
| url | https://arxiv.org/abs/2503.00790 |