Abstract: The readiness and usefulness of Automated Machine Learning (AutoML) methods in classification of railway track defects is explored.
Abstract—The readiness and usefulness of Automated Machine. Learning (AutoML) methods in classification of railway track defects is explored.
47 References � Comparison of sensors and methodologies for effective prognostics on railway turnout systems � Vehicle Remote Health Monitoring and Prognostic�...
The readiness and usefulness of Automated Machine Learning (AutoML) methods in classification of railway track defects is explored.
Machine learning methods were used to detect defects of important components of the track structure that are directly related to safe train operation, including�...
Feb 6, 2023 � In this study, new intelligent automation based on machine learning pattern recognition has been built to detect and predict the deterioration of railway�...
The paper reviews the existing prediction methods for railway track degradation, including traditional methods and prediction methods based on machine learning�...
This paper proposes new data-driven techniques that identify railway track faults using three object detection models: YOLOv5, Faster RCNN, and EfficientDet.
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Jun 14, 2024 � The authors present a novel approach to anticipating poor track performance, in terms of derailment risk, track degradation and passenger�...
A case for track defect prognosis in rail track engineering is presented in this paper. Fatigue defects are very common and are influential on rail maintenance.