On Data-Preprocessing for Effective Predictive Maintenance on Multi-Purpose Machines

Lukas Meitz, Michael Heider, Thorsten Schöler, Jörg Hähner

2023

Abstract

Maintenance of complex machinery is time and resource intensive. Therefore, decreasing maintenance cycles by employing Predictive Maintenance (PdM) is sought after by many manufacturers of machines and can be a valuable selling point. However, currently PdM is a hard to solve problem getting increasingly harder with the complexity of the maintained system. One challenge is to adequately prepare data for model training and analysis. In this paper, we propose the use of expert knowledge–based preprocessing techniques to extend the standard data science–workflow. We define complex multi-purpose machinery as an application domain and test our proposed techniques on real-world data generated by numerous machines deployed in the wild. We find that our techniques enable and enhance model training.

Paper Citation

in Harvard Style

Meitz L., Heider M., Schöler T. and Hähner J. (2023). On Data-Preprocessing for Effective Predictive Maintenance on Multi-Purpose Machines. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 606-612. DOI: 10.5220/0012146700003541

in Bibtex Style

@conference{data23,
author={Lukas Meitz and Michael Heider and Thorsten Schöler and Jörg Hähner},
title={On Data-Preprocessing for Effective Predictive Maintenance on Multi-Purpose Machines},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={606-612},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012146700003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - On Data-Preprocessing for Effective Predictive Maintenance on Multi-Purpose Machines
SN - 978-989-758-664-4
AU - Meitz L.
AU - Heider M.
AU - Schöler T.
AU - Hähner J.
PY - 2023
SP - 606
EP - 612
DO - 10.5220/0012146700003541
PB - SciTePress