A common damage mechanism in aluminum aircraft structures is the development of fatigue cracks at locations with stress concentration, such as fastener holes. Detection at an early stage, ideally before they have grown to through-thickness cracks, is important for aircraft safety. Low frequency guided waves that can propagate long distances along thin components allow for the efficient structural health monitoring (SHM) of large structures such as aircraft wings and fuselage. SHM systems employing distributed guided ultrasonic waves sensors, typically piezoelectric transducers, have been investigated and shown promising sensitivity for efficient monitoring. However, limitations due to the typically employed baseline subtraction method have been reported, as environmental influences, e.g., temperature, can influence the wave propagation characteristics and thus accuracy of the baseline subtraction. Baseline-free SHM methodology has been proposed to overcome some of the limitations caused by environmental factors and to improve the sensitivity and temperature stability. This contribution investigates specifically the use of mode conversion of the fundamental guided wave modes at part-thickness defects. 3D FE simulations were used to predict the mode conversion of the fundamental guided wave modes for a variation of the defect size. Accurate experimental validation of the FE predictions was achieved. A baseline-free SHM algorithm was developed, employing the mode-conversion from the S₀ to the A₀ guided wave mode with a difference of arrival time algorithm. This enabled the accurate localization of damage in a plate with a good agreement between FE simulations and experimental validation. The influence of the excitation and monitoring location layout on the localization accuracy was investigated from FE simulations. From experiments and FE simulations it was demonstrated that the baseline-free SHM worked independent of temperature effects and could accurately localize a part-thickness damage across significant temperature changes.

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.

When diagnosing events and resulting damages through PZT active interrogation, the Environmental and Operational Conditions (EOCs) assessment and compensation becomes a key issue. Variations in EOCs have a substantial impact on diverse material and structural attributes, encompassing factors like stiffness, boundary conditions, and vibration characteristics. In fact, these effects are in the vicinity of the ones caused by damage, potentially resulting in erroneous evaluations of the affected structure. Hence, it becomes imperative to adjust and compensate SHM measurements to account for these fluctuating variables.

For this purpose, a series of thermomechanical tests have been performed by Airbus Defence and Space (ADS) in collaboration with Universidad Politécnica de Madrid (UPM) and Universidad de Jaén (UJA) in order to assess the influence that certain EOCs, such as tensile stress and temperature, have over the damage diagnosis. In this paper, the assessment of the test results, particularly the influence of acoustoelastic and thermal effects, as well as some conclusions, are presented.

These tests have been carried out in controlled conditions of temperature and load, by means of the use of a climatic chamber and a tensile testing machine, respectively, for both metallic and composite specimens, with and without damage.

The topic of adhesively bonded composite joints for primary aircraft structural parts is very challenging. Bond quality assessment and debonding detection are considered as one of the key issues. This paper investigates debonding detection in a small-dimension composite bonded joint placed into an advanced trainer jet. The test specimen was subjected to different environmental conditions and loadings associated with the aircraft operation. All the measurements were performed on the ground in non-equidistant time periods and in different environmental conditions. Several measurements were performed in non-damage states. Although the test specimen was subjected to loads during many test flights, no debonding occurred. An artificial damage was induced manually, and measurements were utilized again several times at different temperatures. A suitable mode and frequency sensitive to debonding was evaluated in a wide frequency range. An analytical approach was proposed to decide whether the measured data variations are caused by the environment or damage. In-house SW LaWaI with the implemented probabilistic algorithm was used for data interpretation and damage visualization.

Guided Waves are commonly used for measurements of thickness loss, presence and gravity of defects in structures such as oil&gas pipes or aeronautical structures due to their capacity to propagate over long distances and their sensibility to materials variations. Common imaging techniques include Delay-And-Sum (DAS), RAPID or Minimum Variance imaging [1][2]. These methods require a baseline to compare the current state of the structure with a previous measured state. Specifically, these techniques may be altered through varying environmental and operational conditions (EOCs) between multiple measurements. Varying EOCs may include aging of the coupling between the sensor and the structure, changes in temperature, humidity, or external loadings of the structure. As the influence of these factors may be comparable to the influence of defects in the structure on the propagating guided waves, they have to be accounted for. While some methods exist to compensate the variations such as Baseline Signal Stretch (BSS) or Optimal Baseline Selection (OBS)[3], they are either insufficient for a full range of varying conditions or very costly in required experiments, and may not be suited for industrial applications with complex environments [4]. More generally, these techniques require precise knowledge about the propagation of guided waves, for example the group velocity to locate a defect in DAS.

To address these problematics, we present an optimization process aiming at retrieving effective material properties, taking into account varying EOCs, enabling to directly simulate a calibrated baseline. This process uses the same instrumentation and signals used for imaging the structure, and relies on an inverse problem posed as an optimization problem over the material properties and effective transfer functions of the sensors. Using the modal decomposition of the elastic field in a waveguide, a generic formulation of guided wave signals is proposed for comparison to experimental signals. This process aims to be robust to the type of structure (plates, pipes, complex geometries), to the presence of a defect as well as to the type of material (isotropic metals, anisotropic composites).

This process is firstly applied to ideal simulation signals obtained through the CIVA software. The process is then applied to signals obtained on an aluminium plate in pristine state over a varying range of temperature (-20 to +50 °C).

[1] Michaels, Jennifer E. 2008. « Detection, Localization and Characterization of Damage in Plates with an in Situ Array of Spatially Distributed Ultrasonic Sensors ». Smart Materials and Structures 17 (3): 035035. https://doi.org/10.1088/0964-1726/17/3/035035

[2] Hall, James, et Jennifer E. Michaels. 2010. « Minimum Variance Ultrasonic Imaging Applied to an in Situ Sparse Guided Wave Array ». IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 57 (10): 2311‑23. https://doi.org/10.1109/TUFFC.2010.1692.

[3] Harley, Joel B., et Jose M.F. Moura. 2012. « Scale transform signal processing for optimal ultrasonic temperature compensation ». IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 59 (10): 2226‑36. https://doi.org/10.1109/TUFFC.2012.2448.

[4] Sohn, Hoon. 2007. « Effects of Environmental and Operational Variability on Structural Health Monitoring ». Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365 (1851): 539‑60. https://doi.org/10.1098/rsta.2006.1935.