A deep learning framework for remaining useful life prediction of turbofan engines with partial sensor failure

Dongdong Tang

doi:10.1371/journal.pone.0347312

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Open Access

Peer-reviewed

Research Article

A deep learning framework for remaining useful life prediction of turbofan engines with partial sensor failure

Dongdong Tang

Roles Conceptualization, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: cafuctdd@126.com

Affiliation Suining Branch, Civil Aviation Flight University of China, Suining, Sichuan, China

https://orcid.org/0009-0005-6219-2233

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A deep learning framework for remaining useful life prediction of turbofan engines with partial sensor failure

Dongdong Tang

Published: April 29, 2026
https://doi.org/10.1371/journal.pone.0347312

About the Authors

Dongdong Tang

Roles Conceptualization, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: cafuctdd@126.com

Affiliation Suining Branch, Civil Aviation Flight University of China, Suining, Sichuan, China

https://orcid.org/0009-0005-6219-2233

Competing Interests

The authors have declared that no competing interests exist.

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Sensory perception
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Forecasting
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Engines
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Damage mechanics
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Deep learning
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Neural networks
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Machine learning
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Statistical data
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