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9. Jiang, J.R., Lee, J.E., Zeng, Y.M.: Time series multiple channel convolutional neural network
with attention-based long short- term memory for predicting bearing remaining useful life.
Sensors 20(1), 166 (2020)
10. Wang, Q., Bu, S.Q., He, Z.Y.: Achieving Predictive and Proactive Maintenance for High-Speed
Railway Power Equipment with LSTM-RNN. IEEE Trans. Ind. Inf. 16(10), 6509–6517 (2020)
11. Essien, A., Giannetti, C.: A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders. IEEE Trans. Ind. Inf. 16(9), 6069–6078
(2020)
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Images Using Variational Autoencoders. In: Leibe B., Matas J., Sebe N., Welling M. (eds)
Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9911.
Springer, Cham (2016)
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problems. Soft Comput. 24, 7999–8009 (2020)
14. Xie, R.M., Jan, N.M., Hao, K.R., Chen, L., Huang, B.: Supervised variational autoencoders
for soft sensor modeling with missing data. IEEE Trans. Industr. Inf. 16(4), 2820–2828 (2020)
15. Wei, J.W., Dong, G.Z., Chen. Z.H.: Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression. IEEE Trans.
Ind. Electron. 65(7), 5634–5643 (2018)
X. Wang et al.
569, 442–446 (2012)
4. Ben Ali, J., 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. 56–57, 150–172 (2015)
5. Deng, S., Hua, L., Han, X.H., Huang. S.: Finite element analysis of fatigue life for deep groove
ball bearing. Proc. Inst. Mech. Eng. Part L: J. Mater.: Design Appl. 227(1), 70–81 (2013)
6. Zhang, Y., Tang, B.P., Xiong, P.: Rolling element bearing life prediction based on multi-scale
mutation particle swarm optimized multi-kernel least square support vector machine. Chinese
J. Sci. Instrum. 37, 2489–2496 (2016)
7. Hong, S., Yin, J.W.: Remaining Useful Life Prediction of Bearing Based on Deep Perceptron
Neural Networks. In: Proceedings of the 2018 2nd International Conference on Big Data and
Internet of Things (BDIOT 2018), pp. 175–179. Association for Computing Machinery, New
York, NY, USA (2018)
8. Ge, Y., Guo, L.Z., Dou, Y.: Remaining useful life prediction of machinery based on K-S distance
and LSTM neural network. Int. J. Performability Eng. 15, 895–901 (2019)
9. Jiang, J.R., Lee, J.E., Zeng, Y.M.: Time series multiple channel convolutional neural network
with attention-based long short- term memory for predicting bearing remaining useful life.
Sensors 20(1), 166 (2020)
10. Wang, Q., Bu, S.Q., He, Z.Y.: Achieving Predictive and Proactive Maintenance for High-Speed
Railway Power Equipment with LSTM-RNN. IEEE Trans. Ind. Inf. 16(10), 6509–6517 (2020)
11. Essien, A., Giannetti, C.: A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders. IEEE Trans. Ind. Inf. 16(9), 6069–6078
(2020)
12. Walker J., Doersch C., Gupta A., Hebert M.: An Uncertain Future: Forecasting from Static
Images Using Variational Autoencoders. In: Leibe B., Matas J., Sebe N., Welling M. (eds)
Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9911.
Springer, Cham (2016)
13. Ohno, H.: Auto-encoder-based generative models for data augmentation on regression
problems. Soft Comput. 24, 7999–8009 (2020)
14. Xie, R.M., Jan, N.M., Hao, K.R., Chen, L., Huang, B.: Supervised variational autoencoders
for soft sensor modeling with missing data. IEEE Trans. Industr. Inf. 16(4), 2820–2828 (2020)
15. Wei, J.W., Dong, G.Z., Chen. Z.H.: Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression. IEEE Trans.
Ind. Electron. 65(7), 5634–5643 (2018)