IEEE/CAA Journal of Automatica Sinica
Citation: | Q. W. Zhu, Q. Y. Xiong, Z. Y. Yang, and Y. Yu, “RGCNU: Recurrent graph convolutional network with uncertainty estimation for remaining useful life prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1640–1642, Jul. 2023. doi: 10.1109/JAS.2023.123369 |
[1] |
F. Li, T. Zheng, N. He, and Q. Cao, “Data-driven hybrid neural fuzzy network and ARX modeling approach to practical industrial process identification,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1702–1705, 2022. doi: 10.1109/JAS.2022.105821
|
[2] |
J. Zhu, N. Chen, and W. Peng, “Estimation of bearing remaining useful life based on multiscale convolutional neural network,” IEEE Trans. Industrial Electronics, vol. 66, no. 4, pp. 3208–3216, 2018.
|
[3] |
R. Jiao, K. Peng, and J. Dong, “Remaining useful life prediction for a roller in a hot strip mill based on deep recurrent neural networks,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1345–1354, 2021. doi: 10.1109/JAS.2021.1004051
|
[4] |
R. Jin, M. Wu, K. Wu, K. Gao, Z. Chen, and X. Li, “Position encoding based convolutional neural networks for machine remaining useful life prediction,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1427–1439, 2022. doi: 10.1109/JAS.2022.105746
|
[5] |
C. Zhang, H.-Y. Zhou, Q. Qiu, Z. Jian, D. Zhu, C. Cheng, L. He, G. Liu, X. Wen, and R. Hu, “Augmented multi-component recurrent graph convolutional network for traffic flow forecasting,” ISPRS Int. J. Geo-Information, vol. 11, no. 2, p. 88, 2022. doi: 10.3390/ijgi11020088
|
[6] |
M. Wang, Y. Li, Y. Zhang, and L. Jia, “Spatio-temporal graph convolutional neural network for remaining useful life estimation of aircraft engines,” Aerospace Systems, vol. 4, no. 1, pp. 29–36, 2021. doi: 10.1007/s42401-020-00070-x
|
[7] |
T. Li, Z. Zhao, C. Sun, R. Yan, and X. Chen, “Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction,” Reliability Engineering &System Safety, vol. 215, p. 107878, 2021.
|
[8] |
A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” in Proc. Adv. Neural Inf. Process. Syst., 2017, pp. 5574–5584.
|
[9] |
A. Saxena, K. Goebel, D. Simon, and N. Eklund, “Damage propagation modeling for aircraft engine run-to-failure simulation,” in Proc. IEEE International Conf. Prognostics Health Management, 2008, pp. 1–9.
|
[10] |
X. Li, Q. Ding, and J.-Q. Sun, “Remaining useful life estimation in prognostics using deep convolution neural networks,” Reliability Engineering &System Safety, vol. 172, pp. 1–11, 2018.
|
[11] |
Z. Chen, M. Wu, R. Zhao, F. Guretno, R. Yan, and X. Li, “Machine remaining useful life prediction via an attention-based deep learning approach,” IEEE Trans. Industrial Electronics, vol. 68, no. 3, pp. 2521–2531, 2020.
|
[12] |
W. Yu, I. Y. Kim, and C. Mechefske, “An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme,” Reliability Engineering &System Safety, vol. 199, p. 106926, 2020.
|
[13] |
H. Liu, Z. Liu, W. Jia, and X. Lin, “Remaining useful life prediction using a novel feature-attention-based end-to-end approach,” IEEE Trans. Industrial Informatics, vol. 17, no. 2, pp. 1197–1207, 2020.
|
[14] |
Y. Mo, Q. Wu, X. Li, and B. Huang, “Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit,” J. Intelligent Manufacturing, vol. 32, no. 7, pp. 1997–2006, 2021. doi: 10.1007/s10845-021-01750-x
|
[15] |
T. Li, Z. Zhou, S. Li, C. Sun, R. Yan, and X. Chen, “The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study,” Mechanical Systems and Signal Processing, vol. 168, p. 108653, 2022. doi: 10.1016/j.ymssp.2021.108653
|
[16] |
Y. Liao, L. Zhang, and C. Liu, “Uncertainty prediction of remaining useful life using long short-term memory network based on bootstrap method,” in Proc. IEEE International Conf. Prognostics Health Management, 2018, pp. 1–8.
|
[17] |
M. Kim and K. Liu, “A Bayesian deep learning framework for interval estimation of remaining useful life in complex systems by incorporating general degradation characteristics,” IISE Transactions, vol. 53, no. 3, pp. 326–340, 2020.
|
[18] |
Y.-H. Lin and G.-H. Li, “A Bayesian deep learning framework for RUL prediction incorporating uncertainty quantification and calibration,” IEEE Trans. Industrial Informatics, vol. 18, no. 10, pp. 7274–7284, 2022. doi: 10.1109/TII.2022.3156965
|
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