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A novel distributed acoustic sensing technique is proposed that exploits both phase and amplitude of the Rayleigh backscattered light to quantify the environmental variation. The system employs a wavelength-scanning laser and an imbalanced Mach-Zehnder interferometer to acquire the reflection spectra and the phase of the detected light, respectively. Fading-free and low-frequency measurements are realized via the crosscorrelation of the reflection spectra. The discrete crosscorrelation is used to circumvent the nonlinear frequency sweeping of the laser. Based on the phase of the backscattered light, it is possible to quantify fast environmental variations. The whole system requires no hardware modification of the existing system and its functionality is experimentally validated. The proposed system has the potential to monitor ground motion/movement at very low frequency band like subsidence around mining areas and at high frequency band like earthquakes and vibrations induced by avalanches.
With the reduction of large oil spills because of stricter regulations and safety measures, the question of how to manage smaller oil spills arises. Few on-site analytical tools are available for first responders or other law enforcement personnel to rapidly test for crude oil in the early management of localized polluted areas. The approach reported here relies on well-described computer-assisted multivariate data analysis of the intrinsic fluorescence fingerprints of crude oils to build a multivariate model for the rapid classification of crude oils and the prediction of their properties. Thanks to a dedicated robust portable reader, the method allowed classification and accurate prediction of various properties of crude oil samples like density (according to API, the American Petroleum Institute and viscosity as well as composition parameters such as volume fractions of paraffins or aromatics. In this way, autonomous operation in on-site or in-the-field applications becomes possible based on the direct (undiluted and untreated) measurement of samples and a rapid, tablet-operated readout system to yield a robust and simple analytical test with superior performance. Testing in real-life scenarios allowed the successful classification and prediction of a number of oil spill samples as well as weathered samples that closely resemble samples collected by first responders.
An observation of the fracture process in front of the crack tip inside a dentin sample by means of ex-situ X-ray computed tomography after uniaxial compression at different deformation values was carried out in this work. This ex-situ approach allowed the microstructure and fracturing process of human dentin to be observed during loading. No cracks are observed up to the middle part of the irreversible deformation in the samples at least visible at 0.4μm resolution. First cracks appeared before the mechanical stress reached the compression strength. The growth of the cracks is realized by connecting the main cracks with satellite cracks that lie ahead of the main crack tip and parallel its trajectory. When under the stress load the deformation in the sample exceeds the deformation at the compression strength of dentin, an appearance of micro-cracks in front of the main cracks is observed. The micro-cracks are inclined (~60°) to the trajectory of the main cracks. The further growth of the main cracks is not realized due to the junction with the micro-cracks; we assume that the micro-cracks dissipate the energy of the main crack and suppressed its growth. These micro-cracks serve as additional stress accommodations, therefore the samples do not break apart after the compression test, as it is usually observed under bending and tension tests.
Communities worldwide face significant threats from Explosive Remnants of War (ERW), which endanger lives and restrict land usage. From forest fires due to ERWs or in ERW-contaminated areas (e.g., in Jüterbog, Germany) to broader global challenges (e.g., the Ukrainian conflict), the need for efficient detection and removal of these remnants, especially for humanitarian demining, is paramount. Traditional methods, like manual demining, have severe limitations in safety and efficiency. Here, we introduce an innovative solution to these challenges: “Chemosensing Smart Dust.” This technology uses chemoselective dyes that change their fluorescence properties when exposed to explosives like 2,4,6-trinitrotoluene (TNT). Fluorescence-based detection offers superior sensitivity, reduced likelihood of false positives, and enhanced accuracy of explosive detection. Drones, equipped with excitation lasers or LEDs, deploy the Chemosensing Smart Dust over areas of interest and actively detect the fluorescence changes using high-resolution cameras, offering a rapid, safe, and adaptable detection method. Beyond demining, this innovative approach has potential applications in monitoring polluted areas, homeland security, and emergency response.
Gasquellenlokalisierungen (Gas Source Localization, GSL) tragen dazu bei, die Folgen von Industrieunfällen und Naturkatastrophen zu mildern. Während die GSL, wenn von Menschen durchgeführt, gefährlich und zeitaufwändig ist, können Schwärme von wendigen und kostengünstigen Nanodrohnen die Effizienz und Sicherheit der Suche erhöhen. Da die geringe Nutzlast von Nanodrohnen die Sensor- und Rechenressourcen einschränkt, werden Strategien zur Koordination des Roboterschwarms verwendet, die von biologischen Schwärmen, wie Kolonien sozialer Insekten, inspiriert sind. Die meisten Schwarm-GSL-Strategien verwenden das Maximum der Gaskonzentrationsverteilung zur Schätzung der Gasquellenposition. Experimente legen jedoch nahe, dass die Intermittenz der Gasverteilung vielversprechender ist. In diesem Beitrag wird eine neuartige GSL-Strategie für Schwärme vorgestellt, die auf Pheromonkommunikation und Intermittenz der Gasverteilung basiert. Die Agenten, d.h. die Nanodrohnen, emittieren Pheromonmarker in einer virtuellen Umgebung, wenn sie eine neue Gaswolke feststellen. Die Agenten werden durch virtuelle Kräfte gesteuert und nutzen abwechselnd das Wissen des Schwarms, indem sie dem Pheromongradienten folgen, oder erkunden den Suchraum, indem sie einen Zufallspunkt ansteuern. Zur Kollisionsvermeidung werden die Agenten durchgehend von anderen Agenten und Wänden abgestoßen. Die Strategie wurde auf drei Nanodrohnen implementiert und durch ein Experiment in einem Innenraum mit einer statischen Gasquelle validiert. Die Ergebnisse zeigen eine Verbesserung gegenüber maximabasierten Verfahren und geringe Lokalisierungsfehler in Windrichtung.
AbstractAir‐coupled ultrasonic testing (ACU) is a pioneering technique in non‐destructive testing (NDT). While contact testing and fluid immersion testing are standard methods in many applications, the adoption of ACU is progressing slowly, especially in the low ultrasonic frequency range. A main reason for this development is the difficulty of generating high amplitude ultrasonic bursts with equipment that is robust enough to be applied outside a laboratory environment. This paper presents the fluidic ultrasonic transducer as a solution to this challenge. This novel aeroacoustic source uses the flow instability of a sonic jet in a bistable fluidic switch to generate ultrasonic bursts up to 60 kHz with a mean peak pressure of 320 Pa. The robust design allows operation in adverse environments, independent of the operating fluid. Non‐contact through‐transmission experiments are conducted on four materials and compared with the results of conventional transducers. For the first time, it is shown that the novel fluidic ultrasonic transducer provides a suitable acoustic signal for NDT tasks and has potential of furthering the implementation of ACU in industrial applications.This article is protected by copyright. All rights reserved
In ultrasonic testing, material and structural properties of a specimen can be derived from the time-of-flight (ToF). Using signal features, such as the first peak or envelope maximum, to calculate the ToF is error-prone in multipath arrangements or dispersive and attenuating materials, which is not the case for the signal onset. Borrowing from seismology, researchers used the Akaike information criterion (AIC) picker to automatically determine onset times. The most commonly used formulation, Maeda's AIC picker, is reassessed and found to be based on inappropriate assumptions for signals often used in ultrasonic testing and dependent on arbitrary parameters. Consequently, an onset picker for ultrasonic through-transmission measurements is proposed, based on a spectral entropy criterion (SEC) to model the signal using the AIC framework. This SEC picker takes into account the spectral properties of the ultrasonic signal and is virtually free of arbitrary parameters. Synthetic and experimental data are used to compare the performance of SEC and AIC pickers. It is shown that the accuracy of onset picking is improved for densely sampled data.
The deployment of machine learning (ML) and deep learning (DL) in structural health monitoring (SHM) faces multiple challenges. Foremost among these is the insufficient availability of extensive high-quality data sets essential for robust training. Within SHM, high-quality data is defined by its accuracy, relevance, and fidelity in representing real-world structural scenarios (pristine as well as damaged). Although methods like data augmentation and creating synthetic data can add to datasets, they frequently sacrifice the authenticity and true representation of the data. Sharing real-world data encapsulating true structural and anomalous scenarios offers promise. However, entities are often reluctant to share raw data, given the potential extraction of sensitive information, leading to trust issues among collaborating entities.
Our study introduces a novel methodology leveraging Federated Learning (FL) to navigate these challenges. Within the FL framework, models are trained in a decentralized manner across different entities, preserving data privacy. In our research, we simulated several scenarios and compared them to traditional local training methods. Employing guided wave (GW) datasets, we distributed the data among different parties (clients) using IID (independent, identically distributed or in other words, statistically identical) mini batches of dataset, as well as non-IID configurations. This approach mirrors real-world data distribution among varied entities, such as hydrogen refueling stations.
In our methodology, the initial round involves individualized training for each client using their unique datasets . Subsequently, the model parameters are sent to the FL server, where they are averaged to construct a global model. In the second round, this global model is disseminated back to the clients to aid in predictive tasks. This iterative process continues for several rounds to reach convergence.
Our findings distinctly highlight the advantages of FL over localized training, evidenced by a marked improvement in prediction accuracy . This research underscores the potential of FL in GW-based SHM, offering a remedy to similar challenges tied to data scarcity in other SHM approaches and paving the way for a new era of collaborative, data-centric monitoring systems.