The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
A comprehensive review on convolutional neural network in machine fault diagnosis
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Sun, B., & Saenko, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-49409-8_35 · Cited by 3,757
Deep learning and its applications to machine health monitoring
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.05.050 · Cited by 2,894
Cited by
A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Zhu, Z., Lei, Y., Qi, G., Chai, Y., Mazur, N., An, Y., & Huang, X. (2023) · Measurement · doi:10.1016/j.measurement.2022.112346 · Cited by 731
Fault diagnosis for small samples based on attention mechanism
Zhang, X., He, C., Lu, Y., Chen, B., Zhu, L., & Zhang, L. (2022) · Measurement · doi:10.1016/j.measurement.2021.110242 · Cited by 299
Pan, T., Chen, J., Zhang, T., Liu, S., He, S., & Lv, H. (2022) · ISA Transactions · doi:10.1016/j.isatra.2021.11.040 · Cited by 283
Hesser, D. F., Mostafavi, S., Kocur, G. K., & Markert, B. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.108 · Cited by 82
Metadata
- Authors
- Jiao, J., Zhao, M., Lin, J., & Liang, K.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 417
- Pages
- 36-63
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W3006342871
- Cited by
- 575
- References
- 41
- FWCI
- 34.64
- Type
- journal article
- Language
- en
- Open access
- green
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- Jiao, J., Zhao, M., Lin, J., & Liang, K. (2020). A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing. https://doi.org/10.1016/j.neucom.2020.07.088
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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.
