A Modeling Method Oriented to Product Multidimensional Correlation Degeneration Failure

A modeling method and degradation model technology, applied in special data processing applications, instruments, electrical digital data processing, etc., to achieve the effect of strong adaptability and promotion ability

Inactive Publication Date: 2017-12-26
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0006] The purpose of the present invention is in order to overcome the defect of existing reliability analysis method based on degradation, establishes the reliability analysis method of more flexible and more general multi-dimensional correlation degradation

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  • A Modeling Method Oriented to Product Multidimensional Correlation Degeneration Failure
  • A Modeling Method Oriented to Product Multidimensional Correlation Degeneration Failure
  • A Modeling Method Oriented to Product Multidimensional Correlation Degeneration Failure

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Embodiment Construction

[0033] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following describes the present invention in further detail with reference to the accompanying drawings and taking a 3-dimensional correlation degradation process as an example. The specific embodiments described here are only used to explain the present invention, but not used to limit the present invention.

[0034] Such as figure 1 As shown, the steps include:

[0035] Step 1: Collect and process the degradation data of each feature quantity.

[0036] The present invention takes the fatigue crack degradation data collected by the gear fatigue test as an example. All gear samples were tested at the same time point, and during the test, crack data was collected for all gear samples every 0.01 million revolutions, and the unit of crack size was inches.

[0037] In order to verify the degradation model based on D-Vine Copula mentioned above, we selected the fatigue crack ...

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Abstract

The invention discloses a modeling method for product multi-dimensional correlation degradation failure; it specifically includes: determining the reliability characteristic quantity of the product, collecting and processing the degradation data of each characteristic quantity, establishing the degradation model of each characteristic quantity, estimating each Parameters in the characteristic quantity degradation model, using D‑Vine Copula to establish a multidimensional correlation degradation model, and determining the types and parameter values ​​of all bivariate Copula functions. The present invention uses D-Vine Copula to connect the joint density function of multi-dimensional correlation variables and its edge density function with multiple bivariate Copula functions, which can effectively deal with multi-dimensional degradation processes with complex correlations, and has better adaptability and promotion ability.

Description

Technical field [0001] The invention belongs to the technical field of reliability analysis of electromechanical products, and in particular is a reliability analysis method for products with multi-dimensional correlation degradation and failure. Background technique [0002] Reliability technology has become an important way to improve product efficiency and reduce product life cycle costs, and it is the focus of enterprise product competition. For some high-reliability and long-life key components in high-tech weaponry, nuclear power plants, aerospace and other fields, the reliability issues are particularly important. Due to the long failure time of high-tech complex products and it is difficult to obtain a large amount of failure time data, traditional reliability analysis methods based on failure time can no longer satisfy the product reliability analysis in this situation. The reliability analysis method based on degradation was put forward under this background and receiv...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/50
Inventor 张志鹏刘宇游志毅姜涛
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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