Intelligent Structural Health Monitoring with Ultrasonic Lamb Waves

Hinders, Mark K.; Miller, Corey

doi:10.1007/978-3-030-49395-0_2

Cited by 2 publications

(2 citation statements)

References 187 publications

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“…A complex computation process is required for interpreting the backscattering Lamb waves because of the higher intersection of the forward and backward waves generated from the sample back-walls, especially in case of multiple defects presence. [12]. Several waves are generated in terms of laser ultrasonic, such as shear, longitudinal waves, rayleigh, and lamb waves make the defect evaluation challenge [13].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Delamination Under Insulation Using Ridge Waveguide

et al. 2022

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Insulation has been widely used to protect metal substrates in various industries such as turbine blades and oil and gas pipelines. Due to aging and cyclic processes, delamination between the metal component and insulation could grow. Such defects represent a critical problem that may result in catastrophic failure of the asset. Therefore, detecting and evaluating delaminated areas is mandatory for providing urgent maintenance solutions before the failure occurrence. In this paper, microwave nondestructive technique (NDT) is proposed for delamination detection and thickness estimation under dielectric insulation. In microwave NDT, the electromagnetic waves are induced into the dielectric material's surface using a ridge waveguide. The complex reflection coefficients are acquired using a vector network analyzer for further analysis in the time domain. Next, a time-domain reflectometry (TDR) technique-based inverse discrete Fourier transform is used to evaluate the complex reflection coefficients in terms of delamination's size and thickness estimation. The delamination evaluation is performed by measuring the maximum peak's time-step variations instead of the magnitude reduction in conventional TDR techniques in terms of the defect and defect-free reflections. Next, a comparison is made between implementing the proposed technique using a ridge waveguide and a regular rectangular waveguide. The results prove the superiority of using the ridge waveguide for delamination evaluation in terms of sizing and thickness with a minimum error rate of 5% and 7.87%, respectively. These novel results of ridge waveguide due to its small aperture and relatively wide bandwidth. The microwave NDT technique reported here could provide a predictive maintenance tool for many industries to minimize the maintenance effort and cost.

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Section: Introductionmentioning

confidence: 99%

Assessment of Delamination Under Insulation Using Ridge Waveguide

et al. 2022

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“…Features automatically extracted by means of genetic algorithms and able to handle Lamb waves for SHM problems have been proposed (Harvey and Todd, 2014). Lamb wave data has also been processed using dynamical wavelet fingerprints and has been demonstrated that features extracted from wavelet analysis of Lamb wave signals can be extremely relevant for SHM purposes (Miller and Hinders, 2014;Hinders and Miller, 2020). Damage detection by means of Lamb waves by selecting the most relevant and discriminant features with wireless applications in mind has also been proposed (Park et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised damage clustering in complex aeronautical composite structures monitored by Lamb waves: An inductive approach

Rahbari

Rébillat

Mechbal

et al. 2021

Engineering Applications of Artificial Intelligence

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Structural Health Monitoring (SHM), i.e. the action of monitoring structures in real-time and in an automated manner, is a major challenge in several industrial fields such as aeronautic. SHM is by nature a very high dimensional data-driven problem that possesses several specificities when addressed as a machine learning problem. First of all data in damaged cases are rare and very costly as the generation of damaged data is not always possible and simulations are not reliable especially when dealing with complex structures. SHM is thus by nature an unsupervised problem. Furthermore, any incoming sample should be instantaneously clustered and handcrafted damage indexes are commonly used as a first dimension reduction step due to large datasets to be processed. As a consequence, unsupervised dimensionality reduction (DR) techniques that project very high dimensional data into a two or three-dimensional space (such as t-SNE or UMAP) are very appealing in such a context. However, these methods suffer from one major drawback which is that they are unable to cluster any unknown incoming sample. To solve this we propose to add inductive abilities to these well know methods by associating their projection bases with Deep Neural Networks (DNNs). The resulting DNNs are then able to cluster any incoming unknown samples. Based on those tools, a SHM methodology allowing for unsupervised damage clustering with dimensionality reduction is presented here. To demonstrate the effectiveness of the method, results of damage classification on large experimental data sets coming from complex aeronautical composite structures monitored through Lamb waves are shown. Furthermore, several DR techniques have been benchmarked and recommendations are derived. It is demonstrated that the use of raw Lamb wave signals instead of the associated damage indexes is more effective. This non-intuitive result helps to reduce the gap between laboratory research and the actual start-up of SHM activities in industrial applications.

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Intelligent Structural Health Monitoring with Ultrasonic Lamb Waves

Cited by 2 publications

References 187 publications

Assessment of Delamination Under Insulation Using Ridge Waveguide

Assessment of Delamination Under Insulation Using Ridge Waveguide

Unsupervised damage clustering in complex aeronautical composite structures monitored by Lamb waves: An inductive approach

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