Details
| Original language | English |
|---|---|
| Title of host publication | Structural health monitoring 2023 |
| Subtitle of host publication | Designing SHM for Sustainability, Maintainability, and Reliability |
| Editors | Saman Farhangdoust, Alfredo Guemes, Fu-Kuo Chang |
| Pages | 1497-1504 |
| Number of pages | 8 |
| ISBN (electronic) | 978-1-60595-693-0 |
| Publication status | Published - 14 Sept 2023 |
| Event | 14th International Workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023 - Stanford, United States Duration: 12 Sept 2023 → 14 Sept 2023 Conference number: 14 |
Abstract
In the wind energy sector, the use of segmented rotor blades poses challenges for Structural Health Monitoring (SHM) systems due to the increasing high loads that can damage bolted joints. This paper investigates the potential of data fusion to improve the reliability of SHM systems for bolt connections in segmented rotor blades. Three CFRP structures with bolted joints are examined and monitored using both piezoelectric and electrical strain gauge sensors. A passive low-frequency monitoring system is built using strain measurements and neural networks to model the relations between measurement data. An additional active high-frequency monitoring system is designed using piezoelectric sensors and guided waves in combination with a subsequent principal components analysis. The results show that the data fusion system successfully detected the damages with an accurate damage occurrence probability prediction even if one monitoring system provided unreliable predictions. By combining two independent monitoring systems that complementarily cover different frequency bands, the data fusion system improved the reliability of the monitoring task and reduced the occurrence of false positive alarms. The approach presented in this study can also be applied to other monitoring systems in various industries, further expanding the impact of the research.
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Engineering(all)
- Building and Construction
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Civil and Structural Engineering
Cite this
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Structural health monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability . ed. / Saman Farhangdoust; Alfredo Guemes; Fu-Kuo Chang. 2023. p. 1497-1504.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Enhancing the Reliability of Structural Health Monitoring for Bolted Joint Connections in Segmented Rotor Blades Using Data Fusion
AU - Abbassi, Abderrahim
AU - Römgens, Niklas
AU - Grießmann, Tanja
AU - Till, Julian
AU - Rolfes, Raimund
N1 - Conference code: 14
PY - 2023/9/14
Y1 - 2023/9/14
N2 - In the wind energy sector, the use of segmented rotor blades poses challenges for Structural Health Monitoring (SHM) systems due to the increasing high loads that can damage bolted joints. This paper investigates the potential of data fusion to improve the reliability of SHM systems for bolt connections in segmented rotor blades. Three CFRP structures with bolted joints are examined and monitored using both piezoelectric and electrical strain gauge sensors. A passive low-frequency monitoring system is built using strain measurements and neural networks to model the relations between measurement data. An additional active high-frequency monitoring system is designed using piezoelectric sensors and guided waves in combination with a subsequent principal components analysis. The results show that the data fusion system successfully detected the damages with an accurate damage occurrence probability prediction even if one monitoring system provided unreliable predictions. By combining two independent monitoring systems that complementarily cover different frequency bands, the data fusion system improved the reliability of the monitoring task and reduced the occurrence of false positive alarms. The approach presented in this study can also be applied to other monitoring systems in various industries, further expanding the impact of the research.
AB - In the wind energy sector, the use of segmented rotor blades poses challenges for Structural Health Monitoring (SHM) systems due to the increasing high loads that can damage bolted joints. This paper investigates the potential of data fusion to improve the reliability of SHM systems for bolt connections in segmented rotor blades. Three CFRP structures with bolted joints are examined and monitored using both piezoelectric and electrical strain gauge sensors. A passive low-frequency monitoring system is built using strain measurements and neural networks to model the relations between measurement data. An additional active high-frequency monitoring system is designed using piezoelectric sensors and guided waves in combination with a subsequent principal components analysis. The results show that the data fusion system successfully detected the damages with an accurate damage occurrence probability prediction even if one monitoring system provided unreliable predictions. By combining two independent monitoring systems that complementarily cover different frequency bands, the data fusion system improved the reliability of the monitoring task and reduced the occurrence of false positive alarms. The approach presented in this study can also be applied to other monitoring systems in various industries, further expanding the impact of the research.
UR - http://www.scopus.com/inward/record.url?scp=85182283417&partnerID=8YFLogxK
M3 - Conference contribution
SP - 1497
EP - 1504
BT - Structural health monitoring 2023
A2 - Farhangdoust, Saman
A2 - Guemes, Alfredo
A2 - Chang, Fu-Kuo
T2 - 14th International Workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023
Y2 - 12 September 2023 through 14 September 2023
ER -