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Authors: Vincenza Carchiolo 1 ; Alessandro Longheu 2 ; Vincenzo di Martino 3 and Niccolo Consoli 2

Affiliations: 1 Dip. di Matematica e Informatica, Universita’ di Catania, Viale Andrea Doria 6, Catania and Italy ; 2 Dip. di Ingegneria Elettrica, Elettronica e Informatica, Universita’ di Catania, Viale Andrea Doria 6, Catania and Italy ; 3 BaxEnergy, Catania and Italy

ISBN: 978-989-758-386-5

Keyword(s): Predictive Maintenance, Natural Language Processing, Ontologies, Wind Turbines, Renewable Energy.

Abstract: The shifting from reactive to predictive maintenance heavily improves the assets management, especially for complex systems with high business value. This occurs in particular in power plants, whose functioning is a mission-critical task. In this work, an NLP-based analysis of failure reports in power plants is presented, showing how they can be effectively used to implement a predictive maintenance aiming to reduce unplanned downtime and repair time, thus increasing operational efficiency while reducing costs.

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Paper citation in several formats:
Carchiolo, V.; Longheu, A.; di Martino, V. and Consoli, N. (2019). Power Plants Failure Reports Analysis for Predictive Maintenance.In Proceedings of the 15th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-386-5, pages 404-410. DOI: 10.5220/0008388204040410

@conference{webist19,
author={Vincenza Carchiolo. and Alessandro Longheu. and Vincenzo di Martino. and Niccolo Consoli.},
title={Power Plants Failure Reports Analysis for Predictive Maintenance},
booktitle={Proceedings of the 15th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2019},
pages={404-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008388204040410},
isbn={978-989-758-386-5},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Power Plants Failure Reports Analysis for Predictive Maintenance
SN - 978-989-758-386-5
AU - Carchiolo, V.
AU - Longheu, A.
AU - di Martino, V.
AU - Consoli, N.
PY - 2019
SP - 404
EP - 410
DO - 10.5220/0008388204040410

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