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- Integrated monitoring system (4)
- Offshore wind turbines (4)
- Structural assessment (4)
- Data management (3)
- Limit values (3)
- Monitoring (3)
- Rotor blades (3)
- Windenergieanlagen (3)
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To ensure a high operational reliability of offshore wind turbines (OWEC) with economically acceptable repair and maintenance efforts, comprehensive diagnosis and supervision concepts are required. Automatic monitoring Systems will be an essential part of such concepts. Because of the fact, that during Operation there will be static and dynamic interaction between the components ‘structure’, ‘machinery’ and ‘blades’ it is necessary to develop the monitoring techniques in an overall concept. These monitoring Systems are supposed to be applied for the design and testing as well as for the Operation and maintenance phases. The knowledge of the dynamic behavior of wind turbines is important both for the design and for a safe Operation. The available monitoring data from a period of three years, allow first conclusions on the long-term Operation of such Systems in terms of quality requirements to the instrumentation to the structure and the rotor blades
This paper presents the development of a continuous dynamic monitoring System and its applications to different structures, with the purpose of understanding structural real behaviours under operational conditions and detecting early structural modifications. The first part of paper introduces a complete continuous dynamic System, consists of signal acquisition and communication, automated signal processing and management, investigation of the interaction between structures and its environmental/operational conditions, feature extraction and detection of structural modification.
The rest of paper describes the applications of continuous dynamic monitoring System to different structures such as a wind turbine System and a highway bridge.
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.
This paper presents the continuous dynamic measurements of an aging prestressed box-beam bridge during the period from 2000 to 2013, which consists of dynamic properties and environmental/operational factors, as well as the Variation of the extracted health features after removing the environmental/operational effects. Firstly, the variations of frequency estimates, temperatures and traffic loads from 2000 to 2013 are described. Subsequently, correlation analysis between them indicates the nonlinear influences of the temperature on the frequency estimates. The Multiple Linear Regression (MLR) method is used to remove the environmental/operational effects and the health index is proposed by performing the Novelty Detection analysis of the residual errors. Finally, the variations of the health index may indicate clear structural modifications that can be also characterized by the long term monitoring of the dynamic strain in the prestressed cable.