The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Bai, S., Kolter, J. Z., & Koltun, V. (2018) · arXiv · doi:10.48550/arxiv.1803.01271 · Cited by 4,655
A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
Wang, B., Lei, Y., Li, N., & Li, N. (2020) · IEEE Transactions on Reliability · doi:10.1109/tr.2018.2882682 · Cited by 1,917
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.06.004 · Cited by 1,594
Exploring Self-Attention for Image Recognition
Zhao, H., Jia, J., & Koltun, V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01009 · Cited by 1,002
PRONOSTIA : An experimental platform for bearings accelerated degradation tests.
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel‐Morello, B., Noureddine Zerhouni, A., & Varnier, C. (2012) · HAL · Cited by 928
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
Lea, C., Vidal, R., Reiter, A., & Hager, G. D. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-49409-8_7 · Cited by 924
Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network
Zhu, J., Chen, N., & Peng, W. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2844856 · Cited by 704
Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach
Chen, Z., Wu, M., Zhao, R., Guretno, F., Yan, R., & Li, X. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2972443 · Cited by 618
Cited by
Multivariate Stacked Bidirectional Long Short Term Memory for Lithium-Ion Battery Health Management
Ardeshiri, R. R., Liu, M., & Ma, C. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108481 · Cited by 94
(2023) · doi:10.5194/hess-27-83-2023 · Cited by 51
Zheng, X., Fan, W., Chen, C., & Peng, Z. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3359212 · Cited by 48
Metadata
- Authors
- Cao, Y., Ding, Y., Jia, M., & Tian, R.
- Venue
- Reliability Engineering & System Safety
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 215
- Pages
- 107813
- ISSN
- 0951-8320 (ISSN-L)
- OpenAlex
- W3173071471
- Cited by
- 361
- References
- 43
- FWCI
- 24.55
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings. https://app.cochat.ai/papers/10.1016/j.ress.2021.107813
- The paper
- Cao, Y., Ding, Y., Jia, M., & Tian, R. (2021). A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings. Reliability Engineering & System Safety. https://doi.org/10.1016/j.ress.2021.107813
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
