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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohd Hilmi Hasan
Universiti Teknologi PETRONAS
Electrical & Electronics Engineering

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Remaining useful life estimation of turbofan engine: a sliding time window approach using deep learning Alawi Alqushaibi; Mohd Hilmi Hasan; Said Jadid Abdulkadir; Shakirah Mohd Taib; Safwan Mahmood Al-Selwi; Ebrahim Hamid Sumiea; Mohammed Gamal Ragab
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.
Surface-mount device design cycle time reduction using hybrid predictive modeling and optimization algorithm Chiah Chin Lim; Hea Choon Ngo; Ummi Raba'ah Hashim; Mohd Hilmi Hasan
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9058

Abstract

This study develops a hybrid predictive and optimization model for surface-mount device (SMD) design, addressing the extended design cycle times in the semiconductor industry caused by high computational demands. Challenges are tackled effectively through integration of convolutional neural network (CNN) for high-accuracy predictions and simulated annealing (SA) algorithm for optimization of SMD physical parameters. CNN model that trained on Monte Carlo simulation (MCS) data, achieved a predictive accuracy of 99.91% in forecasting SMD design errors. Concurrently, SA algorithm refined design parameters and substantially reducing error rates to nearly zero after 800 iterations. Our results indicate that combining predictive modeling with an optimization algorithm significantly enhances SMD design efficiency, providing a robust tool for mitigating time-to-market risks in semiconductor manufacturing.
Co-Authors Alawi Alqushaibi Chiah Chin Lim Ebrahim Hamid Sumiea Hea Choon Ngo Mohammed Gamal Ragab Safwan Mahmood Al-Selwi Said Jadid Abdulkadir Shakirah Mohd Taib Ummi Raba'ah Hashim