Prognostics of unsupported railway sleepers and their severity diagnostics using machine learning

Abstract

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.

Ensemble model for rail surface defects detection

“…The rise and development of machine learning provides a new effective approach for rail defect detection. DNNs have been successfully adopted to detect rail corrugation [ 12 ], rail flat [ 13 ], and have been applied to investigate the condition of railway sleepers [ 14 , 15 ], settlement/dipped joints [ 16 ], and other rail track components [ 17 ]. Recently, object detection has achieved a substantial breakthrough by using Convolutional Neural Networks (CNNs), and has been introduced for rail surface defect detection in the past decades [ 11 , 18 , 19 ].…”

Multi-Hazard Effects of Crosswinds on Cascading Failures of Conventional and Interspersed Railway Tracks Exposed to Ballast Washaway and Moving Train Loads

“…In order to ensure the safety of the interspersed railway lines, some measurements should be taken for the accurate early-age diagnosis and detection of the track defect. For example, the sleeper support conditions can be assessed and predicted using machine learning technologies based on the acceleration data of track components [ 57 ] and the rail displacement from digital video records [ 36 ]. Ground penetrating radar (GPR) can also reflect the ballast layer condition and the void zone [ 58 ], as well as on the bridge ends for digital twin based monitoring [ 59 , 60 ].…”

Detection and assessment of rail discontinuities using a multibody vehicle-track model

“…A machine learning (ML) algorithm is one of the powerful tools that help in detecting the defect. A machine learning algorithm is applied on the axle-box acceleration signal to solve the problems like locating the broken or missing rail fastener clip [16], out of roundness of the wheel [17], broken rail [18] and loss of track support [19].…”