Predicting the Remaining Useful Life of Non-ideal Data Through a Joint Framework of Sequential Contrastive Learning and Attention Mechanisms

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Publication Details

Journal
Journal of Mechanical Engineering
Published
2025-01-01
DOI
https://doi.org/10.3901/jme.2025.18.027
Primary Topic
Domain Adaptation and Few-Shot Learning
Type
article
Field-Weighted Citation Impact
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article

Predicting the Remaining Useful Life of Non-ideal Data Through a Joint Framework of Sequential Contrastive Learning and Attention Mechanisms

Journal of Mechanical Engineering
Domain Adaptation and Few-Shot Learning
article

Predicting the Remaining Useful Life of Non-ideal Data Through a Joint Framework of Sequential Contrastive Learning and Attention Mechanisms

article en

Abstract

No abstract available for this paper.
Journal of Mechanical EngineeringVol. 61(18)
Beijing University of Technology (CN), Beijing University of Chemical Technology (CN)
Openalex Percentile: Top 78%
Domain Adaptation and Few-Shot Learning
DOIOpen Access PDFOpenAlex
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Predicting the Remaining Useful Life of Non-ideal Data Through a Joint Framework of Sequential Contrastive Learning and Attention Mechanisms · Journal of Mechanical Engineering (2025) | TGRS Research Map | TGRS