Discretization Strategies for Improved Health State Labeling in Multivariable Predictive Maintenance Systems

Jean-Victor Autran, Jean-Victor Autran, Véronique Kuhn, Jean-Philippe Diguet, Matthias Dubois, Cédric Buche

2024

Abstract

In machine learning, effective data preprocessing, particularly in the context of predictive maintenance, is a key to achieving accurate predictions. Predictive maintenance datasets commonly exhibit binary health states, offering limited insights into transitional phases between optimal and failure states. This work introduces an approach to label data derived from intricate electronic systems based on unsupervised discretization techniques. The proposed method uses data distribution patterns and predefined failure thresholds to discern the overall health of a system. By adopting this approach, the model achieves a nuanced classification that not only distinguishes between healthy and failure states but also incorporates multiple transitional states. These states act as intermediary phases in the system’s progression toward potential failure, enhancing the granularity of predictive maintenance assessments. The primary objective of this methodology is to increase anomaly detection capabilities within electronic systems. Through the utilization of unsupervised discretization, the model ensures a data-driven approach to system monitoring and health evaluation. The inclusion of multiple transitional states in the labeling process facilitates a more precise predictive maintenance framework, enabling informed decision-making in maintenance strategies. This article contributes to advancing the effectiveness of predictive maintenance applications by addressing the limitations associated with binary labeling, ultimately encouraging a more nuanced and accurate understanding of system health.

Paper Citation

in Harvard Style

Autran J., Kuhn V., Diguet J., Dubois M. and Buche C. (2024). Discretization Strategies for Improved Health State Labeling in Multivariable Predictive Maintenance Systems. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-707-8, SciTePress, pages 434-441. DOI: 10.5220/0012788800003756

in Bibtex Style

@conference{data24,
author={Jean-Victor Autran and Véronique Kuhn and Jean-Philippe Diguet and Matthias Dubois and Cédric Buche},
title={Discretization Strategies for Improved Health State Labeling in Multivariable Predictive Maintenance Systems},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2024},
pages={434-441},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012788800003756},
isbn={978-989-758-707-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Discretization Strategies for Improved Health State Labeling in Multivariable Predictive Maintenance Systems
SN - 978-989-758-707-8
AU - Autran J.
AU - Kuhn V.
AU - Diguet J.
AU - Dubois M.
AU - Buche C.
PY - 2024
SP - 434
EP - 441
DO - 10.5220/0012788800003756
PB - SciTePress