Power Plants Failure Reports Analysis for Predictive Maintenance

Vincenza Carchiolo, Alessandro Longheu, Vincenzo di Martino, Niccolo Consoli

2019

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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