Print Email Facebook Twitter Multiple-axle box acceleration measurements at railway transition zones Title Multiple-axle box acceleration measurements at railway transition zones Author Unsiwilai, S. (TU Delft Railway Engineering) Wang, L. (TU Delft Railway Engineering) Nunez, Alfredo (TU Delft Railway Engineering) Li, Z. (TU Delft Railway Engineering) Date 2023 Abstract This paper presents a methodology for monitoring transition zones using responses of multiple-axle box acceleration (multi-ABA) measurements. The time–frequency characteristics of the vertical ABA signals from four wheelsets are analyzed. The major contributions are as follows. (1) We propose four key performance indicators (KPIs) to quantify local multi-ABA energy differences at different abutments, tracks, entrance and exit sides, and inner and outer rails. (2) The same dominant spatial frequencies are obtained with different measurement speeds, so the proposed method is suitable for multi-ABA systems mounted on operational trains. Transition zones at nine double-track railway bridges are selected as the case study. The KPIs indicate that (1) the energy differences between abutments are above 80% in three bridges; (2) two abutments show that the energy differences between tracks are higher than 100%; (3) three tracks have energy differences above 100% between the entrance and exit sides; and (4) the energy differences between rails are above 80% on three sides. Finally, using measurement with 7 years of difference, the KPIs and track quality index are discussed. These findings suggest that multi-ABA measurement can be used as a health condition monitoring method for railway transition zones to support condition-based maintenance. Subject Transition zonesRailway infrastructureAxle box accelerationTime-frequency analysisOnboard measurement To reference this document use: http://resolver.tudelft.nl/uuid:180d19a8-61c6-4652-8331-0f44941625cb DOI https://doi.org/10.1016/j.measurement.2023.112688 ISSN 0263-2241 Source Measurement, 213 Part of collection Institutional Repository Document type journal article Rights © 2023 S. Unsiwilai, L. Wang, Alfredo Nunez, Z. Li Files PDF 1_s2.0_S026322412300252X_main.pdf 25.74 MB Close viewer /islandora/object/uuid:180d19a8-61c6-4652-8331-0f44941625cb/datastream/OBJ/view