Localization of Damages in Plain And Riveted Aluminium Specimens using Lamb Waves

Andhale, Yogesh Sahebrao; Masurkar, Faeez; Yelve, Nitesh P.

doi:10.20855/ijav.2019.24.11485

Cited by 22 publications

(11 citation statements)

References 14 publications

(32 reference statements)

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“…On the other hand, its low cost due to the simplicity in its construction makes piezoelectric elements ideal when the intention is to seek economy. Additionally, the results obtained in its implementation throughout numerous experiments have been quite satisfactory in the tasks of detection, classification, localization and the quantification of damage in structures that could be metallic or made of composite material [17,50]. These tasks can be carried out with this type of sensors, even when the structure is subjected to temperature variations [4].…”

Section: Sensorsmentioning

confidence: 86%

Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring

Angulo-Saucedo

Leon-Medina

Pineda-Muñoz

et al. 2022

Sensors

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Improvements in computing capacity have allowed computers today to execute increasingly complex tasks. One of the main benefits of these improvements is the possibility of developing machine learning algorithms, of which the fields of application are extensive and varied. However, an area in which this type of algorithms acquires an increasing relevance is structural health monitoring (SHM), where inspection strategies and guided wave-based approaches make the evaluation of the structural conditions of an aircraft, vessel or building among others possible, by detecting and classifying existing damages. The use of sensors, data acquisition systems (DAQ) and computation has also allowed these damage detection and classification tasks to be carried out automatically. Despite today’s advances, it is still necessary to continue with the development of more robust, reliable, and low-cost structural health monitoring systems. For this reason, this work contemplates three key points: (i) the configuration of a data acquisition system for signal gathering from an an active piezoelectric (PZT) sensor network; (ii) the development of a damage classification methodology based on signal processing techniques (normalization and PCA), from which the models that describe the structural conditions of the plate are built; and (iii) the use of machine learning algorithms, more specifically, three variants of the self-organizing maps called CPANN (counterpropagation artificial neural network), SKN (supervised Kohonen) and XYF (X–Y fused Kohonen). The data obtained allowed one to carry out an experimental validation of the damage classification methodology, to determine the presence of damages in two aluminum plates of different sizes, where masses were added to change the vibrational responses captured by the sensor network and a composite (CFRP) plate with real damages, such as delamination and cracks. This classification methodology allowed one to obtain excellent results by validating the usefulness of the SKN and XYF networks in damage classification tasks, showing overall accuracies of 73.75% and 72.5%, respectively, according to the cross-validation process. These percentages are higher than those obtained in comparison with other neural networks such as: kNN, discriminant analysis, classification trees, partial least square discriminant analysis, and backpropagation neural networks, when the cross-validation process was applied.

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Section: Sensorsmentioning

confidence: 86%

Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring

Angulo-Saucedo

Leon-Medina

Pineda-Muñoz

et al. 2022

Sensors

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“…Furthermore, VL=λ+2μρ , VT=μρ, and VR=(0.87+1.12ν1+ν)VT are the longitudinal, shear, and Rayleigh wave velocities, respectively. 1,6 The absolute value of the material nonlinearity can be estimated using equation (2) and the spectral amplitudes of the Rayleigh wave harmonics measured experimentally at several sensing points in the direction of wave propagation.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Nondestructive testing of rails using nonlinear Rayleigh waves

Masurkar

Yelve

Tse

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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The present research focuses on evaluating the dislocation-induced material nonlinearity in rails caused by falling of parapets or large rocks during an earthquake or land-slide and consequently appraising their health status using an amplitude-based nonlinear parameter based on Rayleigh wave propagation. The falling of parapets or large rocks onto the rail is simulated through a laboratory experiment. Several cases are considered here with impacts of varying intensities to demonstrate the capability of the nonlinear parameter in diagnosing such damages in the rails. It is found that for a pristine specimen, the first peak of the amplitude-based nonlinear parameter matches well with the physics-based nonlinear parameter, whereas there is a considerable deviation between the two for the specimen with impact damage. Moreover, the linear ultrasonic parameter is also evaluated for different states of damage for studying its effectiveness in diagnosing impact damages in rails. The results reveal that the nonlinear ultrasonic-based method proposed here is very sensitive and helps characterize the health status of the rails in a baseline-free manner.

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“…Considering the dispersion degree of the modes, the interaction between waves and defects, and the center frequency of transducers, the frequency of the excitation signal is usually lower than 500 kHz in the study. 7,26,28 It is shown within the selection zone in Figure 1(a).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…However, the method does not consider the influence of the measurement accuracy of ToF on the detection results. Andhale et al 28 combined the asteroid algorithm and GA to detect the defect in plain and riveted metal plates. The defect position was approximately located with the asteroid algorithm through analysis of the ToF of signals.…”

Section: Introductionmentioning

confidence: 99%

An intelligent algorithm based on evolutionary strategy and clustering algorithm for Lamb wave defect location

Chen

Liu

et al. 2020

Structural Health Monitoring

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Imaging algorithms for visualization of defects play a significant role in Lamb wave–based research of nondestructive testing and structural health monitoring. In classical algorithms, the position or distribution of defects is located by mapping the amplitude or phase information of signals from the time domain to every discrete spatial grid of the structure. It is time-consuming. In this study, the diversity, statistical, and fuzzy characteristics of the elliptic imaging algorithm are analyzed first; then, an intelligent defect location algorithm is proposed based on the evolutionary strategy and the K-means algorithm. The position of defects can be identified by observing the distribution of individuals. There are six parts in the proposed algorithm, including the data structure design, adaptive population screening, adaptive population reproduction, diversity maintenance mechanism, and cutoff criterion. Considering the statistical and fuzzy characteristics in the detection, several specific input parameters are defined in our algorithm, such as the distance-dependent screening threshold, path-dependent residual vector, and path-independent residual. To maintain the diversity of individuals in the analysis, we have made two adjustments to the evolutionary strategy: one is to optimize the population screening and reproduction steps with the K-means algorithm, and the other is to add a diversity maintenance method into the evolutionary strategy. The effectiveness of the proposed intelligent defect location algorithm is verified by numerical simulations and experiments. Numerical studies indicate that the proposed algorithm has a reliable performance in the detection of defects with different shapes and sizes. In the experimental research, we demonstrate that the efficiency of the proposed algorithm is about 200 times faster than the elliptic imaging algorithm. And the optimum parameter setting of the algorithm is investigated by analyzing the influence of parameter setting on the detection.

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Localization of Damages in Plain And Riveted Aluminium Specimens using Lamb Waves

Cited by 22 publications

References 14 publications

Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring

Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring

Nondestructive testing of rails using nonlinear Rayleigh waves

An intelligent algorithm based on evolutionary strategy and clustering algorithm for Lamb wave defect location

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