A High-speed Railway Track Response Prediction Method Based on Sparse Bayesian Width Learning

Abstract

The present invention proposes a high-speed rail track response prediction method based on sparse Bayesian width learning. The method includes linear and nonlinear feature extraction for input temperature field variables, and maximum output layer weights for hidden layer neuron nodes. Posterior estimation, prediction of structural response output, and preliminary assessment of track structure state, etc. The present invention adopts the sparse Bayesian breadth learning method to mine the correlation relationship of the data of the high-speed rail track monitoring system, and can effectively avoid the over-fitting problem of the regression prediction by sparsely solving the weight value w reflecting the relationship between the data variables, and has relatively good performance. High prediction accuracy, efficient calculation speed and loose equipment hardware requirements, so as to realize the mining of the relationship between temperature load and structural strain hidden in a large amount of monitoring data, and timely discover the evolution of monitoring data models as a way to judge the service life of track structures The basis for the abnormal status.

Description

technical field [0001] The invention belongs to the technical field of machine learning and structural health monitoring, in particular to a high-speed rail track response prediction method based on sparse Bayesian width learning. Background technique [0002] Today, with the extensive development of high-speed rail technology, ensuring the health of high-speed rail tracks in service has become more and more of a concern. At present, the slab ballastless track laid by domestic high-speed railways is a continuous track structure, which is greatly affected by temperature and alignment, and is prone to disease problems. Real-time and effective assessment of whether its state changes are an important issue for high-speed railway operation safety. [0003] In response to the above problems, scientific research and engineering applications of long-term monitoring technology for high-speed railway track structure systems have been carried out at home and abroad in recent years, and...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of existing high-speed rail track state assessment, and propose a high-speed rail track response prediction method based on sparse Bayesian width learning

Images