Hierarchical RUL Prediction for Turbofan Engines Based on Health Stage Classification and Change Point-Guided Data Augmentation
Yazarlar (1)
Dr. Öğr. Üyesi Kıymet ENSARİOĞLU Yalova Üniversitesi, Türkiye
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Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | IEEE Access (Q2) | ||
| Dergi ISSN | 2169-3536 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2025 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-01-2025 |
| Cilt / Sayı / Sayfa | 13 / 1 / 134241–134260 | DOI | 10.1109/ACCESS.2025.3593206 |
| Makale Linki | https://doi.org/10.1109/ACCESS.2025.3593206 | ||
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Yapay Zeka
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| Özet |
| In prognostics, it is essential to accurately predict the Remaining Useful Life (RUL) of complex systems like turbofan engines. Most studies adopt data-driven methods and rely on piecewise linear (PwL) labeling, where a fixed initial RUL is assigned to all engines. To better reflect real-world degradation, this assumption is refined using change point detection to assign engine-specific initial RUL values, offering a more realistic problem formulation. In line with PwL-based labeling approaches, where mechanical systems shift from a normal to a degradation phase at a specific change point, distinct RUL prediction models are developed for each health stage. A classification model is proposed using a novel training methodology to identify the current health stage of an engine. This method segments the dataset into pre- and post-change intervals and applies data augmentation both to simulate the truncated nature of … |
| Anahtar Kelimeler |
| Change point detection | data augmentation | deep learning | explainable artificial intelligence | health stage classification | prognostics and health management | remaining useful life | turbofan engines |
BM Sürdürülebilir Kalkınma Amaçları
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| Scopus | 2 |
| Google Scholar | 4 |