Details
| Original language | English |
|---|---|
| Title of host publication | 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) |
| Pages | 480-485 |
| Number of pages | 6 |
| ISBN (electronic) | 9781665482400 |
| Publication status | Published - 2022 |
Publication series
| Name | IEEE International Symposium on Industrial Electronics |
|---|---|
| Volume | 2022-June |
Abstract
Belt drives have versatile industrial applications. A proper pretension is necessary to achieve high efficiency and low wear. For this purpose, active fault diagnosis (FD), where an auxiliary signal is injected, has shown promising results. But published approaches for input design require high domain knowledge, making them impractical in many real-world applications. We propose a procedure for input design in a data-driven FD setup and apply it to a real-world application. Multisine signals are optimized to achieve maximum separability showing significant performance improvement compared to passive FD. The resulting input signal leads to a high system disturbance which is undesirable if injected during normal operation. A minimum energy signal that still ensures successful FD is designed to solve this problem. In this way, AFD systems are superior to passive approaches while minimizing their downside of disturbing the machine operation. As a result, AFD's feasibility and potential are proven leading to increased reliability of belt drives.
Keywords
- Fault diagnosis, industrial drives, input design, machine learning
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
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2022 IEEE 31st International Symposium on Industrial Electronics (ISIE). 2022. p. 480-485 (IEEE International Symposium on Industrial Electronics; Vol. 2022-June).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Multisine Input Design for Active Data-Driven Fault Diagnosis of Belt Drives
AU - Fehsenfeld, Moritz Johannes
AU - Kühn, Johannes
AU - Ziaukas, Zygimantas
AU - Jacob, Hans-Georg
PY - 2022
Y1 - 2022
N2 - Belt drives have versatile industrial applications. A proper pretension is necessary to achieve high efficiency and low wear. For this purpose, active fault diagnosis (FD), where an auxiliary signal is injected, has shown promising results. But published approaches for input design require high domain knowledge, making them impractical in many real-world applications. We propose a procedure for input design in a data-driven FD setup and apply it to a real-world application. Multisine signals are optimized to achieve maximum separability showing significant performance improvement compared to passive FD. The resulting input signal leads to a high system disturbance which is undesirable if injected during normal operation. A minimum energy signal that still ensures successful FD is designed to solve this problem. In this way, AFD systems are superior to passive approaches while minimizing their downside of disturbing the machine operation. As a result, AFD's feasibility and potential are proven leading to increased reliability of belt drives.
AB - Belt drives have versatile industrial applications. A proper pretension is necessary to achieve high efficiency and low wear. For this purpose, active fault diagnosis (FD), where an auxiliary signal is injected, has shown promising results. But published approaches for input design require high domain knowledge, making them impractical in many real-world applications. We propose a procedure for input design in a data-driven FD setup and apply it to a real-world application. Multisine signals are optimized to achieve maximum separability showing significant performance improvement compared to passive FD. The resulting input signal leads to a high system disturbance which is undesirable if injected during normal operation. A minimum energy signal that still ensures successful FD is designed to solve this problem. In this way, AFD systems are superior to passive approaches while minimizing their downside of disturbing the machine operation. As a result, AFD's feasibility and potential are proven leading to increased reliability of belt drives.
KW - Fault diagnosis
KW - industrial drives
KW - input design
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85135811627&partnerID=8YFLogxK
U2 - 10.1109/isie51582.2022.9831682
DO - 10.1109/isie51582.2022.9831682
M3 - Conference contribution
SN - 978-1-6654-8239-4
SN - 978-1-6654-8241-7
T3 - IEEE International Symposium on Industrial Electronics
SP - 480
EP - 485
BT - 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)
ER -