Abstract
Overall Equipment Effectiveness (OEE) has been used by manufacturers as a key metric to identify how productive a production operation is, during the planned production time. While Industrial Internet of Things (IIoT) can be leveraged with data science, predictive maintenance becomes a better option for manufacturers to maintain their equipment. In this study, a simulation of predictive maintenance was done by using Classification and Regression Tree (CART) algorithm to predict machine failure. We also discussed the possible improvement of predictive maintenance to OEE.
Keywords: Overall Equipment Effectiveness, Industry 4.0, Predictive Maintenance
Authors
Leong Chee Him [1] ; Yu Yong Poh [2] ; Lee Wah Pheng [3]
[1][2] Department of Computer Science and Embedded System, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
[3] Centre For Postgraduate Studies And Research, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
[1] leongcchi@gmail.com; [2] yuyp@tarc.edu.my; [3] leewp@tarc.edu.my
Cite Me
Plain Text:
C.H.Leong, Y.P.Yu, W.P.Lee, "Improvement of Overall Equipment Effectiveness from Predictive Maintenance," International Conference on Digital Transformation and Applications (ICDXA) 2020, 2020, pp. 72-75, doi: https://doi.org/10.56453/icdxa.2020.1005.
BibTex:
@INPROCEEDINGS{ICDXA2020T105,
author={Leong, Chee Him and Yu, Yong Poh and Lee, Wah Pheng},
booktitle={International Conference on Digital Transformation and Applications (ICDXA) 2020},
title={Improvement of Overall Equipment Effectiveness from Predictive Maintenance},
year={2020},
volume={},
number={},
pages={72-75},
doi={https://doi.org/10.56453/icdxa.2020.1005}}
