scholarly journals Use of Time-Frequency Representation of Magnetic Barkhausen Noise for Evaluation of Easy Magnetization Axis of Grain-Oriented Steel

Materials ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3390 ◽  
Author(s):  
Michal Maciusowicz ◽  
Grzegorz Psuj
Keyword(s):  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise ◽  
Barkhausen Effect ◽  
Easy Magnetization ◽  
Time Frequency ◽  
Use Of Time ◽  
Frequency Representation ◽  
Magnetization Axis ◽  
Analysis And Synthesis ◽  
Selection Of

The paper presents a new approach to non-destructive evaluation of easy/hard magnetization axis in grain-oriented SiFe electrical steels based on the Barkhausen phenomenon and its time-frequency (TF) characteristics. Anisotropy in steels is influenced by a number of factors that formulate the global relationship and affect the Barkhausen effect. Due to the observed high variability in the dynamics of magnetic Barkhausen noise (MBN) over time, obtained for various directions in grain-oriented steel, it becomes justified to conduct MBN signal analyses in the time-frequency domain. This representation allows not only global information from MBN signal over entire period to be expressed, but also detailed relationships between properties in time and in frequency to be observed as well. This creates the opportunity to supplement the information obtained. The main aspect considered in the work is to present a procedure that allows an assessment of the resultant angular characteristics in steel. For this purpose, a sample of a conventional grain-oriented SiFe sheet was used. Measurements were made for several angular settings towards the rolling and transverse directions. A data transformation procedure based on short-time Fourier transform (STFT) as well as quantitative analysis and synthesis of information contained in the TF space was presented. Angular characteristics of selected TF parameters were shown and discussed. In addition, an analysis of the repeatability of information obtained using the proposed procedure under various measurement conditions was carried out. The relationship between the selection of calculation parameters used during transformation and the repeatability of the obtained TF distributions were demonstrated. Then the selection of the final values of the calculation parameters was commented upon. Finally, the conclusions of the work carried out were discussed.

scholarly journals Use of Time-Dependent Multispectral Representation of Magnetic Barkhausen Noise Signals for the Needs of Non-Destructive Evaluation of Steel Materials

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1443 ◽  
Author(s):  
Michal Maciusowicz ◽  
Grzegorz Psuj
Keyword(s):  
Extraction Procedure ◽  
Measuring System ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise ◽  
Barkhausen Effect ◽  
Destructive Testing ◽  
Time Frequency ◽  
Use Of Time ◽  
Definition Of ◽  
Non Destructive

Due to the existing relationship between microstructural properties and magnetic ones of the ferromagnetic materials, the application potential of the magnetic Barkhausen noise (BN) method to non-destructive testing is constantly growing. However, the stochastic nature of the Barkhausen effect requires the use of advanced signal processing methods. Recently, the need to apply time-frequency (TF) transformations to the processing of BN signals arose. However, various TF methods have been used in the majority of cases for qualitative signal conditioning and no extensive analysis of TF-based information has been conducted so far. Therefore, in this paper, the wide analysis of BN TF representation was carried out. Considering the properties of TF transformations, the Short-Time Fourier Transform (STFT) was used. A procedure for definition of the envelopes of the TF characteristic was proposed. To verify the quality of extracted features, an analysis was performed on the basis of BN signals acquired during stress loading experiments of steel elements. First, the preliminary experiments were processed for various parameters of the measuring system and calculation procedures. The feature extraction procedure was performed for different modes of TF representations. Finally, the distributions of TF features over the loading stages are presented and their information content was validated using commonly used features derived from time T and frequency F domains.

scholarly journals Analysis of measurement conditions influence on the magnetic Barkhausen noise phenomenon

2020 ◽  
Vol 91 (12) ◽  
pp. 17-24
Author(s):  
Michał Paweł Maciusowicz ◽  
Grzegorz Psuj
Keyword(s):  
Extraction Methods ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise ◽  
Destructive Testing ◽  
Discrimination Ability ◽  
Time Frequency ◽  
Frequency Representation ◽  
Material Conditions ◽  
Transformation Methods ◽  
The Impact

The effectiveness of the magnetic Barkhausen noise method (MBN), used for non-destructive testing of ferromagnetic materials, depends to a large extent on a number of factors determining the measurement conditions. The use of conditions allowing the highest possible level of discrimination between the various states of the materials state is of highest importance. Therefore, this paper presents an analysis of the impact of measurement conditions on Barkhausen noise signals observed for various states of the material conditions. Taking into consideration the stochastic nature of MBN and the complex characterization of its changes, the analysis was based on the time-frequency representation of the MBN signal. The paper presents selected distributions achieved using two transformation methods. In addi- tion, the extraction methods of features allowing the quantification of complex information were given. Finally, the discrimination ability for a number of parameters and features of MBN signals were deter- mined and the obtained results were discussed.

scholarly journals Identification of Grain Oriented SiFe Steels Based on Imaging the Instantaneous Dynamics of Magnetic Barkhausen Noise Using Short-Time Fourier Transform and Deep Convolutional Neural Network

Materials ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 118
Author(s):  
Michal Maciusowicz ◽  
Grzegorz Psuj ◽  
Paweł Kochmański
Keyword(s):  
Fourier Transform ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise ◽  
Short Time Fourier Transform ◽  
Deep Convolutional Neural Networks ◽  
Electrical Steels ◽  
New Approach ◽  
Time Frequency ◽  
Short Time

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.

scholarly journals Using Reduced Interference Distribution to Analyze Abnormal Cardiac Signal

2011 ◽  
Vol 62 (3) ◽  
pp. 168-172 ◽  
Author(s):  
Allam Mousa ◽  
Rashid Saleem
Keyword(s):  
Frequency Analysis ◽  
Proper Time ◽  
Distribution Functions ◽  
Time Frequency ◽  
Use Of Time ◽  
Biological Signals ◽  
Bessel Distribution ◽  
Multicomponent Nature ◽  
Selection Of ◽  
Time Frequency Distribution

Using Reduced Interference Distribution to Analyze Abnormal Cardiac SignalDue to the non-stationary, multicomponent nature of biomedical signals, the use of time-frequency analysis can be inevitable for these signals. The choice and selection of the proper Time-Frequency Distribution (TFD) that can reveal the exact multicomponent structure of biological signals is vital in many applications, including the diagnosis of medical abnormalities. In this paper, the instantaneous frequency techniques using two distribution functions are applied for analysis of biological signals. These distributions are the Wigner-Ville Distribution and the Bessel Distribution. The simulation performed on normaland abnormal cardiac signals show that the Bessel Distribution can clearly detect the QRS complexes. However, Wigner-Ville Distribution was able to detect the QRS complexes in the normal signa, but fails to detect these complexes in the abnormal cardiac signal.

Research on pipeline internal stress detection technology based on the Barkhausen effect

2020 ◽  
Vol 62 (9) ◽  
pp. 550-554
Author(s):  
YiLai Ma ◽  
JinZhong Chen ◽  
RenBi He ◽  
Tao Meng ◽  
RenYang He
Keyword(s):  
Stress Concentration ◽  
Oil And Gas ◽  
Operating Conditions ◽  
Barkhausen Noise ◽  
Stress Detection ◽  
Magnetic Barkhausen Noise ◽  
Barkhausen Effect ◽  
Long Distance ◽  
Optimal Position ◽  
Detection Technology

Focusing on the requirements of pipeline in-line testing for stress concentration, mechanical scratches and corrosion discrimination, a numerical calculation and experimental verification study of the internal testing excitation of oil and gas pipelines based on the Barkhausen effect (magnetic Barkhausen noise (MBN)) is carried out. This paper uses finite element calculation to determine the optimal position of the sensor, quantitatively analyses the influence of parameters, such as the excitation structure size and excitation intensity, on the magnetisation field of the pipeline and obtains the optimal exciting parameters for acquiring continuous Barkhausen signals, which can provide references for designing the pipeline in-line inspection gauge for stress concentration. The feasibility of the continuous Barkhausen noise (CBN) method for long-distance pipeline stress detection is verified by simulating the operating conditions of the internal detector in the pipeline using dynamic rotating excitation.

scholarly journals Analysis of the Possibility of Using Various Time-Frequency Transformation Methods to Barkhausen Noise Characterization for the Need of Magnetic Anisotropy Evaluation in Steels

2021 ◽  
Vol 11 (13) ◽  
pp. 6193
Author(s):  
Michal Maciusowicz ◽  
Grzegorz Psuj
Keyword(s):  
Magnetic Anisotropy ◽  
Barkhausen Noise ◽  
Time Frequency ◽  
Frequency Representation ◽  
Stationary Signals ◽  
Crucial Information ◽  
Frequency Transformations ◽  
Transformation Methods ◽  
Short Time ◽  
Noise Characterization

Magnetic Barkhausen Noise (MBN) is a method being currently considered by many research and development centers, as it provides knowledge about the properties and current state of the examined material. Due to the practical aspects, magnetic anisotropy evaluation is one of such key areas. However, due to the non-stationary and stochastic nature of MBN, it requires searching for postprocessing procedures, allowing the extraction of crucial information on factors influencing the phenomenon. Advances in the field of the analysis of non-stationary signals by various transformations or decompositions resulting into new time- and frequency-related representations, allow the interpretation of complex sets of signals. Therefore, in this paper, several time-frequency transformations were used to analyze the data of MBN for the purpose of the magnetic anisotropy evaluation of electrical steel. The three main transform types with their modifications were considered and compared: the Short-Time Fourier Transform, the Continuous Wavelet Transform and the Smoothed Pseudo Wigner–Ville Transform. By using Exploratory Data Analysis methods and the parametrization of time-frequency representation, the qualitative and quantitative analysis was made. The STFT presented good performance on providing useful information on MBN changes while simultaneously leading to the lowest computational efforts.

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