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Nowadays, 3D eddy current nondestructive characterization of crack and corrosion defects while using ECA remains an industrial challenge because the obtained image permits to determine only the 2D defect shape. Consequently, this article is devoted to determine directly the crack length and width by eddy current images through sensor array. Afterwards, we extract the maximal impedance amplitude to estimate the crack depth while using the deterministic algorithm that we have recently developed. In fact, the obtained results have demonstrated the effectiveness and the reliability of the proposed method.

In this paper, we have carried out an experimental study of the detection of top rail surface cracks. Firstly, we have highlighted the inability to inspect the entire rail head surface by a single sensor with a single scan. To overcome this inspection inability, we have proposed a multisensor system composed of three differential probes arranged within a specific configuration. The yielded results showed the efficiency and the robustness of the proposed configuration in the detection of cracks regardless its size, orientation and location.

The study of 3D eddy current non destructive testing system for cracks characterization using finite element method requires a great amount of computing time and memory space. In this article, we have validated the developed model and then determined directly the crack length by analyzing the complete signal. Afterwards, we have extracted from the complete sensor sweep signal the maximal amplitude that we have exploited to estimate the crack depth.

This article presents a study of a Multi-coils circular eddy current non-destructive testing sensor for determining the fibers orientation as well as the detection of defect in multidirectional car...

This paper presents a new modeling approach of eddy current nondestructive evaluation systems containing magnetic materials. Originally, the proposed model is based on coupled circuits principle an...

This paper presents the development of a multi-coil eddy current (EC) sensor that uses an axial rotating magnetic field for the measurement of electrical resistance to determine the electrical conductivity tensor of stratified carbon fiber reinforced polymer (CFRP) materials. The sensor consists of an identical planar racetrack multi-coil, excited by two-phase sinusoidal current sources that are 90° apart in phase to generate an axial rotating magnetic field and eliminate the need for mechanical rotation. Each sensor's coil's resistance variation is measured using a developed experimental prototype unit and computed using a 3D finite element method (FEM) based on the (A, V–A) formulation. The inverse problem technique that minimizes the difference between the calculated and measured resistances is then used to identify the electrical conductivity tensor components using the particle swarm optimization (PSO) algorithm. The comparison between the computed resistances and the measured ones shows an excellent concordance.

This paper presents a study of a surface crack detection in which the volume is filled by conductive substances due to the polluting environment. Hence, this investigation demonstrates by numerical simulation that electric conductivity is a crucial property that has to be added to the other defect geometrical characteristics in order to complete the developed models. Consequently, introducing the tolerance in percent in the measured impedance is necessary in some conditions. So, the obtained results demonstrate that the signal amplitude passes from 0 to 78% of the maximal amplitude when the defect conductivity rises from 0 to 0.5 Ms/m. On the other hand, the relative difference of the resistance partincreases according to defect volume. For example, for a defect of 0.3 MS/m, the relative difference of the resistance varies from 52 to 62% of the maximal amplitude when the defect depth varies from 0.5 to 2.25 mm. These results can be exploited to show the effect of the conductive substances occupying the crack volume. In fact, the controller using EC-NDT technique must take into consideration the presence of conductive polluting elements in the crack volume. So, this condition becomes primordial and necessary according to the degree and nature of pollution.

The development of fast and accurate method describing the electromagnetic phenomena intervening in eddy current non-destructive systems is very interesting, since it permits the design of reliable systems permitting the detection and the characterisation of defect in conductive materials. The coupled electric field method presented in this article can assume a large part of these objectives, because it is fast in comparison to the finite element method and easily invertible since the sensor impedance variation is an explicit function of target physical and geometrical characteristics. These advantages have motivated us to extend this method for multilayered structures, very interesting in aeronautic industry, by superposing the inductive effects in different layers. The impedance of an absolute sensor operating above three conducting layers will be calculated and compared to those obtained with finite element method. Afterwards, we shall exploit the model to study the effect of defect characteristics on ...

In this paper, a new method of contactless electric conductivity measurement is developed. This method is essentially based on the association of the coupled electric field forward model, which we have recently developed, with a simple and efficient research algorithm. The proposed method is very fast because 1.3 s are sufficient to calculate electric conductivity, in a CPU of 2 GHz and RAM of 3 GB, for a starting research interval of 1.72–17.2 %IACS and tolerance of 1.72 × 10− 5 %IACS. The study of the calculation time according to mesh density and starting interval width has showed that an optimal choice has to be made in order to improve the rapidity while preserving its precision. Considering its rapidity and its simplicity of implementation, this method is more adapted, in comparison to direct current techniques using Van der Pauw geometry, for automated applications.

This paper proposes a contactless method for the identification of the electrical conductivity tensor of a carbon fiber composite materials plate using a rotating magnetic field and multi-coil eddy current sensor. This sensor consists of identical rectangular multi-coil, excited by two-phase sinusoidal current source in order to generate a rotating magnetic field and to avoid the mechanical rotation of the sensor. The fibers orientations, the longitudinal and transverse conductivities in each ply of carbon fiber composite material plate were directly determined with analysis of the impedance variation of each coil as function of its angular position. The inversion process is based on the use of artificial neural networks. The direct calculation associated with artificial neural networks makes use of 3D time-harmonic finite element method based on the A, V–A formulation.

A general and efficient algorithmic implementation is presented for calculating of self and/or mutual inductances using winding function theory for any type of stator and/or rotor layout slot windings. Then, this algorithm is applied on brushless doubly fed induction machines with standard cage. In fact, several kinds of slot windings are treated to prove the efficiency of this technique. The procedure is provided with inputs vector consisted by the machine main dimensions, air gap length, stator and rotor slot numbers, pole pairs, slots per pole and phase and the winding slots sequence defining the winding kind. Consequently, the application of this procedure yields both self and mutual inductances matrix of the stator and the rotor as well as the stator-rotor mutual inductances curve and its derivative. Finally, a dynamic simulation with multiple coupled circuits modelling is carried out and the results are presented and discussed.

Efficiency improvement of motor drives is important not only from the viewpoints of energy loss and hence cost saving, but also from the perspective of environmental pollution. Several efficiency optimization methods for induction motor (IM) drives have been introduced nowadays by researchers. Distinctively, artificial intelligence (AI)-based techniques, in particular Fuzzy Logic (FL) one, have been emerged as a powerful complement to conventional methods. Design objectives that are mathematically hard to express can be incorporated into a Fuzzy Logic Controller (FLC) using simple linguistic terms. The merit of FLC relies on its ability to express the amount of ambiguity in human reasoning. When the mathematical model of a process does not exist or exists with uncertainties, FLC has proven to be one of the best alternatives to move with unknown process. Even when the process model is well-known, there may still be parameter variation issues and power electronic systems, which are known to be often approximately defined. The purpose of this paper is to demonstrate that a great efficiency improvement of motor drive can be achieved and hence a significant amount of energy can be saved by adjusting the flux level according to the applied load of an induction motor by using an on-line fuzzy logic optimization controller based on a vector control scheme. An extensive simulation results highlight and confirm the efficiency improvement with the proposed algorithm.