| Summary: | Due to the increase in demand on freight transportation it becomes necessary to avoid delays to ensure that the goods reach its destination on time. The main factors causing disturbances in the traffic on the mainline is the breakdown of vehicles mainly due to damaged wheels. The damaged wheels are identified through the wheel-rail impact force measurements provided by the wheel impact load detectors (WILD). This calls for the optimal schedule of maintenance of wheelsets and wagons in general. During the maintenance, the officials manually check for defective wheels and the exchange of wheelsets is performed based on the type of damage. The classification of wheel damages plays a vital role in providing ease of damage identification and insights to deduce a strategy for wheelset exchange. In this study, an attempt to classify the damaged wheelsets is done by analysing the wheel-rail impact force data from the wayside detectors. The data from the detectors are acquired from PredgeAB, a Luleå based startup pioneering in providing decision support on optimal maintenance schedules and predictive maintenance of rail wheels. Through their detection and prediction solutions it was observed that of all the wheels marked as damaged by the detectors 10% were undamaged. The source of the deviation in the impact force readings could help Predge make better estimations in damage detection and prediction. In this study, the sources contributing to the deviation is studied using multi-body simulations in GENSYS. A new method for modelling wheel damage is developed to overcome the shortcomings of the software. The findings of this study can then be used appropriately to make classifications of wheel damages. På grund av den ökade efterfrågan på godstransporter är det nödvändigt attundvika förseningar för att säkerställa att varorna når sin destination i tid. Deviktigaste faktorerna som orsakar störningar i trafiken på huvudlinjen är stopp ispår av fordon, främst på grund av skadade hjul. De skadade hjulen identifierasmed ...
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