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All Journal Automotive Experiences Journal of Electronics Technology Exploration (JOeTEx)
Syuhada, Themy Sabri
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mechanical Engineering

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Journal : Journal of Electronics Technology Exploration (JOeTEx)

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Performance Analysis of Long Short-term Memory (LSTM) Model for Remaining Useful Life Prediction on Turbofan Engine Syuhada, Themy Sabri
Journal of Electronics Technology Exploration Vol. 3 No. 1 (2025): June 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i1.585

Abstract

Accurate Remaining Useful Life (RUL) prediction is critical for the predictive maintenance and operational safety of aircraft turbofan engines. This research develops and evaluates a stacked Long Short-Term Memory (LSTM) network for RUL prediction using the NASA C-MAPSS FD001 dataset as a fundamental case study. A systematic data preprocessing pipeline was employed, including sensor selection, RUL value clipping at 130 cycles, and feature normalization to prepare the data for modeling. The LSTM model was trained with regularization techniques and an EarlyStopping callback to ensure robustness and prevent overfitting. Evaluation results on the unseen test data show the final model achieved a solid and competitive performance with a Root Mean Squared Error (RMSE) of 15.22 and a PHM08 Score of 311.20. These results demonstrate that a well-configured LSTM architecture provides a reliable baseline for engine prognostic tasks, exhibiting strong generalization capabilities on new data.
Co-Authors Asep Bayu Dani Nandiyanto Bilad, Muhammad Roil Obie Farobie Piantari, Erna Ragadhita, Risti Syarafah, Karina Nur Syazwany, Aisha Nadhira