The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
Deep learning for computer vision: A brief review
Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/7068349 · Cited by 3,403
Transformer-XL: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1285 · Cited by 3,169
Damage propagation modeling for aircraft engine run-to-failure simulation
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008) · 2008 International Conference on Prognostics and Health Management · doi:10.1109/phm.2008.4711414 · Cited by 1,954
Remaining useful life estimation in prognostics using deep convolution neural networks
Li, X., Ding, Q., & Sun, J.-Q. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2017.11.021 · Cited by 1,755
Self-citationA recurrent neural network based health indicator for remaining useful life prediction of bearings
Guo, L., Li, N., Jia, F., Lei, Y., & Lin, J. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.02.045 · Cited by 1,231
Cited by
Fusing physics-based and deep learning models for prognostics
Chao, M. A., Kulkarni, C. S., Goebel, K., & Fink, O. (2022) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000506035 · Cited by 369
Liang, P., Li, Y., Wang, B., Yuan, X., & Zhang, L. (2023) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2023.107722 · Cited by 101
Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction
Wen, Z., Fang, Y., Wei, P., Liu, F., Chen, Z., & Wu, M. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3592788 · Cited by 34
Ma, M., Wang, Z., & Zhong, Z. (2024) · Aerospace · doi:10.3390/aerospace11040289 · Cited by 18
Tran, K., Vu, H.-C., Pham, L., Boudaoud, N., & Nguyen, H.-S.-H. (2024) · Mathematics · doi:10.3390/math12101569 · Cited by 14
Metadata
- Authors
- Mo, Y., Wu, Q., Li, X., & Huang, B.
- Venue
- Journal of Intelligent Manufacturing
- Published
- 2021-03-15
- Volume
- 32
- Issue
- 7
- Pages
- 1997-2006
- ISSN
- 0956-5515 (ISSN-L)
- 1572-8145
- OpenAlex
- W3137613462
- Cited by
- 328
- References
- 37
- FWCI
- 19.98
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. https://app.cochat.ai/papers/10.1007/s10845-021-01750-x
- The paper
- Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01750-x
Data sources
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.
