Performance and Explainability of Reservoir Computing Models for Industrial Prognosis

  1. Armentia, Unai
  2. Barrio, Irantzu 1
  3. Del Ser, Javier 1
  4. aut
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Book:
16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021)

ISSN: 2194-5357 2194-5365

ISBN: 9783030878689 9783030878696

Year of publication: 2021

Pages: 24-36

Type: Book chapter

DOI: 10.1007/978-3-030-87869-6_3 GOOGLE SCHOLAR lock_openOpen access editor

Bibliographic References

  • Li, Z., Yi, W., Wang, K.: Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers. In: Advances in Manufacturing, pp. 377–387 (2017)
  • Ruschel, E., Alves Portela Santos, E., de Freitas Rocha Loures, E.: Industrial maintenance decision-making: a systematic literature review. J. Manuf. Syst. 45, 180–194 (2017)
  • Okoh, C., Roy, R., Mehnen, J., Redding, L.: Overview of remaining useful life prediction techniques in through-life engineering services. Procedia CIRP 16, 158–163 (2014)
  • Diez-Olivan, A., Del Ser, J., Galar, D., Sierra, B.: Data fusion and machine learning for industrial prognosis: Trends and perspectives towards industry 4.0. Inf. Fusion 50, 92–111 (2019)
  • Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning (2017)
  • Ortego, P., Diez-Olivan, A., Del Ser, J., Sierra, B.: Data augmentation for industrial prognosis using generative adversarial networks. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 113–122 (2020)
  • Diez-Olivan, A., et al.: Adaptive dendritic cell-deep learning approach for industrial prognosis under changing conditions. IEEE Trans. Industr. Inf. 17, 7760–7770 (2021)
  • Fan, Y., Nowaczyk, S., Rognvaldsson, T., Antonelo, E.: Predicting air compressor failures with echo state networks. In: European Conference of the Prognostics and Health Management Society (2016)
  • Westholm, J.: Event detection and predictive maintenance using component echo state networks, Master’s Thesis in Mathematical Statistics (2018)
  • Liu, C., Yao, R., Zhang, L., Liao, Y.: Attention based echo state network: a novel approach for fault prognosis. In: International Conference on Machine Learning and Computing, pp. 489-493 (2019)
  • Liu, K., Zhang, J.: Modelling a penicillin fermentation process using attention-based echo state networks optimized by covariance matrix adaption evolutionary strategy. Comput. Aided Chem. Eng. 48, 1117–1122 (2020)
  • Del Ser, J., et al.: Randomization-based machine learning in renewable energy prediction problems: critical literature review, new results and perspectives. arXiv preprint arXiv:2103.14624 (2021)
  • Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020)
  • Yaguo, L., Li, N., Guo, L., Li, N., Tao, Y., Lin, J.: Machinery health prognostics: a systematic review for data acquisition to RUL prediction. Mech. Syst. Sig. Process. 104, 799–834 (2018)
  • Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks – with an erratum note. German National Research Center for Information Technology GMD Technical Report 148, Bonn, Germany (2001)
  • Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004)
  • Lukoševičius, M.: A practical guide to applying echo state networks, 2nd edn. In: Neural Networks: Tricks of the Trade, pp. 659–686 (2012)
  • Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127–149 (2009)
  • Lukosevicius, M., Jaeger, H.: Overview of reservoir recipes (2007)
  • Gallicchio, C., Micheli, A., Pedrelli, L.: Deep reservoir computing: a critical experimental analysis. Neurocomputing 268, 87–99 (2017)
  • Gallicchio, C., Micheli, A.: Deep echo state network (DeepESN): a brief survey. arXiv preprint arXiv:1712.04323 (2017)
  • Saxena, A., Goebel, K.: Turbofan engine degradation simulation data set. NASA Ames Prognostics Data Repository, pp. 1551–3203 (2008)
  • Rakitianskaia, A., Engelbrecht, A.: Measuring saturation in neural networks. In: IEEE Symposium Series on Computational Intelligence, pp. 1423–1430 (2015)