Skip to main content
SpringerLink
Log in

A New Approach for Remaining Useful Life Estimation Using Deep Learning

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

Prognosis and Health Management (PHM) refer specifically to the prediction phase of the future behavior of the system or subsystem, including the remaining useful life (RUL). It is helpful to early detect incipient failures in many domains as aircraft, nuclear reactor, turbine gas, etc. In this paper we propose a new approach based on the implementation of data-driven methods for fault prognosis. Such methods require the availability of data describing the degradation process; when there is a lack of data, it is difficult to predict the states using deep models, which require a large amount of training data. In this paper, we propose to use a simple data augmentation strategy to solve the problem of data scarcity in prediction of RUL provided through the use of a long-short term memory (LSTM), which is a type of recurrent neural network. The results of our experiments demonstrate that using a simple data augmentation strategy can increase RUL prediction performance by using LSTM technics. We analyze our approach using data from NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

REFERENCES

  1. Li, L.-L., Zhang, X.-B., Tseng, M.-L., and Zhou, Ya-T., Optimal scale Gaussian process regression model in insulated gate bipolar transistor remaining life prediction, Appl. Soft Comput., 2019, vol. 78, pp. 261–273. https://doi.org/10.1016/j.asoc.2019.02.035

    Article  Google Scholar 

  2. Wang, Yu., Deng, Ch., Wu, Ju., Wang, Yi., and Xiong, Ya., A corrective maintenance scheme for engineering equipment, Eng. Failure Anal., 2014, vol. 36, p. 269–283. https://doi.org/10.1016/j.engfailanal.2013.10.006

    Article  Google Scholar 

  3. Liao, L. and Köttig, F., A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction, Appl. Soft Comput., 2016, vol. 44, pp. 191–199. https://doi.org/10.1016/j.asoc.2016.03.013

  4. Ramasso, E., Rombaut, M., and Zerhouni, A., Joint prediction of continuous and discrete states in time-series based on belief functions, IEEE Trans. Cybern., 2013, vol. 43, no. 1, pp. 37–50. https://doi.org/10.1109/TSMCB.2012.2198882

    Article  Google Scholar 

  5. Lei, Y., Li, N., Guo, L., Li, N., Yan, T., and Lin, J., Machinery health prognostics: A systematic review from data acquisition to RUL prediction, Mech. Syst. Signal Process., 2018, vol. 104, pp. 799–834. https://doi.org/10.1016/j.ymssp.2017.11.016

    Article  Google Scholar 

  6. Liu, J. and Zio, E., Prediction of peak values in time series data for prognostics of critical components in nuclear power plants, IFAC-PapersOnLine, 2016, vol. 49, no. 28, pp. 174–178. https://doi.org/10.1016/j.ifacol.2016.11.030

    Article  MathSciNet  Google Scholar 

  7. Hornik, K., Approximation capabilities of multilayer feedforward networks, Neural Networks, 1991, vol. 4, no. 2, pp. 251–257. https://doi.org/10.1016/0893-6080(91)90009-T

    Article  Google Scholar 

  8. Peel, L., Data driven prognostics using a Kalman filter ensemble of neural network models, 2008 Int. Conf. on Prognostics and Health Management, Denver, Colo., 2008, IEEE, 2008, pp. 1–6. https://doi.org/10.1109/PHM.2008.4711423

  9. Tamilselvan, P. and Wang, P., Failure diagnosis using deep belief learning based health state classification, Reliab. Eng. Syst. Saf., 2013, vol. 115, pp. 124–135. https://doi.org/10.1016/j.ress.2013.02.022

    Article  Google Scholar 

  10. Zhang, X., Xiao, L., and Kang, J., Degradation prediction model based on a neural network with dynamic windows, Sensors, 2015, vol. 15, no. 3, pp. 6996–7015. https://doi.org/10.3390/s150306996

    Article  Google Scholar 

  11. Zhao, Z., Liang, B., Wang, X., and Lu, W., Remaining useful life prediction of aircraft engine based on degradation pattern learning, Reliab. Eng. Syst. Saf., 2017, vol. 164, no. 457, pp. 74–83. https://doi.org/10.1016/j.ress.2017.02.007

    Article  Google Scholar 

  12. Ompusunggu, A.P., Papy, J.M., and Vandenplas, S., Kalman-filtering-based prognostics for automatic transmission clutches, IEEE/ASME Trans. Mechatronics, 2016, vol. 21, no. 1, pp. 419–430. https://doi.org/10.1109/TMECH.2015.2440331

    Article  Google Scholar 

  13. Li, X., Ding, Q., and Sun, J.Q., Remaining useful life estimation in prognostics using deep convolution neural networks, Reliab. Eng. Syst. Saf., 2018, vol. 172, pp. 1–11. https://doi.org/10.1016/j.ress.2017.11.021

    Article  Google Scholar 

  14. Khelif, R., Chebel-Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., and Zerhouni, N., Direct remaining useful life estimation based on support vector regression, IEEE Trans. Ind. Electron., 2017, vol. 64, no. 3, pp. 2276–2285. https://doi.org/10.1109/TIE.2016.2623260

    Article  Google Scholar 

  15. Wu, J., Xu, J., and Huang, X., An indirect prediction method of remaining life based on glowworm swarm optimization and extreme learning machine for lithium battery, 36th Chinese Control Conf. (CCC), Dalian, China, 2017, IEEE, 2017, pp. 7259–7264. https://doi.org/10.23919/ChiCC.2017.8028502

  16. Morando, S., Jemei, S., Gouriveau, R., Zerhouni, N., and Hissel, D., Fuel cells remaining useful lifetime forecasting using echo state network, 2014 IEEE Vehicle Power and Propulsion Conf. (VPPC), Coimbra, Portugal, 2014, IEEE, 2014, pp. 1–6. https://doi.org/10.1109/VPPC.2014.7007074

  17. H. Liu, J. Zhou, Y. Zheng, W. Jiang, and Y. Zhang, Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders, ISA Trans., 2018, vol. 77, pp. 167–178. https://doi.org/10.1016/j.isatra.2018.04.005

    Article  Google Scholar 

  18. R. Chandra, Competition and collaboration in cooperative coevolution of elman recurrent neural networks for time-series prediction, IEEE Trans. Neural Networks Learn. Syst., 2015, vol. 26, no. 12, pp. 3123–3136. https://doi.org/10.1109/TNNLS.2015.2404823

    Article  MathSciNet  Google Scholar 

  19. Malhi, A. and Gao, R.X., Recurrent neural networks for long-term prediction in machine condition monitoring, Proc. 21st IEEE Instrumentation Measurement Technology Conf., Como, Italy, 2004, IEEE, 2004, pp. 2048–2053. https://doi.org/10.1109/imtc.2004.1351492

  20. Lukoševičius, M. and Jaeger, H., Reservoir computing approaches to recurrent neural network training, Comput. Sci. Rev., 2009, vol. 3, no. 3, pp. 127–149. https://doi.org/10.1016/j.cosrev.2009.03.005

    Article  MATH  Google Scholar 

  21. Wu, Y., Yuan, M., Dong, S., Lin, L., and Liu, Y., Remaining useful life estimation of engineered systems using vanilla LSTM neural networks, Neurocomputing, 2018, vol. 275, pp. 167–179. https://doi.org/10.1016/j.neucom.2017.05.063

    Article  Google Scholar 

  22. Likhitha, D.L. and Nagaraja, R., Prediction of remaining useful life of an aircraft engine using LSTM network, 2019, vol. 9, no. 6, pp. 329–334.

  23. Li, H., Huang, J., Yang, X., Luo, J., Zhang, L., and Pang, Y., Fault diagnosis for rotating machinery using multiscale permutation entropy and convolutional neural networks, Entropy, 2020, vol. 22, no. 8, pp. 101–110. https://doi.org/10.3390/E22080851

    Article  MathSciNet  Google Scholar 

  24. Zheng, Sh., Ristovski, K., Farahat, A., and Gupta, Ch., Long short-term memory network for remaining useful life estimation, 2017 IEEE Int. Conf. on Prognonstics and Health Management (ICPHM), Dallas, Texas, 2017, IEEE, 2017, pp. 88–95. https://doi.org/10.1109/ICPHM.2017.7998311

  25. Wu, Ju., Hu, K., Cheng, Yi., Zhu, H., Shao, X., and Wang, Yu., Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network, ISA Trans., 2020, vol. 97, pp. 241–250. https://doi.org/10.1016/j.isatra.2019.07.004

    Article  Google Scholar 

  26. Xia, M., Zheng, X., Imran, M., and Shoaib, M., Data-driven prognosis method using hybrid deep recurrent neural network, Appl. Soft Comput., 2020, vol. 93, p. 106351. https://doi.org/10.1016/j.asoc.2020.106351

    Article  Google Scholar 

  27. Hochreiter, S., The vanishing gradient problem during learning recurrent neural nets and problem solutions, Int. J. Uncertainty, Fuzziness Knowl.-Based Syst., 1998, vol. 6, no. 2, pp. 107–116. https://doi.org/10.1142/S0218488598000094

    Article  MATH  Google Scholar 

  28. Hochreiter, S. and Schmidhuber, J., Long short-term memory, Neural Comput., 1997, vol. 9, no. 8, pp. 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  29. Gers, F.A., Schmidhuber, J., and Cummins, F., Learning to forget: Continual prediction with LSTM, Neural Comput., 2000, vol. 12, no. 10, pp. 2451–2471. https://doi.org/10.1162/089976600300015015

    Article  Google Scholar 

  30. Tuncel, K.S. and Baydogan, M.G., Autoregressive forests for multivariate time series modeling, Pattern Recognit., 2018, vol. 73, pp. 202–215. https://doi.org/10.1016/j.patcog.2017.08.016

    Article  Google Scholar 

  31. Saxena, A., Goebel, K., Simon, D., and Eklund, N., Damage propagation modeling for aircraft engine run-to-failure simulation, 2008 Int. Conf. on Prognostics and Health Management, Denver, Colo., 2008, IEEE, 2008, pp. 1–9. https://doi.org/10.1109/PHM.2008.4711414

  32. Fawzi, A., Samulowitz, H., Turaga, D., and Frossard, P., Adaptive data augmentation for image classification, 2016 IEEE Int. Conf. on Image Processing (ICIP), Phoenix, Ariz., 2016, IEEE, 2016, pp. 3688–3692. https://doi.org/10.1109/ICIP.2016.7533048

  33. Kingma, D.P. and Ba, J.L., Adam: A method for stochastic optimization, 3rd Int. Conf. Learning Representation, San Diego, Calif., 2015, pp. 1–15.

  34. Heimes, F.O., Recurrent neural networks for remaining useful life estimation, 2008 Int. Conf. on Prognostics and Health Management, Denver, Colo., 2008, IEEE, 2008, pp. 1–6. https://doi.org/10.1109/PHM.2008.4711422

  35. Yuan, M., Wu, Y., and Lin, L., Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network, AUS 2016—2016 IEEE/CSAA Int. Conf. on Aircraft Utility Systems (AUS), Beijing, 2016, IEEE, 2016, pp. 135–140. https://doi.org/10.1109/AUS.2016.7748035

  36. Sayah, M., Guebli, D., Noureddine, Z., and Al Masry, Z., Deep LSTM enhancement for RUL prediction using Gaussian mixture models, Autom. Control Comput. Sci., 2021, vol. 55, no. 1, pp. 15–25. https://doi.org/10.3103/S014641162

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biskra University, Biskra, Algeria

    Drici Djalel

  2. Research Center in Industrial Technologies CRTI, Annaba, Algeria

    Drici Djalel

  3. LEER, Souk Ahras University, Souk Ahras, Algeria

    Kourd Yahia

  4. Biskra University, Biskra, Algeria

    Touba Mostefa Mohamed

  5. Research Group in Electrotechnical and Automatic University Le Havre Normandie, Le Havre, France

    Lefebvre Dimitri

Authors
  1. Drici Djalel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kourd Yahia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Touba Mostefa Mohamed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lefebvre Dimitri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Drici Djalel or Kourd Yahia.

Ethics declarations

The authors declare that they have no conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Drici Djalel, Yahia, K., Mohamed, T.M. et al. A New Approach for Remaining Useful Life Estimation Using Deep Learning. Aut. Control Comp. Sci. 57, 93–102 (2023). https://doi.org/10.3103/S0146411623010030

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411623010030

Keywords:

Search

Navigation