Railway transition zones connecting conventional embankments and rigid struc-tures, such as bridges and tunnels, usually degrade much faster than other railway sections. Efficient health condition monitoring of transition zones is important for preventative track maintenance. In this paper, a methodology for monitoring rail-way transition zones using acceleration measurements on multiple axle boxes (multi-ABA) of a passing train is presented. To showcase its capability, the measurements in the Netherlands, Sweden, and Norway are analyzed and dis-cussed. It is found that different bridges and transition zones exhibit unique char-acteristics including dominant wavelengths and energy distribution. Based on these unique characteristics, the geometry and support conditions at different lo-cations of a transition zone can be evaluated. Higher train speed makes the char-acteristics more pronounced. The results demonstrate that the multi-ABA meas-urement has the potential to evaluate and thus monitor the health conditions of various transition zones.@en

This work presents the results of a measurement campaign to demonstrate the effectiveness of the axle box acceleration (ABA) technology for detecting rail defects. The measurements were conducted along the Iron Ore line between Sweden and Norway for the IN2TRACK3 project. This line is mostly single-track with passenger-freight mixed traffic and heavy axle load. Historical data and track information data were not considered in this study. By analyzing data acquired from the accelerometers in vertical and longitudinal directions, rail defects were detected in near real-time using big-data analytics. For our validated sections, 100% of rail defects (including squats) were detected using time-frequency analysis and an outlier detection approach. The methodology also allows for identifying priority locations, e.g., defective welds, joints, transition zones, etc., and its use for prescriptive maintenance recommendations is being explored in the framework of the IAM4RAIL project.@en

Inefficient management of rail surface defects can increase maintenance costs, safety hazards, service disruptions, and catastrophic failures like rail breaks. To achieve adequate management, having effective technology capable of timely detecting and frequently monitoring rail defects is of utmost importance. The aim is early detection of defects to maintain safety levels and prevent the re-appearance due to residual damages.

Various measurement technologies, such as visual inspections, geometry profile measurements, and other measurement techniques, have been used for the detection of rail defects. While these methods provide insights, they often lack the capability for early-stage defect detection. Thus, most of these technologies are suitable for reactive maintenance since they detect defects when they reach a certain severity level. Axle box acceleration (ABA) technology provides a solution capable of frequent monitoring, mounted on trains in operation without dedicated measurement vehicles (see figure 71-1). Its basic principle is to use a train as a moving load that excites the infrastructure and to detect defects by evaluating the time-frequency characteristics of the dynamic response measured by accelerometers installed on axle boxes of the train. ABA systems have shown promise in detecting defects in the early stages. However, its widespread application and need for robustness require further validation and development. This work presents the results of detecting and monitoring rail surface defects using ABA technology.@en

Friction/adhesion management in railway networks is a challenge for infrastructure managers and railway operators. Friction/adhesion at the wheel–rail interface influences the braking and traction performance of railway vehicles and the formation of wheel and rail defects. A minimum level of friction/adhesion must be guaranteed to ensure appropriate braking and traction of vehicles, whereas high friction/adhesion increases wear and rolling contact fatigue (RCF) of wheels and rails, noise emissions and carbon footprint (transportation energy consumption). A crucial part of friction/adhesion management is to reliably measure the wheel–rail friction levels and creepage. A train-borne tribometer is desired because the wheelrail friction level depends on, among others, the normal contact load and speed.

A light vehicle will thus experience adhesion different from a heavy train, and the accuracy of hand-pushed tribometers is adversely affected by scaling and low speed. Aiming to contribute to the development of a train-borne tribometer for friction/adhesion management, this study conducted a comprehensive lab test in the V-Track in the Railway lab of TU Delft. The V-Track is a downscaled wheel-rail contact test rig consisting of a 4-meterdiameter ring track and 1~4 wheel assemblies running over it with well-controlled and measurable normal load and friction forces. The coefficient of friction (COF) was measured with two schemes: 1. Increase the angle of attack (AoA) to get friction saturation in the lateral direction and 2. Increase the traction/braking torque of the wheel to get friction saturation in the longitudinal direction. The wheel-rail contact forces in the three directions, AoA, wheel rolling speed and rotational/circumferential speed, and traction/braking torques were measured and analysed to obtain the COF of the V-Track.@en

By modifying friction to the desired level, the application of friction modifiers (FMs) has been considered as a promising emerging tool in the railway engineering for increasing braking/traction force in poor adhesion conditions and mitigating wheel/rail interface deterioration, energy consumption, vibration and noise. Understanding the effectiveness of FMs in wheel–rail dynamic interactions is crucial to their proper applications in practice, which has, however, not been well explained. This study experimentally investigates the effects of two types of top-of-rail FM, i.e. FM-A and FM-B, and their application dosages on wheel–rail dynamic interactions with a range of angles of attack (AoAs) using an innovative well-controlled V-track test rig. The tested FMs have been used to provide intermediate friction for wear and noise reduction. The effectiveness of the FMs is assessed in terms of the wheel–rail adhesion characteristics and friction rolling induced axle box acceleration (ABA). This study provides the following new insights into the study of FM: the applications of the tested FMs can both reduce the wheel–rail adhesion level and change the negative friction characteristic to positive; stick–slip can be generated in the V-Track and eliminated by FM-A but intensified by FM-B, depending on the dosage of the FMs applied; the negative friction characteristic is not a must for stick–slip; the increase in ABA with AoA is insignificant until stick–slip occurs and the ABA can thus be influenced by the applications of FM.

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In this paper, a fuzzy interval-based method is proposed for solving the problem of rail defect detection relying on an on-board measurement system and a multiple spiking neural network architecture. Instead of outputting binary values (defect or not defect), all data will belong to both classes with different spreads that are given by two fuzzy intervals. The multiple spiking neural networks are used to capture different sources of uncertainties. In this paper, we consider uncertainties in the parameters of spiking neural networks during the training phase. The proposed method comprises two steps. In the first step,
multiple sets of the firing times for both classes are obtained from multiple spiking neural networks. In the second step, the obtained multiple sets of firing times are fuzzy numbers and they are used to construct fuzzy intervals. The proposed method is showcased with the problem of rail defect detection. The
numerical analysis indicates that the fuzzy intervals are suitable to make use of the information provided by the multiple spike neural networks. Finally, with the proposed method, we improve the interpretability of the decision making regarding the detection of anomalies.@en

An innovative downscale test rig called V-Track has been constructed for wheel-rail contact experiments under impact loading conditions. In this paper, a force measurement system termed a dynamometer was developed in V-Track to measure the wheel-rail contact forces. The dynamometer consists of four 3-component piezoelectric force sensors and was mounted between the wheel assembly and the steel frame of V-Track, enabling it to measure the forces transmitted from the wheel-rail interface to the frame. Static tests were first carried out to calibrate the dynamometer in three directions. Then, several tests were performed in V-Track to examine the reliability and validity of the dynamometer for measuring the wheel-rail contact forces under running conditions. Experimental results show that the dynamometer is capable of reliably and accurately measuring these forces. Utilizing the measurement results from the dynamometer, the control of the wheel-rail contact forces in V-Track has also been achieved.

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Results on the development of smart technology solutions for lower density railway lines are presented. The goal is to reach a cost effective inspection and asset management to minimize maintenance interventions time/cost without dedicated inspection vehicles. The proposed methods in this paper include: 1) axle box acceleration measurements and 2) ultra‐low cost smartphones. The data is interpreted and converted from monitoring information into management information. Feasibility and preliminary studies were conducted in the railway lines of Romania. The results presented in this paper were obtained in the framework of the H2020 project NeTIRail‐INFRA.@en

In this paper, we present preliminary results on the development of smart technology solutions for lower density railway lines. The goal is to reach a cost effective inspection and asset management to minimize maintenance interventions time/cost without dedicated inspection vehicles. The proposed methods include axle box acceleration measurements and ultra-low cost smartphones. The collected data will be further used to increase knowledge of the condition of the railway track and to estimate the comfort of passengers. In order to make use of the data, the data is interpreted and converted from monitoring information into management information. Feasibility and preliminary studies were conducted in the railway lines of Romania. The results presented in this paper were obtained in the framework of the H2020 project NeTIRail-INFRA, Work Package 4: Monitoring and Smart Technology.@en

In this paper we discuss rail condition monitoring based on axle box acceleration (ABA) measurements. We present three case studies. The first one in The Netherlands, the detection of local defects with different severity levels (squat A, squat B and squat C) is analysed. The second case from the Faurei testing ring in Romania, the detection of rail defects over the whole testing ring is presented and examples of responses at a local defect (wheel-burn) is discussed with measurements at 80km/h (conventional speed measurement) and 200km/h (high speed measurement). In the third case, ABA measurements were obtained during operation in a train with passengers in the railway line near Brasov, Bartolomeu-Zărneşti. Examples of the defects and validations are discussed.@en

In this paper, we propose a methodology based on signal processing and evolutionary multiobjective optimization to facilitate the maintenance decision making of infra-managers in regional railways. Using a train in operation (with passengers onboard), we capture the condition of the rails using Axle Box Acceleration measurements. Then, using Hilbert-Huang Transform, the locations where the major risks are detected and ssessed with a degradation model. Finally,
evolutionary multiobjective optimization is employed to solve the maintenance decision problem, and to facilitate the visualization of the trade-offs between number of interventions and performance. Real-life measurements from the track from Braşov to Zărneşti in Romania are included to show the methodology.@en