This paper presents a hybrid model to estimate the magnetic behaviour of a ferromagnetic structure. The mathematical-physical model has been developed using the Method of Moments combined with a hysteresis model. The mathematical model was derived by a linearisation of the hysteresis curve. The initial magnetic state of a ferromagnetic object is found through inverse computations, including regularisation techniques. The idea of dictionary regularisation is introduced to support the inverse computations with prescribed templates that reflect a priori knowledge of the typical shapes of magnetisation distributions. These templates are extracted from the Method of Moments. Data assimilation is used to update the model in time by means of measurements of the magnetic field near a ferromagnetic structure. The proposed hybrid model is implemented for a typical steel object and verified by means of numerical experiments and measurements in an experimental environment.

This paper proposes an algorithm to localize a magnetic dipole using a limited number of noisy measurements from magnetic field sensors. The algorithm is based on the theory of compressed sensing, and exploits the sparseness of the magnetic dipole in space. Beforehand, a basis consisting of magnetic dipole fields belonging to individual dipoles in an evenly spaced 3D grid within a specified search domain is constructed. In the algorithm, a number of sensors is chosen which measure all three magnetic field components. The sensors are chosen optimally using QR pivoting. Using the pre-constructed basis and the obtained field measurements, a sparse representation in the location domain is computed using $\ell _{{1}}$ optimization. Based on the resulting sparse representation, the location and magnetic moment of the magnetic dipole are estimated. An extension to an iterative method is implemented, where the basis and chosen sensors improve after every location estimate. Numerical simulations have been performed to verify the algorithm, and experiments have been done for validation. The proposed algorithm is shown to be effective in localizing magnetic dipoles.

The purpose of this article is to estimate the parameters of the Jiles-Atherton hysteresis model, based on minor-loop measurement data in weak applied fields. The well-known hysteresis model by Jiles and Atherton serves as a basis of this article with an extension for the closure of minor loops. In order to represent minor loops correctly, a dissipative factor is introduced. A methodology to obtain the initial magnetization of a specimen is defined, based on an expansion in terms of higher order Gaussian functions. The methodology is implemented within a finite-element method using an interconnection between MATLAB and COMSOL. This interconnection allows the investigation of potentially large ferromagnetic objects to be calibrated to the proposed ferromagnetic model in weak fields. The proposed methodology was verified using an original approach. The approach relies on the use of a sensor array that makes it possible to detect local variations of magnetic properties in steel plates. Material parameters for our test specimen are successfully obtained by means of experimental data, using the shuffled frog leaping optimization algorithm. An analysis of the obtained results shows that the calibrated model is able to represent the measurement data accurately.

This paper presents a parameter estimation method to determine the linear behavior of an object constructed of thin plates. Based on the magnetostatic field equations, an integral equation is derived that fully determines the induced magnetization, whenever the spatial magnetic susceptibility distribution is known. This forward problem is used as an underlying physical model for the parameter estimation method. Using near-field magnetic measurements around a thin plate, the parameter estimation yields a distribution of the magnetic susceptibility. Furthermore, a sensitivity analysis is performed to understand the behavior of this parameter estimation method.