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In the present work a new system for nondestructive evaluation of applied stress on the pipeline steels surface is proposed. This system is based on applying a magnetic field on the pipe surface, whose direction changes due to rotatory permanent magnets. The variation of magnetic field direction produces the Barkhausen signal, which is measured by a pick-up coil. The angular dependence of the magnetic Barkhausen noise signal energy is influenced by the pipe surface curvature due to the changing head-probe liftoff. Also, the said angular dependence changes with the applied tensile stress due to the magneto-elastic effect. The result of Barkhausen noise measurements in the pipeline surface submitted to applied tensile stress reveals that, since the effect of curvature on the angular dependence remains constant, the effect of applied stress could be determined by analyzing the angular dependence variation of the Barkhausen signal with the applied stress.

In the present work a model for the correlation between magnetocrystalline anisotropy energy and Barkhausen noise is proposed. The link between the magnetocrystalline anisotropy energy and the magnetic Barkhausen noise is due to the influence of the crystallographic texture on the domain nucleation process which produces the Barkhausen signal in the branch from saturation to remanence. The statistical distributions of magnetic free poles and local fields of nucleation and subsequent growth of reversed domains were obtained for a large number of grain boundaries and used to estimate the number and size of Barkhausen events at each angular position from 0 to 360° in ten degree-steps. The good agreement observed between the modeled magnetocrystalline energy and the prediction of this energy made from X-ray texture and Barkhausen noise measurements corroborates the validity of the proposed model.

This work analyzes the differences between the magnetic Barkhausen noise corresponding to the initial magnetization curve and Barkhausen noise corresponding to one branch of the hysteresis loop in API-5L steel. The outcomes show that the Barkhausen noise signal corresponding to the initial magnetization curve and that corresponding to the hysteresis are significantly different. This difference is due to the presence of different processes of the domain wall dynamics in both phenomena. To study the processes present in magnetization dynamics for an applied field of H > 0, research into the angular dependence of a Barkhausen signal using applied field bands has revealed that a Barkhausen signal corresponding to the initial magnetization curve is more suitable than a Barkhausen signal corresponding to the hysteresis loop.

In this paper, a new portable magnetic perturbation probe using a Giant Magneto-Resistance (GMR) sensor to detect and characterize surface cracks in ferromagnetic steels is presented. Measurements from one tangential component of the magnetic flux density are obtained during a single scan over a crack, and two parameters are defined from the output voltage of the GMR sensor: the distance between the peaks, DX, and their voltage difference, DV. The correlations between DX and DV and crack features such as depth, width, and orientation angle are determined. Sensitivity analyses are performed based on the studied features. The probe permits the user to obtain the crack׳s orientation angle using only one measured tangential component of the magnetic flux density. A simple procedure to estimate the crack׳s features is proposed based on the presented analysis.

The present work analyzes the influence of electric conductivity on the Magnetic Barkhausen Noise (MBN) signal using a microscopic model which includes the influence of eddy currents. This model is also implemented to explain the dependence of MBN on the frequency of the applied magnetic field. The results presented in this work allow analyzing the influence of eddy currents on MBN signals for different values of the material's electric conductivity and for different frequencies of applied magnetic field. Additionally, the outcomes of this research can be used as a reference to differentiate the influence of eddy currents from that of second phase particles in the MBN signal, which has been reported in previous works.

This work studies the influence of the maximum applied magnetic field on the angular dependence of the energy of the Magnetic Barkhausen Noise signal in three different API5L pipeline steels. The results show that the shape of the angular dependence of the Magnetic Barkhausen Noise energy changes with the increase of the amplitude of the applied magnetic field. This phenomenon is a consequence of the presence of unlike magnetization processes at different magnitudes of the applied magnetic field. The outcomes reveal the importance of controlling the value of the maximum applied field as parameter for the improvement of the MBN angular dependence measurements.

Giant magnetoresistance (GMR) sensors have been used in non-destructive testing based on eddy current (NDT-EC). The low-pass filtered GMR-based NDT-EC have been successfully used to estimate the dimensions of artificial near-side cracks in aluminum plates. In this paper, an experimental study has been carried out in order to optimize the characteristics of the probe to improve its sensitivity. It is proposed a new design method for the low-pass filtered GMR-based eddy current probe. Experimental measurements were performed along artificial notches with different widths and depths in three aluminum plates. The asymmetry of the probe is analyzed in detail, giving that the operating point of the probe depends strongly on the asymmetry between the GMR end the Coil axis. The influence of the excitation coil parameters on the average sensitivity of the GMR-based EC probe is established. The outcomes revealed the strong dependence of the probe sensitivity to the inner diameter and filling factor of the excitation coil. Therefore, the methodology for selecting the inner diameter and the filling factor of the excitation coil and the GMR-coil asymmetry value are proposed. The average sensitivity of the designed probe using the proposed methodology is twice as high as the sensitivity of a non-optimized probe.

Recent works in the literature have demonstrated that the angular dependence of magnetocrystalline anisotropy energy could be estimated from magnetic Barkhausen noise in carbon steels using the energy of a Barkhausen signal band associated with the magnetic domain nucleation. However, although the estimations of this magnitude using the Barkhausen noise signal have practical advantages with respect to X-ray method, it is not as precise as the latter. This work proposes a method that uses a deep neural network for significantly enhancing the estimation precision of the shape of the angular dependence of the magneto-crystalline anisotropy energy using Barkhausen signal. Additionally, the proposed algorithm allows to estimate for the first time the amplitude of this angular dependence using the Barkhausen signal. The results are also compared with the determination of magnetocrystalline anisotropy energy using a feature selection method. The most significant features of the Barkhausen signal for computing the magnetocrystalline anisotropy energy are analyzed.

This work proposes a Brain Computer Interface based on using multi-frequency visual stimulation and deep neural networks for signals classification. The use of multi-frequency stimulation, combined with a new proposed coding method codifying up to 220 commands, which could be used to create a large multi-command brain computer interface. The advantages this method for commands codification and classification performance is analyzed in a five commands Brain computer interface. The classification of the electroencephalographic signals used in the interface was performed using several algorithms. The outcomes reveal that the best classification algorithm is a deep neural network, which gives a classification accuracy of 97.78 %. This algorithm, also, allows establishing the most relevant features of the electroencephalographic signal spectrums for the classification and information extraction from the evoked potentials.

This work proposes a method for data condensing. The method is based on the selection of a generator of data prototypes. An algorithm for the front propagation of the prototypes׳ boundaries is performed in order to obtain the class boundaries given by a set of support vectors. The proposed method just has one tuning parameter and presents high classification rates even for complex topological and non-concave classes and low tendency to over-fitting. The most important advantage of the proposed method is its higher condensing rate without a significant detrimental effect on the classification rate. The algorithm is intended to be applied for condensing data in low memory devices and transmission of high-volume of data where data condensing could be crucial.

A method for determination of the magnetic easy axis of the Roll Magnetic Anisotropy in API-5L steels is proposed. The method is based on the fact that the angular dependence of the energy corresponding to the main peak of the Magnetic Barkhausen signal presents uniaxial anisotropy with its easy axis parallel to the rolling direction, independently of the angular dependence of the magnetocrystalline energy in the materials. The proposal is also justified based on the analysis of the influence of microstructural changes, produced by hot-rolling on the domain wall dynamics.

A microscopic model of magnetic Barkhausen noise (MBN) in carbon steel is proposed. The model uses the quasi-static magnetic formulation of Maxwell equations for electromagnetic fields combined with a microscopic model of the magnetic Barkhausen noise, and its equations are solved by means of finite difference formulation. The simulated MBN signal obtained presents high similarities to the measured MBN signal. Using this model, the influences of the uniformity and waveform profile of the excitation magnetic field on the envelope of the MBN signal were studied. The results show that the lack of uniformity of the excitation magnetic field increases the amplitude of the MBN envelope at the right of its main peak, and the waveform profile influences the shape of the MBN envelope. The proposed model can be used as a tool for studying the influence of several excitation parameters on the Barkhausen Noise in order to improve this technique.

The main advantage of Magnetic Barkhausen Noise as a non-destructive testing method is its sensitivity to several parameters such as microstructure, applied tension and plastic deformation. However, this noticeable property of the MBN sometimes could be a drawback. Usually, in measurements of industrial steel samples the variation of parameters occurs simultaneously. Then it is difficult to separate the influence of multiple parameters from the raw signal. This work proposes a method using trajectories traced in a type of neural network known as Self-Organizing Maps, in order to separate the influence of varying parameters on the Magnetic Barkhausen Noise row signal.

This work proposes a system for classification of industrial steel pieces by means of magnetic nondestructive device. The proposed classification system presents two main stages, online system stage and off-line system stage. In online stage, the system classifies inputs and saves misclassification information in order to perform posterior analyses. In the off-line optimization stage, the topology of a Probabilistic Neural Network is optimized by a Feature Selection algorithm combined with the Probabilistic Neural Network to increase the classification rate. The proposed Feature Selection algorithm searches for the signal spectrogram by combining three basic elements: a Sequential Forward Selection algorithm, a Feature Cluster Grow algorithm with classification rate gradient analysis and a Sequential Backward Selection. Also, a trash-data recycling algorithm is proposed to obtain the optimal feedback samples selected from the misclassified ones.

This paper presents a method for rapid quantification of long artificial cracks using a GMR-based eddy current sensor. The quantification is achieved simultaneously through a proposed phenomenological relationship between the crack's dimension and two parameters extracted from the GMR output signal. The results show that the proposed method is valid and advantageous when a quick and reliable estimation of the crack's dimension is necessary. A criterion taking into consideration the filling factor of the excitation coil is proposed in order to improve the system's sensitivity.

This work presents a Feature Selection Algorithm for optimization of a Probabilistic Neural Network. The aim of this Probabilistic Neural Network is classifying signals obtained from magnetic material samples where two micro-structural parameters were changed simultaneously. The combination of the Feature Selection Algorithm with the Probabilistic Neural Network shows both, classification rate outcomes and classification speed, higher than those coming from traditional Probabilistic Neural Networks.

The magnetic Barkhausen noise (MBN) is a phenomenon sensitive to several kinds of magnetic material microstructure changes, as well as to variations in material plastic deformation and stress. This fact stimulates the development of MBN-based non-destructive testing (NDT) techniques for analyzing magnetic materials, being the proposition of such a method, the main objective of the present study. The behavior of the MBN signal envelope, under simultaneous variations of carbon content and plastic deformation, is explained by the domain wall dynamics. Additionally, a non-destructive parameter for the characterization of each of these factors is proposed and validated through the experimental results.

We present a method to simulate the Magnetic Barkhausen Noise using the Random Field Ising Model with magnetic long-range interaction. The method allows calculating the magnetic flux density behavior in particular sections of the lattice reticule. The results show an internal demagnetizing effect that proceeds from the magnetic long-range interactions. This demagnetizing effect induces the appearing of a magnetic pattern in the region of magnetic avalanches. When compared with the traditional method, the proposed numerical procedure neatly reduces computational costs of simulation.

The present work presents the measurements of the magnetic Barkhausen noise (MBN) in ASTM 36 steel samples around a pit under plastic deformation. The contour maps obtained from these Barkhausen noise measurements are compared with the finite element analysis of the ideal plastic deformation. Also, a parameter of the Barkhausen signal to detect the plastic deformation around the pit in ASTM 36 steel is obtained. Additionally to that, we propose another MBN parameter to estimate the pit width using the Barkhausen noise.

Angular-dependent magnetic Barkhausen noise measurements were performed on ASTM 36 steel samples for various values of applied uniaxial stress. The maximum differential permeability and the coercive field are obtained from this Barkhausen signals. The angular dependence of these macroscopic magnetic properties is studied. The tension for which the magneto-elastic energy balances the anisotropic energy is estimated. The results are discussed based on the domain wall theory.

This work presents the development of a Brain Computer Interface (BCI) based on Steady State Visual Evoked Potentials (SSVEP), that enables a user to control a remote control car. The visual stimulus conditions of area, frequency, and shape were evaluated with the purpose of obtaining the SSVEP signal with the highest amplitude in order to obtain the best performance of the developed BCI. A remote control car was built and was controlled by a healthy subject using the developed SSVEP BCI, proving its proper functionality. This BCI has the purpose of helping people with motor disabilities; one of its possible applications could be the control of wheelchairs.

This study presents a model of the microstructural defect influence on the magnetic Barkhausen noise in plain steels. The comparison of the theoretical and experimental results reveals an excellent agreement between them. We show that both model and experimental approach can be very useful, particularly, for the carbon content characterization in commercial steels.

In this work, the dependence of the magnetic Barkhausen Effect (MBE) with carbon content in commercial steels has been studied. The dependence of the MBE amplitude, root mean square voltage and the parameters that characterize the form of the MBE Jumps with different carbon content were obtained. The cause of this dependence was analyzed on the base of domain walls theory.

In this paper, theoretical bases for the development of a software for electromagnetic effects simulation based on the approximation method of finite differences using Scilab are presented. A methodology for programming the algorithm is discussed and a simulation test is conducted in order to validate it, which consists of a coil with a ferromagnetic core. Finally, this software is used in studies related to magnetic flux leakage by a simulation of a system for the measurement of magnetic flux density in plates with different defects, which include both 2D and 3D and defects with orientation of 45 degrees.

This paper presents the development of a portable probe for detecting cracks in steel plates. The probe consists of a magnet and a giant magneto-resistance (GMR) sensor. The magnet provides a radial magnetization at the surface of the steel plate. The GMR sensor detects the tangential component of the magnetic flux leakage due to a crack in the steel plate. Two steel plates were inspected with six cracks of depths: 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 mm, and widths of 0.25 and 0.5 mm, respectively. The cracks were scanned with the GMR sensitivity axis at 90°, 80°, 70°, 60° and 50°. It is demonstrated that the output voltage of the GMR sensor is sensitive to the orientation of the crack.

This work presents a method to identify processes in magnetization dynamics using the angular dependence of the magnetic Barkhausen noise. The analysis reveals that three different processes of the magnetization dynamics could be identified using the angular dependence of the magnetic Barkhausen noise energy. The first process is the reversed domain nucleation which is related to the magneto-crystalline energy of the material, and the second and third ones are associated with 180° and 90° domain walls motions, respectively. Additionally, two transition regions were identified and they are located between the regions associated with the aforementioned processes. The causes involving these processes are analyzed and a method for establishing their location in the Barkhausen noise signal with respect to the applied magnetic field intensity is proposed.

This paper presents experimental evidence of the capability of Barkhausen noise measurements to estimate the angular dependence of the average magnetocrystalline energy in soft magnetic polycrystalline materials. Three different API 5L steel samples, all obtained from out-of-service pipelines, were investigated using crystallographic texture and Barkhausen noise measurements. The angular dependence of the rms voltage of the Barkhausen signal was determined in each sample for the time band corresponding to the saturation-to-remanence part of the hysteresis loop where irreversible domain rotation occurs. For each angular position, the rms voltage of the Barkhausen noise signal in this time band was interpreted as a direct measure of the magnetocrystalline anisotropy energy of the polycrystal in the corresponding direction. A strong correlation between the angular dependence of both the rms voltage of the Barkhausen signal in the time band of interest and the average magnetocrystalline energy obtained from crystallographic texture measurements was found experimentally.

This work presents a model for the magnetic Barkhausen jump in low carbon content steels. The outcomes of the model evidence that the Barkhausen jump height depends on the coercive field of the pinning site and on the mean free path of the domain wall between pinning sites. These results are used to deduce the influence of the microstructural features and of the magnetizing parameters on the amplitude and duration of the Barkhausen jumps. In particular, a theoretical expression, establishing the dependence of the Barkhausen jump height on the carbon content and grain size, is obtained. The model also reveals the dependence of the Barkhausen jump on the applied frequency and amplitude. Theoretical and experimental results are presented and compared, being in good agreement.