Details
| Original language | English |
|---|---|
| Title of host publication | Structural Health Monitoring 2017 |
| Subtitle of host publication | Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017 |
| Editors | Fu-Kuo Chang, Fotis Kopsaftopoulos |
| Pages | 2475-2482 |
| Number of pages | 8 |
| ISBN (electronic) | 9781605953304 |
| Publication status | Published - 2017 |
| Event | 11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017 - Stanford, United States Duration: 12 Sept 2017 → 14 Sept 2017 |
Abstract
Proper normalization of structural response data that accounts for the environmental and operational conditions (EOCs) of a structure is a key step in structural health monitoring (SHM) analyses. Normalizing data based on EOCs enables a more effective comparison of damage sensitive features extracted from structural response data derived from a system operating under a wide variation in its operations. In this paper, structural response data from an operational wind turbine is used for both damage detection as well as for EOC-based data normalization in a damage detection framework. The structure under consideration is the tower of a 3-kW wind turbine located at Los Alamos National Lab. A wireless monitoring system was installed in the turbine tower to record the tower acceleration response when the tower was undamaged and intentionally damaged. Gaussian Process Regression is used to find tower response features that correlate with EOCs, specifically rotor angular velocity and nacelle yaw angle, yet are insensitive to structural damage. The features are then used in the damaged detection framework to enhance the performance of clustering algorithms used for EOC normalization. Damage-sensitive features are then used as condition parameters for damage detection. The efficiency of the proposed EOCs normalization process is evaluated by comparing the Receiver Operating Characteristic (ROC) curves of a threetier damage detection strategy previously proposed for wind turbine systems.
ASJC Scopus subject areas
- Health Professions(all)
- Health Information Management
- Computer Science(all)
- Computer Science Applications
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Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017. ed. / Fu-Kuo Chang; Fotis Kopsaftopoulos. 2017. p. 2475-2482.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Extraction of environmental and operational conditions of wind turbines using tower response data for structural health monitoring
AU - Bahrami, Omid
AU - Tsiapoki, Stavroula
AU - Kane, Michael B.
AU - Lynch, Jerome P.
AU - Rolfes, Raimund
N1 - Funding information: The authors acknowledge the financial support of the National Science Foundation under Grants CMMI-1362513 and CMMI-1436631. Any opinions, findings and conclusions or recommendations expressed on this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors also thank the staff of LANL for their assistance collecting data on the Whisper 500.
PY - 2017
Y1 - 2017
N2 - Proper normalization of structural response data that accounts for the environmental and operational conditions (EOCs) of a structure is a key step in structural health monitoring (SHM) analyses. Normalizing data based on EOCs enables a more effective comparison of damage sensitive features extracted from structural response data derived from a system operating under a wide variation in its operations. In this paper, structural response data from an operational wind turbine is used for both damage detection as well as for EOC-based data normalization in a damage detection framework. The structure under consideration is the tower of a 3-kW wind turbine located at Los Alamos National Lab. A wireless monitoring system was installed in the turbine tower to record the tower acceleration response when the tower was undamaged and intentionally damaged. Gaussian Process Regression is used to find tower response features that correlate with EOCs, specifically rotor angular velocity and nacelle yaw angle, yet are insensitive to structural damage. The features are then used in the damaged detection framework to enhance the performance of clustering algorithms used for EOC normalization. Damage-sensitive features are then used as condition parameters for damage detection. The efficiency of the proposed EOCs normalization process is evaluated by comparing the Receiver Operating Characteristic (ROC) curves of a threetier damage detection strategy previously proposed for wind turbine systems.
AB - Proper normalization of structural response data that accounts for the environmental and operational conditions (EOCs) of a structure is a key step in structural health monitoring (SHM) analyses. Normalizing data based on EOCs enables a more effective comparison of damage sensitive features extracted from structural response data derived from a system operating under a wide variation in its operations. In this paper, structural response data from an operational wind turbine is used for both damage detection as well as for EOC-based data normalization in a damage detection framework. The structure under consideration is the tower of a 3-kW wind turbine located at Los Alamos National Lab. A wireless monitoring system was installed in the turbine tower to record the tower acceleration response when the tower was undamaged and intentionally damaged. Gaussian Process Regression is used to find tower response features that correlate with EOCs, specifically rotor angular velocity and nacelle yaw angle, yet are insensitive to structural damage. The features are then used in the damaged detection framework to enhance the performance of clustering algorithms used for EOC normalization. Damage-sensitive features are then used as condition parameters for damage detection. The efficiency of the proposed EOCs normalization process is evaluated by comparing the Receiver Operating Characteristic (ROC) curves of a threetier damage detection strategy previously proposed for wind turbine systems.
UR - http://www.scopus.com/inward/record.url?scp=85032328666&partnerID=8YFLogxK
U2 - 10.12783/shm2017/14145
DO - 10.12783/shm2017/14145
M3 - Conference contribution
AN - SCOPUS:85032328666
SP - 2475
EP - 2482
BT - Structural Health Monitoring 2017
A2 - Chang, Fu-Kuo
A2 - Kopsaftopoulos, Fotis
T2 - 11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017
Y2 - 12 September 2017 through 14 September 2017
ER -