eprintid: 11331
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/13/31
datestamp: 2023-11-10 03:25:51
lastmod: 2023-11-10 03:25:51
status_changed: 2023-11-10 01:15:00
type: conference_item
metadata_visibility: show
creators_name: Zainuddin, Z.
creators_name: P Akhir, E.A.
creators_name: Aziz, N.
title: Predictive analytics for machine failure using optimized recurrent neural network-gated recurrent unit (GRU)
ispublished: pub
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
note: cited By 1; Conference of 1st International Conference on Artificial Intelligence and Data Sciences, AiDAS 2019 ; Conference Date: 19 September 2019; Conference Code:157266
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.
date: 2019
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079346213&doi=10.1109%2fAiDAS47888.2019.8970725&partnerID=40&md5=ac076132c400990b24e1c17a4bc8214c
id_number: 10.1109/AiDAS47888.2019.8970725
full_text_status: none
publication: Proceedings - 2019 1st International Conference on Artificial Intelligence and Data Sciences, AiDAS 2019
pagerange: 88-93
refereed: TRUE
isbn: 9781728130415
citation:   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.