Random convolution layer: an auxiliary method to improve fault diagnosis performance
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02458-4.pdf
Reference61 articles.
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2. Anowar, F., Sadaoui, S., & Selim, B. (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne). Computer Science Review, 40, 100378.
3. Bai, R., Xu, Q., Meng, Z., Cao, L., Xing, K., & Fan, F. (2021). Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation. Measurement, 184, 109885. https://doi.org/10.1016/j.measurement.2021.109885
4. Chen, L., Li, Q., Shen, C., Zhu, J., Wang, D., & Xia, M. (2021). Adversarial domain-invariant generalization: A generic domain-regressive framework for bearing fault diagnosis under unseen conditions. IEEE Transactions on Industrial Informatics, 18(3), 1790–1800. https://doi.org/10.1109/TII.2021.3078712
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