IPDC Network-Based Image Reconstruction for Damage Detection of CFRP With Electrical Impedance Tomography

Xue, Qing; Zhang, Kai

doi:10.1109/jsen.2022.3177835

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…In this case, the authors used an absolute imaging approach due to its suitability for reducing the discrepancy between simulated data (used for training) and real measurements. Xue and Zhang (2022) used a dual‐channel inception‐dense‐cbam network model integrated with an attention mechanism for damage detection in carbon fiber‐reinforced polymers in a difference imaging approach, which proved particularly robust to noise.…”

Section: Introductionmentioning

confidence: 99%

Crack identification using electrical impedance tomography and transfer learning

Quqa

Landi

Loh

2023

Computer aided Civil Eng

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Sensing skins and electrical impedance tomography constitute a convenient and inexpensive alternative to dense sensor networks for distributed sensing in civil structures. However, their performance can deteriorate with the aging of the sensing film. Guaranteeing high identification performance after minor lesions is crucial to improving their ability to identify structural damage. In this paper, electrical resistance tomography is used to identify the crack locations in nanocomposite paint sprayed onto structural components. The main novelty consists of using crack annotations collected during visual inspections to improve the crack identification performance of deep neural networks trained using simulated datasets through transfer learning. Transfer component analysis is employed for simulation‐to‐real information transfer and applied at a population level, extracting low‐dimensional domain‐invariant features shared by simulated models and structures with similar geometry. The results show that the proposed method outperforms traditional approaches for crack localization in complex damage patterns.

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Section: Introductionmentioning

confidence: 99%

Crack identification using electrical impedance tomography and transfer learning

Quqa

Landi

Loh

2023

Computer aided Civil Eng

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“…COMPARED with traditional metal materials, carbon fiber reinforced polymer (CFRP) has become a hot spot in aerospace industry [1] due to its high specific strength, high corrosion resistance, high structural stability, and large-area integral molding [2]. However, CFRP may be damaged when it is manufactured and used.…”

Section: Introductionmentioning

confidence: 99%

Damage detection of CFRP laminates based on Adamax-INN model for EIT

Xue,

Chen,

Fan

2023

Meas. Sci. Technol.

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Carbon fiber reinforced polymer (CFRP) is electrically conductive, making it possible to detect structural damage of CFRP laminates with electrical impedance tomography (EIT). However, the inverse problem of EIT is severely non-linear, ill-posed, and underdetermined, thus limiting the resolution and accuracy of images reconstructed with EIT. This paper solves the inverse problem of EIT based on invertible neural networks (INN) and optimizes INN with Adamax gradient descent algorithm. Simulation and experimental results demonstrated that, compared with traditional EIT image reconstruction algorithms and radial basis function (RBF) neural network algorithm, the Adamax-INN model largely reduced the artifacts in reconstructed images, improved the damage recognition accuracy and edge clarity, and displayed good noise immunity.

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Machine learning for nano-level defect detection in aligned random carbon nanotubes-reinforced electrically conductive nanocomposite

Akmanov,

Lomov,

Spasennykh

et al. 2025

Composite Structures

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IPDC Network-Based Image Reconstruction for Damage Detection of CFRP With Electrical Impedance Tomography

Cited by 8 publications

References 17 publications

Crack identification using electrical impedance tomography and transfer learning

Crack identification using electrical impedance tomography and transfer learning

Damage detection of CFRP laminates based on Adamax-INN model for EIT

Machine learning for nano-level defect detection in aligned random carbon nanotubes-reinforced electrically conductive nanocomposite

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