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Sensors 2017, 17(10), 2276; https://doi.org/10.3390/s17102276

Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data

Centre for Autonomous Systems (CB 11.09.300), Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia
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Received: 31 July 2017 / Revised: 22 September 2017 / Accepted: 28 September 2017 / Published: 6 October 2017
(This article belongs to the Special Issue Magnetic Sensors and Their Applications)
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Abstract

Remote-Field Eddy-Current (RFEC) technology is often used as a Non-Destructive Evaluation (NDE) method to prevent water pipe failures. By analyzing the RFEC data, it is possible to quantify the corrosion present in pipes. Quantifying the corrosion involves detecting defects and extracting their depth and shape. For large sections of pipelines, this can be extremely time-consuming if performed manually. Automated approaches are therefore well motivated. In this article, we propose an automated framework to locate and segment defects in individual pipe segments, starting from raw RFEC measurements taken over large pipelines. The framework relies on a novel feature to robustly detect these defects and a segmentation algorithm applied to the deconvolved RFEC signal. The framework is evaluated using both simulated and real datasets, demonstrating its ability to efficiently segment the shape of corrosion defects. View Full-Text
Keywords: Remote Field Eddy Current (RFEC); Non-Destructive Evaluation (NDE); defect segmentation; active-contour Remote Field Eddy Current (RFEC); Non-Destructive Evaluation (NDE); defect segmentation; active-contour
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Falque, R.; Vidal-Calleja, T.; Miro, J.V. Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data. Sensors 2017, 17, 2276.

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