We investigated the adaptation and performance of Masked Autoencoders (MAEs) with Vision Transformer (ViT) architectures for self-supervised representation learning on one-dimensional (1D) ultrasound signals. Although MAEs have demonstrated significant success in computer vision and other domains, their use for 1D signal analysis, especially for raw ultrasound data, remains largely unexplored. Ultrasound signals are vital in industrial applications such as nondestructive testing (NDT) and structural health monitoring (SHM), where labeled data are often scarce and signal processing is highly task-specific. We propose an approach that leverages MAE to pre-train on unlabeled synthetic ultrasound signals, enabling the model to learn robust representations that enhance performance in downstream tasks, such as time-of-flight (ToF) classification. This study systematically investigated the impact of model size, patch size, and masking ratio on pre-training efficiency and downstream accuracy. Our results show that pre-trained models significantly outperform models trained from scratch and strong convolutional neural network (CNN) baselines optimized for the downstream task. Additionally, pre-training on synthetic data demonstrates superior transferability to real-world measured signals compared with training solely on limited real datasets. This study underscores the potential of MAEs for advancing ultrasound signal analysis through scalable, self-supervised learning.

New quantitative data processing methods could enable ultrasound as a potential diagnostic method for hip implant integration monitoring. For development of such methods, suitable acoustic simulation tools are essential. In this work, a novel 1D FDTD simulation tool for multilayer structures, considering frequency-dependent properties, is introduced, particularly meeting the special needs of this application. Simulation results show excellent agreement with experimental data, confirming accurate prediction of wave propagation in multilayer systems.

Zusammenfassung

Geführte Akustische Wellen (GAW) haben sich im Themengebiet des Structural Health Monitoring (SHM) etabliert. Neben ihren Vorteilen bei der Überwachung von Objekten und Detektion von Fehlstellen, gibt es jedoch auch einige Herausforderungen. Zu diesen zählt die dispersive Natur der häufig eingesetzten Lambwellen. Dispersion führt zu Signalverzerrung und reduziert dadurch die räumliche Auflösung und erschwert die Erkennung von schwach reflektierenden Fehlstellen. In diesem Beitrag wird der Einsatz eines Phased-Array-Systems zur Delaminationserkennung bei einem Mehrschichtsystem demonstriert, bei dem dispersive Lambwellen zum Einsatz kommen. Durch das Kompensieren der Dispersionseffekte kann die Sign Coherence Factor (SCF) Erweiterung des Total Focusing Method (TFM) Algorithmus eingesetzt und so auch schwach reflektierende Fehlstellen erkannt werden. Des Weiteren wird auf das Entstehen von Modenüberlagerungen bei Mehrschichtsystemen eingegangen, die bei der Auswahl der Arbeitsfrequenz und Sendesignallänge des Phased-Array-Systems berücksichtigt werden müssen.

Guided Acoustic Waves (GAW) are well established in the field of Structural Health Monitoring (SHM). However, in addition to their advantages in monitoring objects and detecting defects, there are also several challenges. These include the dispersive nature of the commonly used Lamb waves. Dispersion leads to signal distortion that reduces spatial resolution and makes it difficult to detect weakly reflecting defects. This paper demonstrates the use of a phased array system for delamination detection in a multilayer system using dispersive Lamb waves. By compensating for the dispersion effects, the Sign Coherence Factor (SCF) extension of the Total Focusing Method (TFM) algorithm can be used to detect even weakly reflective defects. Furthermore, the occurrence of mode superposition in multilayer systems is discussed, which must be taken into account when selecting the operating frequency and transmit signal length of the phased array system.

The precise positioning of objects (especially on a flat surface), is crucial in numerous industrial applications, from large

object movement in machine tools to precise alignment in optical microscopes. This paper focuses on targeted movement

and positioning of smaller objects on a flat surface using piezoelectric actuators. Specifically, it investigates the stick-slip

motor principle for achieving high-speed transport and high-resolution positioning. The stick-slip motor utilizes the friction

between objects and surfaces to generate motion. The experimental setup includes a piezoelectric actuator inducing

asymmetric vibrations in a glass plate (stator), resulting in controlled movement of a slider (the object). An asymmetrical

excitation signal, generated by superimposing two sinusoidal waves, is applied to the piezoelectric actuator. The influence

of the different parameters of the excitation signal on the object speed is examined in more detail. The results demonstrate

successful movement and precise positioning of various objects. It concludes that the developed stick-slip motor can

efficiently move objects of different materials and sizes, offering a versatile solution for precise object positioning in

confined spaces.

Für die nicht-invasive Durchflussmessung werden meist Ultraschallsensoren verwendet, die reversibel an das zu untersuchende Rohrsystem angebaut werden. Die Messgenauigkeit dieser Sensoren wird durch mögliche Ablagerungen im Rohrinneren beeinflusst.

In dem hier vorgestellten Forschungsvorhaben, soll es mit Hilfe eines ultraschallbasierten Messverfahren möglich sein, sowohl die Materialeigenschaften und Wandstärke des Rohres direkt zu ermitteln als auch eventuell vorhandene Schichten im Rohrinneren zu detektieren und zu charakterisieren. Mit Hilfe dieser zusätzlichen Messgrößen soll zukünftig eine präzisere Durchflussmessung von klemmbaren Ultraschalldurchflusssensoren ermöglicht werden. Um die Algorithmen zur Charakterisierung der Materialeigenschaften zu erproben, wurden zunächst Simulationen zur Wellenausbreitung der geführten akustischen Wellen und deren Interaktion mit Schichten durchgeführt. Es erfolgte die Auswertung der beiden Grundmoden, A0 und S0, in einem definierten Frequenzbereich. Im Anschluss erfolgte die experimentelle Überprüfung auf einer ebenen Platte mit definierten Schichten von 415 μm und 780 μm. Die bisherigen Messergebnisse zeigen, dass es möglich ist mit dem entwickelten Algorithmus das Material und die Schichten zu charakterisieren. Die noch vorhandene Abweichung der Materialdaten von den Literaturwerten ergibt sich u. a. aus dem Schwingungsverhalten der Piezokeramik. Zukünftig soll die Auswertung durch direkte Messung der Schwingungseigenschaften der Piezokeramik weiter optimiert werden.

Optoacoustics is a metrology widely used for material characterisation. In this study, a measurement setup for the selective determination of the frequency-resolved phase velocities and attenuations of longitudinal waves over a wide frequency range (3-55 MHz) is presented. The ultrasonic waves in this setup were excited by a pulsed laser within an absorption layer in the thermoelastic regime and directed through a layer of water onto a sample. The acoustic waves were detected using a self-built adaptive interferometer with a photorefractive crystal. The instrument transmits compression waves only, is low-contact, non-destructive, and has a sample-independent excitation. The limitations of the approach were studied both by simulation and experiments to determine how the frequency range and precision can be improved. It was shown that measurements are possible for all investigated materials (silicon, silicone, aluminium, and water) and that the relative error for the phase velocity is less than 0.2%.

Humidity, salt content, and migration in building materials lead to weathering and are a common challenge. To understand damage phenomena and select the right conservation treatments, knowledge on both the amount and distribution of moisture and salt load in the masonry is crucial. It was shown that commercial portable devices addressing moisture are often limited by the mutual interference of these values. This can be improved by exploiting broadband radar reflectometry for the quantification of humidity in historic masonry. Due to the above-mentioned limitations, today’s gold standard for evaluating the moisture content in historic buildings is still conducted by taking drilling samples with a subsequent evaluation in a specially designed laboratory, the so-called Darr method. In this paper, a new broadband frequency approach in the range between 0.4 and 6 GHz with improved artificial-intelligence data analysis makes sure to optimize the reflected signal, simplify the evaluation of the generated data, and minimise the effects of variables such as salt contamination that influence the permittivity. In this way, the amount of water could be determined independently from the salt content in the material and an estimate of the salt load. With new machine learning algorithms, the analysis of the permittivity is improved and can be made accessible for everyday use on building sites with minimal intervention by the user. These algorithms were trained with generated data from different drying studies on single building bricks from the masonries. The findings from the laboratory studies were then validated and evaluated on real historic buildings at real construction sites. Thus, the paper shows a spatially resolved and salt-independent measurement system for determining building moisture.

The loosening of an artificial joint is a frequent and critical complication in orthopedics and trauma surgery. Due to a lack of accuracy, conventional diagnostic methods such as projection radiography cannot reliably diagnose loosening in its early stages or detect whether it is associated with the formation of a biofilm at the bone–implant interface. In this work, we present a non-invasive ultrasound-based interferometric measurement procedure for quantifying the thickness of the layer between bone and prosthesis as a correlate to loosening. In principle, it also allows for the material characterization of the interface. A well-known analytical model for the superposition of sound waves reflected in a three-layer system was combined with a new method in data processing to be suitable for medical application at the bone–implant interface. By non-linear fitting of the theoretical prediction of the model to the actual shape of the reflected sound waves in the frequency domain, the thickness of the interlayer can be determined and predictions about its physical properties are possible. With respect to determining the layer’s thickness, the presented approach was successfully applied to idealized test systems and a bone–implant system in the range of approx. 200 µm to 2 mm. After further optimization and adaptation, as well as further experimental tests, the procedure offers great potential to significantly improve the diagnosis of prosthesis loosening at an early stage and may also be applicable to detecting the formation of a biofilm.

For the investigation of moisture and salt content in historic masonry, destructive drilling samples followed by a gravimetric investigation is still the preferred method. In order to prevent the destructive intrusion into the building substance and to enable a large-area measurement, a nondestructive and easy-to-use measuring principle is needed. Previous systems for moisture measurement usually fail due to a strong dependence on contained salts. In this work, a ground penetrating radar (GPR) system was used to determine the frequency-dependent complex permittivity in the range between 1 and 3 GHz on salt-loaded samples of historical building materials. By choosing this frequency range, it was possible to determine the moisture in the samples independently of the salt content. In addition, it was possible to make a quantitative statement about the salt level. The applied method demonstrates that with ground penetrating radar measurements in the frequency range selected here, a salt-independent moisture determination can be carried out.

Material characterization using contactless excited and detected guided acoustic waves is a well proven, but overall expensive approach, since a cheap method of excitation is missing so far. Ignition-spark-excitation of Lamb waves could be such a method but has not shown to be suitable for material characterization yet. Covering various thicknesses and materials of metal plates, systematic dependencies of the spectral amplitude of the ignition-spark-excited Lamb waves are presented in this work. This enables the approach to be an alternative for exciting Lamb waves for material characterization.

Guided acoustic waves (GAW) have proven to be a useful tool for structural health monitoring (SHM). However, the dispersive nature of commonly used Lamb waves compromises the spatial resolution making it difficult to detect small or weakly reflective defects. Here we demonstrate an approach that can compensate for the dispersive effects, allowing advanced algorithms to be used with significantly higher signal-to-noise ratio and spatial resolution. In this paper, the sign coherence factor (SCF) extension of the total focusing method (TFM) algorithm is used. The effectiveness is examined by numerical simulation and experimentally demonstrated by detecting weakly reflective layers with a highly dispersive A0 mode on an aluminum plate, which are not detectable without compensating for the dispersion effects.