Comparison of Different Excitation Strategies for Fault Diagnosis of Belt Drives: Industrial Application Scenarios

Moritz Fehsenfeld, Johannes Kühn, Zygimantas Ziaukas, Hans-Georg Jacob

2022

Abstract

Machine learning (ML) has received a lot of attention in solving fault diagnosis (FD) tasks. As a result, more and more advanced machine learning algorithms have been developed to increase accuracy. But the system’s excitation has likewise a high impact on the diagnosis performance and applicability. For this purpose, we describe different industrial application scenarios and the related set trajectory. They are divided into passive FD, where normal operation data serves as the input, and active FD, where an optimized excitation is injected. All scenarios are investigated concerning achievable accuracy and data requirement based on comprehensive measurements. We demonstrate that in active scenarios a high accuracy of 97:6% combined with a small number of measurements are obtained by very basic algorithms like a one-nearest neighbor with Euclidean distance. In passive scenarios, where the FD task is generally harder, the demand for large datasets and more advanced ML methods increases. In this way, we illustrate how intelligent use of an optimized excitation strategy leads to feasible, reliable, and accurate fault diagnosis with a broad industrial application spectrum.

Paper Citation

in Harvard Style

Fehsenfeld M., Kühn J., Ziaukas Z. and Jacob H. (2022). Comparison of Different Excitation Strategies for Fault Diagnosis of Belt Drives: Industrial Application Scenarios. In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-585-2, pages 177-184. DOI: 10.5220/0011274100003271

in Bibtex Style

@conference{icinco22,
author={Moritz Fehsenfeld and Johannes Kühn and Zygimantas Ziaukas and Hans-Georg Jacob},
title={Comparison of Different Excitation Strategies for Fault Diagnosis of Belt Drives: Industrial Application Scenarios},
booktitle={Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2022},
pages={177-184},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011274100003271},
isbn={978-989-758-585-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Comparison of Different Excitation Strategies for Fault Diagnosis of Belt Drives: Industrial Application Scenarios
SN - 978-989-758-585-2
AU - Fehsenfeld M.
AU - Kühn J.
AU - Ziaukas Z.
AU - Jacob H.
PY - 2022
SP - 177
EP - 184
DO - 10.5220/0011274100003271