Gaussian process for interpreting pulsed eddy current signals for ferromagnetic pipe profiling

Ulapane, N; Alempijevic, A; Vidal-Calleja, T; Miro, JV; Rudd, J; Roubal, M

Gaussian process for interpreting pulsed eddy current signals for ferromagnetic pipe profiling

Ulapane, N

Alempijevic, A

Vidal-Calleja, T

Miro, JV

Rudd, J Roubal, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014, 2014, pp. 1762 - 1767
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Ulapane, N https://orcid.org/0000-0003-3432-3943	en_US
dc.contributor.author	Alempijevic, A https://orcid.org/0000-0002-1769-8041	en_US
dc.contributor.author	Vidal-Calleja, T https://orcid.org/0000-0002-5763-9644	en_US
dc.contributor.author	Miro, JV https://orcid.org/0000-0002-0083-7797	en_US
dc.contributor.author	Rudd, J	en_US
dc.contributor.author	Roubal, M	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014, 2014, pp. 1762 - 1767	en_US
dc.identifier.isbn	9781479943166	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30536
dc.description.abstract	© 2014 IEEE. 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.	en_US
dc.relation.ispartof	Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014	en_US
dc.relation.isbasedon	10.1109/ICIEA.2014.6931453	en_US
dc.title	Gaussian process for interpreting pulsed eddy current signals for ferromagnetic pipe profiling	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. 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.

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http://hdl.handle.net/10453/30536