Enhancing the Reliability of Structural Health Monitoring for Bolted Joint Connections in Segmented Rotor Blades Using Data Fusion

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

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  • Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) Standort Braunschweig
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Details

OriginalspracheEnglisch
Titel des SammelwerksStructural health monitoring 2023
UntertitelDesigning SHM for Sustainability, Maintainability, and Reliability
Herausgeber/-innenSaman Farhangdoust, Alfredo Guemes, Fu-Kuo Chang
Seiten1497-1504
Seitenumfang8
ISBN (elektronisch)978-1-60595-693-0
PublikationsstatusVeröffentlicht - 14 Sept. 2023
Veranstaltung14th International Workshop on Structural Health Monitoring (IWSHM) - Stanford, USA / Vereinigte Staaten
Dauer: 12 Sept. 202314 Sept. 2023
Konferenznummer: 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 Sachgebiete

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Enhancing the Reliability of Structural Health Monitoring for Bolted Joint Connections in Segmented Rotor Blades Using Data Fusion. / Abbassi, Abderrahim; Römgens, Niklas; Grießmann, Tanja et al.
Structural health monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability . Hrsg. / Saman Farhangdoust; Alfredo Guemes; Fu-Kuo Chang. 2023. S. 1497-1504.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Abbassi, A, Römgens, N, Grießmann, T, Till, J & Rolfes, R 2023, Enhancing the Reliability of Structural Health Monitoring for Bolted Joint Connections in Segmented Rotor Blades Using Data Fusion. in S Farhangdoust, A Guemes & F-K Chang (Hrsg.), Structural health monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability . S. 1497-1504, 14th International Workshop on Structural Health Monitoring (IWSHM), Stanford, California, USA / Vereinigte Staaten, 12 Sept. 2023. <https://www.dpi-proceedings.com/index.php/shm2023/article/view/36897/35473>
Abbassi, A., Römgens, N., Grießmann, T., Till, J., & Rolfes, R. (2023). Enhancing the Reliability of Structural Health Monitoring for Bolted Joint Connections in Segmented Rotor Blades Using Data Fusion. In S. Farhangdoust, A. Guemes, & F.-K. Chang (Hrsg.), Structural health monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability (S. 1497-1504) https://www.dpi-proceedings.com/index.php/shm2023/article/view/36897/35473
Abbassi A, Römgens N, Grießmann T, Till J, Rolfes R. Enhancing the Reliability of Structural Health Monitoring for Bolted Joint Connections in Segmented Rotor Blades Using Data Fusion. in Farhangdoust S, Guemes A, Chang FK, Hrsg., Structural health monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability . 2023. S. 1497-1504
Abbassi, Abderrahim ; Römgens, Niklas ; Grießmann, Tanja et al. / Enhancing the Reliability of Structural Health Monitoring for Bolted Joint Connections in Segmented Rotor Blades Using Data Fusion. Structural health monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability . Hrsg. / Saman Farhangdoust ; Alfredo Guemes ; Fu-Kuo Chang. 2023. S. 1497-1504
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title = "Enhancing the Reliability of Structural Health Monitoring for Bolted Joint Connections in Segmented Rotor Blades Using Data Fusion",
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.",
author = "Abderrahim Abbassi and Niklas R{\"o}mgens and Tanja Grie{\ss}mann and Julian Till and Raimund Rolfes",
note = "ACKNOWLEDGEMENTS The authors express their gratitude to the Federal Ministry for Economic Affairs and Climate Action of the Federal Republic of Germany for providing funding for the collaborative research project SONYA (FKZ 03EE3026B).; 14th International Workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023 ; Conference date: 12-09-2023 Through 14-09-2023",
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AU - Abbassi, Abderrahim

AU - Römgens, Niklas

AU - Grießmann, Tanja

AU - Till, Julian

AU - Rolfes, Raimund

N1 - Conference code: 14

PY - 2023/9/14

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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.

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