Details
| Originalsprache | Englisch |
|---|---|
| Seiten | 488-495 |
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - 2014 |
| Veranstaltung | 7th European Workshop on Structural Health Monitoring, EWSHM 2014 - Nantes, Frankreich Dauer: 8 Juli 2014 → 11 Juli 2014 |
Konferenz
| Konferenz | 7th European Workshop on Structural Health Monitoring, EWSHM 2014 |
|---|---|
| Land/Gebiet | Frankreich |
| Ort | Nantes |
| Zeitraum | 8 Juli 2014 → 11 Juli 2014 |
Abstract
Structural health monitoring systems can help to improve safety and minimize the numerous economical burdens of wind turbines. To detect damage of rotor blades, several research projects focus on an acoustic emission approach. Acoustic emission stands for stress waves emitted by a damage process. For this approach components of the waves are measured with sensors mounted on the surface of the blade. Small damages can be detected, but the amount of sensors is relatively high due to the size of modern blades and high internal damping of composite materials. The damage process and stress waves also emit airborne sound. In contrast to existing approaches we use the airborne sound for damage detection. We developed a detection algorithm based on our signal analysis which showed that airborne sound provides adequate features for cracking sound detection. We optimized and tested the algorithm with our airborne sound recordings of a full-scale rotor blade test. In the first three days of the long term fatigue test our algorithm detects nine events per day, 79% of the events are cracking sounds. In the remaining 73 days of the fatigue test the algorithm detects about one event per day.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Ingenieurwesen (insg.)
- Bauwesen
- Informatik (insg.)
- Angewandte Informatik
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- BibTex
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2014. 488-495 Beitrag in 7th European Workshop on Structural Health Monitoring, EWSHM 2014, Nantes, Frankreich.
Publikation: Konferenzbeitrag › Paper › Forschung › Peer-Review
}
TY - CONF
T1 - Detection of impulse-like airborne sound for damage identification in rotor blades of wind turbines
AU - Krause, Thomas
AU - Preihs, Stephan
AU - Ostermann, Jörn
PY - 2014
Y1 - 2014
N2 - Structural health monitoring systems can help to improve safety and minimize the numerous economical burdens of wind turbines. To detect damage of rotor blades, several research projects focus on an acoustic emission approach. Acoustic emission stands for stress waves emitted by a damage process. For this approach components of the waves are measured with sensors mounted on the surface of the blade. Small damages can be detected, but the amount of sensors is relatively high due to the size of modern blades and high internal damping of composite materials. The damage process and stress waves also emit airborne sound. In contrast to existing approaches we use the airborne sound for damage detection. We developed a detection algorithm based on our signal analysis which showed that airborne sound provides adequate features for cracking sound detection. We optimized and tested the algorithm with our airborne sound recordings of a full-scale rotor blade test. In the first three days of the long term fatigue test our algorithm detects nine events per day, 79% of the events are cracking sounds. In the remaining 73 days of the fatigue test the algorithm detects about one event per day.
AB - Structural health monitoring systems can help to improve safety and minimize the numerous economical burdens of wind turbines. To detect damage of rotor blades, several research projects focus on an acoustic emission approach. Acoustic emission stands for stress waves emitted by a damage process. For this approach components of the waves are measured with sensors mounted on the surface of the blade. Small damages can be detected, but the amount of sensors is relatively high due to the size of modern blades and high internal damping of composite materials. The damage process and stress waves also emit airborne sound. In contrast to existing approaches we use the airborne sound for damage detection. We developed a detection algorithm based on our signal analysis which showed that airborne sound provides adequate features for cracking sound detection. We optimized and tested the algorithm with our airborne sound recordings of a full-scale rotor blade test. In the first three days of the long term fatigue test our algorithm detects nine events per day, 79% of the events are cracking sounds. In the remaining 73 days of the fatigue test the algorithm detects about one event per day.
KW - Acoustic emission
KW - Airborne sound
KW - Damage detection
KW - Rotor blade
KW - Wind turbine
UR - http://www.scopus.com/inward/record.url?scp=84939454471&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:84939454471
SP - 488
EP - 495
T2 - 7th European Workshop on Structural Health Monitoring, EWSHM 2014
Y2 - 8 July 2014 through 11 July 2014
ER -