Utilizing Mel-Frequency Cepstral Coefficients for Acoustic Diagnostics of Damaged UAV Propellers
Year 2024,
Volume: 05 Issue: 02, 79 - 89
Bahadir Cinoglu
,
Umut Durak
,
T. Hikmet Karakoc
Abstract
In this study, the diagnostic potential of the acoustic signatures of Unmanned Aerial Vehicle (UAVs) propellers which is one of the critical components of these vehicles were examined under different damage conditions. For this purpose, a test bench was set up and acoustic data of five different damaged propellers and one undamaged propeller were collected. The methodology emphasized contains using an omnidirectional microphone to collect data under three different thrust levels which correspond to 25%, 50% and 75%. Propeller acoustics sound characteristics extracted using the Mel Frequency Cepstrum Coefficient (MFCC) technique that incorporates Fast Fourier Transform (FFT) in order to obtain feature extracted data, and the visual differences of sound patterns were discussed to underline its importance in terms of diagnostics. The results indicated that there is a potential for classifying slightly and symmetrically damaged and undamaged propellers successfully in an Artificial Intelligence-based diagnostic application using MFCC. This study aimed to demonstrate a way to effectively use MFCC detecting damaged and undamaged propellers through their sound profiles and highlighted its usage potential for future integration into Artificial Intelligence (AI) methods in terms of UAV diagnostics. The findings provided a foundation for creating an advanced diagnostic method for increasing UAV safety and operational efficiency.
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Year 2024,
Volume: 05 Issue: 02, 79 - 89
Bahadir Cinoglu
,
Umut Durak
,
T. Hikmet Karakoc
References
- Abdul, Z.K. and Al-Talabani, A.K., 2022. Mel frequency cepstral coefficient and its applications: A review. IEEE Access, 10, pp.122136-122158.
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- Jamil, S., Fawad, Rahman, M., Ullah, A., Badnava, S., Forsat, M. and Mirjavadi, S.S., 2020. Malicious UAV detection using integrated audio and visual features for public safety applications. Sensors, 20(14), p.3923.
- Jiao, Q., Wang, X., Wang, L. and Bai, H., 2023. Audio features based ADS-CNN method for flight attitude recognition of quadrotor UAV. Applied Acoustics, 211, p.109540.
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- Kołodziejczak, M., Puchalski, R., Bondyra, A., Sladic, S. and Giernacki, W., 2023, June. Toward lightweight acoustic fault detection and identification of UAV rotors. In 2023 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 990-997). IEEE.
- Kucukkor, O., Aras, O., Ozbek, E., Ekici, S. and Karakoc, T.H., 2023. Design and analysis of an IoT enabled unmanned aerial vehicle to monitor carbon monoxide: methodology and application. International Journal of Global Warming, 29(1-2), pp.66-77.
- Liang, B., Iwnicki, S.D. and Zhao, Y., 2013. Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis. Mechanical Systems and Signal Processing, 39(1-2), pp.342-360.
- Lyu, M., Zhao, Y., Huang, C. and Huang, H., 2023. Unmanned aerial vehicles for search and rescue: A survey. Remote Sensing, 15(13), p.3266.
- Mohsan, S.A.H., Khan, M.A., Noor, F., Ullah, I. and Alsharif, M.H., 2022. Towards the unmanned aerial vehicles (UAVs): A comprehensive review. Drones, 6(6), p.147.
- Pourpanah, F., Zhang, B., Ma, R. and Hao, Q., 2018, October. Anomaly detection and condition monitoring of UAV motors and propellers. In 2018 IEEE SENSORS (pp. 1-4). IEEE.
- Salman, S., Mir, J., Farooq, M.T., Malik, A.N. and Haleemdeen, R., 2021, January. Machine learning inspired efficient audio drone detection using acoustic features. In 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST) (pp. 335-339). IEEE.
- Shen, F.Y., Li, W., Jiang, D.N. and Mao, H.J., 2024. Autonomous predictive maintenance of quadrotor UAV with multi-actuator degradation. The Aeronautical Journal, pp.1-25.
- Suman, A., Kumar, C. and Suman, P., 2022. Early detection of mechanical malfunctions in vehicles using sound signal processing. Applied Acoustics, 188, p.108578.
- Tong, B., Wang, J., Wang, X., Zhou, F., Mao, X. and Zheng, W., 2022. Optimal route planning for truck–drone delivery using variable neighborhood tabu search algorithm. Applied sciences, 12(1), p.529.
- Wang, X., Yadav, V. and Balakrishnan, S.N., 2007. Cooperative UAV formation flying with obstacle/collision avoidance. IEEE Transactions on control systems technology, 15(4), pp.672-679.
- Utebayeva, D., Almagambetov, A., Alduraibi, M., Temirgaliyev, Y., Ilipbayeva, L. and Marxuly, S., 2020, November. Multi-label UAV sound classification using Stacked Bidirectional LSTM. In 2020 Fourth IEEE International Conference on Robotic Computing (IRC) (pp. 453-458). IEEE.
- Van Der Maaten, L., Postma, E.O. and van den Herik, H.J., 2009. Dimensionality reduction: A comparative review. Journal of Machine Learning Research, 10(66-71), p.13.
- Yaman, O., Yol, F. and Altinors, A., 2022. A fault detection method based on embedded feature extraction and SVM classification for UAV motors. Microprocessors and Microsystems, 94, p.104683.
- Zhang, B., Song, Z., Zhao, F. and Liu, C., 2022. Overview of propulsion systems for unmanned aerial vehicles. Energies, 15(2), p.455.
- Zhang, X. and Zhao, X., 2020. Architecture design of distributed redundant flight control computer based on time-triggered buses for UAVs. IEEE Sensors Journal, 21(3), pp.3944-3954.