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Lookup NU author(s): Professor Gui Yun TianORCiD, Professor Bin Gao
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2018.
For re-use rights please refer to the publisher's terms and conditions.
IEEE The use of UHF RFID passive tags for defect detection is a promising application in structural health monitoring. However, it’s a challenging task while most related information to tag antenna design is not available as well it suffers from the interference effect on wireless measurements. In this article, we investigated and developed a new technique for crack depth sensing by using a passive UHF RFID tag as a sensor which interrogated by thingmagic M6e platform. Wireless power transfer WPT level and the frequency sweeping are used to match between tag impedance and metal induction effect. The distance between the tag and reader is adjusted at 30cm which can achieve high quality factor. As a result, the tag backscatter signal become rich with maximum peak components. The proposed technique called power peaks feature extraction (PPFE) which is used to detect the artificial crack depth on the surface of the stainless steel and ferromagnetic samples. Skewness is applied on PPFE to offer a direct approximation procedure for the crack depth. A linear relationship of skewness achieves high accuracy result with a maximum estimation error of 0.1 mm for stainless steel sample, the technique is validated and compared with the frequency domain result, and it achieves all most the same accuracy for the stainless steel sample.
Author(s): Omer M, Tian GY, Gao B, Su D
Publication type: Article
Publication status: Published
Journal: IEEE Sensors Journal
Year: 2018
Volume: 18
Issue: 23
Pages: 9867 - 9873
Print publication date: 01/12/2018
Online publication date: 26/09/2018
Acceptance date: 02/04/2018
Date deposited: 15/10/2018
ISSN (print): 1530-437X
ISSN (electronic): 1558-1748
Publisher: IEEE
URL: https://doi.org/10.1109/JSEN.2018.2872174
DOI: 10.1109/JSEN.2018.2872174
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