Abstract
Fast detection of motor failures is crucial for multi-rotor unmanned aerial vehicle (UAV) safety. It is well established in the literature that UAVs can adopt fault-tolerant control strategies to fly even when losing one or more rotors. We present a motor fault detection and isolation (FDI) method for multi-rotor UAVs based on an external wrench estimator and a recurrent neural network composed of long short-term memory nodes. The proposed approach considers the partial or total motor fault as an external disturbance acting on the UAV. Hence, the devised external wrench estimator trains the network to promptly understand whether the estimated wrench comes from a motor fault (also identifying the motor) or from unmodelled dynamics or external effects (i.e., wind, contacts, etc.). Training and testing have been performed in a simulation environment endowed with a physic engine, considering different UAV models operating under unknown external disturbances and unexpected motor faults. To further assess this approach’s effectiveness, we compare our method’s performance with a classical model-based technique. The collected results demonstrate the effectiveness of the proposed FDI approach.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
Code or Data Availability
No code will be released for this work.
References
Amazon.com: Prime Air. https://www.amazon.com/Amazon-Prime-Air/b?ie=UTF8&node=8037720011 (2024). Accessed 29 March 2021
Zhao, S., Ruggiero, F., Fontanelli, G.A., Lippiello, V., Zhu, Z., Siciliano, B.: Nonlinear model predictive control for the stabilization of a wheeled unmanned aerial vehicle on a pipe. IEEE Robot. Autom. Lett. 4(4), 4314–4321 (2019). https://doi.org/10.1109/LRA.2019.2931821
Trujillo, M.A., Martinez-de Dios, J.R., Martín, C., Viguria, A., Ollero, A.: Novel aerial manipulator for accurate and robust industrial ndt contact inspection: A new tool for the oil and gas inspection industry. Sensors 19(6) (2019)
Venkataraman, R., Bauer, P., Seiler, P., Vanek, B.: Comparison of fault detection and isolation methods for a small unmanned aircraft. Control Eng. Pract. 84, 365–376 (2019)
Ruggiero, F., Lippiello, V., Ollero, A.: Aerial manipulation: A literature review. IEEE Robot. Autom. Lett. 3(3), 1957–1964 (2018)
Funahashi, K.-I., Nakamura, Y.: Approximation of dynamical systems by continuous time recurrent neural networks. Neural Netw. 6(6), 801–806 (1993)
Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017)
Furrer, F., Burri, M., Achtelik, M., Siegwart, R.: RotorS - A Modular Gazebo MAV Simulator. Framework 625, 595–625 (2016)
Guo, K., Liu, L., Shi, S., Liu, D., Peng, X.: Uav sensor fault detection using a classifier without negative samples: A local density regulated optimization algorithm. Sensors 19(4) (2019)
Sun, R., Cheng, Q., Wang, G., Ochieng, W.Y.: A novel online data-driven algorithm for detecting uav navigation sensor faults. Sensors 17(10) (2017)
Aboutalebi, P., Abbaspour, A., Forouzannezhad, P., Sargolzaei, A.: A novel sensor fault detection in an unmanned quadrotor based on adaptive neural observer. J. Intell. Robot. Syst. 90 (2018)
Gilmore, J.P., McKern, R.A.: A redundant strapdown inertial reference unit (siru). J. Spacecr. Rocket 9(1), 39–47 (1972)
Saied, M., Lussier, B., Fantoni, I., Shraim, H., Francis, C.: Active versus passive fault-tolerant control of a redundant multirotor uav. Aeronaut. J. 124(1273), 385–408 (2020)
Baskaya, E., Hamandi, M., Bronz, M., Franchi, A.: A novel robust hexarotor capable of static hovering in presence of propeller failure. IEEE Robot. Autom. Lett. 6(2), 4001–4008 (2021)
Mazeh, H., Saied, M., Shraim, H., Francis, C.: Fault-tolerant control of an hexarotor unmanned aerial vehicle applying outdoor tests and experiments. IFAC-PapersOnLine 51(22), 312–317 (2018). 12th IFAC Symposium on Robot Control SYROCO 2018
Mueller, M.W., D’Andrea, R.: Relaxed hover solutions for multicopters: Application to algorithmic redundancy and novel vehicles. Int. J. Robot. Res. 35(8), 873–889 (2016)
Stephan, J., Schmitt, L., Fichter, W.: Linear parameter-varying control for quadrotors in case of complete actuator loss. J. Guid. Control Dyn. 41(10), 2232–2246 (2018)
Lippiello, V., Ruggiero, F., Serra, D.: Emergency landing for a quadrotor in case of a propeller failure: A pid based approach. In: 2014 IEEE International Symposium on Safety, Security, and Rescue Robotics (2014), pp. 1–7 (2014)
Pourpanah, F., Zhang, B., Ma, R., Hao, Q.: Anomaly detection and condition monitoring of uav motors and propellers. 2018 IEEE SENSORS, 1–4 (2018)
Iannace, G., Ciaburro, G., Trematerra, A.: Fault diagnosis for uav blades using artificial neural network. Robotics 8(3) (2019)
Sharifi, F., Mirzaei, M., Gordon, B.W., Zhang, Y.: Fault tolerant control of a quadrotor uav using sliding mode control. In: 2010 Conference on Control and Fault-Tolerant Systems (SysTol), pp. 239–244 (2010)
Freddi, A., Longhi, S., Monteriù, A.: A diagnostic thau observer for a class of unmanned vehicles. J. Intell. Robot. Syst. 67, 61–73 (2012)
Cen, Z., Noura, H.: An adaptive thau observer for estimating the time-varying loe fault of quadrotor actuators. In: 2013 Conference on Control and Fault-Tolerant Systems (SysTol), pp. 468–473 (2013)
Avram, R.C., Zhang, X., Muse, J.: Quadrotor actuator fault diagnosis and accommodation using nonlinear adaptive estimators. IEEE Trans. Control Syst. Technol. 25(6), 2219–2226 (2017). https://doi.org/10.1109/TCST.2016.2640941
Caliskan, F., Hacizade, C.: Sensor and actuator fdi applied to an uav dynamic model. IFAC Proc. Volumes 47(3), 12220–12225 (2014). https://doi.org/10.3182/20140824-6-ZA-1003.01013 . 19th IFAC World Congress
Amoozgar, M.H., Chamseddine, A., Zhang, Y.: Experimental test of a two-stage kalman filter for actuator fault detection and diagnosis of an unmanned quadrotor helicopter. J. Intell. Robotics Syst. 70(1–4), 107–117 (2013). https://doi.org/10.1007/s10846-012-9757-7
Tzoumanikas, D., Yan, Q., Leutenegger, S.: Nonlinear mpc with motor failure identification and recovery for safe and aggressive multicopter flight. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 8538–8544 (2020). https://doi.org/10.1109/ICRA40945.2020.9196690
Guo, D., Wang, Y., Zhong, M., Zhao, Y.: Fault detection and isolation for unmanned aerial vehicle sensors by using extended pmi filter. IFAC-PapersOnLine 51(24), 818–823 (2018). 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2018
Fourlas, G.K., Karras, G.C.: A survey on fault diagnosis methods for uavs. In: 2021 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 394–403 (2021). https://doi.org/10.1109/ICUAS51884.2021.9476733
De Luca, A., Albu-Schaffer, A., Haddadin, S., Hirzinger, G.: Collision detection and safe reaction with the dlr-iii lightweight manipulator arm. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1623–1630 (2006)
Ruggiero, F., Cacace, J., Sadeghian, H., Lippiello, V.: Impedance control of vtol uavs with a momentum-based external generalized forces estimator. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2093–2099 (2014)
Ruggiero, F., Cacace, J., Sadeghian, H., Lippiello, V.: Passivity-based control of vtol uavs with a momentum-based estimator of external wrench and unmodeled dynamics. Robot. Auton. Syst. 72, 139–151 (2015)
Guo, D., Zhong, M., Ji, H., Liu, Y., Yang, R.: A hybrid feature model and deep learning based fault diagnosis for unmanned aerial vehicle sensors. Neurocomputing 319, 155–163 (2018)
Samy, I., Postlethwaite, I., Gu, D.-W.: Sensor fault detection and accommodation using neural networks with application to a non-linear unmanned air vehicle model. Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng. 224(4), 437–447 (2010)
Wen, L., Li, X., Gao, L., Zhang, Y.: A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans. Ind. Electron. 65(7), 5990–5998 (2018)
Zhang, X., Zhao, Z., Wang, Z., Wang, X.: Fault detection and identification method for quadcopter based on airframe vibration signals. Sensors 21(2) (2021)
Sadhu, V., Zonouz, S., Pompili, D.: On-board deep-learning-based unmanned aerial vehicle fault cause detection and identification. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 5255–5261 (2020)
Wang, B., Liu, D., Peng, Y., Peng, X.: Multivariate regression-based fault detection and recovery of uav flight data. IEEE Trans. Instrum. Meas. 69(6), 3527–3537 (2020)
Madani, T., Benallegue, A.: Backstepping control for a quadrotor helicopter. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3255–3260 (2006)
Lee, T., Leok, M., McClamroch, N.H.: Geometric tracking control of a quadrotor uav on se(3). In: 49th IEEE Conference on Decision and Control (CDC), pp. 5420–5425 (2010)
Ruggiero, F., Trujillo, M.A., Cano, R., Ascorbe, H., Viguria, A., Peréz, C., Lippiello, V., Ollero, A., Siciliano, B.: A multilayer control for multirotor uavs equipped with a servo robot arm. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4014–4020 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–80 (1997)
Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. Proceedings of the 30th International Conference on International Conference on Machine Learning - vol. 28, pp. 1310–1318 (2013)
jMavSim. https://github.com/PX4/jMAVSim. (2024) Accessed 29 March 2021
Shah, S., Dey, D., Lovett, C., Kapoor, A.: AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles (2017)
Ferrera, E., Alcántara, A., Capitán, J., Castaño, A.R., Marrón, P.J., Ollero, A.: Decentralized 3d collision avoidance for multiple uavs in outdoor environments. Sensors 18(12) (2018)
Suárez Fernández, R.A., Dominguez, S., Campoy, P.: L1 adaptive control for wind gust rejection in quad-rotor uav wind turbine inspection. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1840–1849 (2017)
Vann, F.W.: Gust loads on aircraft: Concepts and applications. f. m. hoblit. american institute of aeronautics and astronautics, washington, d.c. 1989. 306 pp. illustrated. \(39.95 (aiaa members) \)49.95 (non-members). Aeronaut. J. (1968) 93(930), 406–406 (1989)
Abichandani, P., Lobo, D., Ford, G., Bucci, D., Kam, M.: Wind measurement and simulation techniques in multi-rotor small unmanned aerial vehicles. IEEE Access 8, 54910–54927 (2020)
Zhong, Y., Zhang, Y., Zhang, W., Zuo, J., Zhan, H.: Robust actuator fault detection and diagnosis for a quadrotor uav with external disturbances. IEEE Access 6, 48169–48180 (2018)
Acknowledgements
The research leading to these results has been supported by the COWBOT project, in the frame of the PRIN 2020 research program, grant n. 2020NH7EAZ_002, the AI-DROW project, in the frame of the PRIN 2022 research program, grant n. 2022BYSBYX, funded by the European Union Next-Generation EU, and the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie (grant agreement n. 953454). The authors are solely responsible for its content.
Funding
Open access funding provided by Università degli Studi di Napoli Federico II within the CRUI-CARE Agreement. See acknowledgment section.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dr. Jonathan Cacace and MSc Vincenzo Scognamiglio. The first draft of the manuscript was written by Dr. Jonathan Cacace and MSc Vincenzo Scognamiglio and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics Approval
This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of University of Naples “Federico II”.
Consent to Partecipate
Not applicable.
Consent for Publication
Not applicable.
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Cacace, J., Scognamiglio, V., Ruggiero, F. et al. Motor Fault Detection and Isolation for Multi-Rotor UAVs Based on External Wrench Estimation and Recurrent Deep Neural Network. J Intell Robot Syst 110, 148 (2024). https://doi.org/10.1007/s10846-024-02176-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10846-024-02176-2