A Method of Barkhausen Noise Feature Extraction Based on an Adaptive Threshold

Cheng, Hang; Liu, Yanyi; Wang, Ping

doi:10.3390/app9152964

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…The main reason for discontinuous DW motion can be viewed in their interference with the pinning sites such as dislocation tangles [4,5], grain boundaries [6], surface irregularities [7], precipitates [8], and non-ferromagnetic phases [9], etc. For this reason, MBN signals contain information about the interfering microstructure features as well as the stress state [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Magnetic Anisotropy and Barkhausen Noise Asymmetry Resulting from Uniaxial Plastic Deformation of Steel S235

et al. 2021

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This study investigates alterations in magnetic anisotropy and the marked asymmetry in Barkhausen noise (MBN) signals after the uniaxial plastic straining of steel S235 obtained from a shipyard and used as standard structural steel in shipbuilding. It was found that the initial easy axis of magnetisation in the direction of previous rolling, and also in the direction of loading, becomes the hard axis of magnetisation as soon as the plastic strain attains the critical threshold. This behaviour is due to the preferential matrix orientation and the corresponding realignment of the magneto-crystalline anisotropy. Apart from the angular dependence of MBN, the asymmetry in the consecutive MBN bursts at the lower plastic strains is also analysed and explained as a result of magnetic coupling between the grains plastically strained and those unaffected by the tensile test. It was found that, by increasing the degree of plastic strain, the marked asymmetry in MBN tends to vanish. Moreover, the asymmetry in MBN bursts occurs in the direction of uniaxial tension and disappears in the perpendicular direction. Besides the MBN technique, XRD and EBSD techniques were also employed in order to provide a deeper insight into the investigated aspects.

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Section: Introductionmentioning

confidence: 99%

Investigation of Magnetic Anisotropy and Barkhausen Noise Asymmetry Resulting from Uniaxial Plastic Deformation of Steel S235

et al. 2021

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“…Intensity changes in an MBN signal with material state transformation are usually reflected and described by the representative parameters calculated in time-frequency (TF) domain such as the amplitude, energy, root mean square (RMS), waveform full width at half maximum (FWHM), envelope, peak time, threshold, and power spectrum [ 3 , 4 , 5 , 8 , 9 , 10 ]. However, affected by the microscopic magnetic anisotropy of the material itself, measurement performance, and experimental magnetization parameters (such as magnetization intensity and frequency, excitation waveform), the MBN has an obvious stochastic nature and the application of more automatic signals processing procedures used for extraction, selection, and fusion of signal features containing critical and distinctive information about the material properties are urgently required.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, wide new features are also proposed. Vashista et al [ 17 ] proposed two parameters of “count” and “event” of MBN signal; on this basis, Hang [ 10 ] used a genetic algorithm (GA) for appropriate threshold selection. Su et al [ 4 ] performed first- and second-order derivation on the AR spectrum and manually extracted some peaks from it as new features.…”

Section: Introductionmentioning

confidence: 99%

A Method for Detecting the Randomness of Barkhausen Noise in a Material Fatigue Test Using Sensitivity and Uncertainty Analysis

Hou

Zheng

et al. 2020

Sensors

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The magnetic Barkhausen noise (MBN) signal provides interesting clues about the evolution of microstructure of the magnetic material (internal stresses, level of degradation, etc.). This makes it widely used in non-destructive evaluation of ferromagnetic materials. Although researchers have made great effort to explore the intrinsic random characteristics and stable features of MBN signals, they have failed to provide a deterministic definition of the stochastic quality of the MBN signals. Because many features are not reproducible, there is no quantitative description for the stochastic nature of MBN, and no uniform standards to evaluate performance of features. We aim to make further study on the stochastic characteristics of MBN signal and transform it into the quantification of signal uncertainty and sensitivity, to solve the above problems for fatigue state prediction. In the case of parameter uncertainty in the prediction model, a prior approximation method was proposed. Thus, there are two distinct sources of uncertainty: feature(observation) uncertainty and model uncertainty were discussed. We define feature uncertainty from the perspective of a probability distribution using a confidence interval sensitivity analysis, and uniformly quantize and re-parameterize the feature matrix from the feature probability distribution space. We also incorporate informed priors into the estimation process by optimizing the Kullback–Leibler divergence between prior and posterior distribution, approximating the prior to the posterior. Thus, in an insufficient data situation, informed priors can improve prediction accuracy. Experiments prove that our proposed confidence interval sensitivity analysis to capture feature uncertainty has the potential to determine the instability in MBN signals quantitatively and reduce the dispersion of features, so that all features can produce positive additive effects. The false prediction rate can be reduced to almost 0. The proposed priors can not only measure model parameter uncertainties but also show superior performance similar to that of maximum likelihood estimation (MLE). The results also show that improvements in parameter uncertainties cannot be directly propagated to improve prediction uncertainties.

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Abnormal Signals Elimination in Hardness Evaluation Using Barkhausen Noise and Tangential Magnetic Field

Cheng

Liu

Dobmann³

et al. 2023

J Nondestruct Eval

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A Method of Barkhausen Noise Feature Extraction Based on an Adaptive Threshold

Cited by 6 publications

References 11 publications

Investigation of Magnetic Anisotropy and Barkhausen Noise Asymmetry Resulting from Uniaxial Plastic Deformation of Steel S235

Investigation of Magnetic Anisotropy and Barkhausen Noise Asymmetry Resulting from Uniaxial Plastic Deformation of Steel S235

A Method for Detecting the Randomness of Barkhausen Noise in a Material Fatigue Test Using Sensitivity and Uncertainty Analysis

Abnormal Signals Elimination in Hardness Evaluation Using Barkhausen Noise and Tangential Magnetic Field

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