A Taxonomy for Complexity Estimation of Machine Data in Machine Health Applications

Lukas Meitz, Michael Heider, Thorsten Schöler, Jörg Hähner

2024

Abstract

The Machine Health (MH) sector—which includes, for example, Predictive Maintenance, Prognostics and Health Management, and Condition Monitoring—has the potential to improve efficiency and reduce costs for maintenance and machine operation. This is achieved by data-driven analytics applications, utilising the vast amount of data collected by sensors during machine runtime. While there are numerous possible fields of application, the overall complexity of machines and applications in scientific publications is still low, preventing MH technologies from being implemented in many real-world scenarios. This may be the result of a diffuse understanding of the term complexity in the publications of this field, which results in a lack of focus towards the core problems of real-world MH applications. This article introduces a new way of discerning complexity in data-driven MH applications, enabling an effective discussion and analysis of present and future MH applications. This is achieved by creating a new taxonomy based on observations from relevant literature and substantial domain knowledge. Using this newly introduced taxonomy, we categorise recent applications of MH to demonstrate the usefulness of our approach and illustrate a still-prevalent research gap based on our findings.

Paper Citation

in Harvard Style

Meitz L., Heider M., Schöler T. and Hähner J. (2024). A Taxonomy for Complexity Estimation of Machine Data in Machine Health Applications. 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 341-350. DOI: 10.5220/0012994900003822

in Bibtex Style

@conference{icinco24,
author={Lukas Meitz and Michael Heider and Thorsten Schöler and Jörg Hähner},
title={A Taxonomy for Complexity Estimation of Machine Data in Machine Health Applications},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={341-350},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012994900003822},
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 - A Taxonomy for Complexity Estimation of Machine Data in Machine Health Applications
SN - 978-989-758-717-7
AU - Meitz L.
AU - Heider M.
AU - Schöler T.
AU - Hähner J.
PY - 2024
SP - 341
EP - 350
DO - 10.5220/0012994900003822
PB - SciTePress