Analysis of Barkhausen noise using wavelet-based fractal signal processing for fatigue crack detection

Miesowicz, Krzysztof; Staszewski, Wiesław J.; Korbiel, Tomasz

doi:10.1016/j.ijfatigue.2015.10.002

Cited by 36 publications

(18 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Análise de ruído provenientes da interação entre campo e material vem sendo utilizados para acompanhar a formação de fases em matérias, sendo aplicada análise por FFT, wavelet e comportamento fractal de sinais, indicando como ferramentas para caracterização de mudanças microestruturais nos materiais. [7], [11] [13].…”

Section: Resultados E Discussõesunclassified

See 1 more Smart Citation

Processamento de sinais de ruído da interação entre campo magnético e material para acompanhamento da fragilização em um aço

Silva¹,

Silva²,

Silva³

et al. 2017

Anais De XXXV Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

View full text Add to dashboard Cite

Resumo Este artigo analisa, através de processamento de sinais, a interação entre densidade de fluxo magnético e um material submetido a diferentes condições de tratamento. O objetivo é estudar o ruído gerado dessa interação e correlacionálo com o grau de fragilização do material. Um aço inoxidável duplex SAF 2205 é utilizado. Os sinais são analisados através de FFT e correlacionados com valores de energia absorvida por impacto. Os resultados mostram que as distribuições de frequências dos sinais de FFT acompanham a formação da fase que fragiliza o material e que os valores médios das amplitudes de frequências servem como parâmetro de monitoramento.Palavras-Chave Processamento de sinais, Densidade de fluxo magnético, Fragilização.Abstract This article analyzes, through signal processing, the interaction between magnetic flux density and a material submitted to different treatment conditions. The objective is to study the generated noise of this interaction and correlate it with the degree of embrittlement of the material. A duplex stainless steel SAF 2205 is used. Signals are analyzed through FFT and correlated with values of absorbed energy by impact test. The results show that the frequency distributions of the FFT signals accompany the formation of the phase that embrittle the material and that the average values of the frequency amplitudes serve as a monitoring parameter.

show abstract

Section: Resultados E Discussõesunclassified

“…A formação de ruídos durante a interação entre campo e material vem sendo objeto de vários estudos. Estudos baseados em FFT, wavelet e dimensão fractal de sinais vêm sendo aplicados para acompanhamentos da formação de novas fases dentro do material quando estes são submetidos a variações de ciclos térmicos [7], [11] [13].…”

Section: Introductionunclassified

Processamento de sinais de ruído da interação entre campo magnético e material para acompanhamento da fragilização em um aço

Silva¹,

Silva²,

Silva³

et al. 2017

Anais De XXXV Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

View full text Add to dashboard Cite

show abstract

“…[21,22]. The development of sensing hardware within low consumption and self-powered wireless network node [23,24] makes the development of long-term monitoring of crack propagation come true. As the state-of-the-art crack detection methodologies of crack propagation process, acoustic emission (AE) method is able to determine the crack activity and the source location of structure under complex working condition [25].…”

Section: Experimental Planmentioning

confidence: 99%

Estimation study of structure crack propagation under random load based on multiple factors correction

Zhu

Jia

2016

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

“…According to the relevant literatures, multiple signal processing methods have been applied to get more representative information of MBN signal. Magalas [ 11 ] introduced wavelet transform into MBN signal analysis; then, Miesawicz et al [ 12 ] further expanded the study. Luo et al [ 13 ] applied the auto-regressive modeling method to obtain a single-sided power spectrum (also called a PSD or an AR spectrum), which is a near-deterministic expression of the MBN signal.…”

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

View full text Add to dashboard Cite

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.

show abstract

Analysis of Barkhausen noise using wavelet-based fractal signal processing for fatigue crack detection

Cited by 36 publications

References 18 publications

Processamento de sinais de ruído da interação entre campo magnético e material para acompanhamento da fragilização em um aço

Processamento de sinais de ruído da interação entre campo magnético e material para acompanhamento da fragilização em um aço

Estimation study of structure crack propagation under random load based on multiple factors correction

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

Contact Info

Product

Resources

About