The DOI matches its Crossref record; 166 of the 166 references it cites were found in a registry or an index.
Transfer learning algorithms for bearing remaining useful life prediction: A comprehensive review from an industrial application perspective
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Cites
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542
A Critical Analysis of Crack Propagation Laws
Paris, P., & Erdogan, F. (1963) · Journal of Basic Engineering · doi:10.1115/1.3656900 · Cited by 7,122
A Comprehensive Survey on Transfer Learning
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004555 · Cited by 6,678
2009 IEEE Conference on Computer Vision and Pattern Recognition
(2009) · doi:10.1109/cvpr14067.2009 · Cited by 5,550
Adversarial Discriminative Domain Adaptation
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.316 · Cited by 5,154
Generative Adversarial Networks: An Overview
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540
A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010) · Machine Learning · doi:10.1007/s10994-009-5152-4 · Cited by 3,515
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277
Metadata
- Authors
- Chen, J., Huang, R., Chen, Z., Mao, W., & Li, W.
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 193
- Pages
- 110239
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W4323363550
- Cited by
- 267
- References
- 166
- FWCI
- 29.03
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Transfer learning algorithms for bearing remaining useful life prediction: A comprehensive review from an industrial application perspective. https://app.cochat.ai/papers/10.1016/j.ymssp.2023.110239
- The paper
- Chen, J., Huang, R., Chen, Z., Mao, W., & Li, W. (2023). Transfer learning algorithms for bearing remaining useful life prediction: A comprehensive review from an industrial application perspective. Mechanical Systems and Signal Processing. https://doi.org/10.1016/j.ymssp.2023.110239
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.
