This study focuses on guided wave propagation and its interaction with structural damage. The growing use of 3D-printed (additively manufactured) structural components implies the need to develop effective methods of damage assessment. In addition, the paper shows some preliminary work which aims to separate out data of individual structures or states, from data of the full population. This paper highlights the challenges faced, which motivate PBSHM by showing some examples of the data collected, including frequency response data and estimated modal characteristics. The experimental campaign included testing over a range of temperatures, and included a variety “pseudo-damage” states, where masses were added to the structures. An experimental campaign was conducted to collect vibration data on four nominally identical Grob G102 Astir glider wings, three of which were “healthy” and one of which was damaged this was done to generate a dataset which can be used to develop and test PBSHM methods. This challenge provides the motivation for the field of population-based SHM (PBSHM), where the population is considered as a whole, in order to generate a more robust damage detection strategy. Furthermore, small differences between nominally identical structures may also manifest as changes in the modal characteristics, thus making fully enveloping baselines difficult to obtain for an entire population of structures. Damage causes changes in material/structural properties which affect the modal characteristics however, inconsequential changes in environment can reduce the capability of the damage detector, as they also affect the modal characteristics. A preliminary analysis shows the influence of the adhesive substance used to hold the rubber in place in a variety of testing scenarios.Īpplications of structural health monitoring (SHM) often use vibration data to detect when damage occurs in structures, by detecting changes in the modal properties. This paper investigates the robustness of a transmissibility-based shaker setup for the dynamic characterization of a rubber component extended to long-run operational loading. Existing laboratory vibrational techniques, based on uni-axial displacement-controlled cyclic tests, are capable of extracting storage and loss properties of the rubber under study as a function of frequency and amplitude of the oscillation. On top of that, to ensure the long-term performance and reliability of a rubber component, aging effects and fatigue life assessments are paramount. Rate, amplitude, and temperature, among others, are operational parameters that affect rubber’s stiffness and damping properties. As a consequence, a large variety of materials may be encountered with a rich phenomenological description of their static and dynamic characteristics. The rheological behavior of rubbers depends on chemical composition, presence of additives, and curing process. Thanks to the unique properties of high elongation, reversibility, incompressibility, and energy dissipation, rubber materials are employed in numerous engineering applications in industrial practice. Results show that the proposed method is effective and noise-robust in identifying the location and extent of the damage. Effectiveness and noise-robustness of the proposed method are investigated in a numerical example. The difference is used to yield an accumulative two-dimensional damage index for further damage identification. When the pseudo-pristine model is built, the prediction of flexural guided wavefield can be generated, and those local anomalies can be approximated by the difference between the prediction of flexural guided wavefield and the corresponding measured flexural guided wavefield. Flexural guided wavefield of a damaged beam-like structure is used to build a pseudo-pristine model of the beam-like structure by using physics-informed neural networks. In this paper, a baseline-free damage identification method is proposed to extract these local anomalies under the assumption that the pristine beam-like structures are homogeneous and isotropic. However, the physics of beam-like structures are not considered in these data-driven techniques, leading to a lack of physical consistency in the damage identification results. Usually, these local anomalies can be intensified by data-driven techniques such as the continuous wavelet transform, the gapped smoothing method, etc. Damage in a beam-like structure due to decreases in its stiffness and/or mass can cause local anomalies in its flexural guided wavefield at locations of damage.
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