Unlock instant, AI-driven research and patent intelligence for your innovation.

A method and system for detecting train brake faults based on graph convolutional neural network

A convolutional neural network and fault detection technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as low detection accuracy, and achieve the effect of ensuring safety and reliability and improving accuracy.

Active Publication Date: 2022-05-10
BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY +1
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a train brake fault detection method and system based on a graph convolutional neural network to solve the problem of low detection accuracy of the existing train brake fault detection method

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A method and system for detecting train brake faults based on graph convolutional neural network
  • A method and system for detecting train brake faults based on graph convolutional neural network
  • A method and system for detecting train brake faults based on graph convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0056] The purpose of the present invention is to provide a train brake fault detection method and system based on a graph convolutional neural network to solve the problem of low detection accuracy of the existing train brake fault detection method.

[0057] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanyin...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a method and system for detecting train brake faults based on a graph convolutional neural network. The method includes: acquiring train brake fault data and the connection relationship between monitors, Construct a train brake fault sample set according to the above connection relationship; construct a standard adjacency matrix according to the connection relationship between the monitors; construct an adaptive adjacency matrix according to the train brake fault data; according to the standard adjacency matrix and the described An adaptive adjacency matrix is ​​used to construct a train brake fault detection model; the train brake fault detection model is trained using the train brake fault sample set to obtain a trained train brake fault detection model; A dynamic fault detection model is used to detect train brake faults. The invention can effectively detect the braking fault and its fault type during the running of the train, thereby ensuring the safety and reliability of the running of the train.

Description

technical field [0001] The invention relates to the field of train brake fault detection, in particular to a train brake fault detection method and system based on a graph convolutional neural network. Background technique [0002] With the continuous development of China's high-speed railway technology, my country's railway network has been continuously improved, and the railway transportation volume has become higher and higher. Railway transportation has become one of the indispensable and important forms of transportation in China's economic and social development. The safety of railway transportation is the basic guarantee for the overall operation of the railway, the prerequisite for improving the production efficiency of railway transportation, and has a vital impact on the protection of people's life and property and the maintenance of long-term social stability. Fault detection is an inspection and testing process to judge whether there is a fault in the system and ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/2458G06N3/04G06N3/08G06Q10/00G06Q50/30
CPCG06F16/2462G06F16/2474G06Q10/20G06N3/049G06N3/08G06N3/045G06Q50/40
Inventor 段大高崔岩松韩忠明李胜男陈科良
Owner BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY