Details

Autor(en) / Beteiligte

• Jamshidi, Ali
• Hajizadeh, Siamak
• Su, Zhou
• Naeimi, Meysam
• Núñez, Alfredo
• Dollevoet, Rolf
• De Schutter, Bart
• Li, Zili

Titel

A decision support approach for condition-based maintenance of rails based on big data analysis

Ist Teil von

• Transportation research. Part C, Emerging technologies, 2018-10, Vol.95, p.185-206

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2018

Link zum Volltext

Quelle

ScienceDirect Journals (5 years ago - present)

Beschreibungen/Notizen

• •A decision support approach is proposed for condition-based maintenance of rails.•The methodology uses big data analysis and expert systems.•ABA measurements, video images and prior knowledge of the track are employed.•Integrated estimation of the rail health condition supports maintenance decisions.•Measurements from the track Amersfoort-Weert in the Dutch railway network are used. In this paper, a decision support approach is proposed for condition-based maintenance of rails relying on expert-based systems. The methodology takes into account both the actual conditions of the rails (using axle box acceleration measurements and rail video images) and the prior knowledge of the railway track. The approach provides an integrated estimation of the rail health conditions to support the maintenance decisions for a given time period. An expert-based system is defined to analyse interdependency between the prior knowledge of the track (defined by influential factors) and the surface defect measurements over the rail. When the rail health conditions is computed, the different track segments are prioritized, in order to facilitate grinding planning of those segments of rail that are prone to critical conditions. In this paper, real-life rail conditions measurements from the track Amersfoort-Weert in the Dutch railway network are used to show the benefits of the proposed methodology. The results support infrastructure managers to analyse the problems in their rail infrastructure and to efficiently perform a condition-based maintenance decision making.