An attention-weighted hybrid feature learning framework for bearing fault diagnosis and maintenance cost optimization
- DOI
- 10.1038/s41598-026-55303-4
- Published
- 2026-06-14
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-55303-4,
title = {An attention-weighted hybrid feature learning framework for bearing fault diagnosis and maintenance cost optimization},
author = {Zhenlong Li and Xiaohui Fan and Xue Wang and Zhengchun Weng and Xinyue Cai and Caiyu Wang},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-55303-4},
url = {https://doi.org/10.1038/s41598-026-55303-4}
}RIS
TY - JOUR TI - An attention-weighted hybrid feature learning framework for bearing fault diagnosis and maintenance cost optimization AU - Zhenlong Li AU - Xiaohui Fan AU - Xue Wang AU - Zhengchun Weng AU - Xinyue Cai AU - Caiyu Wang PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-55303-4 UR - https://doi.org/10.1038/s41598-026-55303-4 ER -
APA
Li, Z., Fan, X., Wang, X., Weng, Z., Cai, X., & Wang, C. (2026). An attention-weighted hybrid feature learning framework for bearing fault diagnosis and maintenance cost optimization. Scientific Reports. https://doi.org/10.1038/s41598-026-55303-4
Source records
- crossref · retrieved 2026-09-25T14:24:18.761Z