A fatigue crack growth prediction method based on improved particle filter algorithm

A fatigue crack propagation, particle filter algorithm technology, applied in computing, computer-aided design, special data processing applications, etc., can solve the problems of slow convergence of model parameters and poor particles.

Inactive Publication Date: 2019-01-04
BEIHANG UNIV
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Problems solved by technology

For the state transfer of model parameters, in order to alleviate the problem of particle impoverishment, the existing methods usually adopt the method of adding an artificial dynamic noise, and this method causes the model parameters to converge slowly

Method used

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  • A fatigue crack growth prediction method based on improved particle filter algorithm
  • A fatigue crack growth prediction method based on improved particle filter algorithm
  • A fatigue crack growth prediction method based on improved particle filter algorithm

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Embodiment Construction

[0077] The method of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments of the present invention.

[0078] figure 1 It is a flow chart of the fatigue crack growth prediction method based on the improved particle filter algorithm of the present invention.

[0079] Such as figure 1 As shown, the fatigue crack growth prediction method based on the improved particle filter algorithm specifically includes the following steps:

[0080] Step 11: The step of defining state model and observation model.

[0081]

[0082]

[0083] Among them, k refers to the kth discrete moment, x k is the state vector of the system, x k =f(x k-1 , ω k ) is the state model, ω k is the state transition noise, the state model defines the state transition probability p(x k |x k-1 ), so it is also called the state transition equation; z k is the observation vector of the state, z k =g(x k ,ν k ) is the observation m...

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Abstract

The invention discloses a fatigue crack growth prediction method based on an improved particle filter algorithm, comprising the following steps: A, defining a state model and an observation model; B,transfering the model parameters; C, carrying out crack state transfer; D, when there is a new crack monitoring value, the particle value being brought into the observed likelihood probability densityfor calculation, and the normalized weight value of the particle being obtained; the posterior distribution of crack length and the posterior distribution of model parameters being obtained; E, the parameter of the state model being taken as the propagation of the crack length to obtain a new particle set of the crack length and the model parameters; F, the crack length and the model parameter particle set being brought into the state transfer equation to realize the prediction of the crack development trend, and the probability distribution of the crack length at any time being obtained; fora given crack length threshold, the probability distribution of residual life at any time being calculated. By adopting the invention, the convergence speed of the parameters can be improved and theprediction accuracy can be improved through the parameter transfer process of the new model.

Description

technical field [0001] The invention relates to the field of fault prediction and health management, in particular to a fatigue crack growth prediction method based on an improved particle filter algorithm. Background technique [0002] State prediction is an important part of structural health assessment, including the prediction of structural damage development trend and remaining life. Among them, fatigue crack damage is one of the most common damage forms in metal structures, and the prediction of fatigue crack growth includes the prediction of crack development trend and remaining life. At present, the fatigue crack growth model based on fracture mechanics is mainly used to predict it, such as Paris model, NASGRO model, etc., and its model parameters are usually obtained by fitting laboratory data or based on experience. However, since the fatigue crack growth will be affected by various uncertain factors, such as material properties, loads, environmental factors and i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/20G06F2111/10G06F2119/04
Inventor 张卫方李宁刘晓鹏戴伟任飞飞金博
Owner BEIHANG UNIV
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