Fastformer: An Efficient Attention-Based Framework for Rapid Multi-Class Fault Diagnosis in High-End Equipment Vibration Signals
- DOI
- 10.3390/e28070820
- Published
- 2026-07-19
- Container
- Entropy
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/e28070820,
title = {Fastformer: An Efficient Attention-Based Framework for Rapid Multi-Class Fault Diagnosis in High-End Equipment Vibration Signals},
author = {Xiaohan Zhang and Hailun Dai and Chong Zhou and Qi Shen},
year = {2026},
journal = {Entropy},
doi = {10.3390/e28070820},
url = {https://doi.org/10.3390/e28070820}
}RIS
TY - JOUR TI - Fastformer: An Efficient Attention-Based Framework for Rapid Multi-Class Fault Diagnosis in High-End Equipment Vibration Signals AU - Xiaohan Zhang AU - Hailun Dai AU - Chong Zhou AU - Qi Shen PY - 2026 JO - Entropy DO - 10.3390/e28070820 UR - https://doi.org/10.3390/e28070820 ER -
APA
Zhang, X., Dai, H., Zhou, C., & Shen, Q. (2026). Fastformer: An Efficient Attention-Based Framework for Rapid Multi-Class Fault Diagnosis in High-End Equipment Vibration Signals. Entropy. https://doi.org/10.3390/e28070820
Source records
- crossref · retrieved 2026-11-04T19:02:27.781Z