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Zur Charakterisierung von luftgekoppelten Ultraschallwandlern werden mehrere verschiedene Methoden und Verfahren angewendet. Durch die unterschiedlichen Herangehensweisen werden oft unterschiedliche Prüfkopfpara-meter und Schallfeldgrößen ermittelt, was einen direkten Vergleich von Datenblättern erschwert. Für die Charakterisierung von Kontakt- und Tauchtechnikprüfköpfen hat sich ISO 22232-2:2021 bereits etabliert, allerdings ist diese Norm nicht auf die luftgekoppelten Wandlertechnologien direkt übertragbar. Daher erkannten Hersteller, Forschungseinrichtungen und Anwender den Bedarf an einer Richtlinie zur einheitlichen Charakterisierung von luftgekoppelten Wandlern. Gemeinsam arbeiten sie aktuell im Rahmen des Unterausschusses „Luftgekoppelte Ultraschallprüfung“ der DGZfP an der Erstellung einer solcher Richtlinie mit dem Anspruch, die bestehende Normung um die luftgekoppelte Prüfung zu erweitern und somit die Vereinheitlichung der Charakterisierung dieser Wandler zu erleichtern.
In diesem Beitrag wird ein Überblick über den geplanten Inhalt der Richtlinie und über die Methoden zur akustischen Charakterisierung von luftgekoppelten Ultraschallwandlern gegeben. Insbesondere werden verschiedene Methoden der Ermittlung von wichtigen Wandlerparametern wie z.B. Empfindlichkeit, Mittenfrequenz, Bandbreite, Fokusgröße und Fokusabstand dargestellt und verglichen.
Temperature Compensation Strategies for Lamb Wave Inspection using Distributed Sensor Networks
(2022)
The application of temperature compensation strategies is crucial in structural health monitoring approaches based on guided waves. Actually, the varying temperature influences the performance of the inspection system inducing false alarms or missed detection, with a consequent reduction of reliability. This paper quantitatively describes a method to compensate the temperature effect, namely the optimal baseline selection (OBS), extending its application to the case of distributed sensor networks (DSN). The effect of temperature separation between baseline time-traces in OBS are investigated considering multiple couples of sensors employed in the DSN. A combined strategy that uses both OBS and frequent value warning is considered. Theoretical results are compared, using data from two several experiments, which use different frequency analysis with either predominantly A0 mode or S0 mode data or both. The focus is given on the fact that different paths are available in a sensor network and several possible combination of results are available. Nonetheless, introducing a frequent value warning it is possible to increase the efficiency of the OBS approach making use of fewer signal processing algorithms. These confirm that the performance of OBS quantitatively agrees with predictions and also demonstrate that the use of compensation strategies improve detectability of damage.
Performance assessment for GuidedWave (GW)-based Structural Health Monitoring (SHM) systems is of major importance for industrial deployment.
With conventional feature extraction methods like damage indices, pathbased probability of detection (POD) analysis can be realized. To achieve reliability quantification enough data needs to be available, which is rarely the case.
Alternatives like methods for performance assessment on system level are still in development and in a discussion phase. In this contribution, POD results using an Artificial Intelligence (AI)-based data analysis are compared with those delivered by conventional data analysis. Using an open-access dataset from Open Guided Wave platform, the possibility of performance assessment for GW-based SHM systems using AI-based data analysis is shown in detail. An artificial neural network (ANN) classifier is trained to detect artificial damage in a stiffened CFRP plate. As input for the ANN, classical damage indicators are used. The ANN is tested to detect damage at another position, whose inspection data were not previously used in training. The findings show very high detection capabilities without sorting any specific path but only having a global view of current damage metrics. The systematic evaluation of the ANN predictions with respect to specific damage sizes allows to compute a probability of correct identification versus flaw dimension, somehow equivalent to and compared with the results achieved through classic path-based POD analysis. Also, sensitive paths are detected by ANN predictions allowing for evaluation of maximal distances between path and damage position. Finally, it is shown that the prediction performance of the ANN can be improved significantly by combining different damage indicators as inputs.
Reliability assessment of Structural Health Monitoring (SHM) systems
poses new challenges pushing the research community to address many questions which are still open. For guided wave-based SHM it is not possible to evaluate the system performance without taking into account the target structure and applied system parameters. This range of variables would result in countless measurements.
Factors like environmental conditions, structural dependencies and wave characteristics demand novel solutions for performance analysis of SHM systems compared to those relying on classical non-destructive evaluation. Such novel approaches typically require model-assisted investigations which may not only help to explain and understand performance assessment results but also enable complete studies without costly experiments. Within this contribution, a multi input multi output approach using a sparse transducer array permanently installed on a composite structure to excite and sense guided waves is considered. Firstly, the method and the analysis of path-based performance assessment are presented considering an open-access dataset from the Open Guided Wave platform. Then, a performance analysis of a guided wave-based SHM system using Probability of Detection is presented. To explain some unexpected results, the model-assisted investigations are used to understand the physical phenomena of wave propagation in the test specimen including the interaction with damage. Finally, issues and future steps in SHM systems’ performance assessment and their development are discussed.
Toughening mechanisms and enhanced damage tolerant fatigue behaviour in laminated metal composites
(2022)
In the present study, fatigue crack growth (FCG) in a laminated metal composite (LMC) consisting of Al-based constituents with dissimilar strength was studied. Additionally, the FCG in both monolithic constituent materials was determined and a linear elastic rule of mixture (ROM) concept was calculated as a reference for the FCG of the laminated composite. Crack networks in the laminates were analyzed post-mortem by means of light microscopy and synchrotron X-Ray tomography (SXCT). Significantly reduced FCG rates for the LMC were found at elevated stress intensity ranges compared to both the monolithic constituents as well as the ROM concept. This is the result of the formation of a complex 3D crack network in the laminated architecture caused by the appearance of the two different toughening mechanisms a) crack deflection and b) crack bifurcation at the vicinity of the interfaces.
Up to now, the inspection of wind turbines with industrial climbers has been considered ”state of the art”. However, ever-larger wind turbines and advancing digitization make modern and automated inspection methods indispensable. Passive thermography can serve as such a digital and atomized method while it is well known for its applications in the inspection of buildings or electrical circuits. However, its application relies on thermal gradients in the inspected object such that a temperature contrast exists between damaged and sound areas. This also holds for unheated structures like rotor blades of wind turbines which show no intrinsic temperature gradient and can hardly be heated. Under certain weather conditions with sufficient solar loading and diurnal temperature variations, passive thermography is suitable for the in-service inspection of rotor blades. However, for a reliable use of passive thermography on ”thermal passive” components, the incorporation of these environmental conditions in the planning and evaluation of thermal inspections is crucial. Additionally, the complex inner structure of wind turbine blades in comparison to other objects and buildings require a specific method referencing the individual rotor blades to each other. This allows the distinction between the thermal response of design-specific structural features and damages or irregularities between the three blades. We show thermal signatures of damage in rotor blades and contrast them with structural characteristics by comparing the three blades. In addition to measurements in industrial environments, laboratory measurements are shown and compared to simulations. The long-term goal is to simulate the influence of different weather parameters and thus gain a better understanding of measurements in the field. The results shown here can be seen as one step towards industrial application.
Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.
Larvae and adults of Korynetes caeruleus (de Geer 1775) (Coleoptera: Cleridae) were collected from old churches and reared in the laboratory on Anobium punctatum (de Geer 1774) (Coleoptera, Ptinidea, formerly Anobiidae). Breeding success of K. caeruleus was low, but basic parameters of this species’ developmental cycle were identified. At 21 °C and 75 % relative humidity and a four-month cold period at 4 °C, the development of K. caeruleus from egg to adult appearance lasted 2 years. The pupal stage may be reached and completed after one and a half years. Feeding on larvae of A. punctatum by larvae of K. caeruleus was observed and consisted of a combination of sucking haemolymph and consuming body parts. The sickle-like mandibles of larvae of K. caeruleus penetrate the cuticle of prey larvae followed by pumping and sucking body movements. Adult beetles of A. punctatum were not attacked by K. caeruleus larvae. Feeding behavior of adult K. caeruleus was not investigated.
Brillouin Optical Frequency Domain Analysis (BOFDA) is a powerful and well-established method for static distributed sensing of temperature and strain. Recently, we demonstrated a BOFDA system based on convolutional neural network which shortens the measurement time considerably. In this paper, we apply leave-one-out cross validation to evaluate the generalization performance and provide an unbiased and reliable machine learning model for a time-efficient BOFDA system.