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

Raphael Falque1, Teresa Vidal-Calleja2, Jaime Valls Miro3

  • 1Centre for Autonomous Systems (CB 11.09.300), Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia. Raphael.H.Guenot-Falque@student.uts.edu.au.

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Summary
This summary is machine-generated.

This study introduces an automated framework for detecting and quantifying pipe corrosion using Remote-Field Eddy-Current (RFEC) technology. The method efficiently segments defect shapes from large pipeline data, improving non-destructive evaluation (NDE).

Keywords:
Non-Destructive Evaluation (NDE)Remote Field Eddy Current (RFEC)active-contourdefect segmentation

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Area of Science:

  • Materials Science
  • Non-Destructive Evaluation (NDE)
  • Signal Processing

Background:

  • Remote-Field Eddy-Current (RFEC) technology is crucial for non-destructive evaluation (NDE) of water pipes.
  • Quantifying pipe corrosion requires accurate defect detection, depth, and shape analysis.
  • Manual analysis of RFEC data for large pipelines is time-consuming and inefficient.

Purpose of the Study:

  • To develop an automated framework for locating and segmenting corrosion defects in pipe segments.
  • To improve the efficiency and accuracy of NDE for large-scale pipeline inspections.
  • To enable robust defect quantification from raw RFEC measurements.

Main Methods:

  • Proposed an automated framework utilizing a novel feature for robust defect detection.
  • Applied a segmentation algorithm to the deconvolved RFEC signal for precise defect outlining.
  • Validated the framework using both simulated and real-world pipeline datasets.

Main Results:

  • Demonstrated the framework's capability to efficiently locate and segment corrosion defects.
  • Successfully quantified defect shapes from RFEC data.
  • Achieved robust defect detection using the novel feature and segmentation approach.

Conclusions:

  • The automated framework significantly enhances the efficiency of NDE for pipeline corrosion.
  • The proposed method provides accurate segmentation of defect shapes, aiding in corrosion quantification.
  • This approach offers a scalable solution for inspecting large pipeline networks.