Supporting the Selection of Prognostic-based Decision Support Methods in Manufacturing

Alexandros Bousdekis, Babis Magoutas, Dimitris Apostolou, Gregoris Mentzas

2015

Abstract

In manufacturing enterprises, maintenance is a significant contributor to the total company’s cost. Condition Based Maintenance (CBM) relies on prognostic models and uses them to support maintenance decisions based on the current and predicted health state of equipment. Although decision support for CBM is not an extensively explored area, there exist methods which have been developed in order to deal with specific challenges such as the need to cope with real-time information, to prognose the health state of equipment and to continually update decision recommendations. We propose an approach for supporting analysts selecting the most suitable combination(s) of methods for prognostic-based maintenance decision support according to the requirements of a given maintenance application. Our approach is based on the ID3 decision tree learning algorithm and is applied in a maintenance scenario in the oil and gas industry.

Paper Citation

in Harvard Style

Bousdekis A., Magoutas B., Apostolou D. and Mentzas G. (2015). Supporting the Selection of Prognostic-based Decision Support Methods in Manufacturing . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-096-3, pages 487-494. DOI: 10.5220/0005372104870494

in Bibtex Style

@conference{iceis15,
author={Alexandros Bousdekis and Babis Magoutas and Dimitris Apostolou and Gregoris Mentzas},
title={Supporting the Selection of Prognostic-based Decision Support Methods in Manufacturing},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2015},
pages={487-494},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005372104870494},
isbn={978-989-758-096-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Supporting the Selection of Prognostic-based Decision Support Methods in Manufacturing
SN - 978-989-758-096-3
AU - Bousdekis A.
AU - Magoutas B.
AU - Apostolou D.
AU - Mentzas G.
PY - 2015
SP - 487
EP - 494
DO - 10.5220/0005372104870494