Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics |
| Herausgeber/-innen | Giuseppina Gini, Henk Nijmeijer, Wolfram Burgard, Dimitar P. Filev |
| Seiten | 177-184 |
| ISBN (elektronisch) | 978-989-758-585-2 |
| Publikationsstatus | Veröffentlicht - 3 Aug. 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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Signalverarbeitung
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Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics. Hrsg. / Giuseppina Gini; Henk Nijmeijer; Wolfram Burgard; Dimitar P. Filev. 2022. S. 177-184.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
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TY - GEN
T1 - Comparison of Different Excitation Strategies for Fault Diagnosis of Belt Drives
T2 - Industrial Application Scenarios
AU - Fehsenfeld, Moritz Johannes
AU - Kühn, Johannes
AU - Ziaukas, Zygimantas
AU - Jacob, Hans-Georg
PY - 2022/8/3
Y1 - 2022/8/3
N2 - 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.
AB - 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.
KW - Belt Drives
KW - Fault Diagnosis
KW - Industrial Application
KW - Machine Learning
KW - Mechatronics Systems
UR - http://www.scopus.com/inward/record.url?scp=85176003156&partnerID=8YFLogxK
U2 - 10.5220/0011274100003271
DO - 10.5220/0011274100003271
M3 - Conference contribution
SP - 177
EP - 184
BT - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics
A2 - Gini, Giuseppina
A2 - Nijmeijer, Henk
A2 - Burgard, Wolfram
A2 - Filev, Dimitar P.
ER -