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

A fault identification method and system based on neural network self-learning

A neural network and fault identification technology, applied in the direction of biological neural network models, can solve problems such as heavy workload, low efficiency, and high risk, and achieve the effect of accelerating speed, saving labor costs, and speeding up fault identification

Active Publication Date: 2017-03-29
BEIJING TAILEDE INFORMATION TECH
View PDF2 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the technical problems of heavy workload, low efficiency and high risk in manual diagnosis of railway signal system faults in the prior art, the present invention provides a fault identification method and system based on neural network self-learning

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 fault identification method and system based on neural network self-learning
  • A fault identification method and system based on neural network self-learning
  • A fault identification method and system based on neural network self-learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0037] The present invention will be described in detail below through specific embodiments and accompanying drawings.

[0038] A fault identification method and system based on neural network self-learning in this embodiment is composed of the following parts: CSM-based data acquisition subsystem, data preprocessing subsystem, feature selection subsystem, model training subsystem, real-time data analysis subsystem system and self-learning subsystem. It is used to solve technical problems such as large workload, low efficiency and high risk in manual diagnosis of railway signal system faults in the prior art.

[0039] The neural network is mainly composed of neurons, and the structure of neurons is as follows: figure 2 As shown, a1~an are the components of the input vector

[0040]w1~wn are the weights of each synapse of neurons

[0041] b is bias

[0042] f is a transfer function, usually a nonlinear function. Generally, there are sigmod(), traind(), tansig(), hardlim()...

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

A fault recognition method and system based on neural network self-learning. The method comprises: 1) monitoring and collecting various set monitoring quantities of track traffic equipment, and converting the collected monitoring data into sample data applicable to neural network training; 2) classifying the sample data according to the types of faults, and obtaining a sample data set corresponding to each type of fault; 3) designing one neural network for each type of fault, then using a sample data set of the fault for training, and obtaining a recognition model of the type of fault; and 4) fusing recognition models of all the types of faults into one neutral network, and carrying out fault recognition on the monitoring data collected in real time. The method can calmly cope with complex equipment faults and train operation accidents.

Description

technical field [0001] The invention provides a fault identification method and system based on neural network self-learning, which relates to technical fields such as railway signal data, railway communication data, railway knowledge data, system alarm data, machine learning, neural network, self-learning, expert system, etc. In order to solve the problems faced by the data analysis of rail transit monitoring data. Background technique [0002] In order to improve the modern maintenance level of my country's railway signal system equipment, since the 1990s, TJWX-I and TJWX-2000 have been independently developed and continuously upgraded signal centralized monitoring CSM systems. At present, most of the stations have adopted computer monitoring system to realize the real-time monitoring of the signal equipment status of the station, and by monitoring and recording the main operating status of the signal equipment, it provides a basis for the electric department to grasp 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 Patents(China)
IPC IPC(8): G06N3/02
CPCG06N3/02
Inventor 鲍侠
Owner BEIJING TAILEDE INFORMATION 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