7.2 Ingenieurbau
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- 2020 (43) (entfernen)
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- Vortrag (16)
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- Englisch (38)
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- Brücken (4)
- Damage characterization (4)
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- Structural Health Monitoring (4)
- Ansys Autodyn (3)
- Drucker-Prager (3)
- Linear parameter varying systems (3)
- Model interpolation (3)
- Post-impact evaluation (3)
Organisationseinheit der BAM
- 7.2 Ingenieurbau (43) (entfernen)
Eingeladener Vortrag
- nein (16)
At the Bundesanstalt für Materialforschung und -prüfung (BAM) full scale specimens for nuclear transport and storage containers (casks) are tested for their structural integrity in a series of drop tests on the Test Site Technical Safety in Horstwalde, 50 km south of Berlin. These drop tests cause a major stress not only on the casks, but also on the steel tower structure of the test facility, itself. The load pattern makes the structure very interesting for detailed investigation. The focus of the monitoring lies on the bolted joints of the flange connections that are a typical connection for cylindrical elements if welding is technical or economical unfavorable. The definition of the monitoring takes was done by investigating the existing documents and inspection results accompanied by building an initial digital representation of the structure, consisting of two finite element (FE) models and a geometrical 3D point cloud representation. As a first step the structures behavior during static and dynamic loading was analyzed using measurement data and an updated numerical FE Model. The idea behind is to use models for a digital planning and operation/evaluation of the structural health monitoring. A static FE simulation and a dynamic FE simulation are generated, to investigate how the structure behaves under the load conditions.
Fault detection for linear parameter varying systems under changes in the process noise covariance
(2020)
Detecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varying parameter, an interpolation approach is pursued, where a limited number of reference models – each estimated from data measured in a reference state – are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex when the different points of interpolation correspond to different noise conditions. Then conflicts may arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to different noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system.
Fault detection for linear parameter varying systems under changes in the process noise covariance
(2020)
Detecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only Systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varying parameter, an interpolation approach is pursued, where a limited number of reference models { each estimated from data measured in a reference state { are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex when the different points of interpolation correspond to different noise conditions. Then con icts May arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to different noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system.