Predictive analytics for machine failure using optimized recurrent neural network-gated recurrent unit (GRU)

Zainuddin, Z. and P Akhir, E.A. and Aziz, N. (2019) Predictive analytics for machine failure using optimized recurrent neural network-gated recurrent unit (GRU). In: UNSPECIFIED.

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Abstract

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 1; Conference of 1st International Conference on Artificial Intelligence and Data Sciences, AiDAS 2019 ; Conference Date: 19 September 2019; Conference Code:157266
Uncontrolled Keywords: 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
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 10 Nov 2023 03:25
Last Modified: 10 Nov 2023 03:25
URI: https://khub.utp.edu.my/scholars/id/eprint/11331

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