Print Email Facebook Twitter Robust and predictive fuzzy key performance indicators for condition-based treatment of squats in railway infrastructures Title Robust and predictive fuzzy key performance indicators for condition-based treatment of squats in railway infrastructures Author Jamshidi, A. (TU Delft Railway Engineering) Nunez, Alfredo (TU Delft Railway Engineering) Dollevoet, R.P.B.J. (TU Delft Railway Engineering) Li, Z. (TU Delft Railway Engineering) Date 2017-09-01 Abstract This paper presents a condition-based treatment methodology for a type of rail surface defect called squat. The proposed methodology is based on a set of robust and predictive fuzzy key performance indicators. A fuzzy Takagi-Sugeno interval model is used to predict squat evolution for different scenarios over a time horizon. Models including the effects of maintenance to treat squats, via either grinding or replacement of the rail, are also described. A railway track may contain a huge number of squats distributed in the rail surface with different levels of severity. This paper proposes to aggregate the local squat interval models into track-level performance indicators including the number and density of squats per track partition. To facilitate the analysis of the overall condition, a single fuzzy global performance indicator per track partition is proposed based on a fuzzy expert system that combines all the scenarios and predictions over time. The proposed methodology relies on the early detection of squats using axle box acceleration measurements. Real-life measurements from the Meppel-Leeuwarden track in the Dutch railway network are used to show the benefits of the proposed methodology. The use of robust and predictive fuzzy performance indicators facilitates the visualization of the track health condition and eases the maintenance decision process. Subject Interval fuzzy modelsKey performance indicators designRailway track condition monitoring and maintenance To reference this document use: http://resolver.tudelft.nl/uuid:023d4756-29c5-4650-b83f-ef4ad360d405 DOI https://doi.org/10.1061/(ASCE)IS.1943-555X.0000357 ISSN 1076-0342 Source Journal of Infrastructure Systems, 23 (3) Part of collection Institutional Repository Document type journal article Rights © 2017 A. Jamshidi, Alfredo Nunez, R.P.B.J. Dollevoet, Z. Li Files PDF Jamshidi_et_al_JIS.pdf 1.39 MB Close viewer /islandora/object/uuid:023d4756-29c5-4650-b83f-ef4ad360d405/datastream/OBJ/view