A multi-stress accelerated life test prediction method based on grey support vector machine

An accelerated life test, support vector machine technology, applied in computer parts, instruments, character and pattern recognition and other directions, can solve problems such as establishment, avoid systematic errors, facilitate practical engineering applications, strong engineering applicability and versatility Effect

Active Publication Date: 2011-12-07
BEIHANG UNIV
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Problems solved by technology

[0011] The purpose of the present invention is to overcome the problems of establishing accelerated models and solving multiple likelihood equations in the traditi

Method used

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  • A multi-stress accelerated life test prediction method based on grey support vector machine
  • A multi-stress accelerated life test prediction method based on grey support vector machine
  • A multi-stress accelerated life test prediction method based on grey support vector machine

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Embodiment

[0064] In this embodiment, a Monte Carlo simulation method is used to verify the correctness of a gray support vector machine-based multi-stress accelerated life test prediction method proposed by the present invention.

[0065] Assuming that the life of a product obeys the Weibull distribution, its failure distribution function is

[0066] F(x)=1-exp{(t / η) m}, t≥0 (4)

[0067] Among them, m is the shape parameter. Since the failure mechanism of the product remains unchanged in the accelerated life test, m does not change with the change of stress and is a constant. η is the characteristic life, if the life of the product is mainly affected by temperature and electrical stress, then the acceleration relationship between the above stress and η obeys the generalized Eyling model, that is

[0068] η = A T exp ( B kT ) exp ...

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Abstract

The invention provides a grey support vector machine-based multi-stress accelerated life testing forecasting method, and belongs to the technical field of life forecasting. The method comprises eight steps, namely acquisition of multi-stress accelerated life testing data, determination of reliability with an empirical distribution function method, level ratio inspection of product failure time data, accumulated generating operation (AGO) of the product failure time data, construction of a support vector machine forecasting model, forecasting with the constructed support vector machine model, reduction of an AGO generating sequence forecasting value and life distribution fitting. The grey support vector machine-based multi-stress accelerated life testing forecasting method provided by the invention can be used for forecasting without knowing the information such as a specific accelerated model and product life distribution and the like, so that difficulty in establishing the accelerated model and introduction of system errors in forecasting are avoided, a complex multiplex likelihood equation set does not need to be solved, and the method has stronger engineering applicability and universality for different products or stress categories.

Description

technical field [0001] The invention belongs to the technical field of life prediction, and in particular relates to a multi-stress accelerated life test life prediction method based on a gray support vector machine. Background technique [0002] Accelerated Life Testing (ALT) method is an effective way to quickly evaluate the life and reliability indicators of long-life and high-reliability products. The key to evaluating the life characteristics of products under normal stress levels using accelerated life test data is to establish the relationship between life characteristics and stress levels, which is commonly known as the acceleration model, also known as the acceleration equation. [0003] At present, in the study of acceleration models, single-stress acceleration models are relatively mature, such as the Arrhenius model and Eyring model for temperature stress, and the inverse power law model and exponential model for electrical stress. However, the environmental str...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 孙富强李晓阳姜同敏
Owner BEIHANG UNIV
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