Method for predicting key infrastructure fault propagation

An infrastructure and fault propagation technology, applied in electrical testing/monitoring, testing/monitoring control systems, instruments, etc., and can solve problems such as single object in traditional fault prediction methods

Active Publication Date: 2017-02-01
BEIHANG UNIV
View PDF6 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Traditional fault prediction methods are only aimed at a single object, and the prediction results can only provide limited macro information

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
  • Method for predicting key infrastructure fault propagation
  • Method for predicting key infrastructure fault propagation
  • Method for predicting key infrastructure fault propagation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0035] The present invention provides a method for microcosmic prediction of the fault status of large-scale critical infrastructure with spatial characteristics, which can predict a future failure state according to the current node status of the system and the spatial characteristics of the node, combined with the fault propagation data set of the system. Or the state of each node in the system at several moments.

[0036] like figure 1 As shown, a method for predicting critical infrastructure fault propagation of the present invention includes the following steps:

[0037] Step 1: Construct the system complex network model of the critical infrastructure system, and use the subsystems with independent functions in the critical infrastructure system as...

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 provides a method for predicting key infrastructure fault propagation. The method comprises steps that a system complex network model of a key infrastructure system is constructed; nodes of the system complex network model are numbered; the historical fault data of cascade failure of the key infrastructure system is searched, the historical fault data of the key infrastructure system is cut according to time intervals, and a fault data set of the key infrastructure system is constructed; a machine learning model is established, and input and output of the machine learning model are determined; the fault data set is processed; faults are predicted through the machine learning model. Through the method, when cascade failure of the network occurs, a state of the network at the next moment is predicted through utilizing the state information of a node itself, the state information of a next node adjacent to the node and space attributes of the node, and the useful information is provided for system microscopic dynamic evolution control.

Description

technical field [0001] The invention relates to the field of system fault prediction, in particular to a method for predicting fault propagation of critical infrastructure. Background technique [0002] System fault prediction means that during the operation of the system, it can predict the state of the system at the next or several moments according to the current operating state of the system combined with some structural characteristics, parameters, environmental conditions and historical data of the system itself, and give the system fault Trends and consequences of development. Fault prediction needs to use some data methods such as statistical analysis, fuzzy theory and neural network to analyze and calculate the fault data of the system, and establish a fault prediction model. The present invention aims to provide a method for learning and predicting cascading failure micro-behavior of large-scale critical infrastructure with spatial characteristics, using its own s...

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): G05B23/02
CPCG05B23/0229G05B23/0251G05B2219/33303
Inventor 李大庆张家全路丹杨顺昆
Owner BEIHANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products