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Remaining Useful Life Estimation Using a Recurrent Variational Autoencoder

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Hybrid Artificial Intelligent Systems (HAIS 2021)


A new framework for the assessment of Engine Health Monitoring (EHM) data in aircraft is proposed. Traditionally, prognostics and health management systems rely on prior knowledge of the degradation of certain components along with professional expert opinion to predict the Remaining Useful Life (RUL). In order to avoid reliance on this process while still providing an accurate diagnosis, a data-driven approach using a novel recurrent version of a VAE is introduced. The latent space learned by this model, trained with the historical data recorded by the sensors embedded in these engines, is used to visually evaluate the deterioration progress of the engines. High prognostic accuracy in estimating the RUL is achieved by building a simple classifier on top of the learned features of the VAE. The superiority of the proposed method is compared with other popular and state-of-the-art approaches using Rolls Royce Turbofan engine data. The results of this study suggest that the proposed data-driven prognostic and explainable framework offers a new and promising approach.

Partially supported by the Ministry of Economy, Industry and Competitiveness (“Ministerio de Economía, Industria y Competitividad”) of Spain/FEDER under grants TIN2017-84804-R and PID2020-112726-RB.

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  1. Ali, J.B., Chebel-Morello, B., Saidi, L., Malinowski, S., Fnaiech, F.: Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mech. Syst. Signal Process. 56, 150–172 (2015)

    Google Scholar 

  2. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(Jan), 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  3. Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017)

  4. Jouin, M., Gouriveau, R., Hissel, D., Péra, M.C., Zerhouni, N.: Degradations analysis and aging modeling for health assessment and prognostics of PEMFC. Reliabil. Eng. Syst. Saf. 148, 78–95 (2016)

    Article  Google Scholar 

  5. Khawaja, T., Vachtsevanos, G., Wu, B.: Reasoning about uncertainty in prognosis: a confidence prediction neural network approach. In: NAFIPS 2005–2005 Annual Meeting of the North American Fuzzy Information Processing Society, pp. 7–12. IEEE (2005)

    Google Scholar 

  6. Khelif, R., Chebel-Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., Zerhouni, N.: Direct remaining useful life estimation based on support vector regression. IEEE Trans. Industr. Electron. 64(3), 2276–2285 (2016)

    Article  Google Scholar 

  7. Li, L., Yan, J., Wang, H., Jin, Y.: Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder. IEEE Trans. Neural Netw. Learn. Syst. 32, 1177–1191 (2020)

    Article  Google Scholar 

  8. Li, X., Ding, Q., Sun, J.Q.: Remaining useful life estimation in prognostics using deep convolution neural networks. Reliabil. Eng. Syst. Saf. 172, 1–11 (2018)

    Article  Google Scholar 

  9. Martínez, A., Sánchez, L., Couso, I.: Engine health monitoring for engine fleets using fuzzy radviz. In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8. IEEE (2013)

    Google Scholar 

  10. Miao, H., Li, B., Sun, C., Liu, J.: Joint learning of degradation assessment and RUL prediction for aeroengines via dual-task deep LSTM networks. IEEE Trans. Industr. Inf. 15(9), 5023–5032 (2019)

    Article  Google Scholar 

  11. Singh, S.K., Kumar, S., Dwivedi, J.: A novel soft computing method for engine RUL prediction. Multimed. Tools Appl. 78(4), 4065–4087 (2019)

    Article  Google Scholar 

  12. Tian, Z.: An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. J. Intell. Manuf. 23(2), 227–237 (2012)

    Article  Google Scholar 

  13. Zhang, A., et al.: Transfer learning with deep recurrent neural networks for remaining useful life estimation. Appl. Sci. 8(12), 2416 (2018)

    Article  Google Scholar 

  14. Zhao, Z., Liang, B., Wang, X., Lu, W.: Remaining useful life prediction of aircraft engine based on degradation pattern learning. Reliabil. Eng. Syst. Saf. 164, 74–83 (2017)

    Article  Google Scholar 

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Correspondence to Nahuel Costa or Luciano Sánchez .

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Costa, N., Sánchez, L. (2021). Remaining Useful Life Estimation Using a Recurrent Variational Autoencoder. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham.

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  • Print ISBN: 978-3-030-86270-1

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