loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

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

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.144.86.138

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 - WEBIST; ISBN 978-989-758-386-5; ISSN 2184-3252, SciTePress, 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 - WEBIST},
year={2019},
pages={404-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008388204040410},
isbn={978-989-758-386-5},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Web Information Systems and Technologies - WEBIST
TI - Power Plants Failure Reports Analysis for Predictive Maintenance
SN - 978-989-758-386-5
IS - 2184-3252
AU - Carchiolo, V.
AU - Longheu, A.
AU - di Martino, V.
AU - Consoli, N.
PY - 2019
SP - 404
EP - 410
DO - 10.5220/0008388204040410
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic