Abstract
Accurate prediction of the remaining useful life (RUL) of engineering systems provides decision-makers valuable information to apply more efficient maintenance programs maximizing the equipment usage and avoiding the increase of costs due to failures. In this area, deep learning methods have become increasingly popular because of their capability to learn complex and discriminative non-linear features that can facilitate the RUL prediction task. These network models are generally trained to minimize the mean square error (MSE) between the RUL prediction and its true value. This metric gives equal importance to the error at the beginning and at the end of a system’s useful life. However, the prediction of the RUL is more critical as a system approaches the end of its useful life. In this chapter, a performance metric for evaluating prognostic models is proposed with the objective of establishing a direct relation between RUL prediction and maintenance planning. In addition, a procedure to use this metric for training a recurrent neural network (RNN) is proposed to improve the network’s ability to learn the relationship between the raw data and the corresponding RUL, giving more importance to obtain accurate predictions as the system approaches to the end of its useful life. The procedure is applied to the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. The satisfactory results confirm the validity of the proposal.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alberto-Olivares, M., Gonzalez-Gutierrez, A., Tovar-Arriaga, S., Gorrostieta-Hurtado, E.: Remaining useful life prediction for turbofan based on a multilayer perceptron and kalman filter. In: 2019 16th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1–6. IEEE (2019)
Di Maio, F., Turati, P., Zio, E.: Prediction capability assessment of data-driven prognostic methods for railway applications. In: Proceedings of the Third European Conference of the Prognostic and Health Management Society (2016)
Heimes, F.O.: Recurrent neural networks for remaining useful life estimation. In: 2008 International Conference on Prognostics and Health Management, pp. 1–6. IEEE (2008)
Hinton, G., Srivastava, N., Swersky, K.: Neural networks for machine learning lecture 6a overview of mini-batch gradient descent. Cited on 14(8), (2012)
Huang, C.G., Huang, H.Z., Li, Y.F., Peng, W.: A novel deep convolutional neural network-bootstrap integrated method for RUL prediction of rolling bearing. J. Manuf. Syst. (2021)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014)
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., Lin, J.: Machinery health prognostics: a systematic review from data acquisition to RUL prediction. Mech. Syst. Signal Process. 104, 799–834 (2018)
Li, H., Zhao, W., Zhang, Y., Zio, E.: Remaining useful life prediction using multi-scale deep convolution neural network. Appl. Soft Comput. 89, 106–113 (2020)
Pei, H., Si, X.S., Hu, C.H., Zheng, J.F., Li, T.M., Zhang, J.X., Pang, Z.N.: An adaptive prognostics method for fusing CDBN and diffusion process: application to bearing data. Neurocomputing 421, 303–315 (2021)
Saxena, A., Celaya, J., Saha, B., Saha, S., Goebel, K.: Metrics for offline evaluation of prognostic performance. Int. J. Progn. Health Manag. 1(1), 4–23 (2010)
Saxena, A., Goebel, K., Simon, D., Eklund, N.: Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 International Conference on Prognostics and Health Management, pp. 1–9. IEEE (2008)
Shi, Z., Chehade, A.: A dual-LSTM framework combining change point detection and remaining useful life prediction. Reliab. Eng. Syst. Saf. 205, 107257 (2021)
Song, Y., Shi, G., Chen, L., Huang, X., Xia, T.: Remaining useful life prediction of turbofan engine using hybrid model based on autoencoder and bidirectional long short-term memory. J. Shanghai Jiatong Univ. (Sci.) 23(1), 85–94 (2018)
Wang, B., Lei, Y., Yan, T., Li, N., Guo, L.: Recurrent convolutional neural network: a new framework for remaining useful life prediction of machinery. Neurocomputing 379, 117–129 (2020)
Wang, H., Peng, M.J., Miao, Z., Liu, Y.K., Ayodeji, A., Hao, C.: Remaining useful life prediction techniques for electric valves based on convolution auto encoder and long short term memory. ISA Trans. 108, 333–342 (2021)
Wu, S., Jiang, Y., Luo, H., Yin, S.: Remaining useful life prediction for ion etching machine cooling system using deep recurrent neural network-based approaches. Control Eng. Pract. 109, 104748 (2021)
Zeng, Z., Di Maio, F., Zio, E., Kang, R.: A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods. Proc. Inst. Mech. Eng., Part 0: J. Risk Reliab. 231(1), 36–52 (2017)
Zhang, H., Mo, Z., Wang, J., Miao, Q.: Nonlinear-drifted fractional brownian motion with multiple hidden state variables for remaining useful life prediction of lithium-ion batteries. IEEE Trans. Reliab. 69(2), 768–780 (2019)
Zheng, S., Ristovski, K., Farahat, A., Gupta, C.: Long short-term memory network for remaining useful life estimation. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 88–95. IEEE (2017)
Zhu, J., Chen, N., Peng, W.: Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Trans. Ind. Electron. 66(4), 3208–3216 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Villalón-Falcón, A., Prieto-Moreno, A., Quiñones-Grueiro, M., Llanes-Santiago, O. (2022). A Proposal for Improving Remaining Useful Life Prediction in Industrial Systems: A Deep Learning Approach. In: Shi, P., Stefanovski, J., Kacprzyk, J. (eds) Complex Systems: Spanning Control and Computational Cybernetics: Applications. Studies in Systems, Decision and Control, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-031-00978-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-00978-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-00977-8
Online ISBN: 978-3-031-00978-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)