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A Bayesian dynamic prediction method based on Markov chain Monte Carlo

A Markov chain and dynamic prediction technology, applied in the computer field, can solve problems that do not include reliability

Active Publication Date: 2019-01-18
EAST CHINA JIAOTONG UNIVERSITY
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Problems solved by technology

[0005] Most of the above studies are only aimed at the reliability of the design scheme, and do not include the reliability of the real structure

Method used

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  • A Bayesian dynamic prediction method based on Markov chain Monte Carlo
  • A Bayesian dynamic prediction method based on Markov chain Monte Carlo
  • A Bayesian dynamic prediction method based on Markov chain Monte Carlo

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Embodiment 1

[0070] First of all, this embodiment provides a certain description of the Bayes update theorem:

[0071] The Bayesian update theorem is a standard method for correcting subjective judgments about probability distributions (ie, prior probability) by applying observed phenomena in probability and statistics. The Bayes update formula is about the conditional probability and marginal probability of random events A and B. It can be expressed as: in the sample space Ω there is A 1 ,...,A n are independent and complete groups of random events, namely: A i A j =φ,P(A i )>0; In addition, an event B is also defined in the sample space Ω, namely: B∈Ω, and the random event B must be consistent with the random event A i One or more of have intersection; A∪B=Ω. If random event B occurs, then random event A i The probability of occurrence is:

[0072]

[0073] where Pr(A i ) is A i The prior probability or marginal probability of . It is called "prior" because of its probabil...

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Abstract

The invention provides a Bayesian dynamic prediction method based on Markov chain Monte Carlo, belonging to the computer technical field, comprising the following steps: according to the historical crack length of a component and corresponding time increment, a stochastic degradation model of fatigue crack damage performance of the component is established; The Bayesian dynamic prediction method based on Markov chain Monte Carlo is used to update the stochastic parameters of the stochastic degradation model of fatigue crack damage performance, and the modified parameters are obtained; According to the parameter correction value, the crack length of the component in a certain period in the future is predicted, and the safety margin equation of the fatigue crack length of the component is established according to the crack length; The fatigue crack damage degradation and time-varying reliability are evaluated according to the parameter correction and the safety margin equation of fatiguecrack length. This method reduces the influence of uncertain factors on the sampling results, and can effectively predict the occurrence of product failure events, which can provide a basis for the prediction of maintenance strategy.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to a Bayesian dynamic prediction method based on Markov chain Monte Carlo. Background technique [0002] The main tool of Bayesian decision-making is the classical Bayesian method, and its theoretical basis is Bayesian theorem. Bayes' theorem is a method of correcting the subjective judgment of the relevant probability with the observed data, created by the British mathematician Bayes. The traditional statistical inference technology does not comprehensively use the existing information, so that the credibility of the analysis conclusion is relatively reduced. The Bayesian method can combine people's subjective knowledge and empirical data to obtain more reliable inference results. In Bayesian theory, Bayesian inference and analysis can be realized by using the Bayesian formula to calculate the posterior distribution, but the complexity of using the joint probability ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F2119/04G06F30/20
Inventor 方苇陈梦成谢力许开成黄宏罗睿袁方杨超温清清
Owner EAST CHINA JIAOTONG UNIVERSITY
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