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.

Highlights

  • Water pipelines made of ferromagnetic materials are subject to corrosion, which can lead to expensive and dangerous pipe failures

  • To assess the quality of the pipelines, the water industry relies on Non-Destructive Evaluation (NDE) technologies which allows for a cost-effective inspection

  • The first dataset consists of Remote Field Eddy Current (RFEC) sensor measurements artificially obtained using a calibrated direct model applied over a realistically corroded pipe geometry generated by spatial statistics

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Summary

Introduction

Water pipelines made of ferromagnetic materials are subject to corrosion, which can lead to expensive and dangerous pipe failures. The double through wall phenomenon induces a signal convolution, where the changes in the sensor measurements are due to the change in thickness at, mainly, two different areas of the pipe—near the exciter coil, and near the receiver coil This signal convolution— referred to as signal shadowing—is discussed in the literature for tools with different receiver configurations; for axisymmetrical tools with a single receiver coil [8], for multiple receiver coils distributed along the axial direction [9] and for tools with an array of receivers [10].

Proposed Approach
Signal Deconvolution
Localization of Bell and Spigot Joints
Feature Construction
Classifiers Description
Defect Detection
Defect Segmentation
Region Growing
Active Contour Segmentation
Results
Artificial Dataset
Real Dataset
Classifiers Evaluation
K-Fold Cross-Validation
Confusion Matrix and Standard Metrics
ROC Curves
Bell and Spigot Joint Detection
Discussion and Final
Full Text
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