Time-frequency representation of magnetic Barkhausen noise under various measurement conditions

Maciusowicz, Michal; Psuj, Grzegorz; Chudzik, P.

doi:10.1109/iiphdw.2019.8755437

Cited by 1 publication

(2 citation statements)

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“…As a result, it becomes crucial to track changes in the dynamics of the MBN course over time. Thus, a variety of analytic approaches based on timefrequency (TF) transformations of the MBN signal have recently been developed [21][22][23][24][25]. The article [21] presents an analysis of the MBN TF representations obtained for the determination of the influence of case hardening on MBN characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…In [23], a resultant anisotropy of the steel was analyzed based on the TF spectrograms of the entire MBN burst, while in [22] the possibility of determining the influence of various factors on the magnetization dynamics was considered by extraction of bursts subsequences. In [23] and [24] the authors discussed the effect of signal processing parameters and the influence of measurement conditions. For that reason, various magnetization currents, frequencies and shapes of the excitation signal on the obtained TF spectrograms were considered to achieve the greatest distinguishability between the states of the examined steel.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Grain-Oriented Electrical Steel Sheets by Magnetic Barkhausen Noise Using Time-Frequency Analysis and Selected Machine Learning Algorithms

Maciusowicz

Psuj

2022

Applied Sciences

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In this paper, a combination of Magnetic Barkhausen Noise (MBN) and several classical machine learning (ML) methods were used to evaluate both the grade and the magnetic directions of conventional and high grain oriented electrical sheets subjected to selected surface engineering methods. The presented analysis was conducted to compare the performance of two machine learning approaches, classical ML and deep learning (DL), in reference to the same MBN examination problem and based on the same database. Thus, during the experiment, 26 classical ML algorithms were used including decision trees, discriminant analysis, support vector machines, naïve Bayes, nearest neighbor, artificial neural networks and ensemble classifiers. The experiments were carried out considering a different number of recognized magnetic directions and hence the number of determined classes as well. The results of classification accuracy of the applied ML methods were compared with those obtained for the DL model presented in a previous paper. The highest accuracy was obtained for ML models based on artificial neural networks and ensemble bagged trees. However, the accuracy did not reach 89% in the best case—for the smallest number of determined classes. Nevertheless, the achieved results generally indicated an approx. 10 percent advantage of the deep learning model over the classical ones in terms of accuracy in each of the considered cases.

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