Grinding is regularly conducted on railway tracks to prevent crack propagation and surface deterioration. However, grinding can introduce roughness on rail surfaces, potentially leading to stress and strain concentration that increase the likelihood of crack initiation. This paper proposes the utilization of surface roughness obtained by replicating the ground rail surface to assess its impact on train wheel-rail interactions. A novel approach which integrates the replicated roughness into an elastoplastic contact model, allows for a detailed assessment of its effects on contact pressure, and residual strain and stress distributions. The findings highlight the importance of considering surface roughness in predictive maintenance planning for railway infrastructure, as it can significantly influence the structural integrity and long-term performance of the track system.

Climate change and its severe impacts pose a number of challenges to transport infrastructure, particularly railway infrastructure, requiring immediate action. A railway system is a linear distributed asset passing different geographical locations and exposed to heterogeneous vulnerabilities under diverse environmental conditions. Furthermore, most of the railway infrastructure assets were designed and built without in-depth analysis of future climate impacts. This paper considers the effects of extreme temperatures on urban railway infrastructure assets, including rail, “switches and crossings”. The data for this study were gathered by exploring various railway infrastructure and meteorological databases over 19 years. In addition, a comprehensive nationwide questionnaire survey of Swedish railway infrastructure, railway maintenance companies, and municipalities has been conducted to assess the risks posed by climate change. A risk and vulnerability assessment framework for railway infrastructure assets is developed. The study shows that track buckling and vegetation fires due to the effect of hot temperatures and rail defects and breakage due to the effect of cold temperatures pose a medium risk. On the other hand, supportability losses due to cold temperatures are classified as high risk. The impact analysis helps infrastructure managers systematically identify and prioritize climate risks and develop appropriate climate adaptation measures and actions to cope with future climate change impacts.

This paper deals with field validation of the force response from a defective wheel after normal service. The defective wheel represents a typical defect arising from normal winter operation in a cold climate. The current detector is a typical wheel load impact detector that has been in use for about fifteen years; this type of detector is widely used in the infrastructure. The wheel has defects of up to1.8 mm in depth and an un-roundness of 0.2 mm. The results from this investigation, pertaining to the particular vehicle and wheel defect, show a linear correlation of speed and force response. Each change in speed of 1 km/h changes the force response by about 0.9 kN.

Wheelsets form an indispensable part of the railway rolling stock and need to be periodically inspected to ensure stable, safe, reliable, and sustainable rail operation. Wheel profiles are usually inspected and measured in a workshop environment using handheld equipment or by utilizing wayside measuring equipment. A common practice for both methods is to measure the wheel profile at one position along the circumference of the wheel, resulting in a one-slice measurement strategy, based on the assumption that the wheel profile has the same shape independent of the measurement position along the wheel. In this article, the representability of a one-slice measurement strategy with respect to the wheel profile parameters is investigated using handheld measurement equipment. The calculated range of standard deviation of the parameters estimated such as flange height, flange width, flange slope, and hollow wear from the measurements shows a spread in the parameter value along the circumference of the wheel. As an initial validation of the results, measurements from the wayside monitoring systems were also investigated to see if a similar spread was visible. The spread was significantly higher for flange height, flange width, and flange slope estimated from wayside measurement equipment than for the same parameters estimated using the handheld measurement equipment.

Underground pipelines are an essential part of the transportation infrastructure. The structural deterioration of pipelines crossing railways and their subsequent failures are critical for society and industry resulting in direct and indirect costs for all the related stakeholders. Pipeline failures are complex processes, which are affected by many factors, both static (e.g., pipe material, size, age, and soil type) and dynamic (e.g., traffic load, pressure zone changes, and environmental impacts). These failures have serious impacts on public due to safety, disruption of traffic, inconvenience to society, environmental impacts and shortage of resources. Therefore, continuous and accurate condition assessment is critical for the effective management and maintenance of pipeline networks within transportation infrastructure. The aim of this study is to identify failure modes and consequences related to the crossing of pipelines in railway corridors. Expert opinion have been collected through two set of questionnaires which have been distributed to the 291 municipalities in the whole Sweden. The failure analysis revealed that pipe deformation has higher impact followed by pipe rupture at cross-section with railway infrastructure. For underground pipeline under railway infrastructure, aging and external load gets higher ranks among different potential failure causes to the pipeline.

This study examines the accuracy of a wayside train wheel profile measurement system. This was accomplished by an evaluation of the contact-point function for the wheel–rail interface. The wheel profile measurement system in question generates data about the wheel profiles of passing trains. These data are used for improving the wheel maintenance procedures for the rolling stock operator. Recent work shows that there are differences between the data from the two different units in the system, but how this influences further use of the data, e.g. in wheel–rail contact analysis, has not been investigated so far. Accordingly, this article shows how two key wheel measures (the wheel flange thickness and the wheel profile) impact on the contact-point function and which of these measures has the largest impact on the contact-point function. The data used in this study were generated by two different measurement units for the same wheel and with the same wheel status. The results show that the different units produce different results and that these differences are more prominent when a difference in the flange thickness is detected, with a resulting shift of the front side of the flange and of the tread. With no difference in the flange thickness, i.e. no shift of the front side of the flange and of the tread, a difference was still detected in the contact conditions. Furthermore, this investigation shows that the shape of the tread has a greater impact on the contact-point conditions compared to a change in the flange thickness of up to 2.5 mm. This difference in the tread shape could have originated in measurement noise or different wheel measurement positions. The results of the study also show the importance of managing the measurement quality before using the data, for example for maintenance decisions.

This article summarizes the experiences gained at the Nordic heavy haul line “Malmbanan” located in Northern Sweden and Norway during the years 2007 to 2015 and the resulting best practice. Unique long-term information of field trials and monitoring from the on-going development for maintenance of rail and wheel has been described. The reported results come from the rail profile measurements using MiniProf and HC-recordings with Eddy-current devices and visual inspection on 43 test sections. The monitoring has been continuous since the project started, to reveal a deep insight into the complex wheel–rail interaction and provide understanding of the effect of applying optimized specifications. This was particularly important in view of the increasing traffic load that contributed to doubling of the yearly grinding campaigns. This article presents in particular the new MB5 profile, the wear rate behaviour between two different curves, impacts of gauge widening on rail rolling contact fatigue and the speed of gauge widening as well as the seasonal impact on the crack propagation. The presently applied maintenance strategy is discussed together with other experiences. The article finishes with some conclusions and an outlook into further work.

Purpose

This purpose of this article is to present a framework for maintenance analytics that is useful for the assessment of rail condition and for maintenance decision support. The framework covers three essential maintenance aspects: diagnostic, prediction and prescription. The article also presents principal component analysis (PCA) and local outlier factor (LOF) methods for detecting anomalous rail wear occurrences using field measurement data.

Design/methodology/approach

The approach used in this paper includes a review of the concept of analytics and appropriate adaptation to railway infrastructure maintenance. The diagnotics aspect of the proposed framework is demonstrated with a case study using historical rail profile data collected between 2007 and 2016 for 9 sharp curves on the heavy haul line in Sweden.

Findings

The framework presented for maintenance analytics is suitable for extracting useful information from condition data as required for effective rail maintenance decision support. The findings of the case study include: combination of the two statistics from PCA model (T2 and Q) can help to identify systematic and random variations in rail wear pattern that are beyond normal: the visualisation approach is a better tool for anomaly detection as it categorises wear observations into normal, suspicious and anomalous observations.

Practical implications

A practical implication of this article is that the framework and the diagnostic tool can be considered as an integral part of eMaintenance solution. It can be easily adapted as online or onboard maintenance analytic tool with data from automated vehicle based measurement system.

Originality/value

This research adapts the concept of analytics to railway infrastructure maintenance for enhanced decision making. It proposes a graphical method for combining and visualising different outlier statistics as a reliable anomaly detection tool.