A Machine Learning-Based Approach for Fault Detection of Railway Track and its Components

Sammanfattning: The hard equation of railway safety versus the high commercial profits can only be achieved through the use of new inspection methods supported by modern technologies. The track and its components can have different types of troubles, such as rail surface defects, broken sleepers, missing fasteners, and irregular ballast levels. Each component of the track infrastructure plays a significant role, where the failure or the absence of any of them can pave the way to undesired situations. The rail is designed to carry and direct the train, the sleepers are meant to maintain the level of the rail, and the ballast mission is to keep all components floating on the surface of the ground. The fasteners are used to fasten the rail to the sleepers, and therefore too many missing fasteners can lead to sever unsteady tracks, which can, in turn, result in derailment. Therefore, there is a high demand for advanced inspection methods to monitor the railway track and its components continuously. The presence of such advanced inspection models would help the railway industry avoid obstacles such as high operation and maintenance costs, dangerous accidents, and uncomfortable passenger's experience.   This master thesis aims to present an efficient method to classify the track and its components by combining image processing techniques and deep learning algorithms. This method was able to detect the missing fasteners in the set of images captured by a line camera, continuously monitoring the rail and its associated fasteners. The experimental results obtained in this thesis showed that the proposed method is efficient and robust for detecting the track and its components in complex environments. The thesis also discusses the idea of building one complete model that can process and classify all track components at once. The image processing technique was employed to extract different components of the track, individually: fasteners, rail, ballast, and sleepers. The model was trained and used to classify the state of the fasteners. Classification of other components of the track will be a part of the future work.