Search

Guided waves (GW) are of great interest for non-destructive testing (NDT) and structural health monitoring (SHM) of engineering structures such as for oil and gas pipelines, rails, aircraft components, adhesive bonds and possibly much more. Development of a technique based on GWs requires careful understanding obtained through modelling and analysis of wave propagation and mode-damage interaction due to the dispersion and multimodal character of GWs. The Scaled Boundary Finite Element Method (SBFEM) is a suitable numerical approach for this purpose allowing calculation of dispersion curves, mode shapes and GW propagation analysis. In this article, the SBFEM is used to analyse wave propagation in a plate consisting of an isotropic aluminium layer bonded as a hybrid to an anisotropic carbon fibre reinforced plastics layer. This hybrid composite corresponds to one of those considered in a Type III composite pressure vessel used for storing gases, e.g., hydrogen in automotive and aerospace applications. The results show that most of the wave energy can be concentrated in a certain layer depending on the mode used, and by that damage present in this layer can be detected. The results obtained help to understand the wave propagation in multi-layered structures and are important for further development of NDT and SHM for engineering structures consisting of multiple layers. [ABSTRACT FROM AUTHOR]

Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated. [ABSTRACT FROM AUTHOR]

Pipe integrity is a central concern regarding technical safety, availability, and environmental compliance of industrial plants and pipelines. A condition monitoring system that detects and localizes threats in pipes prior to occurrence of actual structural failure, e.g., leakages, especially needs to target transient events such as impacts on the pipe wall or pressure waves travelling through the medium. In the present work, it is shown that fiber-optic distributed acoustic sensing (DAS) in conjunction with a suitable application geometry of the optical fiber sensor allows to track propagating acoustic waves in the pipeline wall on a fast time-scale. Therefore, short impacts on the pipe may be localized with high fidelity. Moreover, different acoustic modes are identified, and their respective group velocities are in good agreement with theoretical predications. In another set of experiments modeling realistic damage scenarios, we demonstrate that pressure waves following explosions of different gas mixtures in pipes can be observed. Velocities are verified by local piezoelectric pressure transducers. Due to the fully distributed nature of the fiber-optic sensing system, it is possible to record accelerated motions in detail. Therefore, in addition to detection and localization of threatening events for infrastructure monitoring, DAS may provide a powerful tool to study the development of gas explosions in pipes, e.g., investigation of deflagration-to-detonation-transitions (DDT). [ABSTRACT FROM AUTHOR]