Detection of quadcopter propeller failure by machine learning methods

The paper presents a study of options for detecting a failure or defect in the propeller of an unmanned aircraft system (quadcopter) using machine learning methods. An original accuracy evaluation of the known algorithms using in practice the data obtained from the quadcopter in its flight conditions is performed. The proposed method is based on the classification of three propeller states (serviceable propellers, one propeller artificially deformed, one propeller broken) using machine learning algorithms. The input information is the data obtained from the quadcopter measuring system in real time: speed, acceleration and rotation angle relative to three axes. For the correct work of the presented algorithm, data was preprocessed with division into time intervals and applying to the obtained intervals the fast Fourier transform. Based on the processed data, machine learning algorithms were trained using the reference vector method, k-nearest neighbor algorithm, decision tree algorithm, and multilayer perceptron. The obtained accuracy values of the proposed methods are compared. It is shown that the application of machine learning methods can detect and classify the propeller states with an accuracy of up to 96 %. The best result is achieved using the decision tree algorithm. The results of the study can be of practical importance for real-time systems to detect propeller defect and breakage for unmanned aerial vehicles. It is possible to predict with high accuracy the propeller wear; it is possible to improve the stability and safety of the flight.

1. Zenkin A.M., Osinkin E.A., Pachkovskii K.A. Detection of the fire boundaries by camera using the computing power of an unmanned aerial vehicle. Almanac of Scientific Works of Young Scientists at ITMO University. Vol. 2. St. Petersburg, 2019, pp. 81–83. (in Russian)

2. Tsvetkov V., Oznamets V. Monitoring transport infrastructure using intelligent UAVS. Automation, Communications, Informatics, 2020, no. 8, pp. 18–21. (in Russian)

3. Ageeva E.G., Evseev O.V. Prospects for the use of UAVs as a means of cargo delivery. Modern problems of foreign economic activity management: Materials of the International Scientific Conference. Moscow, Russian Foreign Trade Academy Ministry of economic development of the Russian Federation, 2018, pp. 161–167. (in Russian)

4. Morozov Y.V. Emergency control of a quadrocopter in case of failure of two symmetric propellers. Automation and Remote Control, 2018, vol. 79, no. 3, pp. 463–478. https://doi.org/10.1134/S0005117918030062

5. Zotin N.A. Development of the optical means for monitoring the operation of propellers of unmanned aerial vehicles. Actual problems and prospects for the civil aviation development: Proceedings of the IX International Scientific and Practical Conference. Irkutsk, Irkutsk Branch of Moscow State Technical University of Civil Aviation, 2020, pp. 237–242. (in Russian)

6. Bondyra A., Gasior P., Gardecki S., Kasinski A. Fault diagnosis and condition monitoring of UAV rotor using signal processing. Proc. of the 21st Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2017, pp. 233–238. https://doi.org/10.23919/SPA.2017.8166870

7. Rago C., Prasanth R., Mehra R.K., Fortenbaugh R. Failure detection and identification and fault tolerant control using the IMM-KF with applications to the Eagle-Eye UAV. Proc. of the 37th IEEE Conference on Decision and Control (CDC). Vol. 4, 1998, pp. 4208–4213. https://doi.org/10.1109/CDC.1998.761963

8. Abbaspour A., Aboutalebi P., Yen K.K., Sargolzaei A. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV. ISA Transactions, 2017, vol. 67, pp. 317–329. https://doi.org/10.1016/j.isatra.2016.11.005

9. Abdalla A.S., Marojevic V. Machine learning-assisted UAV operations with the UTM: Requirements, challenges, and solutions. Proc. of the 92nd IEEE Vehicular Technology Conference (VTC), 2020, pp. 9348605. https://doi.org/10.1109/VTC2020 - Fall49728.2020.9348605

10. Phanindra B.R., Pralhad R.N., Raj A.A. B. Machine learning based classification of ducted and non-ducted propeller type quadcopter. Proc. of the 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020, pp. 1296–1301. https://doi.org/10.1109/ICACCS48705.2020.9074307

11. Baskaya E., Bronz M., Delahaye D. Fault detection & diagnosis for small UAVs via machine learning. Proc. of the 36th IEEE/AIAA Digital Avionics Systems Conference (DASC), 2017, pp. 8102037. https://doi.org/10.1109/DASC.2017.8102037

12. Yang P., Wen C., Geng H., Liu P. Intelligent fault diagnosis method for blade damage of quad-rotor UAV based on stacked pruning sparse denoising autoencoder and convolutional neural network. Machines, 2021, vol. 9, no. 12, pp. 360. https://doi.org/10.3390/machines9120360

13. Chen X.M., Wu C.-X., Wu Y., Xiong N.-X., Han R., Ju B.-B., Zhang S. Design and analysis for early warning of rotor UAV based on data-driven DBN. Electronics, 2019, vol. 8, no. 11, pp. 1350. https://doi.org/10.3390/electronics8111350

14. Parfentyev K.V., Zhiltsov A.I. Intelligent system development for assessing the unmanned aerial vehicle state based on neural network technologies. Radio Engineering, 2018, no. 2, pp. 13–28. (in Russian). https://doi.org/10.24108/rdeng.0218.0000134

15. Kotsiantis S.B. Supervised machine learning: A review of classification techniques. Frontiers in Artificial Intelligence and Applications, 2007, vol. 160, pp. 3–24.

16. Viugin V. Mathematical Basics of Machine Learning and Forecasting. Moscow, MCCME Publ., 2017, 383 p. (in Russian)

17. Toledo-Pérez D.C., Rodríguez-Reséndiz J., Gómez-Loenzo R.A., Jauregui-Correa J.C. Support vector machine-based EMG signal classification techniques: A review. Applied Sciences, 2019, vol. 9, no. 20, pp. 4402. https://doi.org/10.3390/app9204402

18. Zhou X., Wu Y., Yang B. Signal classification method based on support vector machine and high-order cumulants. Wireless Sensors Network, 2010, vol. 2, no. 1, pp. 48–52. https://doi.org/10.4236/wsn.2010.21007

19. Dhanabal S., Chandramathi S. A review of various k-nearest neighbor query processing techniques. International Journal of Computer Applications, 2011, vol. 31, no. 7, pp. 14–22. https://doi.org/10.5120/3836-5332

20. Zhang S., Li X., Zong M., Zhu X., Cheng D. Learning k for kNN classification. ACM Transactions on Intelligent Systems and Technology, 2017, vol. 8, no. 3, pp. 43. https://doi.org/10.1145/2990508

21. Guo G., Wang H., Bell D., Bi Y., Greer K. KNN model-based approach in classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2003, vol. 2888, pp. 986–996. https://doi.org/10.1007/978-3-540-39964-3_62

22. Myles A.J., Feudale R.N., Liu Y., Woody N.A., Brown S.D. An introduction to decision tree modeling. Journal of Chemometrics, 2004, vol. 18, no. 6, pp. 275–285. https://doi.org/10.1002/cem.873

23. Song Y.-Y., Lu Y. Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry, 2015, vol. 27, no. 2, pp. 130. https://doi.org/10.11919/j.issn.1002-0829.215044

24. Zanaty E.A. Support vector machines (SVMs) versus multilayer perception (MLP) in data classification. Egyptian Informatics Journal, 2012, vol. 13, no. 3, pp. 177–183. https://doi.org/10.1016/j.eij.2012.08.002