# Evaluation of Measurement Strategy for Track Side Monitoring of Railway Wheels
## 1 Introduction  
Rail transport has emerged as a significant and sustainable mode of transportation, forming a key enabler of the socioeconomic development of modern society through passenger and freight services. To reduce the carbon emission arising from the transport sector, the European commission aims to shift 50% of medium-distance passenger and freight transports from road transportation to railway and waterborne transport by 2050 [1]. In Sweden, the use of rail transport has increased significantly over the last 26 years, with passenger-kilometer doubled and a 12% increase in freight traffic [2]. An average annual growth of 3% of passenger traffic and 1% of freight traffic is estimated in Sweden up to the year 2050 [3, 4]. This growing demand has put a certain strain on the Swedish railway network in terms of operational capacity and service quality. With the rapid development of railway transportation, maintenance and renewal process have become the important factors to ensure a high quality of service (i.e., punctuality, comfort, and safety) [5]. The wheel-rail interface plays a crucial role in the performance of the railway system, and to ensure a high quality of service it needs to be managed well and treated as a system [5, 6]. In addition to the rail and wheel, other factors such as track curvature, wheel and rail profiles, friction, and the status of the track and wheel themselves have significant influence on the wheel-rail interface [7]. In Sweden, the track and its components are measured on a regular basis according to internal regulations of the Swedish Transport Administration [3, 6, 8], and hence infrastructure managers usually have good knowledgeof rail status. However, an infrastructure manager's knowledge of the wheel in service is limited and has become an issue of great relevance in Sweden, as the ownership of the infrastructure and rolling stock is divided. The wheelset is one of the most important and costly on-maintenance components [9] in the rolling stock, and the health status of the same can be related to operation safety, stability, and noise emission [10]. Wheels are subject to numerous defects that influence their smooth revolutions, and these defects can be broadly classified into four categories: surface defects (flatness, spalling, shelling), profile defects, polygonization (corrugation, roughness, etc. ), and subsurface defects [11; 12; 13; 14]. The presence of such defects on the wheel can also lead to high-impact forces in the wheel-rail interface, which can subsequently induce damage on the rail and other track components. Further, the presence of such damages can reduce the service life of a wheel, leading to accelerated deterioration and excessive costs. The presence of such damages on wheels can lead to unplanned maintenance activities that are a major source of train delays [15]. Thus, early detection and prediction of wheel defects are essential to reduce systemwide maintenance cost, reduce delays due to unplanned maintenance activities, and ensure safe train operations [16]. The condition assessment of a dynamic system such as a railway wheel can be carried out using various methods: statistical modelling, physical modelling, or condition monitoring. For an in-service train with multiple wheels, the wheel fatigue parameters and wheel wear, and consequently the degradation rate, depend on various factors including varying environmental and operational conditions (speed, axle load, rail profile, etc.). Further, the wheel-rail interaction and the degradation pattern vary between the right and left wheels of an axle, the back and front axles in a bogie, and from bogie to bogie [17; 18; 19]. These varying factors hinder the use of numerical, statistical, and analytical models for condition assessment of an in-service wheel. Accordingly, condition monitoring techniques can be considered as the best plausible tool for condition assessment for an in-service wheel. An imperative step in any condition monitoring process is data acquisition.