Remaining Useful Life Estimation of Turbofan Engines with Deep Learning Using Change-Point Detection Based Labeling and Feature Engineering
Yazarlar (3)
Arş. Gör. Dr. Kıymet ENSARİOĞLU
Yalova Üniversitesi, Türkiye
Tülin İnkaya
Bursa Uludağ Üniversitesi, Türkiye
Erdal Emel
Bursa Uludağ Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Applied Sciences Switzerland (Q2) | ||
| Dergi ISSN | 2076-3417 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2023 |
| Cilt / Sayı / Sayfa | 13 / 21 / – | DOI | 10.3390/app132111893 |
| Makale Linki | http://dx.doi.org/10.3390/app132111893 | ||
| Özet |
| Accurate remaining useful life (RUL) prediction is one of the most challenging problems in the prognostics of turbofan engines. Recently, RUL prediction methods for turbofan engines mainly involve data-driven models. Preprocessing the sensor data is essential for the performance of the prognostic models. Most studies on turbofan engines use piecewise linear (PwL) labeling, which starts with a constant initial RUL value in normal/healthy operating time. In this study, we designed a prognostic procedure that includes difference-based feature construction, change-point-detection-based PwL labeling, and a 1D-CNN-LSTM (one-dimensional–convolutional neural network–long short-term memory) hybrid neural network model for RUL prediction. The procedure was evaluated on the subset FD001 of the C-MAPSS dataset. The proposed procedure was compared with machine learning and deep learning models with and without the new difference feature. Also, the results were compared with the studies that used similar labeling approaches. Our analysis of the numerical results underscores the clear superiority of the proposed 1D-CNN-LSTM model with the difference feature in RUL prediction, with a score of 437.2 and an RMSE value of 16.1. This result illustrates the superior predictive capability of the 1D-CNN-LSTM model, which outperformed traditional machine learning methods and one of the earliest deep learning methods. These findings emphasize the superior predictive capability of the 1D-CNN-LSTM model and underline the potential of the feature engineering process for more accurate and robust RUL prediction in the context of turbofan engine prognostics. |
| Anahtar Kelimeler |
| remaining useful life | prognostics and health management | feature engineering | turbofan engines | change point detection | convolutional neural network | long short-term memory network |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 16 |
| SCOPUS | 19 |
| Google Scholar | 33 |