Structural health monitoring (SHM) strategies based on ultrasonic guided waves are very promising regarding thin-walled lightweight structures. To study the performance of such systems, validated numerical analysis tools have to be used. For that procedure, a benchmark between two numerical methods will be presented. The first promising approach is the elastodynamic finite integration technique (EFIT). Miscellaneous research shows that its capability of capturing wave characteristics and interactions is advanced in various media and structures, including thin-walled composites. The second approach employs conventional shell-type finite elements following the Reissner–Mindlin theory for modelling layered composite structures. The advantage of using such finite element methods (FEM) is their high availability in general purpose simulation tools. As a reference model, the measurement data coming from the Open Guided Waves Project (OGW) was taken into account. The OGW dataset provides the experimental data of ultrasonic guided wave propagation in carbon fiber composite plates with an additional omega stringer. By using this contribution, this experiment was reproduced by simulation. The paper presents the results of a validation and motivates further research, such as in research related to the probability of detection analysis and numerical performance.

Composite overwrapped pressure vessels are increasingly used for hydrogen storage because of their lightweight construction. Ensuring their structural integrity therefore becomes an important requirement for safe operation. Ultrasonic guided waves are well suited for this task because they are highly sensitive to structural changes in thin-walled pressure vessels. In this work, we developed a machine learning framework based on interpretable Best Daubechies Wavelet features and k-Nearest Neighbors novelty detection. The framework identifies a persistent transition in the UGW response during overpressurization that is indicative of a permanent structural change and occurs prior to burst failure. It was validated using measurements acquired from a real-world pressure vessel. For the a priori selected sensor pair 11–12, located in the highly stressed cylindrical section, the method achieved a balanced accuracy of 98.28% and a true negative rate of 100%. In addition, the proposed methodology identified the pressure level at which the persistent structural transition first became detectable and showed that this transition remained detectable after the vessel had returned to its normal operating pressure.

Pipes made of glass fiber reinforced plastic (GFRP) are used in a wide range of applications. The combination of high strength and rigidity with low weight and excellent corrosion and chemical resistance makes them superior to conventional plastic or metal pipes in many areas of application. They are characterized by their chemical resistance to a wide range of acids and salts, lack of electrical conductivity and good tightness. Due to their properties, they are primarily used in drinking water and sewage pipes, in the chemical industry, as pressure pipes and downhole tubing and casings in geothermal applications. Matrix cracking and, to a lesser extent, delamination are considered to be the main reason for the failure of GFRP pipes. This involves the detachment of laminate layers or the liner from the structural composite, which can lead to leaks or reduced load-bearing capacity and sudden failure. Matrix cracking and delamination are primarily caused by existing operating loads. GFRP pipes are primarily tested using visual inspection, thermography, ultrasound or acoustic emission testing. Apart from acoustic emission testing, it is not possible to test or monitor large areas. Therefore, guided waves are another promising testing method where waves propagate along the pipe and are guided by the inner and outer surfaces. Compared to ultrasonic waves, guided waves are less strongly attenuated and can propagate several meters through pipes which enable monitoring large areas. In this paper, guided waves were used to distinguish between matrix cracked/delaminated and intact pipe segments. During a pressure test, pipes were pressurized to burst, and matrix cracked/delaminated areas occurred as a result. Measurements were then taken on five damaged pipe segments and, with five intact pipes serving as a control group. Statistical methods were implemented to identify the damaged pipes and to distinguish from the control group of intact pipes. A network of piezoelectric sensors on the pipes was used to send and receive guided waves. Repeated application of the network to the pipes caused deliberate variability in the measurement data, which is used to evaluate the fault detection probability of the method. To compare the measurements with a model, the dispersion curve of the GFRP pipes was calculated in advance and the recorded measurement data was verified against it. With an adapted model for the material parameters, the combined damage mechanisms in the pipes are also simulated using the dispersion curve.

Continuous structural health monitoring is essential for ensuring the safe operation of critical engineering systems. Ultrasonic guided waves are widely used for damage detection and localization due to their ability to cover large areas with high sensitivity to structural changes. This work evaluates the robustness of a previously proposed guided-wave localization approach by applying it to the complex geometry of carbon fiber composite plates with integrated omega stringers. The measurement data used in this study were provided within the framework of the Open Guided Waves project. We employ an interpretable machine-learning framework based on the normalized root mean square deviation to extract damage-sensitive features. Damage localization is further improved by modeling the spatial damage probability using a normal distribution, which enhances spatial coverage of the structure. The influence of 13 damages with progressively increasing size on classification and localization performance is systematically analyzed. The proposed method achieves an average classification accuracy of 95% and a mean localization error of 5 mm, demonstrating its suitability for damage characterization in complex composite structures.

Structural Health Monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides detailed information about their structural integrity and overall condition. Due to their intricated characteristics, UGWs are highly sensitive to material properties as well as environmental and operational factors such as temperature and pre-stress. To advance the development and validation of UGW-based SHM evaluation techniques, benchmark data sets are therefore essential for enabling transparent comparison of emerging algorithms. With the growing relevance of composite overwrapped pressure vessels (COPVs) in various industries, this work introduces a comprehensive open-access dataset of UGW measurements on a COPV, to be published on the Open Guided Waves platform. The COPV was placed in a high-pressure hydraulic system, and it was exposed to varying temperature and pressure levels to simulate realistic environmental and operational conditions. UGWs were excited and recorded using a distributed network of piezoelectric transducers attached to the surface of the specimen. Tests were repeated by introducing artificial and real damages on the vessel to evaluate their effect under similar conditions. The paper provides a brief overview of the experimental methodology, key results demonstrating the dataset’s scope and a short section on technical validation, including machine learning-based damage detection, and localisation.