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Ragab, Mohammed Gamal
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Remaining useful life estimation of turbofan engine: a sliding time window approach using deep learning Alqushaibi, Alawi; Hasan, Mohd Hilmi; Abdulkadir, Said Jadid; Taib, Shakirah Mohd; Al-Selwi, Safwan Mahmood; Sumiea, Ebrahim Hamid; Ragab, Mohammed Gamal
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp283-299

Abstract

System degradation is a common and unavoidable process that frequently oc curs in aerospace sector. Thus, prognostics is employed to avoid unforeseen breakdowns in intricate industrial systems. In prognostics, the system health status, and its remaining useful life (RUL) are evaluated using numerous sen sors. Numerous researchers have utilized deep-learning techniques to estimate RUL based on sensor data. Most of the studies proposed solving this problem with a single deep neural network (DNN) model. This paper developed a novel turbofan engine RUL predictor based on several DNN models. The method includes a time window technique for sample preparation, enhancing DNN’s ability to extract features and learn the pattern of turbofan engine degradation. Furthermore, the effectiveness of the proposed approach was confirmed using well-known model evaluation metrics. The experimental results demonstrated that among four different DNNs, the long short-term memory (LSTM)-based predictor achieved the better scores on an independent testing dataset with a root mean-square error of 15.30, mean absolute error score of 2.03, and R-squared score of 0.4354, which outperformed the previously reported results of turbofan RULestimation methods.
Co-Authors Abdulkadir, Said Jadid Al-Selwi, Safwan Mahmood Alqushaibi, Alawi Hasan, Mohd Hilmi Sumiea, Ebrahim Hamid Taib, Shakirah Mohd