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- Damage detection (2)
- Data management (2)
- Integrated monitoring system (2)
- Limit values (2)
- Offshore wind turbines (2)
- Rotor blades (2)
- Structural assessment (2)
- Concrete bridge (1)
- Convidence intervals (1)
- Data analysis (1)
For the last decades vibration based damage detection of engineering structures has become an important issue for maintenance operations on transport infrastructure. Research in vibration based structural damage detection has been rapidly expanding from classic modal parameter estimation to modern operational monitoring. Methodologies from control Engineering especially of aerospace applications have been adopted and converted for the application on civil structures. Here the difficulty is to regard to the specific environmental and operational influence to the structure under observation. A null space based damage detection algorithm is tested for its sensitivity to structural damage of a prestressed concrete road bridge. Specific techniques and extensions of the algorithm are used to overcome difficulties from the size of the structure which is associated with the number of recorded sensor channels as well as from the operational disturbances by a nearby construction site. It can be shown that for concrete bridges the proposed damage detection methodology is able to clearly indicate the presence of structural damage, if the damage leads to a significant change of the structural system. Small damage which do not result in a System change when not activated by loading, do not lead to a modification of the dynamic response behavior and for that cannot be detected with the proposed global monitoring method.
The concept and technical details of the implementation of the developed integrated monitoring system within the IMO-WIND project are presented. The tasks of the components of the system and its requirements are described. Selected results from the continuous monitoring during operation of the plant M5000_2 regarding the task design verification and dynamic structural analysis are given.
For the last decades vibration based identification of damage on civil Engineering structures has become an important issue for maintenance operations on transport infrastructure.
Research in that field has been rapidly expanding from classic modal Parameter estimation using measured excitation to modern operational monitoring. Here the difficulty is to regard to the specific environmental and operational influence to the structure under observation. In this paper, two methods accounting for statistical and/or operational uncertainties are applied to measurement data of a progressive damage test on a prestressed concrete bridge. On the base of covariance driven Stochastic Subspace Identification (SSI) an algorithm is developed to monitor and automatically compute confidence intervals of the obtained modal parameters. Furthermore, a null space based non-parametric damage detection method, utilizing a statistical χ2 type test is applied to the measurement data. It can be shown that for concrete bridges the proposed methodology is able to clearly indicate the presence of structural damage, if the damage leads to a change of the structural system.
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 describes a structural reliability analysis utilizing monitoring data in the ultimate limit state with consideration of the uncertainties of the monitoring procedure. For this purpose the uncertainties of the monitoring data are modeled utilizing a new framework for the determination of measurement uncertainties. The approach is based on a process equation and Statistical models of observations for the derivation of a posterior measurement uncertainty by Bayesian updating. This facilitates the quantification of a measurement uncertainty using all available data of the measurement process. For the reliability analysis in the ultimate limit state, monitoring data can be utilized as a loading model Information and as proof loading, i.e. resistance model Information. Both approaches are discussed with generic examples and it is shown that the modeling of monitoring data in a reliability analysis can result in a reduction of uncertainties and as a consequence in the reduction of the probability of failure. Furthermore, the proof loading concept is developed further to account for the uncertain characteristic of proof loading due to the measurement uncertainties which is consistent with the framework for the determination of measurement uncertainties. These approaches and findings can be utilized for the assessment of structures for life cycle extension and the design of monitoring Systems.
In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios and mode shapes) obtained from Stochastic Subspace Identification (SSI) of a structure, are afflicted with statistical uncertainty. For evaluating the quality of the obtained results it is essential to know the respective confidence intervals of these figures. In this paper we present algorithms that automatically compute the confidence intervals of modal parameters obtained from covarianceand data-driven SSI of a structure based on vibration measurements. They are applied to the monitoring of the modal parameters of a prestressed concrete highway bridge during a progressive damage test that was accomplished within the European research project IRIS. Results of the covariance- and data-driven SSI are compared.