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A Deep Learning-Based Stacking Ensemble Framework for Turbofan Engine Remaining Useful Life Prediction — arXiv:2608.27940

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.27940
License (as archived): CC0-1.0
Archived: 2026-10-17T10:47:19.559Z
SHA-256 of the archived text: a6f2d9dd90c2ca2a7ce82dd1d669436a9c32f986a27261208d0227fb9db437c6
Stable URL of this page: https://metraipolis.com/documento/dc_7658a7280bb6

How to cite

A Deep Learning-Based Stacking Ensemble Framework for Turbofan Engine Remaining Useful Life Prediction — arXiv:2608.27940 — arXiv (en), https://arxiv.org/abs/2608.27940 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7658a7280bb6 (Metraipolis archive dc_7658a7280bb6, sha256 a6f2d9dd90c2ca2a7ce82dd1d669436a9c32f986a27261208d0227fb9db437c6)

BibTeX

@misc{dc7658a7280bb6,
  title = {A Deep Learning-Based Stacking Ensemble Framework for Turbofan Engine Remaining Useful Life Prediction — arXiv:2608.27940},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.27940},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7658a7280bb6; sha256 a6f2d9dd90c2ca2a7ce82dd1d669436a9c32f986a27261208d0227fb9db437c6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.27940. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This study proposes a two-level stacking ensemble framework for Remaining Useful Life (RUL) prediction of turbofan engines, evaluated on the NASA C-MAPSS benchmark using the FD001 and FD003 subsets. The framework integrates four heterogeneous deep learning base learners: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM, and CNN-GRU, whose out-of-fold predictions are combined by an XGBoost meta-learner to capture complex degradation patterns while mitigating individual model biases. Comprehensive experiments demonstrate that the stacking ensemble achieves superior predictive performance, with Root Mean Square Error (RMSE) of 9.989 and 8.613, Mean Absolute Error (MAE) of 7.081 and 5.195, and R-squared values of 0.899 and 0.906 for FD001 and FD003, respectively. Compared to the best-reported baseline (TCAT: RMSE 11.12 and 11.02), the proposed method achieves RMSE reductions of 10.2 percent and 21.8 percent for FD001 and FD003, respectively. Feature correlation analysis, residual diagnostics, and training convergence curves validate the model's robustness. These findings underscore the efficacy of stacking ensemble methods for prognostics and health management in safety-critical aerospace applications.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-31 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.27940 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T10:33:42.369Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).