Multiple regression based prediction correlations for enhanced sensor design of magnetic induction tomography systems

Yessica Arellano, Andrew Hunt, Olivier Haas, Hafiz Ahmed, Lu Ma

Research output: Contribution to journalArticle

Abstract

Magnetic Induction Tomography (MIT) is an imaging technology that measures changes in the electric properties of a sample located within the imaging region. Measurement of low conductivity contrasts such as biological tissue or ionised water flow in pipelines requires highly accurate systems due to the small amplitude of the measured signals. Optimisation of the sensors results in enhanced MIT performance. Geometric characteristics of MIT sensors impact the intensity of the electromagnetic field, and the inductive coupling between a) the sensors and b) the sensors and the medium. Three correlation models are derived here to help developers predict the relative performance of MIT systems for a given set of coil characteristics. Bivariate and multiple regression analyses are performed on a dataset from Finite Element Method (FEM) simulations to validate the significant relationship between the sensor geometry and three performance parameters. Correlations models are provided for prediction of induced voltage level, eddy currents and system sensitivity relative to the geometric characteristics of the sensors. The performance of the computed models is validated using a dataset of 180 coil designs and four electrical conductivities values. Predictions from the developed correlations are compared to reference data from simulations. Errors estimated for the predicted performance parameters together with the variance for each correlation are presented. The predicted data fitted the reference values within ±15%, showing reasonable accuracy of the models and a balanced variance-bias trade-off. It has been found that the performance of MIT systems is largely affected by the coil dimensions and the number of turns, as well as by the coil shape and wire diameter to a lesser degree.
Original languageEnglish
Pages (from-to)(In-Press)
Number of pages6
JournalMeasurement Science and Technology
Volume(In-Press)
Early online date31 Jul 2019
DOIs
Publication statusE-pub ahead of print - 31 Jul 2019

Fingerprint

Electromagnetic induction
magnetic induction
Multiple Regression
Tomography
regression analysis
Proof by induction
tomography
Coil
Sensor
Prediction
sensors
Sensors
coils
predictions
Imaging
Imaging techniques
low conductivity
Biological Tissue
Eddy Currents
photographic developers

Keywords

  • Magnetic Induction Tomography, sensor design, multiple regression

Cite this

Multiple regression based prediction correlations for enhanced sensor design of magnetic induction tomography systems. / Arellano, Yessica; Hunt, Andrew; Haas, Olivier; Ahmed, Hafiz; Ma, Lu.

In: Measurement Science and Technology, Vol. (In-Press), 31.07.2019, p. (In-Press).

Research output: Contribution to journalArticle

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