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Unplanned track inspections can be a direct consequence of any disruption to the operation of on-board track geometry monitoring activities. A novel response strategy to enhance the value of the information for supplementary track measurements is thus established to construct a data generation model. In this model, artificial (synthetic) data is assigned on each measurement point along the affected track segment over a short period of time. To effectively generate artificial track measurement data, this study proposes a NARX (nonlinear autoregressive with exogenous variables) model, which incorporates short-range memory dependencies in the dependent variable and integrates interdependent effects from external factors. Nonlinearities in the proposed model have been determined using an artificial neural network that allowed fast computation of a mapping function in line with the needs of effective disruption management. The risk of over fitting the data generation model, which reflected its generalisation ability, has been effectively managed through risk aversion concept. For the model evaluation, the deviation of track longitudinal level has been taken as a case study, predicted using its degradation rate and track alignment and gauge as exogenous variables. Simulation results on two datasets that are statistically different showed that the data generation model for disrupted track measurements is reliable, accurate, and easy-to-use. This novel model is an essential breakthrough in railway track integrity prediction and resilient operation management. [ABSTRACT FROM AUTHOR]

Railway track health monitoring and maintenance are crucial stages in railway asset management, aiming to enhance the train operation quality and service life. For this aim, various inspection means (using diverse non-destructive testing techniques) have been applied, however, these means are mostly not able to monitor whole railway track network or track underlying layers (e.g., ballast and subgrade). The use of remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR), can expedite the defect diagnosis process for railway tracks, elevating the scope of health monitoring to a network-wide level. The Ground Penetrating Radar (GPR) has emerged as a particularly reliable method, especially for detecting structural deficiencies in underlying layers. As a result, combining the two distinct non-destructive testing techniques – GPR and InSAR – presents a promising strategy for efficient railway asset management. Recognizing the significance of embracing newer and more advanced monitoring strategies, this paper reviews the fusion of GPR and InSAR methodologies, and explores the potential integration of machine learning models to develop a predictive health monitoring and condition-based maintenance approach for railway tracks. • A review of InSAR/GPR with potential fusion for railway monitoring was discussed. • A general framework for fusion of InSAR/GPR data and machine learning integration was proposed. • Predictive strategy based on fusion of characterized techniques for railway health monitoring/maintenance plan was focused. • Future research directions are recommended based on the review of literature and discussion of technology. [ABSTRACT FROM AUTHOR]

Track quality affects passenger comfort and safety. To maintain the quality of the track, track geometry and track component defects are inspected routinely. Track geometry is inspected using a track geometry car (TGC). Measured values are stored in the machine and processed to evaluate the track quality. However, track component defects require more effort to inspect. Track component defects can be inspected manually which is time- and workload-consuming or using sensors installed at additional cost. This study presents an approach using track geometry obtained by a TGC to detect track component defects, namely, rail, switch and crossing, fastener and rail joint defects. Detection models are developed using several supervised machine learnings. The relationships between track component defects are analysed to gain insights using unsupervised machine learnings. From the study, the best model for detecting track component defects using track geometry is a deep neural network with an accuracy of 94.31% followed by a convolutional neural network with an accuracy of 93.77%. For the exploration of insights, k-means clustering is used to cluster the track components defects, and association rules are used to find the relationships between them. Examples of the insights from applying these two techniques are that switch and crossing defects are usually found where the radius of curvature is less than 2000 m and the gradient is positive, the most common defects when the radius of curvature higher than 4000 m are rail defects, or a worn wing rail will be found when the rail section has failed, ties in switches and worn point blades are found with the confidence of 92.17%. The findings of the study can be applied to detect track component defects using track geometry where additional cost is not required and unsupervised machine learning provides the insights that will be beneficial for railway maintenance. The information obtained from machine learning models will be complementary information to support decision making and improve the maintenance efficiency in the railway industry. [ABSTRACT FROM AUTHOR]

Railway sleepers are safety–critical components of a railway structure. They support ballasted track superstructure and are a critical factor in track geometry and track components' deterioration. Unsupported sleepers are a common issue incurred after tracks have been utilized. When unsupported sleepers are present, they cause differential settlements of track superstructures, additional dynamic loading, and excessive train-track vibrations which affect passenger comfort, safety, and maintenance cost. This study is the world's first to develop new machine learning models to prognose and better diagnose defect severities of unsupported sleepers aligned with practical track inspection guidelines. Data used to develop machine learning models are based on a verified finite element model with actual field measurements, enabling unbiased full data ranges that govern all defect conditions. Different conditions of unsupported sleepers can be explored by varying locations of unsupported sleepers and the number of unsupported sleepers. Also, other operational parameters can be addressed such as speeds of rolling stock, the roughness of rail surface, and vertical stiffness of wheel-rail contact. In total, 2016 data sets have been obtained. Axle box accelerations are adopted as key indicators for machine learning models. Machine learning techniques used in the study are the convolutional neural network, recurrent neural network, ResNet, and fully convolutional neural network. Data fusion and assimilation have been conducted since the data points are dependent on the train speeds. Our new results reveal a breakthrough essential for real-world applications that the convolutional neural network model provides the best accuracy in both unsupported sleeper prognostics and severity identification. The accuracies of detection and severity identification are 99.34% and 97.02% respectively. [ABSTRACT FROM AUTHOR]