relation: https://khub.utp.edu.my/scholars/15822/
title: Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data
creator: Zainuddin, Z.
creator: P. Akhir, E.A.
creator: Hasan, M.H.
description: Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time series data that continuously update each second. The arising of machine learning may help in managing the data. It can forecast future instance while handling large data issues. Forecasting is related to predicting task of an upcoming event to avoid any circumstances happen in current environment. It helps those sectors such as production to foresee the state of machine in line with saving the cost from sudden breakdown as unplanned machine failure can disrupt the operation and loss up to millions. Thus, this paper offers a deep learning algorithm named recurrent neural network-gated recurrent unit (RNN-GRU) to forecast the state of machines producing the time series data in an oil and gas sector. RNN-GRU is an affiliation of recurrent neural network (RNN) that can control consecutive data due to the existence of update and reset gates. The gates decided on the necessary information to be kept in the memory. RNN-GRU is a simpler structure of long short-term memory (RNN-LSTM) with 87 of accuracy on prediction. © 2021, Institute of Advanced Engineering and Science. All rights reserved.
publisher: Institute of Advanced Engineering and Science
date: 2021
type: Article
type: PeerReviewed
identifier:   Zainuddin, Z. and P. Akhir, E.A. and Hasan, M.H.  (2021) Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data.  Bulletin of Electrical Engineering and Informatics, 10 (2).  pp. 870-878.  ISSN 20893191     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102993747&doi=10.11591%2feei.v10i2.2036&partnerID=40&md5=a2cf14ad3b4fbdd43574b42008b22a7d
relation: 10.11591/eei.v10i2.2036
identifier: 10.11591/eei.v10i2.2036