Deep learning-based proactive fault detection method for enhanced quadrotor safety
Abstract
The early detection of faults in advanced technological systems is imperative for ensuring operational reliability and safety. While there is a growing interest in using artificial intelligence for fault detection, current methodologies often exhibit limitations in utilizing comprehensive system information and sensor data. Hidden faults within collected data further highlight the need for advanced analysis techniques. This study introduces a novel deep learning-based framework designed to predict faults and extract insights from complex system datasets. The model, consisting of LSTM-autoencoder and BiLSTM classification components, effectively reduces feature dimensions, thereby enhancing fault detection accuracy. The autoencoder’s latent layer identifies prominent features across various dimensions, while BiLSTM classification conducts bidirectional analysis using these features from both healthy and faulty states, facilitating early fault detection. Experimental results demonstrate the model’s efficacy, achieving an accuracy of 79.48% in predicting incipient faults 30 seconds before a serious malfunction occurs. This underscores the significant potential of the proposed framework in enhancing operational safety and reliability in complex systems. Moreover, the study emphasizes the importance of leveraging comprehensive data and advanced analysis techniques for early fault detection.
Keyword : quadrotor fault prognostics, early fault detection, deep learning, autoencoders, LSTM
How to Cite
Ozcan, M., & Perkgoz, C. (2024). Deep learning-based proactive fault detection method for enhanced quadrotor safety. Aviation, 28(3), 175–187. https://doi.org/10.3846/aviation.2024.22173
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Ai, S., Shang, W., Song, J., & Cai, G. (2021). Fault diagnosis of the four-rotor unmanned aerial vehicle using the optimized deep forest algorithm based on the wavelet packet translation. In 2021 8th International Conference on Dependable Systems and Their Applications (DSA). IEEE. https://doi.org/10.1109/DSA52907.2021.00085
Al Younes, Y., Rabhi, A., Noura, H., & El Hajjaji, A. (2016). Sensor fault diagnosis and fault tolerant control using intelligent-output-estimator applied on quadrotor UAV. In 2016 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE. https://doi.org/10.1109/ICUAS.2016.7502557
Altinors, A., Yol, F., & Yaman, O. (2021). A sound based method for fault detection with statistical feature extraction in UAV motors. Applied Acoustics, 183, Article 108325. https://doi.org/10.1016/j.apacoust.2021.108325
Anidjar, O. H., Barak, A., Ben-Moshe, B., Hagai, E., & Tuvyahu, S. (2023). A stethoscope for drones: Transformers-based methods for UAVs acoustic anomaly detection. IEEE Access, 11, 33336–33353. https://doi.org/10.1109/ACCESS.2023.3262702
Belcastro, C. M., Klyde, D. H., Logan, M. J., Newman, R. L., & Foster, J. V. (2017). Experimental flight testing for assessing the safety of unmanned aircraft system safety-critical operations. In 17th AIAA Aviation Technology, Integration, and Operations Conference. ResearchGate. https://doi.org/10.2514/6.2017-3274
Bondyra, A., Gasior, P., Gardecki, S., & Kasinski, A. J. (2018). Development of the sensory network for the vibration-based fault detection and isolation in the multirotor UAV propulsion system. In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (CINCO 2018) (Vol. 2, pp. 102–109). Scitepress Digital Library. https://doi.org/10.5220/0006846801120119
Bondyra, A., Kołodziejczak, M., Kulikowski, R., & Giernacki, W. (2022). An acoustic fault detection and isolation system for multirotor UAV. Energies, 15(11), Article 3955. https://doi.org/10.3390/en15113955
Chen, B., Peng, Y., Gu, B., Luo, Y., & Liu, D. (2021). A fault detection method based on enhanced GRU. In 2021 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). IEEE. https://doi.org/10.1109/ICSMD53520.2021.9670769
Duncan Imbassahy, D. W., Costa Marques, H., Conceição Rocha, G., & Martinetti, A. (2020). Empowering predictive maintenance: A hybrid method to diagnose abnormal situations. Applied Sciences, 10(19), Article 6929. https://doi.org/10.3390/app10196929
Erfanian, A. M., & Ramezani, A. (2022). Using deep learning network for fault detection in UAV. In 2022 8th International Conference on Control, Instrumentation and Automation (ICCIA). IEEE. https://doi.org/10.1109/ICCIA54998.2022.9737206
Fu, J., & Che, G. (2021). Fusion fault diagnosis model for six-rotor UAVs based on conformal Fourier transform and improved self-organizing feature map. IEEE Access, 9, 14422–14436. https://doi.org/10.1109/ACCESS.2021.3052317
Guo, D., Zhong, M., Ji, H., Liu, Y., & Yang, R. (2018). A hybrid feature model and deep learning based fault diagnosis for unmanned aerial vehicle sensors. Neurocomputing, 319, 155–163. https://doi.org/10.1016/j.neucom.2018.08.046
Hajiyev, C. (2016). An innovation approach based sensor fault detection and isolation. IFAC-PapersOnLine, 49(17), 420–425. https://doi.org/10.1016/j.ifacol.2016.09.072
Huang, J., Li, M., Zhang, Y., Mu, L., Ao, Z., & Gong, H. (2021). Fault detection and classification for sensor faults of UAV by deep learning and time-frequency analysis. In 2021 40th Chinese Control Conference (CCC). IEEE. https://doi.org/10.23919/CCC52363.2021.9550141
Iannace, G., Ciaburro, G., & Trematerra, A. (2019). Fault diagnosis for UAV blades using artificial neural network. Robotics, 8(3), Article 59. https://doi.org/10.3390/robotics8030059
Ignatovich, S., Menou, A., Karuskevich, M., & Maruschak, P. (2013). Fatigue damage and sensor development for aircraft structural health monitoring. Theoretical and Applied Fracture Mechanics, 65, 23–27. https://doi.org/10.1016/j.tafmec.2013.05.004
Jiang, Y., Zhiyao, Z., Haoxiang, L., & Quan, Q. (2015). Fault detection and identification for quadrotor based on airframe vibration signals: a data-driven method. In 2015 34th Chinese Control Conference (CCC). IEEE. https://doi.org/10.1109/ChiCC.2015.7260639
Jing, C. S., & Pebrianti, D. (2016). Fault detection and identification in Quadrotor system (Quadrotor robot). In 2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). IEEE. https://doi.org/10.1109/I2CACIS.2016.7885281
Jing, C. S., Pebrianti, D., Qian, G. M., & Bayuaji, L. (2017). Fault detection in Quadrotor MAV. In 2017 7th IEEE International Conference on System Engineering and Technology (ICSET). IEEE. https://doi.org/10.1109/ICSEngT.2017.8123422
Lazzara, M., Chevalier, M., Colombo, M., Garcia, J. G., Lapeyre, C., & Teste, O. (2022). Surrogate modelling for an aircraft dynamic landing loads simulation using an LSTM AutoEncoder-based dimensionality reduction approach. Aerospace Science and Technology, 126, Article 107629. https://doi.org/10.1016/j.ast.2022.107629
Liu, L., Ma, Y., Xu, B., Xiang, C., & Yang, X. (2016). Fault detection and isolation based on UKFs for a novel ducted fan UAV. In 2016 IEEE International Conference on Aircraft Utility Systems (AUS). IEEE. https://doi.org/10.1109/AUS.2016.7748049
Liu, W., Chen, Z., & Zheng, M. (2020). An audio-based fault diagnosis method for quadrotors using convolutional neural network and transfer learning. In 2020 American Control Conference (ACC). IEEE. https://doi.org/10.23919/ACC45564.2020.9148044
Olyaei, M. H., Jalali, H., Noori, A., & Eghbal, N. (2018). Fault detection and identification on UAV system with CITFA algorithm based on deep learning. In Iranian Conference on Electrical Engineering (ICEE). IEEE. https://doi.org/10.1109/ICEE.2018.8472529
Ouadine, A. Y., Mjahed, M., Ayad, H., & El Kari, A. (2020). UAV quadrotor fault detection and isolation using artificial neural network and Hammerstein-Wiener model. Studies in Informatics and Control, 29(3), 317–328. https://doi.org/10.24846/v29i3y202005
Ozkat, E. C., Bektas, O., Nielsen, M. J., & la Cour-Harbo, A. (2023). A data-driven predictive maintenance model to estimate RUL in a multi-rotor UAS. International Journal of Micro Air Vehicles, 15. https://doi.org/10.1177/17568293221150171
Pose, C., Giribet, J., Torre, G., & Marzik, G. (2023). Neural network-based propeller damage detection for multirotors. In 2023 International Conference on Unmanned Aircraft Systems (ICUAS). ResearchGate. https://doi.org/10.1109/ICUAS57906.2023.10156355
Puchalski, R., & Giernacki, W. (2022). UAV fault detection methods, state-of-the-art. Drones, 6(11), Article 330. https://doi.org/10.3390/drones6110330
Said Elsayed, M., Le-Khac, N.-A., Dev, S., & Jurcut, A. D. (2020). Network anomaly detection using LSTM based autoencoder. In Q2SWinet’20: Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks (pp. 37–45). ACM Digital Library. https://doi.org/10.1145/3416013.3426457
Vural, S. Y., & Hacızade, C. (2016). Sensor/actuator fault detection, isolation and accommodation applied to UAV model. Journal of Aeronautics and Space Technologies, 9(2), 1–12.
Wang, B., Liu, D., Peng, Y., & Peng, X. (2019). Multivariate regression-based fault detection and recovery of UAV flight data. IEEE Transactions on Instrumentation and Measurement, 69(6), 3527–3537. https://doi.org/10.1109/TIM.2019.2935576
Wei, Y., Wu, D., & Terpenny, J. (2020). Robust incipient fault detection of complex systems using data fusion. IEEE Transactions on Instrumentation and Measurement, 69(12), 9526–9534. https://doi.org/10.1109/TIM.2020.3003359
Yang, P., Geng, H., Wen, C., & Liu, P. (2021). An intelligent quadrotor fault diagnosis method based on novel deep residual shrinkage network. Drones, 5(4), Article 133. https://doi.org/10.3390/drones5040133
Yasniy, O., Mytnyk, M., Maruschak, P., Mykytyshyn, A., & Didych, I. (2024). Machine learning methods as applied to modelling thermal conductivity of epoxy-based composites with different fillers for aircraft. Aviation, 28(2), 64–71. https://doi.org/10.3846/aviation.2024.21472
Zhang, W., Tong, J., Liao, F., & Zhang, Y. (2023). Simulation-to-reality UAV fault diagnosis with deep learning. In arXiv preprint arXiv:2302.04410. Cornell University.
Zhao, Z., Wang, X., Xu, J., & Yu, J. (2018). A performance evaluation algorithm of stochastic hybrid systems based on fuzzy health degree and its application to quadrotors. IEEE Access, 6, 37581–37594. https://doi.org/10.1109/ACCESS.2018.2838149
Zheng, K., Jia, G., Yang, L., & Wang, J. (2021). A Compound fault labeling and diagnosis method based on flight data and BIT record of UAV. Applied Sciences, 11(12), Article 5410. https://doi.org/10.3390/app11125410