Abstract
This paper presents a new approach to the extraction and analysis of information contained in magnetic Barkhausen noise (MBN) for evaluation of grain oriented (GO) electrical steels. The proposed methodology for MBN analysis is based on the combination of the Short-Time Fourier Transform for the observation of the instantaneous dynamics of the phenomenon and deep convolutional neural networks (DCNN) for the extraction of hidden information and building the knowledge. The use of DCNN makes it possible to find even complex and convoluted rules of the Barkhausen phenomenon course, difficult to determine based solely on the selected features of MBN signals. During the tests, several samples made of conventional and high permeability GO steels were tested at different angles between the rolling and transverse directions. The influences of the angular resolution and the proposed additional prediction update algorithm on the DCNN accuracy were investigated, obtaining the highest gain for the angle of 3.6°, for which the overall accuracy exceeded 80%. The obtained results indicate that the proposed new solution combining time–frequency analysis and DCNN for the quantification of information from MBN having stochastic nature may be a very effective tool in the characterization of the magnetic materials.
Highlights
SiFe electrical steel sheets are used in many technical solutions, including electric motors or transformers [1,2,3,4,5]
The aim this study investigate feasibility of theapplying proposed deviation of of it from
A new approach to analysis of the magnetic Barkhausen noise (MBN) based on time–frequency algorithm
Summary
SiFe electrical steel sheets are used in many technical solutions, including electric motors or transformers [1,2,3,4,5]. The proposed TF-based method made it possible to visualize the changes in dynamics or to determine the level of the background of the Barkhausen phenomenon during the entire magnetization period [37], and to identify any periodic disturbances constituting a constant contribution to the measured signal and disturbing the obtaining of information about the investigated anisotropy from the MBN signal [38]. Due to the possibilities of Deep Neural Networks (DNN) and their sensitivity to even subtle changes in the input data, it is possible to apply them to the analysis of the MBN signal obtained from electrical steel sheets. To avoid the above described problems in this study, the new approach to MBN analysis based on STFT transformation and the use of deep convolutional neural networks (DCNN), to identify the type of GO sheet and to evaluate its magnetic directions, is proposed. The assessment of the proposed approach accuracy as a function of various angular resolutions applied during testing was carried out and the optimal conditions were discussed
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