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System health state prediction method based on Bayesian neural network

A Bayesian network, health state technology, applied in neural learning methods, biological neural network models, neural architectures, etc., to avoid data overlap and coupling, clear method processes, and ensure prediction accuracy.

Active Publication Date: 2021-12-03
BEIHANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Able to consider the problems of overlapping multi-feature data and ambiguous node data relationships in the establishment methods of common system state prediction models, effectively deal with complex node relationships in multi-level systems, and establish health state prediction models

Method used

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  • System health state prediction method based on Bayesian neural network
  • System health state prediction method based on Bayesian neural network
  • System health state prediction method based on Bayesian neural network

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Embodiment Construction

[0024] 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.

[0025] 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 accompanying drawings and specific embodiments.

[0026] A system health state prediction method based on Bayesian neural network, comprising:

[0027] Dividing each node of the system structure to obtain a first integration node and a second integration node;

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Abstract

The invention relates to a system health state prediction method based on a Bayesian neural network. The method comprises the steps: dividing each node of a system structure to obtain a first integrated node and a second integrated node; establishing an original multi-level system Bayesian network model based on the first integration node to obtain a state distribution prediction result of the first integration node, and establishing a deep learning model and a Bayesian network model based on the second integration node to obtain a state distribution prediction result of the second integration node; and integrating the state distribution prediction result of the first integration node and the state distribution prediction result of the second integration node to realize health state prediction of the whole system. According to the method, health state monitoring data is effectively utilized, and the conditions of data overlapping and coupling are avoided; and the Bayesian neural network is used to divide and predict the system state, and the operation and maintenance of the subsequent system are effectively guided.

Description

technical field [0001] The invention belongs to the technical field of system modeling and state prediction, and in particular relates to a system health state prediction method based on a Bayesian neural network. Background technique [0002] With the continuous improvement of system function requirements and performance requirements, the complexity of system structure and function shows a trend of rapid growth. Hierarchical relationships exist, forming a multi-level system. Multi-level complex systems are widely used in important fields such as aviation, aerospace, navigation, railways, weapons and equipment, and manufacturing. The large scale and complex internal relationships of multi-level systems will cause system failures to be transmitted interactively between components and subsystems, and small failures may lead to system failure. Therefore, it is necessary to grasp the changes in the health status and future changes of multi-level systems Trends, in order to tak...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/34G06N3/04G06N3/08
CPCG06F11/3495G06N3/08G06N3/047
Inventor 王晓红王立志孙雅宁姚梦菲苏霖
Owner BEIHANG UNIV
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