Machine Learning and Optimization for Predictive Maintenance based on Predicting Failure in the Next Five Days

Eman Ouda, Maher Maalouf, Andrei Sleptchenko

2021

Abstract

This study proposes a framework to predict machine failures using sensor data and optimize predictive/corrective maintenance schedule. Using historical data, machine learning (ML) models are trained to predict the failure probabilities for the next five days. Multiple algorithms, including feature extraction techniques, selections, and ML models (both regression and classification based) are compared. The machine learning models’ output is fed to an optimization model to propose an optimized maintenance policy, and we demonstrate how prediction models can help increase system reliability at lower costs.

Paper Citation

in Harvard Style

Ouda E., Maalouf M. and Sleptchenko A. (2021). Machine Learning and Optimization for Predictive Maintenance based on Predicting Failure in the Next Five Days.In Proceedings of the 10th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-485-5, pages 192-199. DOI: 10.5220/0010247401920199

in Bibtex Style

@conference{icores21,
author={Eman Ouda and Maher Maalouf and Andrei Sleptchenko},
title={Machine Learning and Optimization for Predictive Maintenance based on Predicting Failure in the Next Five Days},
booktitle={Proceedings of the 10th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2021},
pages={192-199},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010247401920199},
isbn={978-989-758-485-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Machine Learning and Optimization for Predictive Maintenance based on Predicting Failure in the Next Five Days
SN - 978-989-758-485-5
AU - Ouda E.
AU - Maalouf M.
AU - Sleptchenko A.
PY - 2021
SP - 192
EP - 199
DO - 10.5220/0010247401920199