Train wheel damage detection based on deep learning

Conference paper (2020)

Authors

Wen-Jun Cao Resources & Recycling - CEG
Research Group
Resources & Recycling (CEG) (TU Delft)
Copyright: © 2020 Wen-Jun Cao
Deep learning Damage quantification Train wheel flat
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Published Date

2020

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Engineering Structures

Research Group

Resources & Recycling

Abstract

The train wheel flat is one of the most common damages in the railway system. It occurs when a wheel locks up while the train is moving. The early detection of wheel-flat severity is crucial for passenger comfort and the safety of the railway operation. However, it is still challenging to quantify the properties of wheel flats (e.g., sizes) without interrupting the operations. One way is to transform this damage detection task into a model updating (parameter identification) task. In this abstract, a deep-learning approach is adopted to solve this inverse problem. It has been successfully applied to a field train track test in Singapore. The identified damage size obtained is consistent with on-site measurements.