Method for predicting service lives in consideration of long-range correlation and uncertainty of components

An uncertainty and life prediction technology, which is applied in the testing of machines/structural components, testing of engines, measuring electricity, etc., can solve problems such as non-stationary temperature degradation, existence of correlation, and influence on temperature measurement, so as to improve prediction accuracy , Accurate remaining life, and the effect of improving accuracy

Inactive Publication Date: 2018-02-13
SHANDONG UNIV OF SCI & TECH
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Problems solved by technology

However, Brownian motion is actually a Markov process, i.e. memory effects are completely ignored
Still taking the blast furnace temperature as an example, the complex heat conduction in the furnace greatly affects the temperature measurement, making the temperature degradation non-stationary and correlated
[0006] Obviously,

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  • Method for predicting service lives in consideration of long-range correlation and uncertainty of components
  • Method for predicting service lives in consideration of long-range correlation and uncertainty of components
  • Method for predicting service lives in consideration of long-range correlation and uncertainty of components

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Embodiment Construction

[0048] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0049] 1. If figure 1 As shown, a life prediction method considering long-range correlation and component uncertainty includes the following steps:

[0050] Step 1: Read in M ​​groups of state monitoring degradation data, denoted as X=(X 1 ,X 2 ,...,X M );

[0051]Step 2: Perform zero-initialization processing on the degraded data, and initialize parameters for the following models, including the mean value of the drift coefficient μ α and standard deviation σ α , the nonlinear coefficient β, the diffusion coefficient σ, and the Hurst exponent H;

[0052] x(t) = αt β +σB H (t)(1);

[0053] Among them, x(t) is the degraded state at time t, α is the drift coefficient obeying the Gaussian distribution, β is the fixed nonlinear coefficient, σ is the diffusion coefficient, B H (t) represents the standard fraction Brownian motion;

[0054...

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Abstract

The invention discloses a method for predicting service lives in consideration of long-range correlation and uncertainty of components, and belongs to the field of prediction and prediction of residual service lives in health management. The method includes steps of reading state monitoring degradation data and initializing model parameters for specified drift structures (such as exponential functions, power functions and linear functions); estimating unknown parameters of models by the aid of maximum likelihood algorithms; carrying out approximation on obtained degradation processes on the basis of weak convergence criteria and computing probability density functions of residual service lives; judging fitting effects and the prediction ability of the models by the aid of AIC (akaike information criteria) and mean-square errors. Compared with existing methods, the method has the advantages of high universality and prediction precision.

Description

technical field [0001] The invention belongs to the field of remaining life prediction in prediction and health management, and in particular relates to a life prediction method considering long-range correlation and component uncertainty. Background technique [0002] For actual industrial systems, remaining life prediction is an important technology to effectively reduce maintenance costs. Based on condition monitoring data, the core of remaining life prediction technology is to estimate the probability density function of the time from the degradation process to the first reaching the predetermined failure threshold. High-precision remaining life prediction method is the theoretical basis in the field of predictive maintenance. [0003] At present, the remaining life prediction algorithms proposed by domestic and foreign scholars in the field of prediction and health management can be divided into two types based on direct monitoring data and based on indirect monitoring ...

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Application Information

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IPC IPC(8): G06F17/50G01R31/36G01M15/00
CPCG01M15/00G01R31/392G06F30/20
Inventor 周东华陈茂银席霄鹏卢晓
Owner SHANDONG UNIV OF SCI & TECH
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