Train wheel damage detection based on deep learning

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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.
Original languageEnglish
Title of host publicationProceedings of the Joint International Resilience Conference 2020
Subtitle of host publicationInterconnected: Resilience Innovations for Sustainable Development Goals 23 - 27 November, 2020
EditorsTina Comes, Christoph Hölscher
Number of pages3
Publication statusPublished - 2020
EventJoint International Resilience Conference: Interconnected: Resilience Innovations for Sustainable Development Goals - online event, Delft, Netherlands
Duration: 23 Nov 202027 Nov 2020


ConferenceJoint International Resilience Conference
Abbreviated titleJIRC
Internet address


  • Damage quantification
  • Train wheel flat
  • Deep learning


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