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The new crack luminescence method offers the possibility of making fatigue surface cracks in metallic materials more visible during inspections through a special coating system. This coating system consists of two layers, whereby the first layer has fluorescent properties and emits visible light as soon as it is irradiated by UV light. The top layer is black and is designed to prevent the fluorescent layer from emitting if no crack develops in the underlying material. The technique proved particularly useful in a wide variety of fatigue tests of steel components under laboratory conditions. Moreover, it has the potential to be used in various industrial applications. To enable industrial deployment and integration into maintenance strategies, a concept study is developed in this contribution, resulting in a qualification framework that can serve as a foundation for determining the reliability of the crack luminescence system in terms of a probability of detection curve. Within this study, factors causing measurement variability and uncertainty are being determined and their influences assessed. Due to the extension of the system by a moving computer vision system for automated crack detection using artificial intelligence, additional long-term effects associated with structural health monitoring systems need to be incorporated into an extended probability of detection study as part of the technical justification. Finally, important aspects and findings related to design of experiments are discussed, and a framework for reliability assessment of a new optical crack monitoring method is presented, emphasizing the influence of various uncertainty parameters, including long-term effects such as system ageing.
Support structures of wind turbines in German offshore wind farms are regularly inspected. Currently, inspection outcomes are generally not systematically recorded. This prevents their consistent handling and processing, which is a key requirement to enable an efficient structural integrity management. As part of the DiMoWind-Inspect project, a data model and reference designation system for such inspection results is developed to facilitate their identification, localization, quantification, tracing and linking throughout the lifetime of a wind farm and beyond. The inspection results together with structural health monitoring results and information on repairs form the basis for assessing and predicting the structural condition, estimating the remaining lifetime, and planning of future inspections, structural health monitoring, and repairs. As a basis for developing a digital structural integrity management, a generic framework is proposed in this paper, which describes the stakeholders, data, models, processes and workflows of the integrity management of support structures in offshore wind farms and their interrelations. The framework adopts a building information modelling approach to describe the information relevant to the structural integrity management. The central digital space of the framework is a common data environment. An implementation of the framework will enable a digital structural integrity management in which inspection, structural health monitoring, repair, assessment and planning outcomes generated throughout the life cycle of a wind farm can be consistently collected, managed, shared and processed by the relevant shareholders.
Right from the beginning of applying SHM to bridge structures it was obvious that environmental based perturbations on the measurement significantly influence the ability to identify structural damage.
Strategies are needed to classify such effects and consider them appropriately in SHM. Various methods have been developed and analyzed to separate environmental based effects from damage induced changes in the measures. Generally, two main approaches have emerged from research activity in this fields: (a) statistics based tools analyzing patterns in the data or in computed parameters and (b) methods, utilizing the structural model of the bridge taking into account environmental as well as damage based changes of stiffness values.
With the back-ground of increasing affordability of sensing and computing technology, effort should be made to increase sensitivity, reliability and robustness of procedures, separating environmental from damage caused changes in SHM measures. The contribution describes both general strategies and points out their Advantages and drawbacks. As basis, a review on relevant methods was conducted. The aim of the study is to classify approaches for separating damage describing information from environmental based perturbations in dependency of the SHM objective. And such, it is intended to describe a best practice in designing concepts for Monitoring infrastructure, naturally effected by environmental influences.
The local asymptotic approach is promising for vibration-based fault diagnosis when associated to a subspace-based residual function and efficient hypothesis testing tools. It has the ability of detecting small changes in some chosen system parameters. In the residual function,the left null space of the observability matrix associated to a reference model is confronted to the Hankel matrix of output covariances estimated from test data. When this left null space is not perfectly known from a model, it should be replaced by an estimate from data to avoid model errors in the residual computation. In this paper, the asymptotic distribution of the resulting data-driven residual is analyzed and its covariance is estimated, which includes also the covariance related to the reference null space estimate. The advantages of the data-driven residual are demonstrated in a numerical study, and the importance of including the covariance of the reference null space estimate is shown, which increases the detection Performance.
Anhand verschiedener Bespiele wird diskutiert, wie Temperaturbeanspruchungen in einer Bauwerksüberwachung sichtbar werden und wie sie zu bewerten sind. Insbesondere der Einfluss auf die Zustandsbewertung im Sinne der Standsicherheit stellt häufig eine große Schwierigkeit dar. Behelfsweise besteht eine grundlegende Möglichkeit darin, ein Tragwerk immer zu seiner schwächsten Phase, d.h. in der Regel im Sommer zu beurteilen. Wie aufgezeigt, können zudem statistische Verfahren geeignet sein, Tragfähigkeitsänderungen zu detektieren.
Als wesentliche Zukunftsaufgaben werden verbesserte Detektionsalgorithmen und physikalisch begründete Modelle zum mechanischen Verhalten angesehen.
Die Akzeptanz schwingungsbasierter Verfahren zur Detektion und Lokalisierung von Schäden in der praktischen Anwendung ist alleinig von der Fähigkeit abhängig, Einflüsse aus veränderlichen Umwelt- und Betriebsbedingungen, insbesondere aber die aus der Temperatur zu eliminieren. Da die in den letzten Jahren immer wichtiger werdenden stochastischen Verfahren zur Schadensde-tektion und -lokalisierung die (statistische) Charakteristik der Messdaten bereits implizit nutzen, sind hier Entwicklung, Anpassung und Anwendung statistischer Algorithmen zur Eliminierung der variierenden Umwelteinflüsse naheliegend.
Auf der anderen Seite wird der saisonale Einfluss auf Bauwerksmessungen in sehr vielen Veröf-fentlichungen dargestellt und es werden häufig verdachtsweise begründete physikalische Zu-sammenhänge für das Verhalten aufgezeigt. Im Regelfall sind die Aussagen aber nicht experi-mentell überprüft und von eher allgemeiner Natur („Asphalt reagiert temperaturabhängig“). Ob-jektspezifische Zusammenhänge, beispielsweise über Versuch an Repräsentanten in der Klima-kammer ermittelt, könnten jedoch sehr hilfreich sein.
At present, to produce renewable energy offshore wind farms play an important role. The available space combined with the more valuable wind conditions make offshore locations very attractive for wind powered energy production. In Europe a significant number of offshore wind farms already exist, especially in the North and Baltic Sea. In future this trend will continue, and further offshore wind farms will be built. The majority of offshore wind turbines are mounted on steel foundation structures. Due to the high-cyclic loading by wind and waves fatigue stress plays a substantial role regarding structural safety.
Besides the consideration of fatigue within the design process, to monitor existing steel structures for potential fatigue cracks during their life time is a major topic and a challenge.
For the structures of the offshore wind turbines are large and partially under water effective reliable methods for the detection of fatigue cracks are required.
This contribution presents investigations on different crack detection methods applied at high-cycle fatigue tests on small-scale welded steel samples as well as on large-scale welded steel components. The tests were conducted at the BAM laboratories. For crack detection mainly three different methods were used and compared. The first method regards to the measurement of strain by conventionally strain gauges. Secondly, the crack luminescence was used as a new and effective optical method for surface monitoring. And finally, crack detection by pressure differentials of the inner and outer section of tubular steel elements was investigated. A comparison study will emphasize the advantages and disadvantages of the different methods and show which of the described methods is potentially more suitable for an application on real offshore wind structures.
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.
The contribution discusses the processing and analysis of data generated on two different ways of investigations for impact damage in reinforced concrete structures. Damage investigations are essential to determine type and characteristics of damage and thus the residual capacity. Damage describing data is generated using two different types of investigation, a non-destructive tomographic as well as numerical examination. Subsequently, data of both sources was merged and analysed. Within the research project “Behaviour of structural components during impact load conditions caused by aircraft fuel tank collision” reinforces concrete plates were damaged by impact loading, see Hering (2020). Afterwards the damaged specimens were investigated tomographically as well as numerically using several methods and models. Aim of the presented research work was to specify an objective comparability of numerical data with experimentally determined damage patterns and based on this, to establish a quantitative damage evaluation.
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.