Abstract
When a model of an industrial system is developed, it is expected that this model performs consistently when applied to other identically designed systems. However, different operating hours, degradation or maintenance, among other circumstances, cause a change in the dynamics of the system and result in the model not performing as expected. For this reason, it is necessary to build a model that continuously adapts to changes in the dynamics of the system, in order to handle such deviations and thus reduce the estimation error.
This paper proposes the development of an adaptive model based on Echo State Networks to estimate the level of a water tank. For this purpose, two identically designed industrial pilot plants are used, taking one of them as a reference for the parameterization, training and validation of the model, and applying the developed model to the other one in order to evaluate the adaptation to changes in the dynamics of the system.
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References
Antonelo, E.A., Camponogara, E., Foss, B.: Echo state networks for data-driven downhole pressure estimation in gas-lift oil wells. Neural Netw. 85, 106–117 (2017)
Dai, J., Venayagamoorthy, G.K., Harley, R.G.: Harmonic identification using an echo state network for adaptive control of an active filter in an electric ship. In: 2009 International Joint Conference on Neural Networks, pp. 634–640. IEEE (2009)
Fuertes, J., Prada, M.A., Domínguez, M., Reguera, P., Díaz, I., Díez, A.: Visualization of dynamic parameters of a multivariable system using self-organizing maps. IFAC Proc. Volumes 41(2), 11001–11006 (2008)
Fuertes, J.J., Prada, M.A., Rodríguez-Ossorio, J.R., González-Herbón, R., Pérez, D., Domínguez, M.: Environment for education on industry 4.0. IEEE Access 9, 144395–144405 (2021). https://doi.org/10.1109/ACCESS.2021.3120517
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148(34), 13(2001)
Jaeger, H.: Echo state network. Scholarpedia 2, 2330 (2007). https://doi.org/10.4249/SCHOLARPEDIA.2330
Johansson, K.H.: The quadruple-tank process: a multivariable laboratory process with an adjustable zero. IEEE Trans. Control Syst. Technol. 8(3), 456–465 (2000). https://doi.org/10.1109/87.845876
Jordanou, J.P., Antonelo, E.A., Camponogara, E.: Online learning control with echo state networks of an oil production platform. Eng. Appl. Artif. Intell. 85, 214–228 (2019)
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014)
Lukoševičius, M.: A practical guide to applying echo state networks. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 659–686. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8_36
Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127–149 (2009)
Salmen, M., Ploger, P.G.: Echo state networks used for motor control. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 1953–1958. IEEE (2005)
Sánchez, L., Anseán, D., Otero, J., Couso, I.: Assessing the health of LiFePO4 traction batteries through monotonic echo state networks. Sensors 18(1), 9 (2018). https://doi.org/10.3390/s18010009
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F.: Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 94(9-12), 3563–3576 (2018). https://doi.org/10.1007/s00170-017-0233-1
Trouvain, N., Pedrelli, L., Dinh, T.T., Hinaut, X.: ReservoirPy: an efficient and user-friendly library to design echo state networks. In: ICANN 2020 - 29th International Conference on Artificial Neural Networks. Bratislava, Slovakia (Sep 2020)
Venayagamoorthy, G.K.: Online design of an echo state network based wide area monitor for a multimachine power system. Neural Netw. 20(3), 404–413 (2007)
Acknowledgements
This work was supported by the Spanish State Research Agency, MCIN/ AEI/ 10.13039/ 501100011033 under Grant PID2020-117890RB-I00 and Grant PID2020-115401GB-I00. The work of José Ramón Rodríguez-Ossorio was supported by a grant from the 2020 Edition of Research Programme of the University of León.
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Rodríguez-Ossorio, J.R., Morán, A., Fuertes, J.J., Prada, M.A., Díaz, I., Domínguez, M. (2023). Adaptive Model for Industrial Systems Using Echo State Networks. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_31
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