Details
| Original language | English |
|---|---|
| Title of host publication | Structural Health Monitoring 2013 |
| Subtitle of host publication | A Roadmap to Intelligent Structures - Proceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM 2013 |
| Editors | Fu-Kuo Chang |
| Pages | 1310-1317 |
| Number of pages | 8 |
| ISBN (electronic) | 9781605951157 |
| Publication status | Published - 2013 |
| Event | 9th International Workshop on Structural Health Monitoring: A Roadmap to Intelligent Structures, IWSHM 2013 - Stanford, United States Duration: 10 Sept 2013 → 12 Sept 2013 |
Publication series
| Name | Structural Health Monitoring 2013: A Roadmap to Intelligent Structures - Proceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM 2013 |
|---|---|
| Volume | 1 |
Abstract
The analysis presented focuses on long term behavior of modal parameters of a 5 MW AREVA M5000 offshore wind turbine (WT). For the analysis, 30.000 data sets, with ten minutes length each, over a period of 17 months were investigated. Each data set holds 24 channels of 50 Hz acceleration data at six different levels and seven environmental & operational conditions (EOCs, 10 min mean). For the given data classification, the major operational states of the WT serve as orientation. Modal parameters are extracted on the basis of stabilization diagrams for different orders of data-driven stochastic subspace identification (SSI-data) in combination with a newly developed, triangulation-based extraction of modal parameters (TEMP) which allows an automated and fast analy sis for all collected data sets. Linear trends of modal frequencies and damping are extracted, giving a broad overview of the structures dynamic behavior under varying operational states. Results yield important knowledge for future model updating, simulations and SHM-applications.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Health Professions(all)
- Health Information Management
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Structural Health Monitoring 2013: A Roadmap to Intelligent Structures - Proceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM 2013. ed. / Fu-Kuo Chang. 2013. p. 1310-1317 (Structural Health Monitoring 2013: A Roadmap to Intelligent Structures - Proceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM 2013; Vol. 1).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Long-term monitoring of modal parameters for SHM at a 5 MW offshore wind turbine
AU - Häckell, M. W.
AU - Rolfes, R.
PY - 2013
Y1 - 2013
N2 - The analysis presented focuses on long term behavior of modal parameters of a 5 MW AREVA M5000 offshore wind turbine (WT). For the analysis, 30.000 data sets, with ten minutes length each, over a period of 17 months were investigated. Each data set holds 24 channels of 50 Hz acceleration data at six different levels and seven environmental & operational conditions (EOCs, 10 min mean). For the given data classification, the major operational states of the WT serve as orientation. Modal parameters are extracted on the basis of stabilization diagrams for different orders of data-driven stochastic subspace identification (SSI-data) in combination with a newly developed, triangulation-based extraction of modal parameters (TEMP) which allows an automated and fast analy sis for all collected data sets. Linear trends of modal frequencies and damping are extracted, giving a broad overview of the structures dynamic behavior under varying operational states. Results yield important knowledge for future model updating, simulations and SHM-applications.
AB - The analysis presented focuses on long term behavior of modal parameters of a 5 MW AREVA M5000 offshore wind turbine (WT). For the analysis, 30.000 data sets, with ten minutes length each, over a period of 17 months were investigated. Each data set holds 24 channels of 50 Hz acceleration data at six different levels and seven environmental & operational conditions (EOCs, 10 min mean). For the given data classification, the major operational states of the WT serve as orientation. Modal parameters are extracted on the basis of stabilization diagrams for different orders of data-driven stochastic subspace identification (SSI-data) in combination with a newly developed, triangulation-based extraction of modal parameters (TEMP) which allows an automated and fast analy sis for all collected data sets. Linear trends of modal frequencies and damping are extracted, giving a broad overview of the structures dynamic behavior under varying operational states. Results yield important knowledge for future model updating, simulations and SHM-applications.
UR - http://www.scopus.com/inward/record.url?scp=84945180789&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84945180789
T3 - Structural Health Monitoring 2013: A Roadmap to Intelligent Structures - Proceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM 2013
SP - 1310
EP - 1317
BT - Structural Health Monitoring 2013
A2 - Chang, Fu-Kuo
T2 - 9th International Workshop on Structural Health Monitoring: A Roadmap to Intelligent Structures, IWSHM 2013
Y2 - 10 September 2013 through 12 September 2013
ER -