Print Email Facebook Twitter A big data analysis approach for rail failure risk assessment Title A big data analysis approach for rail failure risk assessment Author Jamshidi, A. (TU Delft Railway Engineering) Faghih Roohi, S. (TU Delft Team Bart De Schutter) Hajizadeh, S. (TU Delft Team Bart De Schutter) Nunez, Alfredo (TU Delft Railway Engineering) Babuska, R. (TU Delft Learning & Autonomous Control) Dollevoet, R.P.B.J. (TU Delft Railway Engineering) Li, Z. (TU Delft Railway Engineering) De Schutter, B.H.K. (TU Delft Team Bart De Schutter) Date 2017 Abstract Railway infrastructure monitoring is a vital task to ensure rail transportation safety. A rail failure could result in not only a considerable impact on train delays and maintenance costs, but also on safety of passengers. In this article, the aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that are detected automatically among the huge number of records from video cameras. We propose an image processing approach for automatic detection of squats, especially severe types that are prone to rail breaks. We measure the visual length of the squats and use them to model the failure risk. For the assessment of the rail failure risk, we estimate the probability of rail failure based on the growth of squats. Moreover, we perform severity and crack growth analyses to consider the impact of rail traffic loads on defects in three different growth scenarios. The failure risk estimations are provided for several samples of squats with different crack growth lengths on a busy rail track of the Dutch railway network. The results illustrate the practicality and efficiency of the proposed approach. Subject Big data analysisRail failure riskRail surface defects To reference this document use: http://resolver.tudelft.nl/uuid:fffde912-a3ab-4f95-a30c-1b9d31200a1c DOI https://doi.org/10.1111/risa.12836 ISSN 0272-4332 Source Risk Analysis: an international journal, 37 (8), 1495–1507 Part of collection Institutional Repository Document type journal article Rights © 2017 A. Jamshidi, S. Faghih Roohi, S. Hajizadeh, Alfredo Nunez, R. Babuska, R.P.B.J. Dollevoet, Z. Li, B.H.K. De Schutter Files PDF Jamshidi_et_al_2017_Risk_ ... alysis.pdf 1.12 MB Close viewer /islandora/object/uuid:fffde912-a3ab-4f95-a30c-1b9d31200a1c/datastream/OBJ/view