TY - CONF A1 - Viefhues, Eva T1 - Fault detection for linear parameter varying systems under changes in the process noise covariance N2 - 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. T2 - 21st IFAC World Congress CY - Berlin, Germany DA - 13.07.2020 KW - Linear parameter varying systems KW - Fault detection KW - Changing process noise KW - Subspace-based residual KW - Model interpolation PY - 2020 AN - OPUS4-52366 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Herrmann, Ralf T1 - Vorstellung des Bauwerksmonitoring der Maintalbrücke Gemünden im Vorhaben AISTEC N2 - Im Abteilungsseminar wird der aktuelle Umsetzungsstand des Bauwerksmonitorings an der Maintalbrücke Gemünden vorgestellt, sowie die nächsten Schritte zur Systemintegration von Konzepten an Realbauwerken. T2 - Abteilungs-Vortragsseminar Abteilung 7. Bauwerkssicherheit CY - Online meeting DA - 03.03.2021 KW - Structural Health Monitoring KW - Maintalbrücke Gemünden KW - Bauwerksüberwachung KW - Datenmanagement PY - 2021 AN - OPUS4-52221 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Sensorbasiertes Monitoring (AP4 + AP7): Ein Überblick N2 - Im AISTEC Projekt erforscht der FB 7.2 Verfahren zur Bewertung von Verkehrsbrücken auf der Gruandlage von sensorbasierten Bauwerksmessungen. In diesem Vortrag wird ein Überlick über die Forschungsarbeiten des FB 7.2 präsentiert. Des Weiteren wird ein Ausblick zur quantitativen Integration von sensorbasierten Bauwerksmessungen in die risiko-basierte prädiktive Planung von Inspektionen und Reparaturen von Ingenieurbauwerken gegeben. T2 - AISTEC 5. Verbundtreffen CY - Online meeting DA - 24.06.2021 KW - AISTEC KW - Structural Health Monitoring KW - Brücken PY - 2021 AN - OPUS4-52982 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Herrmann, Ralf T1 - AISTEC 5. Verbundtreffen - Messfahrt auf der Maintalbrücke Gemünden N2 - Der Vortrag stellt die Durchführung und die ersten Ergebnisse der Belastungsfahrten der BAM im Rahmen des AISTEC Projekts an der Maintalbrücke am 19.05. und 20.05.2021 vor. Es wurden Tragwerksreaktionen des Bauwerks mit dem installierten Dauermonitoringsystem und insbesondere dem Betongelenk mit zusätzlich installierter Sensorik aufgezeichnet. Für die Ermittlung der Lastposition wurden mehrere Verfahren eingesetzt und verglichen. T2 - AISTEC 5. Verbundtreffen CY - Online meeting DA - 24.06.2021 KW - AISTEC KW - Structural Health Monitoring KW - Einflusslinie KW - Messfahrt KW - Belastungszug PY - 2021 AN - OPUS4-52927 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Viefhues, Eva A1 - Döhler, M. A1 - Simon, Patrick A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Mevel, L. ED - Cunha, A. ED - Caetano, E. T1 - Stochastic subspace-based damage detection of a temperature affected beam structure N2 - Structural health monitoring (SHM) of civil structures often is limited due to changing environmental conditions, as those changes affect the structural dynamical properties in a similar way like damages can do. In this article, an approach for damage detection under changing temperatures is presentedand applied to a beam structure. The used stochastic subspace-based algorithm relies on a reference null space estimate, which is confronted to data from the testing state in a residual function. For damage detection the residual is evaluated by means of statistical hypothesis tests. Changes of the system due to temperature effects are handled with a model interpolation approach from linear parameter varying system theory. From vibration data measured in the undamaged state at some few reference temperatures, a model of the dynamic system valid for the current testing temperature is interpolated. The reference null space and the covariance matrix for the hypothesis test is computed from this interpolated model. This approach has been developed recently and was validated in an academic test case on simulations of a mass-spring-damper. In this paper, the approach is validated experimentally on a beam structure under varying temperature conditions in a climate chamber. Compared to other approaches, the interpolation approach leads to significantly less false positive alarms in the reference state when the structure is exposed to different temperatures, while faults can still be detected reliably. T2 - 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 10 CY - Online meeting DA - 30.06.2021 KW - Damage detection KW - Subspace methods KW - Temperature effects KW - Model interpolation KW - Climate chamber KW - Laboratory beam structure PY - 2021 SP - 1 EP - 6 AN - OPUS4-52999 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Viefhues, Eva T1 - Stochastic subspace-based damage detection of a temperature affected beam structure N2 - Structural health monitoring (SHM) of civil structures often is limited due to changing environmental conditions, as those changes affect the structural dynamical properties in a similar way like damages can do. In this article, an approach for damage detection under changing temperatures is presentedand applied to a beam structure. The used stochastic subspace-based algorithm relies on a reference null space estimate, which is confronted to data from the testing state in a residual function. For damage detection the residual is evaluated by means of statistical hypothesis tests. Changes of the system due to temperature effects are handled with a model interpolation approach from linear parameter varying system theory. From vibration data measured in the undamaged state at some few reference temperatures, a model of the dynamic system valid for the current testing temperature is interpolated. The reference null space and the covariance matrix for the hypothesis test is computed from this interpolated model. This approach has been developed recently and was validated in an academic test case on simulations of a mass-spring-damper. In this paper, the approach is validated experimentally on a beam structure under varying temperature conditions in a climate chamber. Compared to other approaches, the interpolation approach leads to significantly less false positive alarms in the reference state when the structure is exposed to different temperatures, while faults can still be detected reliably T2 - 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 10 CY - Online meeting DA - 30.06.2021 KW - Damage detection KW - Subspace methods KW - Temperature effects KW - Model interpolation KW - Climate chamber KW - Laboratory beam structure PY - 2021 AN - OPUS4-53000 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Baeßler, Matthias A1 - Recknagel, Christoph ED - Cunha, Á. ED - Caetano, E. T1 - Enhancing structural models with material tests and static response data - a case study considering a steel beam with asphalt layer subject to temperature variations N2 - Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems that enable a prediction of the structural behavior under varying ambient condition. Within this framework, different types of data obtained from real the structural system can be applied to update the digital twin of the structural system using Bayesian methods and thus enhance predictions of the structural behavior. In this contribution, we implement the framework to develop a digital twin of a simply supported steel beam with an asphalt layer. It is formulated such that it can predict the static response of the beam in function of its temperature. In a climate chamber, the beam was subject to varying temperatures and its static response wass monitored. In addition, tests are performed to determine the temperature-dependent properties of the asphalt material. Bayesian system identification is applied to enhance the predictive capabilities of the digital twin based on the observed data. T2 - International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-10) CY - Online meeting DA - 30.06.2021 KW - Digital twin KW - Structural health monitoring KW - Material tests KW - Bayesian updating PY - 2021 SN - 2564-3738 SP - 1537 EP - 1544 AN - OPUS4-54126 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Baeßler, Matthias T1 - Dynamisches Verhalten und Überwachung des Großen Fallturms der BAM in Horstwalde N2 - Falltürme sind Bauwerke, die als Versuchsanlagen sehr speziellen, impulsartigen Belastungen ausgesetzt sind. Am Fallturm auf dem Testgelände Technische Sicherheit (TTS) der BAM wurde bei Routineinspektionen eine Abnahme der strukturellen Integrität in Form von Vorspannungsverlusten in den Bolzen der Stahlverbindungen festgestellt. Um ein Verständnis für die zugrundeliegenden Trag- und Schädigungsmechanismen zu erlangen, wurde ein umfassendes Structural Health Monitoring (SHM) System geplant und am Bauwerk installiert unter Nutzung von digitalen Bauwerksmodellen. Für die Auslegung des Überwachungssystems, insbesondere aber zur Unterstützung der Untersuchung des Schädigungsprozesses, wurden Finite-Elemente-Modelle erstellt. Um sicherzustellen, dass die FE-Modelle das reale Tragwerksverhalten mit ausreichender Genauigkeit abbilden, mussten sie jedoch in Bezug auf die gemessenen Antworten des Tragwerks kalibriert werden. Der vorliegende Beitrag beschreibt experimentelle und numerische Untersuchungen zur Identifizierung des strukturellen Systems des Stahlrohrgitterturms in Vorbereitung einer Überwachungskampagne. Die Auswertung von gemessenen Schwingungen unter ambienter Anregung ermöglichte die Identifizierung der Eigenfrequenzen mehrerer globaler Schwingungsmoden des Fallturms. Zur Modellvalidierung wurde zunächst eine Sensitivitätsanalyse durchgeführt, um die Parameter mit dem größten Einfluss zu ermitteln. Anschließend wurde ein evolutionärer Algorithmus (EA) zur Optimierung nach dem Prinzip der Minimierung der Differenzen zwischen gemessenen und simulierten charakteristischen Antworten eingesetzt. Das aktualisierte Modell wurde schließlich an der dynamischen Reaktion der Turmstruktur infolge einer realen Falltest-induzierten Stoßbelastung validiert. Die Ergebnisse zeigten eine gute Übereinstimmung zwischen numerischen und experimentellen Ergebnissen. N2 - Drop test towers are structures subjected to very specific dynamic loadings. At the drop test tower at the Test Site Technical Safety (TTS) of BAM routine inspections revealed a decline of structural integrity in form of loss of pretension in bolts of structural steel connections. To get an understanding about the underlying damage mechanisms as well as to assure the structural safety in operation, a multifaceted Structural Health Monitoring (SHM) system was planned and installed at the structure with the help of digital models. For planning the monitoring system but especially for supporting the investigation of the damaging process finite element models are deployed. Though, to ensure the capability of the FE model to reproduce the real structural behavior in sufficient accuracy, it needs to be calibrated in respect to measured responses of the structure. The present contribution describes experimental and numerical investigations to identify the structural system of the steel tube lattice tower in preparation of the monitoring campaign. An ambient vibration test allowed the identification of the natural frequencies of several global vibrational modes of the drop test tower. For model updating first a sensitivity analysis is applied to identify the most influencing parameter. In succession a genetic algorithm was applied for optimization in the sense of minimization of the difference between measured and simulated characteristic responses. The updated model is finally validated on the dynamic response of the tower structure to a real drop test induced impact loading. The results showed a good agreement between numerical and experimental results though the robustness of the simulation should be improved by more sophisticated numerical modelling. T2 - 7. VDI-Fachtagung Baudynamik CY - Würzburg, Germany DA - 27.04.2022 KW - Großer Fallturm Horstwalde KW - Structural Health Monitoring KW - Belastungsversuch KW - Modalanalyse KW - Schwingungsdynamik KW - Finite Elemente Simulation PY - 2022 AN - OPUS4-55470 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Auersch, Lutz T1 - Characteristic Frequencies of Train-Induced Bridge, Track, Ground and Building Vibrations – Excitation and Mitigation N2 - The characteristic frequencies of train-induced vibrations are discussed in theory and experiment following the propagation of vibrations from the source to the receiver: 1. Out-of-roundness frequencies of the wheels, 2. sleeper-passage frequency, 3. the vehicle-track eigenfrequency, 4. band frequency of the impulses of the passing static axle loads, 5. car-length frequency and multiples, 6. axle-distance frequencies with two characteristic zeros, 7. bridge eigenfrequencies, 8. the cut-on frequency due to the layering, and 9. the cut-off frequency due to the material damping of the soil, 10. the building-soil eigenfrequency, 11. as a rigid building or flexible wall/column mode, 12. floor eigenfrequencies, 13. acoustic room resonances, 14. the „resonance“ frequency or cut-off frequency of a base isolation. Coincidences of some of these characteristic frequencies or frequency ranges can be typically problematic and mitigation measures at the track or at the building can be necessary. The bridge response to the passing static loads is deter¬mined by the axle-sequence spectrum, the eigenfrequency (transfer function) of the bridge, and the modal force or mode shape spectrum. The ground vibration has typically high frequencies for a stiff soil and low frequencies for a soft soil. The high amplitudes between the zeros of the axle-sequence spectrum are often measured in the ground vibrations, and they can be mitigated by soft support elements or a higher bending stiffness of the track. T2 - EVACES 2025 CY - Porto, Portugal DA - 02.07.2025 KW - Train-induced vibration KW - Axle-sequence spectrum KW - Vehicle-track eigenfrequency KW - Axle impulses PY - 2025 AN - OPUS4-63654 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Auersch, Lutz T1 - Railway vibration – fast physics-based models for the prediction of ground vibration and the identification of track damage N2 - The following applications of machine learning will be discussed: 1. The prediction of the wave propagation from a railway line (completely physics based for surface lines, physics-based machine learning for tunnel lines) 2. The track behaviour for the emission of train-induced ground vibration (physics based for homogeneous soil, machine learning for layered soil) 3. Track damage detection and quantification from frequency response functions and moving load responses 4. Bridge damage detection and localisation from modal analysis and moving load 5. The use of axle-box acceleration for the identification of track/sub-soil condition and bridge resonances. The prediction of railway vibration usually needs time-consuming finite element, boundary element and wavenumber domain calculations. For a user-friendly prediction software however, fast calculations are needed. Several time-consuming detailed calculations have been used to develop simpler and fast models for the surface railway lines. The more challenging prediction from tunnel lines will be attacked by purely mathematical and by physics-informed machine learning. The dynamic stiffnesses of isolated or un-isolated railway tracks from detailed calculations with a continuous soil have been approximated with the simpler Winkler soil. The vehicle-track resonance (P2 resonance) rules the effect of the mitigation measures, and it can also be used for the on-board monitoring of the track and sub-soil condition. For the identification of track damage such as gaps between sleepers, track slabs and layers, detailed models with a continuous soil have been updated to get the best fit to the measured frequency response functions from hammer tests and the deformation pattern from the moving load response. Whereas the track damage can be locally identified, this is more difficult for bridges where the modal analysis gives mainly global information. The influence lines of the inclination for statically passing vehicles (locomotive, truck, compaction roller) have been used to localise bridge damage (stiffness variations). The on-board monitoring of rail bridges needs special conditions (regular trains with special speeds) to excite and measure the bridge resonance. T2 - 11th European Workshop on Structural Health Monitoring (EWSHM 2024) CY - Potsdam, Germany DA - 10.06.2024 KW - Vibration prediction KW - Track damage detection KW - Human and machine learning KW - Wave propagation KW - Surface line KW - Tunnel KW - Bridge resonance PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612462 DO - https://doi.org/10.58286/29865 SN - 1435-4934 SP - 1 EP - 9 PB - NDT.net CY - Kirchwald AN - OPUS4-61246 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -