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.

scholarly journals Time-Frequency Analysis of Barkhausen Noise for the Needs of Anisotropy Evaluation of Grain-Oriented Steels

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 768 ◽  
Author(s):  
Michal Maciusowicz ◽  
Grzegorz Psuj
Keyword(s):  
Magnetic Anisotropy ◽  
Supplementary Information ◽  
Barkhausen Noise ◽  
New Approach ◽  
Time Frequency ◽  
Crucial Information ◽  
Discrimination Level ◽  
Main Activity ◽  
Short Time ◽  
Steel Properties

The paper presents a new approach to obtain information on magnetic anisotropy in Si–Fe grain oriented ferromagnetic steel based on the observation of the magnetic Barkhausen noise (MBN). Until now, in the literature one can only notice the MBN study of magnetic anisotropy in steels carried out in a single time or frequency domain. However, due to the observed high variability of the dynamics of the MBN phenomenon over its occurrence period, depending on the steel properties, the idea of utilization of combined time and frequency representations to obtain new or supplementary information arises. For this purpose, the MBN phenomenon was observed in various directions for steels with oriented magnetic properties. Then, using the short-time Fourier transform, time-frequency (TF) distributions were determined and features vectors enabling the quantification of crucial information were determined. Before performing the final experiments, a series of tests were carried out for different measuring conditions. As a result, it was possible to adjust the conditions enabling us to obtain the highest possible sensitivity for MBN and discrimination level between directional properties in the material. Then, an algorithm of detailed analysis and division of the TF representation into subranges was proposed, enabling the extraction of more detailed information about the phenomena occurring during the magnetization process. This allowed us to clearly indicate and then separate three areas of MBN main activity. Finally, the obtained angular distributions of selected features were presented and discussed, and further conclusions were given.

scholarly journals Representation and Denoising of ECG Signal Using Hybrid Filtering Approach

2021 ◽  
Vol 9 (VII) ◽  
pp. 3281-3286
Author(s):  
Neeraj Venkat
Keyword(s):  
Feature Extraction ◽  
External Electromagnetic Field ◽  
Health Condition ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Time Frequency ◽  
Interference Noise ◽  
Frequency Representation ◽  
Stationary Signals ◽  
Short Time

Electrocardiogram (ECG) signal plays an imperative role in monitoring and examining the health condition of the heart. ECG signal represents the electrical activity of the heat. The most consequential noises that degrade important features in ECG signal are powerline interference noise, external electromagnetic field interference noise, baseline wandering and electroencephalogram noise. The features of ECG signal obtained in time domain is not sufficient for analyzing the ECG signal. As the signal is non-stationary, the time-frequency representation can be used for feature extraction. The Short Time Fourier Transform can be used but its time frequency precision is not optimal. In this current project, we will be able to implement the ideology proposed to overcome the problem among various time frequency transformation. The discrete wavelet transform (DWT) is used which gives effective results for non-stationary signals like ECG signal which may be often contaminated. The combination of Savitzky-Golay filtering and DWT can be used for ECG denoising and feature extraction which has the advantage of preserving the important feature by elimination the noise components. The method is applied for the database which is taken from MIT- BIH arrhythmia and the algorithm is implemented in MATLAB platform.

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 Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid
Keyword(s):  
Fourier Transform ◽  
Guided Waves ◽  
Strain Level ◽  
Energy Concentration ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Fourier Transform ◽  
Short Time ◽  
Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.

Evaluation of potential auras in generalized epilepsy from EEG signals using deep convolutional neural networks and time-frequency representation

2020 ◽  
Vol 65 (4) ◽  
pp. 379-391 ◽  
Author(s):  
Hasan Polat ◽  
Mehmet Ufuk Aluçlu ◽  
Mehmet Siraç Özerdem
Keyword(s):  
Eeg Signals ◽  
Deep Convolutional Neural Networks ◽  
One Dimensional ◽  
Time Frequency ◽  
Specificity And Sensitivity ◽  
Frequency Representation ◽  
Proposed Model ◽  
Generalized Epilepsy ◽  
Short Time

AbstractThe general uncertainty of epilepsy and its unpredictable seizures often affect badly the quality of life of people exposed to this disease. There are patients who can be considered fortunate in terms of prediction of any seizures. These are patients with epileptic auras. In this study, it was aimed to evaluate pre-seizure warning symptoms of the electroencephalography (EEG) signals by a convolutional neural network (CNN) inspired by the epileptic auras defined in the medical field. In this context, one-dimensional EEG signals were transformed into a spectrogram display form in the frequency-time domain by applying a short-time Fourier transform (STFT). Systemic changes in pre-epileptic seizure have been described by applying the CNN approach to the EEG signals represented in the image form, and the subjective EEG-Aura process has been tried to be determined for each patient. Considering all patients included in the evaluation, it was determined that the 1-min interval covering the time from the second minute to the third minute before the seizure had the highest mean and the lowest variance to determine the systematic changes before the seizure. Thus, the highest performing process is described as EEG-Aura. The average success for the EEG-Aura process was 90.38 ± 6.28%, 89.78 ± 8.34% and 90.47 ± 5.95% for accuracy, specificity and sensitivity, respectively. Through the proposed model, epilepsy patients who do not respond to medical treatment methods are expected to maintain their lives in a more comfortable and integrated way.

The Gearbox Fault Detection of Machine Center by Using Time Frequency Order Spectrum

2012 ◽  
Vol 452-453 ◽  
pp. 1329-1333 ◽  
Author(s):  
C.C. Wang ◽  
Y. Kang ◽  
Y.L. Chung
Keyword(s):  
Fault Detection ◽  
Back Propagation ◽  
Machine Center ◽  
Rotor Systems ◽  
Time Frequency ◽  
Order Spectrum ◽  
Signal Features ◽  
Stationary Signals ◽  
Short Time ◽  
Frequency Features

Previously, for the case of fixed or steady state rotation rate, spectrum analysis can be used to extract the frequency features as the basis for the gearbox fault detection of machine center. However, the gearbox of machine center for increasingly instant speed variations mostly generate non-stationary signals, and the signal features must be averaged with analysis time which makes it difficult to identify the causes of failures. This study proposes a time frequency order spectrum method combining the short-time Fourier transform (STFT) and speed frequency order method to capture the order features of non-stationary signals. Such signal features do not change with speed, and are thus effective in identifying faults in mechanical components under non-stationary conditions. In this study, back propagation neural networks (BPNN) and time frequency order spectrum methods were used to verify faults diagnosis and obtained superior diagnosis results in non-stationary signals of gear-rotor systems in machine center.

scholarly journals A Study of Parameters Setting of the STADZT

10.14311/1654 ◽  
2012 ◽  
Vol 52 (5) ◽  
Author(s):  
Václav Turoň
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Frequency Resolution ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Stationary Signals ◽  
Zolotarev Polynomials ◽  
Non Stationary Signal ◽  
Short Time ◽  
Main Influence

This paper deals with the new time-frequency Short-Time Approximated Discrete Zolotarev Transform (STADZT), which is based on symmetrical Zolotarev polynomials. Due to the special properties of these polynomials, STADZT can be used for spectral analysis of stationary and non-stationary signals with the better time and frequency resolution than the widely used Short-Time Fourier Transform (STFT). This paper describes the parameters of STADZT that have the main influence on its properties and behaviour. The selected parameters include the shape and length of the segmentation window, and the segmentation overlap. Because STADZT is very similar to STFT, the paper includes a comparison of the spectral analysis of a non-stationary signal created by STADZT and by STFT with various settings of the parameters.

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 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.

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