Research rticle An Index for Rail Weld Health Assessment in Urban Metro Using In-Service Train Morad Shadfar , Habibollah Molatefi , and Asghar Nasr Iran University of Science and Technology, Tehran, Iran Correspondence should be addressed to Habibollah Molatef; molatef@iust.ac.ir Received 8 October 2022; Revised 27 November 2022; Accepted 30 November 2022; Published 27 December 2022 Academic Editor: Madalina Dumitriu Copyright©2022MoradShadfaretal.TisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Rail welds are considered as the weak part of a railway track. Teir defects and health can directly afect wheel•rail interaction, track safety, and reliability. Current practices for rail welds health assessment are based on 2D vertical and lateral wear measurement which needs time and track blocking. Te development of inertia•based condition monitoring methods such as measuring axle box acceleration (ABA) comes with a crucial question on criteria or index for each rail track component health monitoring. In this study, an index for evaluation of rail weld health is proposed through integrated numerical and feld ex• perimentdatawithinametrolineusingtheABAtechnique.Terelationshipbetweenthespeed,wheelstructuralvibration,and acceleration amplitude is investigated using fast Fourier transformation (FFT) and a nonlinear neural network principal componentanalysis(PCA)model.Anindexisintroducedtoassessweldseveritylevelbasedonthestatisticalmethod.Tisindexis simple and applicable for maintenance practice. 1. Introduction Rail track maintenance actions are divided into three cat• egories according to EN13848•5 standard: alert limit (AL), interventionlimit(IL),andimmediateactionlimit(IAL)[1]. Tebasisforidentifyingdefectsanddeterminingthepriority ofeachrepairisdoneusingtrackrecordingvehicles(TRVs) whichimplementdatacollectionatintervalsofabout0.25m. Terefore,defectswithveryshortwavelengths(suchasworn weld, rail spalling, and corrugation) cannot be detected in this way. Most railway operators use visual inspection and nondestructive testing (NDT) methods to identify local defects. Te main challenge in visual inspection is the possibility of human error and inaccurate estimation of the severity of defects. Also, the use of NDT requires track occupation which limits the use of these methods for high• trafc corridors. Current standards forrailweldsinspection are based on measuring vertical and lateral wear which do not consider welds geometry, wheel•rail interaction, and contact forces which is an indication of track deterioration. Using in•service train acceleration for maintenance purposes is introduced to cover the abovementioned challenges. Te accelerometer is mounted on the axle box andbyanalyzingthecollectedaccelerationdata,information can be obtained about the rail defects. Extensive work has been done by Molodova et al. in this area [2–6]. Tey were able to identify squat defects in a railway track through waveletanalysis.Itisalsopossibletodeterminetheboltspre• loadinfshplatesusingthismethod[7].N´ uñezetal.in2018 used the ABA technique to establish a cost•efective in• spection and asset management to minimize maintenance interventiontime/costwithoutdedicatedinspectionvehicles [8].In2018,Boczetal.withthehelpoftheABAandscaled average wavelet power (SAP) studied possibility of the tramway track condition monitoring [9]. In 2021, Cho and Park used the ABA to detect squats in the Korean railway using wavelet spectrum. Tey concluded that the most probable areas for squats formation are rail welds and joint sections [10]. In 2022, Xu et al. with the help of the ABA method estimated rail corrugation in a high•speed track using the energy factor and inverse STFTmethod [11]. Te use of axle box acceleration for condition monitoring of a track has been taken into consideration by many railway’s companies in recent years. Tis method is based on the Hindawi Mathematical Problems in Engineering Volume 2022, Article ID 4911952, 10 pages https://doi.org/10.1155/2022/4911952