The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Remaining useful life estimation in prognostics using deep convolution neural networks
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
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
Cited by
Diez-Olivan, A., Del Ser, J., Galar, D., & Sierra, B. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.10.005 · Cited by 633
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107864 · Cited by 581
Listou Ellefsen, A., Bjørlykhaug, E., Æsøy, V., Ushakov, S., & Zhang, H. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.11.027 · Cited by 523
Deep residual learning-based fault diagnosis method for rotating machinery
Zhang, W., Li, X., & Ding, Q. (2019) · ISA Transactions · doi:10.1016/j.isatra.2018.12.025 · Cited by 416
Self-citationIntelligent rotating machinery fault diagnosis based on deep learning using data augmentation
Li, X., Zhang, W., Ding, Q., & Sun, J.-Q. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1456-1 · Cited by 404
Self-citation
Metadata
- Authors
- Li, X., Ding, Q., & Sun, J.-Q.
- Venue
- Reliability Engineering & System Safety
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 172
- Pages
- 1-11
- ISSN
- 0951-8320 (ISSN-L)
- OpenAlex
- W2772084711
- Cited by
- 1,755
- References
- 5
- FWCI
- 56.82
- Type
- journal article
- Language
- en
- Open access
- bronze
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- The paper
- Li, X., Ding, Q., & Sun, J.-Q. (2018). Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety. https://doi.org/10.1016/j.ress.2017.11.021
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.
