A real-time feature-based clustering approach for vibration-based SHM of large structures

Prasad, Sneha; Kumar, David; Kalra, Sumit; Khandelwal, Arpit

doi:10.1016/j.measurement.2024.114222

Cited by 3 publications

(6 citation statements)

References 37 publications

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“…As such, the sensitivity of each combination is calculated based on the relative value of the objective function (ROF), Equation (8). The ROF is calculated from the value of the objective function, Equation (7), before and after the execution of the Damage Detection Framework for the specific sensor combination. The first term of Equation ( 8) shows the influence of the simulated damage on the healthy structure.…”

Section: Relativementioning

confidence: 99%

“…Before the execution of the methodology, a comparison was made between the simulated and experimental data. The comparison was made by calculating Equation (7). For the experimental data, this equation is calculated between the real-world healthy structure and the damaged structure each time by taking into account only the acceleration signals of the active sensor combination, shown in Figure 9.…”

Section: Optimal Sensor Placementmentioning

confidence: 99%

“…Regarding case D3, Figures 14 and 15 show the values of the objective function, Equation (7), between the healthy and damaged states of the experimental structure and the FE model, respectively, when it is calculated separately for each combination. It is obvious that both images again follow the same pattern as was the case for D1.…”

Section: Optimal Sensor Placementmentioning

confidence: 99%

“…Following are combinations "9-11", "6-8", and a combination that includes a location on a part of the structure that is fixed, "7-BLL". Regarding case D7, Figures 18 and 19 show the values of the objective function, Equation (7), between the healthy and damaged states of the experimental structure and the FE model accordingly. Figures 20 and 21 present the ROF values of all the evaluated sensor combinations, and the successful sensor combinations, respectively.…”

Section: Optimal Sensor Placementmentioning

confidence: 99%

“…The current work focuses on vibration-based methods that rely on the fact that structural damage will affect the dynamic characteristics of a structure. Different approaches that belong in this category have been applied in the past [ 2 , 3 , 4 , 5 , 6 ], and methods that can be used in real-time scenarios have also been presented [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Sensor Placement for Vibration-Based Damage Localization Using the Transmittance Function

Zacharakis,

Giagopoulos

2024

Sensors

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A methodology for optimal sensor placement is presented in the current work. This methodology incorporates a damage detection framework with simulated damage scenarios and can efficiently provide the optimal combination of sensor locations for vibration-based damage localization purposes. A classic approach in vibration-based methods is to decide the sensor locations based, either directly or indirectly, on the modal information of the structure. While these methodologies perform very well, they are designed to predict the optimal locations of single sensors. The presented methodology relies on the Transmittance Function. This metric requires only output information from the testing procedure and is calculated between two acceleration signals from the structure. As such, the outcome of the presented method is a list of optimal combinations of sensor locations. This is achieved by incorporating a damage detection framework that has been developed and tested in the past. On top of this framework, a new layer is added that evaluates the sensitivity and effectiveness of all possible sensor location combinations with simulated damage scenarios. The effectiveness of each sensor combination is evaluated by calling the damage detection framework and feeding as inputs only a specific combination of acceleration signals each time. The final output is a list of sensor combinations sorted by their sensitivity.

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Section: Relativementioning

confidence: 99%

Section: Optimal Sensor Placementmentioning

confidence: 99%

Section: Optimal Sensor Placementmentioning

confidence: 99%

Section: Optimal Sensor Placementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Sensor Placement for Vibration-Based Damage Localization Using the Transmittance Function

Zacharakis,

Giagopoulos

2024

Sensors

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Automated AI-DIC marker-less technique for tracking displacements of large structures

Prasad,

Chiang,

Kumar

et al. 2024

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII

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Markers are crucial for measuring the displacement of large structures using digital image correlation (DIC). In general, creating or affixing artificial markers on large structures is cha llenging. Several studies in the past have addressed this challenge by employing marker-less or target-free approaches. In this work, we integrate an AIbased non-intersecting poly-shape marker identification technique with the DIC program that uses the structural pattern as a marker and automates the selection of the region of interest by enabling strong correlation criteria to obtain the displacements in real-time. The proposed AI-DIC algorithm segments non-intersecting poly-shape markers from the images of the target structure based on the features detected by the KAZE feature detector and descriptors. Further, the investigated marker or the natural structural pattern is automatically given as an input region of interest to the DIC program. Moreover, it considers the marker as a template, correlates it with all subsequent images, and analyzes the displacements and frequencies of the target structure. In addition, the AI-DIC algorithm is realized on an in-house cantilever beam experiment where the images are acquired and processed in real-time.

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Feature-based template approach for optimizing digital image correlation on complex deformations

Prasad,

Kumar,

Kalra

et al. 2024

Health Monitoring of Structural and Biological Systems XVIII

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The initialization method in Digital Image Correlation (DIC) is essential for optimizing the correlation criteria and accurately computing the deformations of a material under load. At present, feature-based initialization techniques are widely explored for predicting the deformations of various complex circumstances, such as large deformations for soft materials, non-continuous deformations in heterogeneous materials, etc. However, due to the non-uniform distribution of the detected features, the initialization process goes through biased prediction. This bias occurs due to the sparsity of features in different regions of the sample, which can lead to inaccuracy in identifying the shape of deformation. This study addresses the issue of feature distribution and develops a featurebased template approach for providing initialization points for each subset on a finer scale. The features (interest points) are determined using KAZE feature detector and descriptor algorithm in nonlinear scale space due to its ability to determine consistent, repeatable, distinct features invariant to scale and rotation. The proposed algorithm uses bi-cubic b-spline interpolation to identify the strongest interest point at the subpixel level for each subset (of the input sample images), which works as an initial value for estimating the deformation. Further, a threshold-based incremental reference approach is developed for measuring large deformations and avoiding the cumulative errors associated with the commonly used incremental reference strategy, which is compute-intensive because of the comparison between every previous image and the subsequent images.

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A real-time feature-based clustering approach for vibration-based SHM of large structures

Cited by 3 publications

References 37 publications

Optimal Sensor Placement for Vibration-Based Damage Localization Using the Transmittance Function

Optimal Sensor Placement for Vibration-Based Damage Localization Using the Transmittance Function

Automated AI-DIC marker-less technique for tracking displacements of large structures

Feature-based template approach for optimizing digital image correlation on complex deformations

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