Rail surface damage identification method, system and device based on improved discrete entropy

By improving the discrete entropy method and using the normal cumulative distribution function and Rényi entropy calculation, the problems of inaccurate entropy values ​​and loss of amplitude information in the existing technology are solved, realizing high-precision identification of rail surface damage and improving the accuracy and noise resistance of identification.

CN116305908BActive Publication Date: 2026-01-06BEIJING JIAOTONG UNIV +1
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Patent Information

Application Number
CN202310234253.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-03-06
Publication Date
2026-01-06
Estimated Expiration
2043-03-06

AI Technical Summary

Technical Problem

Existing rail surface damage identification methods based on information entropy suffer from inaccurate entropy measurement, loss of amplitude information, and lack of preprocessing, resulting in low identification accuracy and sensitivity to noise.

Method used

An improved discrete entropy method is adopted to standardize rail profile data through a normal cumulative distribution function, generate vector groups and calculate weighted probability distributions, and use Rényi entropy to calculate improved discrete entropy values ​​to identify rail surface damage.

Benefits of technology

It improves the accuracy and noise resistance of rail surface damage identification, can extract amplitude information more accurately, reduce information loss, and achieve high-precision automated identification.

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Abstract

The application discloses a rail surface damage identification method and system based on improved discrete entropy, and equipment. The method comprises the following steps: adopting a normal cumulative distribution function to perform standardization processing on an original rail profile data sequence; generating a vector group based on the standardized sequence; corresponding each vector in the vector group with each discrete mode, and calculating the weighted probability distribution of each discrete mode according to the complexity of each vector; calculating the improved discrete entropy entropy value by adopting Rényi entropy according to the weighted probability distribution; obtaining the entropy value distribution range of each type of damage; and identifying the rail surface damage based on the improved discrete entropy entropy value and the entropy value distribution range. The application can automatically and high-precisely realize the extraction of various damage characteristics, mine valuable information in the damage data, and provide decision-making help and information support for railway staff.
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