Detecting internal defects of a steel plate by using low-frequency magnetic flux leakage method

Nannan Song, Yuta Haga, Tsuyoshi Goda, Kenji Sakai, Toshihiko Kiwa, Keiji Tsukada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Steel is an important material in modern society. It is widely used in our lives, such as in buildings, bridges, and airplanes. We previously reported an analysis method for examining the internal defects of metal structures with eddy current testing (ECT) over a wide range of frequencies. However, internal defects in steel plates are difficult to detect using ECT. In this paper, we present a low-frequency magnetic flux leakage (MFL) method for the nondestructive evaluation of internal defects in steel structures, and an analysis and imaging method that uses the phase of a magnetic field to examine internal defects. By analyzing the phase change in the detected signal, the internal defects can be detected with no magnetic fluctuation. The phase map of the measured magnetic field exhibits good correlation with the simulation results. The two-dimensional magnetic phase map can be used to visualize the internal defects. This new method has been validated through both experiment and simulation.

Original languageEnglish
Title of host publicationSAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509032020
DOIs
Publication statusPublished - Apr 6 2017
Event12th IEEE Sensors Applications Symposium, SAS 2017 - Glassboro, United States
Duration: Mar 13 2017Mar 15 2017

Other

Other12th IEEE Sensors Applications Symposium, SAS 2017
CountryUnited States
CityGlassboro
Period3/13/173/15/17

Fingerprint

Steel
Magnetic flux
magnetic flux
leakage
steels
low frequencies
Defects
defects
Magnetic Fields
Eddy current testing
eddy currents
Aircraft
Magnetic fields
steel structures
Metals
Steel structures
magnetic fields
simulation
Imaging techniques
evaluation

Keywords

  • internal defects
  • low-frequency magnetic field
  • Magnetic flux leakage method
  • steel plate

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering
  • Health Informatics

Cite this

Song, N., Haga, Y., Goda, T., Sakai, K., Kiwa, T., & Tsukada, K. (2017). Detecting internal defects of a steel plate by using low-frequency magnetic flux leakage method. In SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings [7894069] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SAS.2017.7894069

Detecting internal defects of a steel plate by using low-frequency magnetic flux leakage method. / Song, Nannan; Haga, Yuta; Goda, Tsuyoshi; Sakai, Kenji; Kiwa, Toshihiko; Tsukada, Keiji.

SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 7894069.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Song, N, Haga, Y, Goda, T, Sakai, K, Kiwa, T & Tsukada, K 2017, Detecting internal defects of a steel plate by using low-frequency magnetic flux leakage method. in SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings., 7894069, Institute of Electrical and Electronics Engineers Inc., 12th IEEE Sensors Applications Symposium, SAS 2017, Glassboro, United States, 3/13/17. https://doi.org/10.1109/SAS.2017.7894069
Song N, Haga Y, Goda T, Sakai K, Kiwa T, Tsukada K. Detecting internal defects of a steel plate by using low-frequency magnetic flux leakage method. In SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. 7894069 https://doi.org/10.1109/SAS.2017.7894069
Song, Nannan ; Haga, Yuta ; Goda, Tsuyoshi ; Sakai, Kenji ; Kiwa, Toshihiko ; Tsukada, Keiji. / Detecting internal defects of a steel plate by using low-frequency magnetic flux leakage method. SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017.
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