Automated machine learning techniques in prognostics of railway track defects

Kocbek, S; Gabrys, B

Automated machine learning techniques in prognostics of railway track defects

Kocbek, S Gabrys, B

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Data Mining Workshops, ICDMW, 2020, 2019-November, pp. 777-784
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Kocbek, S
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.date	2019-11-08
dc.date.accessioned	2021-04-14T19:41:51Z
dc.date.available	2021-04-14T19:41:51Z
dc.date.issued	2020
dc.identifier.citation	IEEE International Conference on Data Mining Workshops, ICDMW, 2020, 2019-November, pp. 777-784
dc.identifier.isbn	9781728146034
dc.identifier.issn	2375-9232
dc.identifier.issn	2375-9259
dc.identifier.uri	http://hdl.handle.net/10453/148104
dc.description.abstract	© 2019 IEEE. The readiness and usefulness of Automated Machine Learning (AutoML) methods in classification of railway track defects is explored. Safety of railway networks is the top priority in the railroad industry, and track defects are a common cause of train accidents and service disruptions around the world. Effective classification and prediction of these defects based on historical inspection data can help in planning maintenance activities before critical defects occur. This increases safety of the network and lowers costs of the maintenance. The experimental analysis carried out on data from an international predictive modelling competition has shown that the proposed AutoML approaches resulted in an improved performance in comparison to the competition winning solutions and have an excellent potential for building robust predictive models in railway industry.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE International Conference on Data Mining Workshops, ICDMW
dc.relation.ispartof	2019 International Conference on Data Mining Workshops (ICDMW)
dc.relation.isbasedon	10.1109/ICDMW.2019.00115
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Automated machine learning techniques in prognostics of railway track defects
dc.type	Conference Proceeding
utslib.citation.volume	2019-November
utslib.location.activity	China
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/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-14T19:41:49Z
pubs.finish-date	2019-11-11
pubs.publication-status	Published
pubs.start-date	2019-11-08
pubs.volume	2019-November

Abstract:

© 2019 IEEE. The readiness and usefulness of Automated Machine Learning (AutoML) methods in classification of railway track defects is explored. Safety of railway networks is the top priority in the railroad industry, and track defects are a common cause of train accidents and service disruptions around the world. Effective classification and prediction of these defects based on historical inspection data can help in planning maintenance activities before critical defects occur. This increases safety of the network and lowers costs of the maintenance. The experimental analysis carried out on data from an international predictive modelling competition has shown that the proposed AutoML approaches resulted in an improved performance in comparison to the competition winning solutions and have an excellent potential for building robust predictive models in railway industry.

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http://hdl.handle.net/10453/148104