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Maintenance analytics for railway infrastructure decision support
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
2017 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, 310-325 p.Article in journal (Refereed) Published
Abstract [en]

Purpose

This purpose of this article is to present a framework for maintenance analytics that is useful for the assessment of rail condition and for maintenance decision support. The framework covers three essential maintenance aspects: diagnostic, prediction and prescription. The article also presents principal component analysis (PCA) and local outlier factor (LOF) methods for detecting anomalous rail wear occurrences using field measurement data.

Design/methodology/approach

The approach used in this paper includes a review of the concept of analytics and appropriate adaptation to railway infrastructure maintenance. The diagnotics aspect of the proposed framework is demonstrated with a case study using historical rail profile data collected between 2007 and 2016 for 9 sharp curves on the heavy haul line in Sweden.

Findings

The framework presented for maintenance analytics is suitable for extracting useful information from condition data as required for effective rail maintenance decision support. The findings of the case study include: combination of the two statistics from PCA model (T2 and Q) can help to identify systematic and random variations in rail wear pattern that are beyond normal: the visualisation approach is a better tool for anomaly detection as it categorises wear observations into normal, suspicious and anomalous observations.

Practical implications

A practical implication of this article is that the framework and the diagnostic tool can be considered as an integral part of eMaintenance solution. It can be easily adapted as online or onboard maintenance analytic tool with data from automated vehicle based measurement system.

Originality/value

This research adapts the concept of analytics to railway infrastructure maintenance for enhanced decision making. It proposes a graphical method for combining and visualising different outlier statistics as a reliable anomaly detection tool.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2017. Vol. 23, no 3, 310-325 p.
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-65099DOI: 10.1108/JQME-11-2016-0059Scopus ID: 2-s2.0-85027972757OAI: oai:DiVA.org:ltu-65099DiVA: diva2:1133312
Note

Validerad;2017;Nivå 2;2017-08-28 (andbra)

Available from: 2017-08-15 Created: 2017-08-15 Last updated: 2017-09-04Bibliographically approved

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