Open Access
| Issue |
JNWPU
Volume 39, Number 2, April 2021
|
|
|---|---|---|
| Page(s) | 407 - 413 | |
| DOI | https://doi.org/10.1051/jnwpu/20213920407 | |
| Published online | 09 June 2021 | |
- Rodrigues L R. Remaining useful life prediction for multiple-component systems based on a system-level performance indicator[J]. IEEE/ASME Trans on Mechatronics, 2017, 231:141–150 [Article] [Google Scholar]
- Elthalji I, Jantunen E. A summary of fault modelling and predictive health monitoring of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2015, 60: 252–272 [Article] [Google Scholar]
- Qin S J. Survey on data-driven industrial process monitoring and diagnosis[J]. Annual Reviews in Control, 2012, 36(2):220–234 [Article] [Google Scholar]
- Khelif R, Chebelmorello B, Malinowski S, et al. Direct remaining useful life estimation based on support vector regression[J]. IEEE Trans on Industrial Electronics, 2017, 64(3):2276–2285 [Article] [Google Scholar]
- Zhang C, Lim P, Qin A K, et al. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics[J]. IEEE Trans Neural Netw Learn Syst, 2017, 28(10):2306–2318 [Article] [Google Scholar]
- Wang B, Lei Y, Li N, et al. Deep separable convolutional network for remaining useful life prediction of machinery[J]. Mechanical Systems and Signal Processing, 2019, 134: 106330 [Article] [Google Scholar]
- Babu G S, Zhao P, Li X L. Deep convolutional neural network based regression approach for estimation of remaining useful life[C]//International Conference on Database Systems for Advanced Applications, Cham, 2016: 214–228 [Google Scholar]
- Li X, Ding Q, Sun J Q. Remaining useful life estimation in prognostics using deep convolution neural networks[J]. Reliability Engineering & System Safety, 2018, 172: 1–11 [Article] [Google Scholar]
- Li H, Zhao W, Zhang Y, et al. Remaining useful life prediction using multi-scale deep convolutional neural network[J]. Applied Soft Computing, 2020, 89: 106113 [Article] [Google Scholar]
- Zheng S, Ristovski K, Farahat A K, et al. Long short-term memory network for remaining useful life estimation[C]//IEEE International Conference on Prognostics and Health Management, 2017: 88–95 [Google Scholar]
- Kong Z, Cui Y, Xia Z, et al. Convolution and long short-term memory hybrid deep neural networks for remaining useful life prognostics[J]. Applied Sciences, 2019, 9(19):4156 [Article] [Google Scholar]
- Xia T, Song Y, Zheng Y, et al. An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation[J]. Computers in Industry, 2020, 115: 103182 [Article] [Google Scholar]
- Kurata G, Ramabhadran B, Saon G, et al. Language modeling with highway LSTM[C]//2017 IEEE Automatic Speech Recognition and Understanding Workshop, 2017: 244–251 [Google Scholar]
- Saranya C, Manikandan G. A study on normalization techniques for privacy preserving data mining[J]. International Journal of Engineering and Technology, 2013, 5(3):2701–2704 [Article] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.