A Convolutional Neural Network with Hybrid Loss Function for Bearing Fault Diagnosis
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49413-0_50
Reference9 articles.
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2. Han, S., Feng, Z.: Deep residual joint transfer strategy for cross-condition fault diagnosis of rolling bearings. J. Dyn. Monit. Diagnostics 2(1), 51–60 (2023). https://doi.org/10.37965/jdmd.2023.147
3. Shao, S., McAleer, S., Yan, R., Baldi, P.: Highly accurate machine fault diagnosis using deep transfer learning. IEEE Trans. Industr. Inf. 15(4), 2446–2455 (2019). https://doi.org/10.1109/TII.2018.2864759
4. Han, T., Zhou, T., Xiang, Y., Jiang, D.: Cross-machine intelligent fault diagnosis of gearbox based on deep learning and parameter transfer. Struct. Control. Health Monit. 29(3), e2898 (2022). https://doi.org/10.1002/stc.2898
5. Zhang, D., Yang, Z., Cheng, S.: Wheel hub recognition based on ResNet50 and transfer learning. Acta Metrologica Sinica. https://doi.org/10.3969/j.issn.1000-1158.2022.11.04
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