%0 Conference Paper
%A Zainuddin, Z.
%A P Akhir, E.A.
%A Aziz, N.
%D 2019
%F scholars:11331
%I Institute of Electrical and Electronics Engineers Inc.
%K Forecasting; Gas industry; Genetic algorithms; Predictive analytics; Public utilities; Time series, Forecasting time series; High-accuracy; Hyper-parameter; Machine failure; Oil and gas companies; Optimization method; Time-series data, Recurrent neural networks
%P 88-93
%R 10.1109/AiDAS47888.2019.8970725
%T Predictive analytics for machine failure using optimized recurrent neural network-gated recurrent unit (GRU)
%U https://khub.utp.edu.my/scholars/11331/
%X This paper proposed a technique named Recurrent Neural Network-Gated Recurrent Unit (RNN-GRU) to predict the condition of machines by using time series data generated by oil and gas company. The problem raised due to limited research of RNN-GRU in improving the accuracy through hyperparameter tuning. Hence, this paper will provide an optimization method that can improve the accuracy of RNN-GRU in forecasting time series data. The preliminary findings of the experiment conducted shows that RNN-GRU can utilize time series data to predict machine failure with improved high accuracy. © 2019 IEEE.
%Z cited By 1; Conference of 1st International Conference on Artificial Intelligence and Data Sciences, AiDAS 2019 ; Conference Date: 19 September 2019; Conference Code:157266