Multimodal Data Fusion for Digital Monitoring of Railway Vehicle Wheelset Health Status

Jun Zhang; Jiaqi Zhang

doi:10.54097/srcgy424

Authors

Jun Zhang
Jiaqi Zhang

DOI:

https://doi.org/10.54097/srcgy424

Keywords:

Multimodal Data Fusion, Wheelset Health Monitoring, Cross-modal Attention Mechanism, Failure Early Warning, Edge Computing

Abstract

This study proposes a multimodal data fusion-based method for monitoring the health status of railway wheelsets by integrating vibration, acoustic, temperature and visual sensing information, and constructing an ‘edge-cloud’ collaborative computing architecture. The research innovatively adopts a deep fusion model with cross-modal attention mechanism, and realises high-precision identification and early warning of early failure of wheelsets. Practical engineering applications verify that the method significantly reduces the number of unscheduled wheelset replacements and maintenance costs, and shows strong robustness under severe operating conditions. The reliability and early warning capability of the system are greatly improved, providing a new paradigm for early warning of wheelset failures with obvious technical and economic benefits.

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