Seasonal Feature Augmentation for Interpretable Turbofan RUL Prediction via Bayesian-Optimized XGBoost

Ertuğrul Metehan Sertdemir; Hülya Kaftelen Odabaşı

doi:10.30518/jav.1940225

Seasonal Feature Augmentation for Interpretable Turbofan RUL Prediction via Bayesian-Optimized XGBoost

Abstract

The operational safety and sustainability of gas turbine engines in the aviation industry are directly contingent upon the accurate prediction of the Remaining Useful Life (RUL) of critical components. Existing RUL prediction models in the aviation literature predominantly focus on in-engine sensor data, largely overlooking the cumulative impact of external environmental variables—particularly ambient temperature and seasonal fluctuations—on material fatigue. To address this gap, the present study proposes a novel hybrid model grounded in the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset (FD001–FD004), integrating standard operational data with seasonal parameters. The proposed model architecture is based on the Extreme Gradient Boosting (XGBoost) algorithm, with hyperparameter optimization conducted via the Bayesian-based Optuna framework. Experimental findings demonstrate that the model achieves RMSE = 15.84 and R² = 0.85, exhibiting competitive or superior predictive accuracy relative to complex deep learning architectures reported in the literature. Furthermore, the study explores the contribution of a synthetically constructed periodic feature to the predictive behavior and interpretability of the model.

Keywords

References

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Details

Primary Language

English

Subjects

Deep Learning

Journal Section

Research Article

Authors

Ertuğrul Metehan Sertdemir
0000-0002-4374-6289
Türkiye

Hülya Kaftelen Odabaşı ^*
0000-0003-1111-5420
Türkiye

Early Pub Date

October 18, 2026

Publication Date

October 25, 2026

Submission Date

April 30, 2026

Acceptance Date

August 2, 2026

Published in Issue

Year 2026 Volume: 10 Number: 3

DOI

https://doi.org/10.30518/jav.1940225

IZ

https://izlik.org/JA55CE78DE

Cite

RIS / Bibtex

APA

Sertdemir, E. M., & Kaftelen Odabaşı, H. (2026). Seasonal Feature Augmentation for Interpretable Turbofan RUL Prediction via Bayesian-Optimized XGBoost. Journal of Aviation, 10(3), 413-419. https://doi.org/10.30518/jav.1940225

AMA

1.Sertdemir EM, Kaftelen Odabaşı H. Seasonal Feature Augmentation for Interpretable Turbofan RUL Prediction via Bayesian-Optimized XGBoost. JAV. 2026;10(3):413-419. doi:10.30518/jav.1940225

Chicago

Sertdemir, Ertuğrul Metehan, and Hülya Kaftelen Odabaşı. 2026. “Seasonal Feature Augmentation for Interpretable Turbofan RUL Prediction via Bayesian-Optimized XGBoost”. Journal of Aviation 10 (3): 413-19. https://doi.org/10.30518/jav.1940225.

EndNote

Sertdemir EM, Kaftelen Odabaşı H (October 1, 2026) Seasonal Feature Augmentation for Interpretable Turbofan RUL Prediction via Bayesian-Optimized XGBoost. Journal of Aviation 10 3 413–419.

IEEE

[1]E. M. Sertdemir and H. Kaftelen Odabaşı, “Seasonal Feature Augmentation for Interpretable Turbofan RUL Prediction via Bayesian-Optimized XGBoost”, JAV, vol. 10, no. 3, pp. 413–419, Oct. 2026, doi: 10.30518/jav.1940225.

ISNAD

Sertdemir, Ertuğrul Metehan - Kaftelen Odabaşı, Hülya. “Seasonal Feature Augmentation for Interpretable Turbofan RUL Prediction via Bayesian-Optimized XGBoost”. Journal of Aviation 10/3 (October 1, 2026): 413-419. https://doi.org/10.30518/jav.1940225.

JAMA

1.Sertdemir EM, Kaftelen Odabaşı H. Seasonal Feature Augmentation for Interpretable Turbofan RUL Prediction via Bayesian-Optimized XGBoost. JAV. 2026;10:413–419.

MLA

Sertdemir, Ertuğrul Metehan, and Hülya Kaftelen Odabaşı. “Seasonal Feature Augmentation for Interpretable Turbofan RUL Prediction via Bayesian-Optimized XGBoost”. Journal of Aviation, vol. 10, no. 3, Oct. 2026, pp. 413-9, doi:10.30518/jav.1940225.

Vancouver

1.Ertuğrul Metehan Sertdemir, Hülya Kaftelen Odabaşı. Seasonal Feature Augmentation for Interpretable Turbofan RUL Prediction via Bayesian-Optimized XGBoost. JAV. 2026 Oct. 1;10(3):413-9. doi:10.30518/jav.1940225