Modelos Neural ODE controlados y GRU para detección multiclasede fallas basada en física en drones cuadrorrotor
DOI:
https://doi.org/10.46842/ipn.cien.v30n2a04Palabras clave:
detección de fallas, dron cuadrorrotor, red Neural ODE, red GRU, simulación numéricaResumen
Este trabajo presenta una metodología de diagnóstico para detectar fallas progresivas en drones cuadrorrotadores mediante una red Neural ODE controlada y una red GRU utilizada como referencia recurrente. La información de entrenamiento y evaluación se obtuvo mediante una simulación numérica que integra la dinámica de vuelo, el comportamiento eléctrico de la batería, la eficiencia de los motores, el calentamiento térmico y el ruido de los sensores. Las fallas consideradas corresponden a la degradación de la batería, la pérdida de efectividad del motor y el sobrecalentamiento, con inicios aleatorios y evoluciones graduales, para representar escenarios de larga duración. El estudio compara ambas arquitecturas bajo la misma partición de datos, las mismas clases y la misma función de pérdida, de modo que la evaluación se enfoque en su capacidad para extraer patrones temporales a partir de la telemetría. El manuscrito desarrolla los modelos físicos empleados para generar las señales, define la formulación de aprendizaje, describe la configuración de la simulación numérica y organiza los resultados mediante tablas y figuras que permiten interpretar la clasificación multiclase y la detección de operaciones anómalas.
Referencias
[1] G. K. Fourlas, G. C. Karras, "A survey on fault diagnosis and fault-tolerant control methods for unmanned aerial vehicles," Machines, vol. 9, no. 9, art. no. 197, Sep. 2021, doi: https://doi.org/10.3390/machines9090197
[2] S. Yin, S. X. Ding, X. Xie, H. Luo, "A review on basic data-driven approaches for industrial process monitoring," IEEE Transactions on Industrial Electronics, vol. 61, no. 11, pp. 6418-6428, Nov. 2014, doi: https://doi.org/10.1109/TIE.2014.2301773
[3] R. Isermann, "Model-based fault-detection and diagnosis - status and applications," Annual Reviews in Control, vol. 29, no. 1, pp. 71-85, 2005, doi: https://doi.org/10.1016/j.arcontrol.2004.12.002
[4] N. P. Nguyen, S. K. Hong, "Fault diagnosis and fault-tolerant control scheme for quadcopter UAVs with a total loss of actuator," Energies, vol. 12, no. 6, art. no. 1139, Mar. 2019, doi: https://doi.org/10.3390/en12061139
[5] P. Pounds, R. Mahony, P. Corke, "Modelling and control of a large quadrotor robot," Control Engineering Practice, vol. 18, no. 7, pp. 691-699, Jul. 2010, doi: https://doi.org/10.1016/j.conengprac.2010.02.008
[6] G. M. Hoffmann, H. Huang, S. L. Waslander, C. J. Tomlin, "Quadrotor helicopter flight dynamics and control: theory and experiment," in AIAA Guidance, Navigation and Control Conference and Exhibit, Hilton Head, SC, USA, Aug. 2007, doi: https://doi.org/10.2514/6.2007-6461.
[7] D. Mellinger, V. Kumar, "Minimum snap trajectory generation and control for quadrotors," in 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, May 2011, pp. 2520-2525, doi: https://doi.org/10.1109/ICRA.2011.5980409
[8] O. Tremblay, L.-A. Dessaint, "Experimental validation of a battery dynamic model for EV applications," World Electric Vehicle Journal, vol. 3, no. 2, pp. 289-298, 2009, doi: https://doi.org/10.3390/wevj3020289
[9] C. Forgez, D. Vinh Do, G. Friedrich, M. Morcrette, C. Delacourt, "Thermal modeling of a cylindrical LiFePO4/graphite lithium-ion battery," Journal of Power Sources, vol. 195, no. 9, pp. 2961-2968, May 2010, doi: https://doi.org/10.1016/j.jpowsour.2009.10.105
[10] R. T. Q. Chen, Y. Rubanova, J. Bettencourt, D. Duvenaud, "Neural ordinary differential equations," in Advances in Neural Information Processing Systems, vol. 31, 2018, pp. 6572-6583, doi: https://doi.org/10.48550/arXiv.1806.07366
[11] P. Kidger, J. Morrill, J. Foster, T. Lyons, "Neural controlled differential equations for irregular time series," in Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 6696-6707, doi: https://doi.org/10.48550/arXiv.2005.08926
[12] K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, "Learning phrase representations using RNN encoder-decoder for statistical machine translation," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, Oct. 2014, pp. 1724-1734, doi: https://doi.org/10.3115/v1/D14-1179
[13] C. Solis, J. Morales, C. Montelongo, S. Palomino, "Transformer- and GRU-Based Identification of Open-Chain Robot Kinematics Using Product-of-Exponentials Coordinates," Technologies, vol. 14, no. 6, p. 333, 2026, doi: https://doi.org/10.3390/technologies14060333
[14] D. P. Kingma, J. Ba, "Adam: A method for stochastic optimization," in International Conference on Learning Representations, San Diego, CA, USA, 2015, doi: https://doi.org/10.48550/arXiv.1412.6980
[15] T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollár, "Focal loss for dense object detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 318-327, Feb. 2020, doi: https://doi.org/10.1109/TPAMI.2018.2858826
[16] T. Fawcett, "An introduction to ROC analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861-874, Jun. 2006, doi: https://doi.org/10.1016/j.patrec.2005.10.010
[17] T. Saito, M. Rehmsmeier, "The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets," PLoS ONE, vol. 10, no. 3, art. no. e0118432, Mar. 2015, doi: https://doi.org/10.1371/journal.pone.0118432
[18] D. Chicco, G. Jurman, "The advantages of the Matthews correlation coefficient over F1 score and accuracy in binary classification evaluation," BMC Genomics, vol. 21, art. no. 6, Jan. 2020, doi: https://doi.org/10.1186/s12864-019-6413-7
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Derechos de autor 2026 César Solís Cervantes, Jorge Morales Mercado, Carlos Montelongo Vázquez , Gerardo Sonck Martínez (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
