Abstract
The deterioration of engineering systems due to wear and working conditions impact directly on their performance, requiring more efficient maintenance programs to prevent unexpected stops and increase production quality. Neural networks have shown significant results in predicting the remaining useful life (RUL) of systems. A neural network for prognostic is 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. Therefore, making an accurate evaluation of prognostic models requires to take this into account. In this paper, a new performance metric for the evaluation of 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 multilayer perceptron (MLP) network is proposed to improve the network’s capacity to learn degradation patterns near the end of the useful life. The procedure is applied to NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset, improving the prediction results significantly.
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Project No. 27 of National Program of Research and Innovation ARIA of CITMA, Cuba.
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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, Franceso an 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)
Ellefsen, A.L., Bjørlykhaug, E., Æsøy, V., Ushakov, S., Zhang, H.: Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture. Reliab. Eng. Syst. Saf. 183, 240–251 (2019)
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)
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)
Li, X., Ding, Q., Sun, J.Q.: Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab. Eng. Syst. Saf. 172, 1–11 (2018)
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, 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)
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)
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Villalón-Falcón, A., Prieto-Moreno, A., Quiñones-Grueiro, M., Llanes-Santiago, O. (2021). A Proposal of Metric for Improving Remaining Useful Life Prediction in Industrial Systems. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2021. Lecture Notes in Computer Science(), vol 13055. Springer, Cham. https://doi.org/10.1007/978-3-030-89691-1_18
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DOI: https://doi.org/10.1007/978-3-030-89691-1_18
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