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An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction

Research article (Nondestructive Testing And Evaluation, 2024) · cited 24× · AI/ML
academia openalex:W4401172597
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An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction

Summary

An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction is a scholarly article[1].

Key Facts

  • An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction's instance of is recorded as scholarly article[2].

References

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction. Retrieved May 24, 2026, from https://4ort.xyz/entity/an-intelligent-hybrid-deep-learning-model-for-rolling-bearing-remaining-useful-life-prediction
MLA “An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/an-intelligent-hybrid-deep-learning-model-for-rolling-bearing-remaining-useful-life-prediction.
BibTeX @misc{4ortxyz_an-intelligent-hybrid-deep-learning-model-for-rolling-bearing-remaining-useful-life-prediction_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction}}, year = {2026}, url = {https://4ort.xyz/entity/an-intelligent-hybrid-deep-learning-model-for-rolling-bearing-remaining-useful-life-prediction}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction — https://4ort.xyz/entity/an-intelligent-hybrid-deep-learning-model-for-rolling-bearing-remaining-useful-life-prediction (retrieved 2026-05-24)

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