Abstract
Remaining Useful Life (RUL) prediction of equipment can estimate the time when equipment reaches the safe operating limit, which is essential for strategy formulation to reduce the possibility of loss due to unexpected shutdowns. This paper proposes a novel RUL prediction model named AA-LSTM. We use a Bi-LSTM-based autoencoder to extract degradation information contained in the time series data. Meanwhile, a generative adversarial network is used to assist the autoencoder in extracting abstract representation, and then a predictor estimates the RUL based on the abstract representation learned by the autoencoder. AA-LSTM is an end-to-end model, which jointly optimizes autoencoder, generative adversarial network, and predictor. This training mechanism improves the model’s feature extraction and prediction capabilities for time series. We validate AA-LSTM on turbine engine datasets, and its performance outperforms state-of-the-art methods, especially on datasets with complex working conditions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Xia, T., Dong, Y., Xiao, L., Du, S., Pan, E., Xi, L.: Recent advances in prognostics and health management for advanced manufacturing paradigms. Reliab. Eng. Syst. Safety 178, 255–268 (2018)
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. Ind. Electron. 64(3), 2276–2285 (2017)
Zhu, K., Liu, T.: Online tool wear monitoring via hidden semi-Markov model with dependent durations. IEEE Trans. Ind. Inform. 14, 69–78 (2018)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
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) (2017)
Listou Ellefsen, A., Bjørlykhaug, E., Æsøy., Ushakov, S., Zhang, H.: Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture. Reliab. Eng. Syst. Safety 183, 240–251 (2019)
Huang, C.G., Huang, H.Z., Li, Y.F.: A bidirectional LSTM prognostics method under multiple operational conditions. IEEE Trans. Ind. Electron. 66, 8792–8802 (2019)
Wang, Q., et al.: Deep image clustering using convolutional autoencoder embedding with inception-like block. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 2356–2360 (2018)
Li, X., Ding, Q., Sun, J.Q.: Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab. Eng. Syst. Safety 172, 1–11 (2018)
Long, W., Yan, D., Liang, G.: A new ensemble residual convolutional neural network for remaining useful life estimation. Math. Biosci. En. MBE 16(2), 862–880 (2019)
Hong, C.W., Lee, K., Ko, M.S., Kim, J.K., Oh, K., Hur, K.: Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis. In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) (2020)
Xia, T., Song, Y., Zheng, Y., Pan, E., Xi, L.: An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation. Comput. Ind. 115, 103182 (2020)
Liu, H., Liu, Z., Jia, W., Lin, X.: Remaining useful life prediction using a novel feature-attention based end-to-end approach. IEEE Trans. Ind. Inform. PP(99), 1 (2020)
Zhang, W., Jin, F., Zhang, G., Zhao, B., Hou, Y.: Aero-engine remaining useful life estimation based on 1-dimensional FCN-LSTM neural networks. In: 2019 Chinese Control Conference (CCC), pp. 4913–4918 (2019)
Al-Dulaimi, A., Zabihi, S., Asif, A., Mohammadi, A.: Hybrid deep neural network model for remaining useful life estimation. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (2014)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I.: Adversarial autoencoders. In: ICLR (2016)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
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 (2008)
Liu, X., et al.: Multiple kernel k-means with incomplete kernels. IEEE Trans. Pattern Anal. Machine Intell. 42, 1191–1204 (2017)
Heimes, F.O.: Recurrent neural networks for remaining useful life estimation. In: 2008 International Conference on Prognostics and Health Management (2008)
Zhang, C., Lim, P., Qin, A.K., Tan, K.C.: Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Trans. Neural Netw. Learn Syst. 28(10), 2306–2318 (2017)
Liao, Y., Zhang, L., Liu, C.: Uncertainty prediction of remaining useful life using long short-term memory network based on bootstrap method. In: 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 1–8 (2018)
Acknowledgments
This work is jointly funded by the National Science Foundation of China (U1811462), the National Key R&D project by Ministry of Science and Technology of China (2018YFB1003203), and the open fund from the State Key Laboratory of High Performance Computing (No. 201901-11).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, D., Wu, C., Xu, C., Wang, Z. (2021). AA-LSTM: An Adversarial Autoencoder Joint Model for Prediction of Equipment Remaining Useful Life. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-75762-5_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75761-8
Online ISBN: 978-3-030-75762-5
eBook Packages: Computer ScienceComputer Science (R0)