Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data
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 dept...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-10-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/10/2276 |
| _version_ | 1818002071781113856 |
|---|---|
| author | Raphael Falque Teresa Vidal-Calleja Jaime Valls Miro |
| author_facet | Raphael Falque Teresa Vidal-Calleja Jaime Valls Miro |
| author_sort | Raphael Falque |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T03:42:08Z |
| format | Article |
| id | doaj.art-3d04556e92b94b4082f5043f8939d943 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T03:42:08Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3d04556e92b94b4082f5043f8939d9432022-12-22T02:14:29ZengMDPI AGSensors1424-82202017-10-011710227610.3390/s17102276s17102276Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor DataRaphael Falque0Teresa Vidal-Calleja1Jaime Valls Miro2Centre for Autonomous Systems (CB 11.09.300), Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, AustraliaCentre for Autonomous Systems (CB 11.09.300), Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, AustraliaCentre for Autonomous Systems (CB 11.09.300), Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, AustraliaRemote-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.https://www.mdpi.com/1424-8220/17/10/2276Remote Field Eddy Current (RFEC)Non-Destructive Evaluation (NDE)defect segmentationactive-contour |
| spellingShingle | Raphael Falque Teresa Vidal-Calleja Jaime Valls Miro Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data Sensors Remote Field Eddy Current (RFEC) Non-Destructive Evaluation (NDE) defect segmentation active-contour |
| title | Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data |
| title_full | Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data |
| title_fullStr | Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data |
| title_full_unstemmed | Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data |
| title_short | Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data |
| title_sort | defect detection and segmentation framework for remote field eddy current sensor data |
| topic | Remote Field Eddy Current (RFEC) Non-Destructive Evaluation (NDE) defect segmentation active-contour |
| url | https://www.mdpi.com/1424-8220/17/10/2276 |
| work_keys_str_mv | AT raphaelfalque defectdetectionandsegmentationframeworkforremotefieldeddycurrentsensordata AT teresavidalcalleja defectdetectionandsegmentationframeworkforremotefieldeddycurrentsensordata AT jaimevallsmiro defectdetectionandsegmentationframeworkforremotefieldeddycurrentsensordata |