Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Maglev train levitation system fault detection method based on data driving

A technology for maglev trains and system failures, which can be used in railway vehicle testing, complex mathematical operations, etc., and can solve problems such as high model accuracy requirements

Active Publication Date: 2020-08-14
NAT UNIV OF DEFENSE TECH +1
View PDF6 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional fault diagnosis methods are mainly based on model expansion, but they have higher requirements for model accuracy

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
  • Maglev train levitation system fault detection method based on data driving
  • Maglev train levitation system fault detection method based on data driving
  • Maglev train levitation system fault detection method based on data driving

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0087] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. And the features in the embodiments can be combined with each other.

[0088] figure 1 It is a flow chart of a data-driven fault detection method for a suspension system of a maglev train in the present invention. Such as figure 1 As shown, a data-driven fault detection method for the suspension system of the maglev train provided by the embodiment of the present invention includes the following steps: S1, using the data identification of the suspension system to obtain the residual generator; S2, according to the data of the suspension system under normal operation Statistical feature quantities related to fault detection are obtained through training; S3. Construct fault detection statistics according to sample data to p...

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 discloses a maglev train levitation system fault detection method based on data driving. The maglev train levitation system fault detection method comprises the following steps: S1, obtaining a residual generator through levitation system data identification; S2, according to data training under normal work of a suspension system, obtaining statistical characteristic quantity relatedto fault detection; and S3, constructing fault detection statistics according to the sample data to perform fault detection. According to the diagnosis method, the support of a system model is not needed, the residual generator is directly identified according to the suspension system data, whether the fault occurs or not can be directly judged by comparing the input data with a threshold value set by the training data, the uncertainty caused by the model is reduced, and the fault detection is accurate.

Description

technical field [0001] The invention belongs to the technical field of maglev trains, and in particular relates to a data-driven fault detection method for the levitation system of the maglev train. Background technique [0002] With the popularization of maglev trains, the safety and reliability of the levitation system has attracted more and more attention. During the operation of the maglev train, once the suspension system fails, the train will not be able to operate. If the system failure can be detected quickly when the suspension system failure occurs, accidents will be avoided to a large extent. The traditional fault diagnosis method is mainly based on model development, but it requires high accuracy of the model. Therefore, how to accurately detect the failure of the suspension system without the support of the system model, so as to reduce the uncertainty caused by the model, has become an urgent problem to be solved by those skilled in the art. Contents of the...

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 Applications(China)
IPC IPC(8): G06F17/18G06F17/16G01M17/08
CPCG06F17/18G06F17/16G01M17/08
Inventor 王志强龙志强高明罗婕翟明达张丽崔玉萌
Owner NAT UNIV OF DEFENSE TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com