An Opening Profile Recognition Method for Magnetic Flux Leakage Signals of Defect.

Saved in:
Bibliographic Details
Title: An Opening Profile Recognition Method for Magnetic Flux Leakage Signals of Defect.
Authors: Huang, Songling1 huangsling@tsinghua.edu.cn, Peng, Lisha1 pls14@mails.tsinghua.edu.cn, Wang, Qing2 qing.wang@durham.ac.uk, Wang, Shen1 wangshen@mail.tsinghua.edu.cn, Zhao, Wei1 zhaowei@mail.tsinghua.edu.cn
Source: IEEE Transactions on Instrumentation & Measurement. Jun2019, Vol. 68 Issue 6, p2229-2236. 8p.
Subjects: Magnetic flux leakage, Right angle, Edge detection (Image processing), Magnetization, Magnetic fields
Abstract: The defect opening profile recognition is of great concern in the magnetic flux leakage (MFL) measurement technique. The detected spatial MFL signal has three components: horizontal, vertical, and normal components. Horizontal and normal component signals are commonly used to estimate the defect profile, while the vertical component has always been neglected. With the development of the high resolution and the 3-D MFL testing techniques, the vertical component signal is becoming more available. This paper analyzes the essential right-angle features of the vertical component signal, which is useful for the defect opening profile recognition. After obtaining the initial profile from the horizontal or normal component, the types of the right angle is identified from the vertical component, and the opening profile is further optimized based on these right-angle features. The opening profile recognition method is put forward in this paper to improve the accuracy of the recognition result of the defect. Both simulation and experimental tests are conducted to verify the good performance of the proposed method. Compared with the opening profiles recognized merely by the horizontal component signal, the proposed method shows better recognition results, which also validates that the vertical component signal can also be a useful information for the defect estimation. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Instrumentation & Measurement is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:The defect opening profile recognition is of great concern in the magnetic flux leakage (MFL) measurement technique. The detected spatial MFL signal has three components: horizontal, vertical, and normal components. Horizontal and normal component signals are commonly used to estimate the defect profile, while the vertical component has always been neglected. With the development of the high resolution and the 3-D MFL testing techniques, the vertical component signal is becoming more available. This paper analyzes the essential right-angle features of the vertical component signal, which is useful for the defect opening profile recognition. After obtaining the initial profile from the horizontal or normal component, the types of the right angle is identified from the vertical component, and the opening profile is further optimized based on these right-angle features. The opening profile recognition method is put forward in this paper to improve the accuracy of the recognition result of the defect. Both simulation and experimental tests are conducted to verify the good performance of the proposed method. Compared with the opening profiles recognized merely by the horizontal component signal, the proposed method shows better recognition results, which also validates that the vertical component signal can also be a useful information for the defect estimation. [ABSTRACT FROM AUTHOR]
ISSN:00189456
DOI:10.1109/TIM.2018.2869438