The railway industry relies heavily on the efficient operation of its infrastructure to facilitate the transportation of goods and passengers over long distances. In the last decades, wayside monitoring systems have emerged as crucial tools for ensuring the safety, reliability, and optimal performance of railway vehicles. This article investigates the evolving role of wayside monitoring, particularly focusing on the utilization of hot-box and hot-wheel detectors for proactive maintenance strategies. Traditional approaches to hot-box monitoring have been reactive, primarily focusing on detecting critical states of vehicles. However, a shift towards predictive maintenance using these classical systems may still be feasible by analysing deeply the detector data and extracting insights into the condition of bearings. The methodology involves reorganizing and redefining HB/HW data to identify anomalies indicative of changes in bearing operation or condition. Moreover, by assessing the quality of detector data and implementing adaptive thresholding and anomaly detection algorithms, false alarms and false negatives can be minimized, enhancing the efficiency of maintenance operations, and improving the reliability of railway networks. Overall, this study investigates and highlights the potential of utilising classical wayside monitoring systems to improve railway maintenance practices and contributing to safer and more efficient railway operations.

The use of greenhouse gas (GHG) emissions as a criterion for decision-making within the rail industry is increasing. The demand for considering this criterion affects the type of decision models acceptable by railway infrastructure managers in the planning, construction, and maintenance of railway assets. The total amount of GHG emitted from a track solution in tunnels during its service life depends on the track form (i.e., ballasted track or ballastless track), the type of construction, maintenance machines used, current traffic profile, and tunnel length. However, the development in the design of ballastless track systems during recent decades to make them environmentally friendly motivates infrastructure managers to rethink and consider the use of the system. This study examines the effect of several design and maintenance factors not adequately addressed in previous research. These factors are (i) the modulus of elasticity of track support affecting the design of track forms, (ii) differences in maintenance and renewal required for track forms in the corresponding line condition, and (iii) recent developments in optimizing the environmental impact of ballastless tracks. The GHG emissions, represented by life cycle carbon dioxide equivalent (CO2e) emissions, are calculated using the climate impact software developed by the Swedish Transport Administration Trafikverket. The result is compared with the estimated emission from the conventional ballasted tracks. The method proposed in this paper is applied in a case study to study the effect of applying the optimized ballastless track system Rheda 2000® in a railway tunnel (the Hallsberg-Stenkumla tunnel) as part of a new line project in Sweden. The model applied in the study is an integral part of an integrated decision support system for effectively selecting track solutions from a lifecycle perspective. The study´s findings are: (i) the life cycle CO2 equivalent emissions by a ballastless track during its life cycle are 10% lower than that of the ballasted track, (ii) the primary total emission driver for both track form solutions is the emissions generated at the manufacturing of rails. (iii) the second important emission factor for the ballasted track solution is the emission from the renewal of the track form during its life cycle, and (iv) the second important emission factor for the ballastless track solution is concrete manufacturing.

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.

Switches and crossings (S&Cs) are also known as turnouts or railway points. They are important assets in railway infrastructures and a defect in such a critical asset might lead to a long delay for the railway network and decrease the quality of service. A squat is a common rail head defect for S&Cs and needs to be detected and monitored as early as possible to avoid costly emergent maintenance activities and enhance both the reliability and availability of the railway system. Squats on the switchblade could even potentially cause the blade to break and cause a derailment. This study presented a method to collect and process vibration data at the point machine with accelerometers on three axes to extract useful features. The two most important features, the number of peaks and the total power, were found. Three different unsupervised machine learning algorithms were applied to cluster the data. The results showed that the presented method could provide promising features. The k-means and the agglomerative hierarchical clustering methods are suitable for this data set. The density-based spatial clustering of applications with noise (DBSCAN) encounters some challenges.

Railway switches and crossings (S&C) are among the most important high-value components in a railway network and a failure of such an asset could result in severe network disturbance. Therefore, potential defects need to be detected at an early stage to prevent traffic-disturbing downtime or even severe accidents. A squat is a common defect of S&Cs that has to be monitored and repaired to reduce such risks. In this study, a testbed including a full-scale S&C and a bogie wagon was developed. Vibrations were measured for different squat sizes by an accelerometer mounted at the point machine. A method of processing the vibration data and the speed data is proposed to investigate the possibility of detecting and quantifying the severity of a squat. One key technology used is wavelet denoising. The study shows that it is possible to monitor the development of the squat size on the rail up to around 13 m from the point machine. The relationships between the normalised peak-to-peak amplitude of the vibration signal and the squat depth were also estimated.

Railway switch and crossing (S&C) systems have complicated moving structures compared with regular rail. They require multiple components that vary in complexity. The complexity of railway S&C, together with the fact that they are discontinuous points of the system, makes them vulnerable to defects such as squats. A squat on the switching rail could potentially cause rail breakage and lead to catastrophic results, such as derailment. In this study, a method based on anomaly scoring was investigated to estimate the status of an S&C system with respect to squat defects. The proposed method was tested in a real environment under controlled measurement sequences. The results show that the methods can differ between an S&C with squats and another one without them.

Railway switches and crossings (S&Cs) are critical, high-value assets in railway networks. A single failure of such an asset could result in severe network disturbance and considerable economical losses. Squats are common rail surface defects of S&Cs and need to be detected and estimated at an early stage to minimise maintenance costs and increase the reliability of S&Cs. For practicality, installation of wired or wireless sensors along the S&C may not be reliable due to the risk of damages of power and signal cables or sensors. To cope with these issues, this study presents a method for collecting and processing vibration data from an accelerometer installed at the point machine to extract features related to the squat defects of the S&C. An unsupervised anomaly-detection method using the isolation forest algorithm is applied to generate anomaly scores from the features. Important features are ranked and selected. This paper describes the procedure of parameter tuning and presents the achieved anomaly scores. The results show that the proposed method is effective and that the generated anomaly scores indicate the health status of an S&C regarding squat defects.

The rail fastening system plays a crucial role in railway tracks as it ensures operational safety by fixing the rail on to the sleeper. Early detection of rail fastener system defects is crucial to ensure track safety and to enable maintenance optimization. Fastener inspections are normally conducted either manually by trained maintenance personnel or by using automated 2-D visual inspection methods. Such methods have drawbacks when visibility is limited, and they are also found to be expensive in terms of system maintenance cost and track possession time. In a previous study, the authors proposed a train-based differential eddy current sensor system based on the principle of electromagnetic induction for fastener inspection that could overcome the challenges mentioned above. The detection in the previous study was carried out with the aid of a supervised machine learning algorithm. This study reports the finding of a case study, along a heavy haul line in the north of Sweden, using the same eddy current sensor system mounted on an in-service freight train. In this study, unsupervised machine learning models for detecting and analyzing missing clamps in a fastener system were developed. The differential eddy current measurement system was set to use a driving field frequency of 27 kHz. An anomaly detection model combining isolation forest (IF) and connectivity-based outlier factor (COF) was implemented to detect anomalies from fastener inspection measurements. To group the anomalies into meaningful clusters and to detect missing clamps within the fastening system, an unsupervised clustering based on the DBSCAN algorithm was also implemented. The models were verified by measuring a section of the track for which the track conditions were known. The proposed anomaly detection model had a detection accuracy of 96.79% and also exhibited a high score of sensitivity and specificity. The DBSCAN model was successful in clustering missing clamps, both one and two missing clamps, from a fastening system separately.