Real-time monitoring method and system for the wheel-rail matching state and wheel wear of rail vehicles

By establishing a vehicle multi-body dynamic model and spectrum model, the axle box vibration acceleration is monitored in real time, the uncertainty of the matching performance of high-speed train wheels and rails is solved, real-time monitoring and prediction are achieved, and train operation safety is improved and maintenance costs are reduced.

CN118551644BActive Publication Date: 2025-08-05EAST CHINA JIAOTONG UNIVERSITY
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Patent Information

Application Number
CN202410612597.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-05-17
Publication Date
2025-08-05
Estimated Expiration
2044-05-17

AI Technical Summary

Technical Problem

In the prior art, there is uncertainty in the matching performance of wheel-rails of high-speed trains, resulting in wear and fatigue of wheel-rails and rolling contact, frequent maintenance and waste of resources, and inability to achieve intelligent operation and maintenance.

Method used

By establishing a vehicle multi-body dynamic model, collecting and analyzing the axle box vibration acceleration, building a spectrum model, monitoring the wheel wear and wheel track matching status in real time, and verifying the mapping relationship between wheel wear and equivalent taper using relevant models to achieve real-time monitoring and prediction.

Benefits of technology

Real-time monitoring of the matching status of high-speed train wheels and rails is realized, which improves train operation safety, reduces maintenance costs, and extends the service life of wheels and rails.

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Abstract

The present invention discloses a method and system for real-time monitoring of the wheel-rail matching state and wheel wear of a rail vehicle. The method comprises the following steps: establishing and performing wear simulation processing on a multi-body dynamics model to obtain vehicle wheel wear at different mileages; collecting axle box vibration accelerations of wheels at different wheel wear levels and performing feature analysis on the axle box vibration accelerations, and extracting axle box lateral vibration accelerations from the axle box vibration accelerations as a training data set; calculating that there is a positive relationship between the wheel wear level and the equivalent taper, and using a correlation model to verify that there is a mapping relationship between wheel wear, equivalent taper, and axle box lateral vibration acceleration; constructing a pre-trained spectrum model, and inputting the training data set into the pre-trained spectrum model for feature training to obtain a spectrum model; collecting a set of axle box lateral vibration accelerations as a test data set and inputting the test data set into the spectrum model to obtain the vehicle wheel-rail matching state and wheel wear level under the axle box lateral vibration accelerations.
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