Super-resolution spectral analysis and signal reconstruction of magnetic Barkhausen noise

Luo, Xiaoyu; Wang, Yilin; Zhu, Bo; Zhang, Yisheng

doi:10.1016/j.ndteint.2014.12.002

Cited by 15 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, in this paper MBN properties expressed in both the time and frequency domains, which allows us also to observe the dynamics of the signal changes, were considered. Time-frequency transformation methods have found several applications for Barkhausen signal analysis in various applications until now [32][33][34][35][36][37]. However, to the authors' best knowledge, the TF methods have not been used yet to analyse the Barkhausen effect for the needs of magnetic anisotropy assessment.…”

Section: Introductionmentioning

confidence: 99%

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

Maciusowicz

Psuj

2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Maciusowicz

Psuj

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Consequently, these obstacles arising in the material require additional energy and cause discontinuous changes of the magnetic state. This phenomenon has been studied and gradually used for non-destructive testing (NDT) of materials [4,5,6,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33]. The stepwise change of the magnetization level during operation of the alternating magnetizing field can be observed by coil placed in the neighbourhood of the material.…”

Section: Introductionmentioning

confidence: 99%

“…In such cases, the traditional methods of frequency analysis are not adequate while they generalize the dynamics of non-stationary signal over whole considered period of time. Thus, the combined TF domain analysis can provide one with complementary information and should also be carried out [12,15,16,21,22,28,30,41,42].…”

Section: Introductionmentioning

confidence: 99%

“…In [28] TF representation was used for qualitative assessment of the time-frequency distributions and observation of frequency bandwidth at low and high levels of magnetizing field. The application of TF transformation to signal conditioning and reconstruction for the needs of stress and hardness evaluation was presented in [30]. The BN signal was decomposed into the TF domain where it was modified and then reconstructed resulting in reduction of background signal level.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Maciusowicz

Psuj

2019

Sensors

View full text Add to dashboard Cite

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.

show abstract

Micromagnetic Automatic Detection Method for Mechanical Properties of Automobile B-Pillar

Ning,

He,

Liu

et al. 2023

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

Super-resolution spectral analysis and signal reconstruction of magnetic Barkhausen noise

Cited by 15 publications

References 23 publications

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

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

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

Micromagnetic Automatic Detection Method for Mechanical Properties of Automobile B-Pillar

Contact Info

Product

Resources

About