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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Taib, Shakirah Mohd
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Computer Science & IT Electrical & Electronics Engineering

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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.
Short term residential load forecasting using long short-term memory recurrent neural network Muneer, Amgad; Ali, Rao Faizan; Almaghthawi, Ahmed; Taib, Shakirah Mohd; Alghamdi, Amal; Abdullah Ghaleb, Ebrahim Abdulwasea
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5589-5599

Abstract

Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.
Co-Authors Abdulkadir, Said Jadid Abdullah Ghaleb, Ebrahim Abdulwasea Al-Selwi, Safwan Mahmood Alghamdi, Amal Ali, Rao Faizan Almaghthawi, Ahmed Alqushaibi, Alawi Hasan, Mohd Hilmi Muneer, Amgad Ragab, Mohammed Gamal Sumiea, Ebrahim Hamid