The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions
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Citations
Cites
(2014) · Cited by 33,419
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Applications of machine learning to machine fault diagnosis: A review and roadmap
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997
Smith, W. A., & Randall, R. B. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.04.021 · Cited by 2,719
Convolutional Neural Network Based Fault Detection for Rotating Machinery
Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., Van de Walle, R., & Van Hoecke, S. (2016) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2016.05.027 · Cited by 1,255
Lu, C., Wang, Z.-Y., Qin, W.-L., & Ma, J. (2017) · Signal Processing · doi:10.1016/j.sigpro.2016.07.028 · Cited by 775
A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
Shao, H., Jiang, H., Zhao, H., & Wang, F. (2017) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.03.034 · Cited by 684
Rolling bearing fault diagnosis using an optimization deep belief network
Shao, H., Jiang, H., Zhang, X., & Niu, M. (2015) · Measurement Science and Technology · doi:10.1088/0957-0233/26/11/115002 · Cited by 517
Jia, F., Lei, Y., Guo, L., Lin, J., & Xing, S. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.07.032 · Cited by 517
Metadata
- Authors
- Su, H., Xiang, L., Hu, A., Xu, Y., & Yang, X.
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2022-04
- Volume
- 169
- Pages
- 108765
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W4211058054
- Cited by
- 281
- References
- 33
- FWCI
- 24.03
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions. https://app.cochat.ai/papers/10.1016/j.ymssp.2021.108765
- The paper
- Su, H., Xiang, L., Hu, A., Xu, Y., & Yang, X. (2022). A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions. Mechanical Systems and Signal Processing. https://doi.org/10.1016/j.ymssp.2021.108765
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.
