Intelligent railway quality evaluation based on unequal time interval grey evaluation model

Dongyan Dong; Yue Zhang; Junlin Sha

doi:10.1117/12.2646539

23 August 2022 Intelligent railway quality evaluation based on unequal time interval grey evaluation model

Dongyan Dong, Yue Zhang, Junlin Sha

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123301S (2022) https://doi.org/10.1117/12.2646539
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

The causes of track irregularity are determined by many factors. It is very difficult to evaluate and quantitatively analyze the causes of the natural environment. On this basis, the track irregularity is regarded as a grey system, and the GM (1,1) model is used to model and evaluate it. However, the traditional grey model with equal time intervals is used to evaluate the deterioration trend of track irregularity, which can not fully reflect the changing trend of track irregularity in a single maintenance cycle. Therefore, to reduce the impact of various factors and improve the accuracy of the assessment results, this paper combines the actual characteristics of the trend assessment of track irregularity based on the grey model theory, which optimizes and improves the traditional grey model from four directions: time interval, calculation of background value, determination of initial value, and weight of detection data. The evaluation accuracy of the model in this paper is more than 1. 64% higher than that of other comparison methods, which can reduce the solution error of the model, improve the sensitivity of the model, and make the model accurately and timely predict the deterioration of track irregularity.

Citation Download Citation

Dongyan Dong, Yue Zhang, and Junlin Sha "Intelligent railway quality evaluation based on unequal time interval grey evaluation model", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123301S (23 August 2022); https://doi.org/10.1117/12.2646539

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available