Hydrogen is one of the future green energy sources that could resolve issues related to fossil fuels. The widespread use of hydrogen can be enabled by composite-overwrapped pressure vessels for storage. It offers advantages due to its low weight and improved mechanical performance. However, the safe storage of hydrogen requires continuous monitoring. Combining ultrasonic guided waves with interpretable machine learning provides a powerful tool for structural health monitoring. In this study, we developed a feature extraction approach based on a similarity method that enables interpretability in the proposed machine learning model for damage detection and localization in pressure vessels. Furthermore, a systematic optimization was performed to explore and tune the model’s parameters. This resulting model provides accurate damage localization and is capable of detecting and localizing damage on hydrogen pressure vessels with an average localization error of 2 cm and a classification accuracy of 96.5% when using quantized classification. In contrast, binarized classification yields a higher accuracy of 99.5%, but with a larger localization error of 6 cm.

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.

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.

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.