Smart data for sustainable Railway Asset Management: railway track: assessment - aggregation - asset management

Research output: Book/ReportBookResearchpeer-review

Abstract

Cost pressure forces infrastructure managers to work sustainably and efficiently. There-fore, track engineers face increasing difficulty to carry out necessary measures owing to budget restrictions. Consequently, they should be supported in prioritising. This requires an objective tool enabling proper condition monitoring as well as component-specific, preventive maintenance and renewal planning. Hence, the right measures are to be executed at the right time. This dissertation deals with a description of the railway track condition. A bottom-up approach provides an in-depth assessment of track using a variety of measurement signals and an aggregated component-specific assessment. Since the approach is based on well positioned measurement signals, it is valid for monitoring specific track sections as well as whole networks. Innovative analyses of various measurement signals form a sound basis to grasp their characteristics enabling a component specific condition evaluation of railway track. The use of historical measurement data allows for an analysis of track behaviour over time. A thorough validation process, including on-site inspections and excavations, shows that the presented approach is able to evaluate the actual condition of railway track. The assessment of the specific components condition can be used for timely maintenance as well as renewal planning. Based on correlation analyses, the component specific evaluations are aggregated into one holistic quality figure. This enables asset managers to monitor the asset condition network-wide as well as to predict future budget demands.
Original languageEnglish
Place of PublicationGraz
PublisherVerlag der Technischen Universität Graz
Number of pages138
ISBN (Print)978-3-85125-569-0
DOIs
Publication statusPublished - Sep 2018

Publication series

NameMonographic Series TU Graz : Railway Research
Volume3

Cite this

Landgraf, M. (2018). Smart data for sustainable Railway Asset Management: railway track: assessment - aggregation - asset management . (Monographic Series TU Graz : Railway Research; Vol. 3). Graz: Verlag der Technischen Universität Graz. https://doi.org/10.3217/978-3-85125-569-0

Smart data for sustainable Railway Asset Management : railway track: assessment - aggregation - asset management . / Landgraf, Matthias.

Graz : Verlag der Technischen Universität Graz, 2018. 138 p. (Monographic Series TU Graz : Railway Research; Vol. 3).

Research output: Book/ReportBookResearchpeer-review

Landgraf, M 2018, Smart data for sustainable Railway Asset Management: railway track: assessment - aggregation - asset management . Monographic Series TU Graz : Railway Research, vol. 3, Verlag der Technischen Universität Graz, Graz. https://doi.org/10.3217/978-3-85125-569-0
Landgraf M. Smart data for sustainable Railway Asset Management: railway track: assessment - aggregation - asset management . Graz: Verlag der Technischen Universität Graz, 2018. 138 p. (Monographic Series TU Graz : Railway Research). https://doi.org/10.3217/978-3-85125-569-0
Landgraf, Matthias. / Smart data for sustainable Railway Asset Management : railway track: assessment - aggregation - asset management . Graz : Verlag der Technischen Universität Graz, 2018. 138 p. (Monographic Series TU Graz : Railway Research).
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abstract = "Cost pressure forces infrastructure managers to work sustainably and efficiently. There-fore, track engineers face increasing difficulty to carry out necessary measures owing to budget restrictions. Consequently, they should be supported in prioritising. This requires an objective tool enabling proper condition monitoring as well as component-specific, preventive maintenance and renewal planning. Hence, the right measures are to be executed at the right time. This dissertation deals with a description of the railway track condition. A bottom-up approach provides an in-depth assessment of track using a variety of measurement signals and an aggregated component-specific assessment. Since the approach is based on well positioned measurement signals, it is valid for monitoring specific track sections as well as whole networks. Innovative analyses of various measurement signals form a sound basis to grasp their characteristics enabling a component specific condition evaluation of railway track. The use of historical measurement data allows for an analysis of track behaviour over time. A thorough validation process, including on-site inspections and excavations, shows that the presented approach is able to evaluate the actual condition of railway track. The assessment of the specific components condition can be used for timely maintenance as well as renewal planning. Based on correlation analyses, the component specific evaluations are aggregated into one holistic quality figure. This enables asset managers to monitor the asset condition network-wide as well as to predict future budget demands.",
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