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

A Bayesian-based complex system reliability analysis method

An analysis method and complex system technology, applied in reliability engineering, Bayesian-based complex system reliability analysis field, to achieve the effect of improving prediction and estimation accuracy and improving accuracy

Active Publication Date: 2019-04-26
ARMY ENG UNIV OF PLA
View PDF3 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In order to solve the above-mentioned defects in the prior art, the present invention aims to provide a Bayesian-based complex system reliability analysis method, which can integrate information from various sources (including expert engineering judgment and test data), and in In the case of limited system test data or no system test data, the reliability of complex systems and their components can be predicted consistently, which improves the accuracy of predicting the reliability of complex systems and their components

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 Bayesian-based complex system reliability analysis method
  • A Bayesian-based complex system reliability analysis method
  • A Bayesian-based complex system reliability analysis method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0043] Embodiment Bayesian-based complex system reliability analysis method

[0044] Using this embodiment to study complex system C 1 reliability, including the following steps in sequence:

[0045] a. The complex system C 1 According to the structure diagram of the system, it is divided into four-level nested subsystems and components, such as figure 1 The event tree diagram is shown to describe C 1 , the complex system C 1 by C 2 、C 3 、C 4 Composition; C 2 by C 5 、C 6 、C 7 (by C 10 to C 17 composition) composition, C 4 by C 8 、C 9 Composition, components and subsystems are connected in series to form the entire complex system C 1 , that is, in order to make the whole system C 1 To run work, all components and subsystems are required to run work. e.g. Subsystem C 7 Probability of normal work p 7 equal to the probability p 10 to p 17 product of C 10 to C 17 Every component is fully functional. thereby,

[0046]

[0047] Similarly, subsystem C 4 nor...

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 Bayesian-based complex system reliability analysis method. The method sequentially comprises the following steps of: firstly, describing a complex system as an event tree diagram consisting of subsystems and components; Secondly, establishing a likelihood function of system reliability; Then establishing priori distribution, and integrating priori distribution of three types of components with accurate engineering judgment, component grouping information and no accurate engineering judgment given by a plurality of experts to obtain priori distribution of the system component; And finally, fusing priori distribution, system test data and subsystem test data of the complex system by using a Markov chain Monte Carlo method to obtain the reliability of the complex system and components. The prediction and estimation precision of the reliability of the complex system can be greatly improved. The method is suitable for the technical field of reliability engineering.

Description

technical field [0001] The invention belongs to the field of system analysis and relates to reliability engineering technology, specifically a Bayesian-based complex system reliability analysis method. Background technique [0002] With the increasing complexity and cost of some systems, comprehensive testing of the system gradually becomes unfeasible. If traditional methods are used to predict and evaluate the reliability of the system, its accuracy will be greatly reduced. For example, the military usually reserves a large number of weapons, ammunition and spare parts. How to evaluate and analyze the reliability of such complex systems is not only related to the management and maintenance of these weapon systems, but also has an impact on training and combat use. However, as weapons become more and more advanced , the cost is getting higher and higher, and destructive system-wide testing has become very infeasible. Second, in order to meet operational requirements, modern ...

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): G06F17/50
CPCG06F30/20
Inventor 苏续军胡起伟赵新会于贵波邓士杰袁占杰熊超张晓良
Owner ARMY ENG UNIV OF PLA
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