Details
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE) |
| ISBN (electronic) | 979-8-3503-9971-4 |
| Publication status | Published - 2023 |
Abstract
Toothed belts are a popular drive solution in many industrial sectors. However, they are little noticed as fault diagnosis (FD) applications. For optimal operating conditions, the belt must be properly tensioned. An active FD combined with machine learning is pursued in this work to provide reliable belt looseness recognition. Active fault diagnosis increases the performance by injecting an additional excitation signal. This work addresses the two crucial steps of active, data-driven fault diagnosis which are (1) input signal design and (2) algorithm selection. For this purpose, test signals are investigated for an appropriate excitation. Based on the obtained data, FD is done by learning a time series regression (TSR) model. State-of-the-art TSR algorithms are benchmarked on multiple industrial datasets which are created by attaching different loads to the belt drive. In this way, we figure out how excitation and algorithm selection help to establish a safe and robust fault diagnosis that meets industrial requirements.
Keywords
- Fault Detection in Machines and Drives, Fault Diagnosis, Machine Learning, Time Series Regression
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
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2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE). 2023.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Comparative Study of Excitation Signals for Active Fault Diagnosis of Belt Drives
AU - Fehsenfeld, Moritz
AU - Kühn, Johannes
AU - Kortmann, Karl-Philipp
PY - 2023
Y1 - 2023
N2 - Toothed belts are a popular drive solution in many industrial sectors. However, they are little noticed as fault diagnosis (FD) applications. For optimal operating conditions, the belt must be properly tensioned. An active FD combined with machine learning is pursued in this work to provide reliable belt looseness recognition. Active fault diagnosis increases the performance by injecting an additional excitation signal. This work addresses the two crucial steps of active, data-driven fault diagnosis which are (1) input signal design and (2) algorithm selection. For this purpose, test signals are investigated for an appropriate excitation. Based on the obtained data, FD is done by learning a time series regression (TSR) model. State-of-the-art TSR algorithms are benchmarked on multiple industrial datasets which are created by attaching different loads to the belt drive. In this way, we figure out how excitation and algorithm selection help to establish a safe and robust fault diagnosis that meets industrial requirements.
AB - Toothed belts are a popular drive solution in many industrial sectors. However, they are little noticed as fault diagnosis (FD) applications. For optimal operating conditions, the belt must be properly tensioned. An active FD combined with machine learning is pursued in this work to provide reliable belt looseness recognition. Active fault diagnosis increases the performance by injecting an additional excitation signal. This work addresses the two crucial steps of active, data-driven fault diagnosis which are (1) input signal design and (2) algorithm selection. For this purpose, test signals are investigated for an appropriate excitation. Based on the obtained data, FD is done by learning a time series regression (TSR) model. State-of-the-art TSR algorithms are benchmarked on multiple industrial datasets which are created by attaching different loads to the belt drive. In this way, we figure out how excitation and algorithm selection help to establish a safe and robust fault diagnosis that meets industrial requirements.
KW - Fault Detection in Machines and Drives
KW - Fault Diagnosis
KW - Machine Learning
KW - Time Series Regression
UR - http://www.scopus.com/inward/record.url?scp=85172079452&partnerID=8YFLogxK
U2 - 10.1109/isie51358.2023.10228157
DO - 10.1109/isie51358.2023.10228157
M3 - Conference contribution
SN - 979-8-3503-9972-1
BT - 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE)
ER -