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Electrical Resistance Tomography (ERT) offers a non-destructive evaluation (NDE) technique that takes advantage of the inherent electrical properties in carbon fiber reinforced polymer (CFRP) composites for internal damage characterization. This paper investigates a method of optimum selection of sensing configurations for delamination detection in thick cross-ply laminates using ERT. Reduction in the number of sensing locations and measurements is necessary to minimize hardware and computational effort. The present work explores the use of an effective independence (EI) measure originally proposed for sensor location optimization in experimental vibration modal analysis. The EI measure is used for selecting the minimum set of resistance measurements among all possible combinations resulting from selecting sensing electrode pairs. Singular Value Decomposition (SVD) is applied to obtain a spectral representation of the resistance measurements in the laminate for subsequent EI based reduction to take place. The electrical potential field in a CFRP laminate is calculated using finite element analysis (FEA) applied on models for two different laminate layouts considering a set of specified delamination sizes and locations with two different sensing arrangements. The effectiveness of the EI measure in eliminating redundant electrode pairs is demonstrated by performing inverse identification of damage using the full set and the reduced set of resistance measurements. This investigation shows that the EI measure is effective for optimally selecting the electrode pairs needed for resistance measurements in ERT based damage detection.
In Electrical Impedance Tomography (EIT), we apply patterns of currents on a set of electrodes at the external boundary of an object, measure the resulting potentials at the electrodes, and, given the aggregate data set, reconstruct the complex conductivity and permittivity within the object. It is possible to maximize sensitivity to internal conductivity changes by simultaneously applying currents and measuring potentials on all electrodes but this approach also maximizes sensitivity to changes in impedance at the interface. We have therefore developed algorithms to assess contact impedance changes at the interface as well as to efficiently and simultaneously reconstruct internal conductivity/permittivity changes within the body. We use simple linear algebraic manipulations, the generalized SVD, and a dual-mesh finite-element-based framework to reconstruct images in real time. We are also able to efficiently compute the linearized reconstruction for a wide range of regularization parameters and to compute both the Generalized Cross-Validation (GCV) parameter as well as the L-curve, objective approaches to determining the optimal regularization parameter, in a similarly efficient manner. Results are shown using data from a normal subject and from a clinical ICU patient, both acquired with the GE GENESIS prototype EIT system, demonstrating significantly reduced boundary artifacts due to electrode drift and motion artifact.
Electrical impedance mammography (EIM) belongs to nonlocal techniques of image creation. It is based on a number of data collection methods, including the cross-sectional approach, the back-projection method with the weight function applied horizontally and vertically, and the static image method. The analysis of data acquired by applying the above methods enabled to work out the EIM diagnostic system. It involves the following diagnostic categories: structural percentile limits and the mammary gland structure, agerelated percentile limits and age-related electric conductivity, outlying values statistics and early diagnostics of breast cancer, D-statistics and distortion of the mammographic scheme in the presence of breast cancer, diagnostic table, and the assessment of the electrical impedance image.
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