Szczegóły publikacji
Opis bibliograficzny
XGBoost regression model for Remaining Useful Life prediction - case study of turbofan engines / Mateusz Gajda, Rafał MULARCZYK, Edyta KUCHARSKA // W: MMAR 2026 [Dokument elektroniczny] : 30th international conference on Methods and Models in Automation and Robotics : 18-21 August 2026, Międzyzdroje, Poland : technical papers : on line proceedings. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2026. — ( International Conference on Methods and Models in Automation and Robotics ; ISSN 2835-2815 ). — USB ISBN:979-8-3195-1920-7. — Print on Demand(PoD) ISBN:979-8-3195-1922-1. — e-ISBN: 979-8-3195-1921-4. — S. 422-427. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 427, Abstr. — Publikacja dostępna online od: 2026-09-02
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 169812 |
|---|---|
| Data dodania do BaDAP | 2026-10-08 |
| Tekst źródłowy | URL |
| DOI | 10.1109/MMAR70562.2026.11667903 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | International Conference on Methods and Models in Automation and Robotics 2026 |
| Czasopismo/seria | International Conference on Methods and Models in Automation and Robotics |
Abstract
This paper focuses on the prediction of Remaining Useful Life (RUL) for turbofan engines in the context of Predictive Maintenance (PdM) in Industry 4.0. The study is based on the NASA C-MAPSS dataset and focuses on the development of a predictive stage, including data preprocessing, feature engineering, normalisation, and sensor selection. An XGBoost regression model was evaluated on four benchmark subsets of increasing operational complexity. The obtained results show that prediction accuracy strongly depends on operating conditions, with RMSE values ranging from 20.97 to 29.64 cycles. The analysis indicates that operational variability has a greater impact on model performance than the number of fault modes alone. Additional investigation of sensor degradation trends, data distributions, and outliers provided insight into the factors affecting prediction quality. The results confirm that carefully designed machine learning pipelines can effectively support condition monitoring and maintenance planning in complex engineering systems.
