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Temperature Compensation Strategies for Lamb Wave Inspection using Distributed Sensor Networks
(2022)
The application of temperature compensation strategies is crucial in structural health monitoring approaches based on guided waves. Actually, the varying temperature influences the performance of the inspection system inducing false alarms or missed detection, with a consequent reduction of reliability. This paper quantitatively describes a method to compensate the temperature effect, namely the optimal baseline selection (OBS), extending its application to the case of distributed sensor networks (DSN). The effect of temperature separation between baseline time-traces in OBS are investigated considering multiple couples of sensors employed in the DSN. A combined strategy that uses both OBS and frequent value warning is considered. Theoretical results are compared, using data from two several experiments, which use different frequency analysis with either predominantly A0 mode or S0 mode data or both. The focus is given on the fact that different paths are available in a sensor network and several possible combination of results are available. Nonetheless, introducing a frequent value warning it is possible to increase the efficiency of the OBS approach making use of fewer signal processing algorithms. These confirm that the performance of OBS quantitatively agrees with predictions and also demonstrate that the use of compensation strategies improve detectability of damage.
The availability of non-destructive methods is particularly important for composites, e.g., carbon or glass fiber-reinforced polymers because their properties strongly depend on the manufacturing process and in-service conditions. Composites are mostly used in thin-walled components which are perfectly suited for the characterisation with ultrasonic guided waves (UGWs).
In this contribution, we demonstrate sensitivities of UGW to different elastic constants of a composite material. Then we present a characterisation procedure which can be used to in-fer elastic properties. Our approach is based on an inverse procedure in which the numeri-cally calculated dispersion curves are fitted to the measured curves. The acquisition is done by applying a broadband piezoelectric transducer (PZT) to excite various UGW modes and a 3D laser Doppler vibrometer (3D LDV) to record them. Compared to the approaches based on laser excitation, the PZT provides a better signal-to-noise ratio because more energy is brought into the structure. Whereas the 3D LDV compared to a 1D LDV or a PZT allows cap-turing in-plane and out-of-plane components giving more dispersion information for a better characterisation. Since the inverse procedure requires many iterations before elastic prop-erties are retrieved, an efficient tool for the calculation of the dispersion curves is necessary. For this, the Scaled Boundary Finite Element Method is used. All in all, a good agreement be-tween theoretical and experimental curves is demonstrated.
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.
Uncertainty quantification of multimodal surface wave inversion using artificial neural networks
(2023)
An inversion of surface waves dispersion curves is a non-unique and ill-conditioned problem. The inversion result has a probabilistic nature, which becomes apparent when simultaneously restoring the shear wave (S-wave) velocity and layer thickness. Therefore, the problem of uncertainty quantification is relevant. Existing methods through deterministic or global optimization approaches of uncertainty quantification via posterior probability density (PPD) of the model parameters are not computationally efficient since they demand multiple solutions of the inverse problem. We present an alternative method based on a multi-layer fully connected artificial neural network (ANN). We improve the current uni-modal approach, which is known from publications, to multi-modal inversion. The learned ANN maps the phase velocity dispersion curves to values of the S-wave velocity and layers thickness. To estimate the uncertainties, we adapt the Monte-Carlo simulation strategy and project onto the resulting velocity model both frequency-dependent data noise and inverse operator errors, which are evaluated by the prediction of the training data set. The proposed combination of surface waves data processing methods, configured with each other, provides a novel surface waves multi-modal dispersion data inversion and uncertainty quantification approach. We first test our approach on synthetic experiments for various velocity models: a positive velocity gradient, a low-velocity layer and a high-velocity layer. This is done considering uni-modal inversion at first and then compared to the multi-modal inversion. Afterwards, we apply our approach to field data and compare resulting models with the body S-wave processing by the generalized reciprocal method (GRM). The experiments show high-potential results using ANN yields the possibility to accurately estimate PPD of restored model parameters without a significant computational effort. The PPD-based comparison demonstrates advantages of a multi-modal inversion over uni-modal inversion. The trained ANN provides reasonable model parameters predictions and related uncertainties in real-time.
Бегущие упругие волны часто используются в области неразрушающего контроля для определения механических характеристик материалов. Поскольку склеивание различных материалов является широко используемым методом в автомобильной и авиационной промышленности, необходима надежная система измерения для определения качества таких клеевых соединений. Дисперсионные кривые для многослойных волноводов при наличии клеевых прослоек характеризуются появлением эффекта расталкивания нормальных мод в тех областях, где соответствующие моды для отдельных материалов пересекались бы. Таким образом, в зависимости от качества клеевого соединения расстояние между этими модами изменяется. Используя комбинацию экспериментальных и численных данных, в настоящей работе представлен подход к определению параметра, указывающего на качество клеевого соединения.
Бегущие упругие волны часто используются в области неразрушающего контроля для определения механических характеристик материалов. Поскольку склеивание различных материалов является широко используемым методом в автомобильной и авиационной промышленности, необходима надежная система измерения для определения качества таких клеевых соединений. Дисперсионные кривые для многослойных волноводов при наличии клеевых прослоек характеризуются появлением эффекта расталкивания нормальных мод в тех областях, где соответствующие моды для отдельных материалов пересекались бы. Таким образом, в зависимости от качества клеевого соединения расстояние между этими модами изменяется. Используя комбинацию экспериментальных и численных данных, в настоящей работе представлен подход к определению параметра, указывающего на качество клеевого соединения.