Related papers: A System for Train Condition Monitoring and Structural Health Assessment of Rail Vehicles

A System for Train Condition Monitoring and Structural Health Assessment of Rail Vehicles

URL: http://arxiv.org/abs/2608.05221v1
Date: Wed, 05 Aug 2026 11:01:54 GMT
Title: A System for Train Condition Monitoring and Structural Health Assessment of Rail Vehicles
Abstract summary: This paper presents a novel approach for real-time vehicle condition monitoring and impact detection that integrates structural sensor technologies with AI-based data analysis.<n>The proposed framework addresses three key applications: (1) automated detection of impacts, structural damage, and driving-over events, (2) condition-based maintenance enabled by continuous monitoring, and (3) long-term data analytics to support vehicle design optimization.
Score: 0.0
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: The ongoing digitalization of rail systems and the increasing use of artificial intelligence (AI) are fundamentally transforming the design, operation, and maintenance of rail vehicles. While fully automated operation at Grade of Automation 4 (GoA4) is well established in metro systems, its deployment in mainline rail remains limited. This is primarily due to stringent safety requirements and the complexity of open operational environments. Current perception systems based on cameras, radar, and lidar are effective in detecting objects but provide limited capability for reliably identifying impacts, collisions, and driving-over events. This paper presents a novel approach for real-time vehicle condition monitoring and impact detection that integrates structural sensor technologies with AI-based data analysis. The proposed framework addresses three key applications: (1) automated detection of impacts, structural damage, and driving-over events, (2) condition-based maintenance enabled by continuous monitoring, and (3) long-term data analytics to support vehicle design optimization. The results demonstrate the feasibility of the proposed approach and highlight its potential to enhance operational safety, enable predictive maintenance strategies, and support the transition toward fully automated operation in mainline rail systems

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