Prediction of the hardness of X12m using Barkhausen noise and component analysis methods

Li, Zibo; Sun, Guangmin; He, Cunfu; Li, Yu; Liu, Xiucheng; Zhao, Dequn

doi:10.1016/j.jmmm.2019.01.084

Cited by 4 publications

(5 citation statements)

References 49 publications

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“…The Barkhausen effect specifically refers to the phenomenon in which the magnetic flux of a magnetized material changes discontinuously due to magnetic domain inversion during magnetization [ 1 ]. As an important nondestructive testing (NDT) method, the magnetic Barkhausen noise (MBN) signal is sensitive to the changes in many material properties and has many applications in the fields of material stress and hardness detection [ 2 , 3 , 4 , 5 ], metal fatigue state analysis [ 6 ], metal microstructure transformation, and grain size measurement [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…Su et al [ 4 ] performed first- and second-order derivation on the AR spectrum and manually extracted some peaks from it as new features. Li et al [ 5 ] adopted a modified slow feature analysis (m-SFA) to enrich the content of PSD by fusing the AR spectrum with different orders. There are many types of features, which can be roughly divided into energy feature, statistical feature, and shape feature, and great differences may manifest for different features used in different applications.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…6 BN features (such as peak value, mean value of BN profile) and 5 tangential magnetic field (TMF) features (such as amplitude of the 3 rd , 5 th and 7 th harmonics) were extracted as the input to the two regression models. In our previous work, [8] we have focused on the feature extraction methods of BN for hardness prediction. Compared with the conventional BN features (called isolated features in [8] ), a stable and unified feature that was generated by signal component analysis (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work, [8] we have focused on the feature extraction methods of BN for hardness prediction. Compared with the conventional BN features (called isolated features in [8] ), a stable and unified feature that was generated by signal component analysis (i.e. slow feature analysis and discriminant incoherent component analysis) was proposed to predict the hardness of X12m.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Hardness of X12m Using Barkhausen Noise and Chebyshev Polynomials Regression Methods

Li²,

Wang³

et al. 2020

Studies in Applied Electromagnetics and Mechanics

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Barkhausen noise (BN) is electromagnetic pulse sequence that could be used to nondestructively predict the properties of materials such as hardness, residual stress and carbon content. Current BN signal analysis methods fail to describe the highly variated BN signal and achieve high regression accuracy due to the low interpretability of neural network and limited capacity of mathematical regression tools. In this paper, two multi-variable regression tools, named partial Chebyshev polynomial regression (PCPR) and Mutual Information-based Feature Selection with Class-dependent Redundancy and multi-variable Chebyshev polynomials regression (MIFS-CR+MCPR), are employed for the first time to predict the hardness of Cr12MoV steel (i.e. X12m). Combined with Chebyshev polynomials, our regression tools are designed on the basis of cascaded regression and mutual-information-based feature selection. As represented by the experimental results for predicting the hardness of X12m, the proposed method outperforms other comparative methods including neural network and partial linear square regression method.

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Study of some thermo-mechanical parameters in Se70Te30-xMx (x = 0, 2; M = Cd, Ag, and Zn) alloys

Dahshan

Mehta

Dohare

et al. 2022

Chinese Journal of Physics

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Prediction of the hardness of X12m using Barkhausen noise and component analysis methods

Cited by 4 publications

References 49 publications

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

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

Prediction of the Hardness of X12m Using Barkhausen Noise and Chebyshev Polynomials Regression Methods

Study of some thermo-mechanical parameters in Se70Te30-xMx (x = 0, 2; M = Cd, Ag, and Zn) alloys

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