Data-Driven Predictive Maintenance for Component Life-Cycle Extension

Margarida Moreira, Eliseu Pereira, Eliseu Pereira, Gil Gonçalves, Gil Gonçalves

2024

Abstract

In the era of Industry 4.0, predictive maintenance is crucial for optimizing operational efficiency and reducing downtime. Traditional maintenance strategies often cost more and are less reliable, making advanced predictive models appealing. This paper assesses the effectiveness of different survival analysis models, such as Cox Proportional Hazards, Random Survival Forests (RSF), Gradient Boosting Survival Analysis (GBSA), and Survival Support Vector Machines (FS-SVM), in predicting equipment failures. The models were tested on datasets from Gorenje and Microsoft Azure, achieving C-index values on test data such as 0.792 on the Cox Model, 0.601 using RSF, 0.579 using the GBSA model and 0.514 when using the FS-SVM model. These results demonstrate the models’ potential for accurate failure prediction, with FS-SVM showing significant improvement in test data compared to its training performance. This study provides a comprehensive evaluation of survival analysis methods in an industrial context and develops a user-friendly dashboard for real-time maintenance decision-making. Integrating survival analysis into Industry 4.0 frameworks can significantly enhance predictive maintenance strategies, paving the way for more efficient and reliable industrial operations.

Paper Citation

in Harvard Style

Moreira M., Pereira E. and Gonçalves G. (2024). Data-Driven Predictive Maintenance for Component Life-Cycle Extension. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 126-136. DOI: 10.5220/0013014200003822

in Bibtex Style

@conference{icinco24,
author={Margarida Moreira and Eliseu Pereira and Gil Gonçalves},
title={Data-Driven Predictive Maintenance for Component Life-Cycle Extension},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={126-136},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013014200003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Data-Driven Predictive Maintenance for Component Life-Cycle Extension
SN - 978-989-758-717-7
AU - Moreira M.
AU - Pereira E.
AU - Gonçalves G.
PY - 2024
SP - 126
EP - 136
DO - 10.5220/0013014200003822
PB - SciTePress