Advanced Condition Assessment and Pipe Failure Prediction ProjectAdvanced Condition Assessment and Pipe Failure Prediction Project
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GAUSSIAN PROCESS FOR INTERPRETING PULSED EDDY CURRENT SIGNALS FOR FERROMAGNETIC PIPE PROFILING

by Nalika Ulapane∗, Alen Alempijevic∗, Teresa Vidal-Calleja∗, Jaime Valls Miro∗, Jeremy Rudd†, Martin Roubal†

∗Centre for Autonomous Systems, University of Technology Sydney, Australia
† Rock Solid Group, Melbourne, Australia

Conference:

The 9th IEEE Conference on Industrial Electronics and Applications (ICIEA 2014)

Date of Conference:
9 – 11 June 2014

Page(s):
1762 – 1767

Conference Location :
Hangzhou, China

Key words:
ferromagnetic, Gaussian process, machine learning, non-destructive testing, pulsed Eddy current, sensor model

Abstract

This paper describes a Gaussian Process based machine learning technique to estimate the remaining volume of cast iron in ageing water pipes. The method utilizes time domain signals produced by a commercially available pulsed Eddy current sensor. Data produced by the sensor are used to train a Gaussian Process model and perform inference of the remaining metal volume. The Gaussian Process model was learned using sensor data obtained from cast iron calibration plates of various thicknesses. Results produced by the Gaussian Process model were validated against the remaining wall thickness acquired using a high resolution laser scanner after the pipes were sandblasted to remove corrosion. The evaluation shows agreement between model outputs and ground truth. The paper concludes by discussing the implications or results and how the proposed method can potentially advance the current technological setup by facilitating real time pipe profiling.

Click here to download the Paper

Information about Pipes

In August 2011 international water research organisations, Australian water utilities and three Australian universities came together through a collaborative research agreement, and committed overall funding of $16 million (including $4 million cash) over five years to undertake this research through the Advanced Condition Assessment and Pipe Failure Prediction Project.

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Recent posts

  • The final meeting of the Committee of Management

    December 6, 2016

  • Final Technical Assessment Committee meeting

    November 24, 2016

  • Critical Pipes Project wins B/HERT award

    November 16, 2016

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