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Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks
Research article (IET Electric Power Applications, 2021) · cited 31× · AI/ML
Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks
Summary
Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks is a scholarly article[1].
Key Facts
Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks. Retrieved May 24, 2026, from https://4ort.xyz/entity/extracting-spatially-global-and-local-attentive-features-for-rolling-bearing-fault-diagnosis-in-electrical-machines-usin
MLA“Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/extracting-spatially-global-and-local-attentive-features-for-rolling-bearing-fault-diagnosis-in-electrical-machines-usin.
BibTeX@misc{4ortxyz_extracting-spatially-global-and-local-attentive-features-for-rolling-bearing-fault-diagnosis-in-electrical-machines-usin_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks}}, year = {2026}, url = {https://4ort.xyz/entity/extracting-spatially-global-and-local-attentive-features-for-rolling-bearing-fault-diagnosis-in-electrical-machines-usin}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks — https://4ort.xyz/entity/extracting-spatially-global-and-local-attentive-features-for-rolling-bearing-fault-diagnosis-in-electrical-machines-usin (retrieved 2026-05-24)
