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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Huthaifa AL-Khazraji
University of Technology
Computer Science & IT Electrical & Electronics Engineering Engineering

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A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance Ahmed Nasser; Huthaifa AL-Khazraji
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp721-730

Abstract

Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and also reduces the complexity of the model. To evaluate the proposed model, two comparisons with regular LSTM and gradient boosting decision tree (GBDT) methods using a freely available dataset have been made. The PdM model based on CNN-LSTM method demonstrates better prediction accuracy compared to the regular LSTM, where the average F-Score increases form 93.34% in the case of regular LSTM to 97.48% for the proposed CNN-LSTM. Compared to the related works the proposed hybrid CNN-LSTM PdM approach achieved better results in term of accuracy.
An intelligent demand forecasting model using a hybrid of metaheuristic optimization and deep learning algorithm for predicting concrete block production Huthaifa AL-Khazraji; Ahmed R. Nasser; Sohaib Khlil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp649-657

Abstract

Demand forecasting aims to optimize the production planning of industrial companies by ensuring that the production planning meets the future demand. Demand forecasting utilizes historical data as an input to predict future trends of the demand. In this paper, a new approach for developing an intelligent demand forecasting model using a hybrid of metaheuristic optimization and deep learning algorithm is presented. Firefly algorithmbased gated recurrent units (FA-GRU) is used to tackle the production forecasting problem. The proposed model has been evaluated and compared with the standard gated recurrent unit (GRU) and standard long short-term memory model (LSTM) using historical data of 36 months of concrete block manufacturing at dler company in Iraq. The prediction accuracy of the three models is evaluated using the root mean square error (RMSE), the mean absolute percentage error (MAPE) and the statistical coefficient of determination (R2 ) indicators. The outcomes of the study show that the proposed FA-GRU gives better forecasting results compared to the standard GRU and standard LSTM.
Predictive maintenance of electromechanical systems based on enhanced generative adversarial neural network with convolutional neural network Azhar Muneer Abood; Ahmed Raoof Nasser; Huthaifa Al-Khazraji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1704-1712

Abstract

Predictive maintenance (PdM) is a cost-cutting method that involves avoiding breakdowns and production losses. Deep learning (DL) algorithms can be used for defect prediction and diagnostics due to the huge amount of data generated by the integration of analog and digital systems in manufacturing operations. To improve the predictive maintenance strategy, this study uses a hybrid of the convolutional neural network (CNN) and conditional generative adversarial neural network (CGAN) model. The proposed CNN-CGAN algorithm improves forecast accuracy while substantially reducing model complexity. A comparison with standalone CGAN utilizing a public dataset is performed to evaluate the proposed model. The results show that the proposed CNN-CGAN model outperforms the conditional GAN (CGAN) in terms of prediction accuracy. The average F-Score is increased from 97.625% for the CGAN to 100% for the CNN-CGAN.
Co-Authors Ahmed Nasser Ahmed R. Nasser Ahmed Raoof Nasser Azhar Muneer Abood Sohaib Khlil