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- 2013 (2) (entfernen)
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- Englisch (2)
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- Damage detection (2)
- Automated operational modal analysis (1)
- Environmental and operational effects (1)
- Environmental/operational effects (1)
- Feature extraction (1)
- Multiple linear regression (1)
- Principal component regression (1)
- Structural health monitoring (1)
- Vibration-based structural health monitoring system (1)
- Wind turbine (1)
The implementation of a continuous dynamic monitoring System in Pedro e Ines footbridge at Coimbra, Portugal, operating since June 2007, enabled to detect different environmental/operational effects on the modal properties, based on appropriate processing of monitoring data collected by a set of accelerometers and thermal sensors.
In order to remove or mitigate such environmental/operational effects with the purpose of damage detection, two different Statistical methods have been adopted, One of them consists in the multiple linear regression (MLR) by performing correlation analysis between measured modal properties and environmental/operational variables. Another is based on the identification of the linear subspace within the modal properties by applying principal component regression (PCR) without using measured values of environmental and operational variables.
This paper presents a comparison ofthe performance of these two alternative approaches on the basis of continuous monitoring data acquired front the instrumented Pedro e Ines footbridge.
In the context of national innovative project IMO-WIND, an integrated long term monitoring System was installed on a prototype of an offshore wind turbine System of 5 megawatt dass. The Federal Institute for Materials Research and Testing (BAM) was responsible for development of the SHM System. It consists of a signal acquisition System and a Signal processing and management System.
The signal acquisition System is composed of 14 acceleration sensors, 110 strain gauges and 4 inclination sensors, installed at the tripod foundation and the tower. It began to work in August 2007. In order to investigate the structural dynamic properties, extract efficient damage index and manage huge amounts of Vibration Signals and analysis results, an automated signal processing and management Software System is developed in LabVIEW® environment. It includes functions such as automated Operational Modal Analysis (OMA) on the basis of Stochastic Subspace Identification (SSI) method and poly-reference Least-Squares Complex Frequency Domain (p-LSCF) approach, investigation of environmental/operational effects on structural dynamic properties under operational conditions, features extraction using Principal Component Regression (PCR) and Multiple Linear Regression (MLR) as well as data/result management and visualization.
Besides, this paper also presents variations of structural modal parameters of a wind turbine system under complex environmental/operational conditions. Comparison of modal parameters automatically extracted by SSI method and poly-reference p-LSCF approach indicates that the latter method provides more stable modal identification results ffom the viewpoint of long term OMA. Subsequently, the influences of temperature, wind velocity, rotation speed and nacelle direction on modal parameters are generally discussed. Finally, variations of indentified modal parameters during two years are presented.