A novel technique for multiple failure modes classification based on deep forest algorithm
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-023-02185-2.pdf
Reference29 articles.
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2. Ding, J., Luo, Q., Jia, L., & You, J. (2020). Deep forest-based fault diagnosis method for chemical process. Mathematical Problems in Engineering. https://doi.org/10.1155/2020/5281512
3. Guan, K., Yang, G., Du, L., Li, Z., & Yang, X. (2023). Method for fusion of neighborhood rough set and XGBoost in welding process decision-making. Journal of Intelligent Manufacturing, 34, 1229–1240. https://doi.org/10.1007/s10845-021-01844-6.
4. Huang, J., & Ting, C. (2022). Deep learning object detection applied to defect recognition of memory modules. International Journal of Advanced Manufacturing Technology, 121, 8433–8445. https://doi.org/10.1007/s00170-022-09716-w.
5. Jakobsson, E., Frisk, E., Krysander, M., & Pettersson, R. (2022). Time series fault classification for wave propagation systems with sparse fault data. https://doi.org/10.48550/arXiv.2203.16121.
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