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A Predictive Maintenance Model Using Recurrent Neural Networks

  • Alberto RivasEmail author
  • Jesús M. Fraile
  • Pablo Chamoso
  • Alfonso González-Briones
  • Inés Sittón
  • Juan M. Corchado
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 950)

Abstract

One of the main goals of Industry 4.0 is to anticipate machine breakdowns. Being able to prevent failures is important because downtime implies high cost and production loss. For this reason, the calculation of the number of remaining cycles or Remaining Useful Life (RUL) until a breakdown occurs is essential for machine maintenance. The calculation of the RUL should be based on previous observations, if possible under the same conditions. Research on RUL estimation has become central to the development of systems that monitor the current state of machines. Although this field has been studied in-depth, there is no single universal method. The lack of a universal method is the motivation behind this proposal in which the designed system uses recurrent neural networks (RNN) in a predictive maintenance problem.

Keywords

Remaining useful life Recurrent neural network Predictive maintenance Industry 4.0 

Notes

Acknowledgments

This research has been partially supported by the European Regional Development Fund (ERDF) under the IOTEC project grant 0123_IOTEC_3_E and by the Spanish Ministry of Economy, Industry and Competitiveness.

References

  1. 1.
    Briones, A.G., Chamoso, P., Rivas, A., Rodríguez, S., De La Prieta, F., Prieto, J., Corchado, J.M.: Use of gamification techniques to encourage garbage recycling. A smart city approach. In: International Conference on Knowledge Management in Organizations, pp. 674–685. Springer, Heidelberg (2018)Google Scholar
  2. 2.
    Candanedo, I.S., Nieves, E.H., González, S.R., Martín, M.T.S., Briones, A.G.: Machine learning predictive model for industry 4.0. In: International Conference on Knowledge Management in Organizations, pp. 501–510. Springer, Heidelberg (2018)Google Scholar
  3. 3.
    Chamoso, P., González-Briones, A., Rivas, A., De La Prieta, F., Corchado, J.M.: Social computing in currency exchange. Knowl. Inf. Syst., pp. 1–21 (2019)Google Scholar
  4. 4.
    Do, P., Voisin, A., Levrat, E., Iung, B.: A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions. Reliab. Eng. Syst. Saf. 133, 22–32 (2015)CrossRefGoogle Scholar
  5. 5.
    Funahashi, K.-I.: On the approximate realization of continuous mappings by neural networks. Neural Netw. 2(3), 183–192 (1989)CrossRefGoogle Scholar
  6. 6.
    Gers, F.A., Schraudolph, N.N., Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. J. Mach. Learn. Res. 3(Aug), 115–143 (2002)MathSciNetzbMATHGoogle Scholar
  7. 7.
    González-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., Corchado, J.M.: Energy optimization using a case-based reasoning strategy. Sensors 18(3), 865 (2018)CrossRefGoogle Scholar
  8. 8.
    González-Briones, A., Rivas, A., Chamoso, P., Casado-Vara, R., Corchado, J.M.: Case-based reasoning and agent based job offer recommender system. In: The 13th International Conference on Soft Computing Models in Industrial and Environmental Applications, pp. 21–33. Springer, Heidelberg (2018)Google Scholar
  9. 9.
    Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D.: Draw: A recurrent neural network for image generation. arXiv preprint arXiv:1502.04623 (2015)
  10. 10.
    Higgins, L.R., Mobley, R.K., Smith, R., et al.: Maintenance Engineering Handbook. McGraw-Hill, New York (2002)Google Scholar
  11. 11.
    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  12. 12.
    Krishnanand, K.R., Dash, P.K., Naeem, M.H.: Detection, classification, and location of faults in power transmission lines. Int. J. Electr. Power Energy Syst. 67, 76–86 (2015)CrossRefGoogle Scholar
  13. 13.
    Na, M.G.: Auto-tuned PID controller using a model predictive control method for the steam generator water level. IEEE Transact. Nucl. Sci. 48(5), 1664–1671 (2001)CrossRefGoogle Scholar
  14. 14.
    Rivas, A., Martín, L., Sittón, I., Chamoso, P., Martín-Limorti, J.J., Prieto, J., González-Briones, A.: Semantic analysis system for industry 4.0. In: International Conference on Knowledge Management in Organizations, pp. 537–548. Springer, Heidelberg (2018)Google Scholar
  15. 15.
    Rivas, A., Martín-Limorti, J.J., Chamoso, P., González-Briones, A., De La Prieta, F., Rodríguez, S.: Human-computer interaction in currency exchange. In: International Conference on Knowledge Management in Organizations, pp. 390–400. Springer, Heidelberg (2018)Google Scholar
  16. 16.
    Saxena, A., Goebel, K.: Turbofan engine degradation simulation data set. NASA Ames Prognostics Data Repository (2008)Google Scholar
  17. 17.
    Smith, C.A., Corripio, A.B., Basurto, S.D.M.: Control automático de procesos: teoría y práctica. Limusa (1991). Number 968-18-3791-6. 01-A3 LU. AL-PCS. 1Google Scholar
  18. 18.
    Swanson, L.: Linking maintenance strategies to performance. Int. J. Prod. Econ. 70(3), 237–244 (2001)CrossRefGoogle Scholar
  19. 19.
    Taher, S.A., Sadeghkhani, I.: Estimation of magnitude and time duration of temporary overvoltages using ann in transmission lines during power system restoration. Simul. Model. Pract. Theory 18(6), 787–805 (2010)CrossRefGoogle Scholar
  20. 20.
    Trinh, H.C., Kwon, Y.K.: An empirical investigation on a multiple filters-based approach for remaining useful life prediction. Machines 6(3), 35 (2018)CrossRefGoogle Scholar
  21. 21.
    Zhou, D., Zhang, H., Weng, S.: A novel prognostic model of performance degradation trend for power machinery maintenance. Energy 78, 740–746 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alberto Rivas
    • 1
    Email author
  • Jesús M. Fraile
    • 1
  • Pablo Chamoso
    • 1
  • Alfonso González-Briones
    • 1
  • Inés Sittón
    • 1
  • Juan M. Corchado
    • 1
    • 2
    • 3
    • 4
  1. 1.BISITE Research Group, University of SalamancaSalamancaSpain
  2. 2.Air Institute, IoT Digital Innovation Hub (Spain)SalamancaSpain
  3. 3.Department of Electronics, Information and Communication, Faculty of EngineeringOsaka Institute of TechnologyOsakaJapan
  4. 4.Pusat Komputeran dan InformatikUniversiti Malaysia KelantanKota BharuMalaysia

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