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News/Remaining Useful Life Estimation for Turbofan Engines: A Comparative Study of Classical, CNN, and LSTM Approaches
arxiv
PublishedMay 1, 2026 at 4:00 AM
—neutral

Remaining Useful Life Estimation for Turbofan Engines: A Comparative Study of Classical, CNN, and LSTM Approaches

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.27234v1 Announce Type: new Abstract: Remaining Useful Life (RUL) estimation is a critical component of Prognostics and Health Management (PHM), enabling proactive maintenance scheduling and reducing unplanned failures in industrial equipment. This paper presents a comparative study of mac

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Discussion
Mentioned models
05
  • 01
    Ridge Regression
  • 02
    Polynomial Ridge
  • 03
    XGBoost
  • 04
    1D Convolutional Neural Network (CNN)
  • 05
    Long Short-Term Memory (LSTM) network
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machinelearning#prognostics#industrialequipment#comparativestudy
Mentioned companies
01
NASA

No replies yet. Be first.

Mentioned models
05
  • 01
    Ridge Regression
  • 02
    Polynomial Ridge
  • 03
    XGBoost
  • 04
    1D Convolutional Neural Network (CNN)
  • 05
    Long Short-Term Memory (LSTM) network
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machinelearning#prognostics#industrialequipment#comparativestudy
Mentioned companies
01
NASA

Related coverage

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arxivVeriEvolve: Evaluator-First Evolution for Tool-Using Agents20harxivTNODEV: Toolbox for Neural ODE Verification20harxivLearning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding20harxivOn the Expressive Power and Limitations of Multi-Layer SSMs20h
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