Transition zones in railway tracks often degrade faster than other locations, yet traditional health assessments rely on infrequent track geometry measurements, limiting early detection of dynamic changes. This research presents an approach for more frequent evaluation of transition zone health by integrating data sources from multiple monitoring technologies: track geometry, interferometric synthetic aperture radar (InSAR), and axle box acceleration (ABA). Missing InSAR data are addressed through spatio-temporal interpolation, and track longitudinal levels are predicted using a hybrid neural model that includes a hybrid convolutional neural network (CNN) with gated recurrent units (GRU) network and a hybrid CNN with a long short-term memory (LSTM) network. The models fuse historical and interpolated data from InSAR and ABA, enabling higher-frequency insights. A novel key performance index (KPI) based on predicted longitudinal levels is proposed to quantify track condition. The framework is validated on a transition zone at a railway bridge between Dordrecht and Lage Zwaluwe in the Netherlands. Results show that the hybrid model outperforms standalone methods and offers a good balance between accuracy and computational efficiency. The proposed approach enables earlier detection of irregularities, supporting prescriptive maintenance decisions.

Modern smartphones, widely available and equipped with multiple sensors, offer the potential for railway infrastructure condition monitoring through mobile crowdsensing without disrupting operational railway services. This paper investigates key factors influencing the use of smartphone accelerometers for railway track quality assessment and highlights the associated challenges. Differences in accelerometer characteristics across three budget smartphones, including variations in sampling frequencies and dynamic sensitivity, are analyzed. A case study conducted on in-service passenger trains examines the effects of operational conditions, specifically vehicle speed and smartphone placement within the car body, on the frequency content and magnitude of recorded signals. Additionally, this study compares smartphone measurement results with a conventional track quality index derived from track geometry measured by specialized vehicles. Promising correlations are observed between the standard deviation of the smartphone vertical acceleration signals and the longitudinal level-based track quality index, demonstrating the potential of smartphones to assess track quality and detect local anomalies. However, variations caused by vehicle speed and smartphone placement pose challenges for standardizing track quality assessments. These findings highlight the potential of mobile crowdsensing for railway infrastructure monitoring while emphasizing the need for strategies to address variability in operational conditions and device characteristics.

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.

This study presents a measuring framework for railway transition zones using a case study on the Swedish line between Boden and Murjek. The final goal is to better understand the vertical dynamics of transition zones using hammer tests, falling weight measurements, and axle box acceleration (ABA) measurements. Frequency response functions (FRFs) from hammer tests indicate two track resonances, for which the FRF magnitudes on the plain track are at least 30% lower than those at the abutment. The falling weight measurements indicate that the track on the bridge has a much higher deflection than the track on the embankment. Two features from ABA signals, the dominant spatial frequency and the scale average wavelet power, show variation along the transition zone. These variations indicate differences in track conditions per location. Finally, the ABA features in the range of 1.05–2.86 m⁻¹ are found to be related to the track resonance in the range of 30–60 Hz. The findings in this paper provide additional support for physically interpreting train-borne measurements for monitoring transition zones.

The railway track is the most critical element that infrastructure managers have to ensure satisfactory health conditions to provide safe, sustainable, and adequate service quality. The condition of railway tracks is managed through maintenance strategies that benefit enormously from the availability of accurate and updated information from measurement devices. This thesis focuses on estimating the track support condition using Axle Box Acceleration (ABA) measurements. The ABA characteristics under different track support conditions are evaluated, effects of influential parameters in the measurements are analyzed, key performance indicators (KPIs) for condition monitoring of ballast and substructure layers are proposed, and results with traditional techniques such as impact tests are compared. Finally, an enhanced track quality index is proposed to improve the effectiveness of maintenance decision-making. Case studies in The Netherlands, Sweden, and Norway are considered.

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.

The conventional vertical track quality index (TQI) based on the standard deviation of longitudinal levels yields standardized railway track condition assessment. Nevertheless, its capability to identify problems is limited, particularly in the ballast and substructure layers when abrupt changes affect train-track interaction. Previous research shows that dynamic responses from moving trains via axle box acceleration (ABA) measurements can quantify abrupt changes in the vertical dynamic responses. Thus, this paper proposes a framework to design an enhanced vertical TQI, called EnVTQI, by integrating track longitudinal levels and dynamic responses from ABA measurements. First, measured ABA signals are processed to mitigate the influence of variation in measurement speed. Then, substructure and ballast-related features are extracted, including scale average wavelet power (SAWP) in the ranges 0.04 m^-1 to 0.33 m^-1 (substructure) and 1.25 m^-1 to 2.50 m^-1 (ballast). This enables identifying track conditions at different track layers. Finally, EnVTQI is determined by weight averaging between the conventional vertical TQI and the ABA features from moving trains. The performance of EnVTQI is evaluated based on 48 segments of a 200-m track on a Dutch railway line. The results indicate that EnVTQI helps to distinguish track segments that cause poor train-track interaction, which the conventional TQI does not indicate. EnVTQI can supplement the conventional TQI, improving the effectiveness of track maintenance decision-making.

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.

This paper presents a methodology for monitoring transition zones using responses of multiple-axle box acceleration (multi-ABA) measurements. The time–frequency characteristics of the vertical ABA signals from four wheelsets are analyzed. The major contributions are as follows. (1) We propose four key performance indicators (KPIs) to quantify local multi-ABA energy differences at different abutments, tracks, entrance and exit sides, and inner and outer rails. (2) The same dominant spatial frequencies are obtained with different measurement speeds, so the proposed method is suitable for multi-ABA systems mounted on operational trains. Transition zones at nine double-track railway bridges are selected as the case study. The KPIs indicate that (1) the energy differences between abutments are above 80% in three bridges; (2) two abutments show that the energy differences between tracks are higher than 100%; (3) three tracks have energy differences above 100% between the entrance and exit sides; and (4) the energy differences between rails are above 80% on three sides. Finally, using measurement with 7 years of difference, the KPIs and track quality index are discussed. These findings suggest that multi-ABA measurement can be used as a health condition monitoring method for railway transition zones to support condition-based maintenance.