CN115809569B

CN115809569B - Reliability evaluation method and device based on coupling competition failure model

Info

Publication number: CN115809569B
Application number: CN202310049362.9A
Authority: CN
Inventors: 潘广泽; 李丹; 丁小健; 陈勃琛; 孙立军; 王远航; 刘文威; 杨剑锋
Original assignee: China Electronic Product Reliability and Environmental Testing Research Institute
Current assignee: China Electronic Product Reliability and Environmental Testing Research Institute
Priority date: 2023-02-01
Filing date: 2023-02-01
Publication date: 2023-06-20
Anticipated expiration: 2043-02-01
Also published as: CN115809569A

Abstract

The application relates to a reliability evaluation method and device based on a coupling competition failure model. The method comprises the following steps: acquiring a target correlation coefficient of a target product under an acceleration test, and determining a target linear conversion function to which the target correlation coefficient belongs; based on the target linear conversion function, establishing a nonlinear wiener process of a target product; according to the solving result of parameters in the nonlinear wiener process, a first unreliable function when the multi-element performance of the target product is degraded and fails is obtained, and a second unreliable function when the target product is suddenly failed is obtained; constructing a coupling competition failure model based on a coupling competition relation between the multiple performance degradation failure and the burst failure, a first unreliable degree function and a second unreliable degree function, and determining a target reliable degree function of a target product according to the coupling competition failure model; and obtaining a reliability evaluation result of the target product under the acceleration test according to the target reliability function. The method can improve the accuracy of the reliability evaluation result of the product.

Description

Reliability evaluation method and device based on coupling competition failure model

Technical Field

The application relates to the technical field of product performance degradation and reliability analysis, in particular to a reliability evaluation method and device based on a coupling competition failure model.

Background

The acceleration test is to accelerate the failure of the tested product through the reinforcement test condition on the premise of not changing the failure mechanism of the product so as to obtain enough information in a short time and evaluate the reliability or life index of the product under the normal use condition. Since the acceleration test can evaluate the reliability and life of the product in a short time, it is widely used for the reliability evaluation of the highly reliable long-life product.

However, the reliability evaluation method under the conventional acceleration test only considers the competition relationship between multiple degradation failures or the competition relationship between single degradation failure and burst failure, thereby causing the problem of lower accuracy of the reliability evaluation result of the product.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a reliability evaluation method and apparatus based on a coupling competition failure model, which can improve accuracy of reliability evaluation results of products.

In a first aspect, the present application provides a reliability evaluation method based on a coupling competition failure model, where the method includes:

Acquiring a target correlation coefficient of a target product under an acceleration test, and determining a target linear conversion function to which the target correlation coefficient belongs; the target correlation coefficient represents a linear relationship between performance degradation and time of the target product;

based on the target linear conversion function, establishing a nonlinear wiener process of the target product;

according to the solving result of the parameters in the nonlinear wiener process, a first unreliable function of the target product in the case of multiple performance degradation failure is obtained, and a second unreliable function of the target product in the case of sudden failure is obtained;

constructing a coupling competition failure model based on a coupling competition relation between the multi-element performance degradation failure and the burst failure, the first unreliable degree function and the second unreliable degree function, and determining a target reliable degree function of the target product according to the coupling competition failure model;

and obtaining a reliability evaluation result of the target product under the acceleration test according to the target reliability function.

In one embodiment, the obtaining the target correlation coefficient of the target product under the acceleration test and determining the target linear transformation function to which the target correlation coefficient belongs includes:

Acquiring performance degradation data and a plurality of linear conversion functions of a plurality of target products under an acceleration test;

substituting the performance degradation data of a plurality of target products into a function formed by the linear conversion functions aiming at each linear conversion function to obtain a correlation coefficient corresponding to the linear conversion function;

determining a target correlation coefficient meeting a target condition in the correlation coefficients based on the numerical value of each correlation coefficient;

and determining a target linear conversion function for establishing a nonlinear wiener process according to the target correlation coefficient.

In one embodiment, the determining a target linear transformation function for establishing a nonlinear wiener process according to the target correlation coefficient includes:

determining a maximum likelihood function corresponding to a function term of the target correlation coefficient based on the value of the target correlation coefficient;

solving a maximum likelihood function corresponding to a function term of the target correlation coefficient to obtain a value of each parameter in a target linear conversion function to which the target correlation coefficient belongs;

a target linear transformation function for establishing a nonlinear wiener process is determined based on the value of each of the parameters.

In one embodiment, after the nonlinear wiener process of the target product is established based on the target linear transformation function, the method includes:

constructing a probability density function of the target product when the multiple performance is degraded and invalid;

determining a maximum likelihood function of a nonlinear wiener process of the target product based on the probability density function;

and solving the maximum likelihood function of the nonlinear wiener process of the target product to obtain a solving result of each parameter in the nonlinear wiener process.

In one embodiment, the obtaining a first unreliable degree function of the target product when the multiple performance degradation fails according to the solving result of the parameters in the nonlinear wiener process, and obtaining a second unreliable degree function of the target product when the target product fails suddenly includes:

obtaining a solving result of each parameter in the nonlinear wiener process, and constructing an unreliable degree sub-function corresponding to each performance of the target product;

substituting the solving result into each unreliable degree sub-function corresponding to the performance to obtain a first unreliable degree function of the target product when the multi-element performance is degraded and invalid;

Establishing a burst unreliability function of the target product in burst failure, and acquiring the burst failure time of the target product;

and carrying out parameter solving processing on parameters in a maximum likelihood function corresponding to the burst unreliable function based on the burst unreliable function and the burst failure time to obtain a second unreliable function of the target product in the burst failure.

In one embodiment, the determining the target reliability function of the target product according to the coupling competition failure model includes:

establishing a maximum likelihood function corresponding to the coupling competition failure model to calculate a parameter value in the coupling competition failure model, so as to obtain a parameter solving result;

substituting the parameter solving result into the coupling competition failure model to obtain a target reliability function.

In one embodiment, the obtaining the reliability evaluation result of the target product under the acceleration test according to the target reliability function includes:

integrating the function term of the target reliability function to obtain a first average fault interval time of the target product under the acceleration test;

Performing product processing on the first average fault interval time and the acceleration factor of the target product to obtain a second average fault interval time of the target product under a normal test;

and obtaining a reliability evaluation result of the target product according to the first average fault interval time and the second average fault interval time.

In a second aspect, the present application provides a reliability evaluation device based on a coupling competition failure model, the device including:

the target linear conversion function determining module is used for acquiring a target correlation coefficient of a target product under an acceleration test and determining a target linear conversion function to which the target correlation coefficient belongs;

the nonlinear wiener process construction module is used for establishing a nonlinear wiener process of the target product based on the target linear conversion function;

the unreliable degree function determining module is used for obtaining a first unreliable degree function of the target product when the multi-element performance is degraded and invalid according to the solving result of the parameters in the nonlinear wiener process, and obtaining a second unreliable degree function of the target product when the target product is suddenly invalid;

the target reliability function determining module is used for constructing a coupling competition failure model based on the coupling competition relation between the multi-element performance degradation failure and the burst failure, the first unreliable degree function and the second unreliable degree function, and determining a target reliability function of the target product according to the coupling competition failure model;

And the reliability evaluation result acquisition module is used for acquiring the reliability evaluation result of the target product under the acceleration test according to the target reliability function.

In a third aspect, the present application provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.

In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method described above.

In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method described above.

According to the reliability evaluation method and the reliability evaluation device based on the coupling competition failure model, the target conversion function is obtained from the plurality of linear conversion functions according to the obtained target correlation coefficient, and the nonlinear wiener process of the target product is established according to the target conversion function, so that the optimization of the conversion function and the description of the nonlinear characteristics of the performance degradation of the target product can be realized, and the problem of low accuracy of the traditional reliability evaluation method is solved; by considering the coupling competition relation between the multiple performance degradation failure and the burst failure and establishing a coupling competition failure model and a target reliability function of the target product according to the first unreliable degree function and the second unreliable degree function, the accuracy of the reliability evaluation result of the target product can be further improved.

Drawings

FIG. 1 is an application environment diagram of a reliability evaluation method based on a coupled race failure model in one embodiment;

FIG. 2 is a flow chart of a reliability evaluation method based on a coupling competition failure model in one embodiment;

FIG. 3 is a flow chart of a reliability evaluation method based on a coupling competition failure model in another embodiment;

FIG. 4 is a block diagram of a reliability evaluation device based on a coupled race failure model in one embodiment;

fig. 5 is an internal structural diagram of a computer device in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.

The reliability evaluation method based on the coupling competition failure model provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 104 can obtain the target correlation coefficient of the target product under the acceleration test by strengthening the test condition of the target product, and determine the target linear transformation function to which the target correlation coefficient belongs. The server 104 can establish a nonlinear wiener process of the target product under an acceleration test according to the obtained target linear conversion function, and can obtain a first unreliable degree function of the target product when the multi-element performance is degraded and invalid by solving parameters in the nonlinear wiener process. Server 104 also obtains a second unreliability function of the target product in the event of a sudden failure. The server 104 can obtain the coupling competition failure model and the target reliability function of the target product by utilizing the first unreliable function of the target product in the multiple performance degradation failure and the second unreliable function of the target product in the burst failure according to the coupling competition relationship between the multiple performance degradation failure and the burst failure of the target product. The server 104 performs an operation process on the target reliability function, so as to obtain a reliability evaluation result of the target product under the acceleration test. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.

In one embodiment, as shown in fig. 2, a reliability evaluation method based on a coupling competition failure model is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:

step 202, obtaining a target correlation coefficient of a target product under an acceleration test, and determining a target linear conversion function to which the target correlation coefficient belongs. The target correlation coefficient represents a linear relationship between performance degradation and time of the target product.

Wherein, the target product refers to a product which can be subjected to an acceleration test.

The acceleration test refers to that under the premise of ensuring that the failure mechanism of the target product is not changed, the tested target product is accelerated to fail through the intensified test condition so as to obtain necessary information in a short time, and the reliability or life index of the target product under the normal condition is evaluated. By means of the acceleration test, the failure reason of the target product can be quickly ascertained, and the reliability index of the target product can be quickly assessed. For example, increasing the temperature of the subject target product to a temperature point exceeding the material property change or the dormant activation threshold temperature results in failure that does not occur during normal use, thereby completing the accelerated test of the subject target product.

The target correlation coefficient is a conditional correlation coefficient selected from among a plurality of correlation coefficients obtained, and is a quantity of the degree of linear correlation between the study variables. For example, the use of a target correlation coefficient may be used to describe a linear relationship between performance degradation and time of a target product.

The target linear transfer function is a linear transfer function to which the target correlation coefficient belongs. For example, the correlation coefficient obtained using the linear conversion function 1 is A ₁ The correlation coefficient obtained by using the linear conversion function 2 is A ₂ But the correlation coefficient A ₁ Greater than the correlation coefficient A ₂ Correlation coefficient A ₁ The target correlation coefficient is the linear transfer function 1 to which the target correlation coefficient belongs is the target linear transfer function.

Optionally, the server first selects a target correlation coefficient that meets the condition from the plurality of correlation coefficients, and then determines a linear conversion function to which the target correlation coefficient belongs as the target linear conversion function.

Step 204, based on the target linear conversion function, a nonlinear wiener process of the target product is established.

Wherein the nonlinear wiener process is a tool for describing nonlinear characteristics of degradation of the target product performance.

Optionally, the server converts the target linear conversion function to which the target correlation coefficient belongs

Is regarded as a parameter and substituted into another function, thereby obtaining the nonlinear wiener process of the target product under the acceleration test

，yAt the target productxThe value of the performance at the moment in time,μto the intermediate unknown parameters that need to be solved,μobeying mean value ofηSum of variances ofσ _η Normal distribution of->

As a function of the brownian drift,σ、ηandσ _η Is an intermediate unknown parameter that needs to be solved.

Step 206, according to the solving result of the parameters in the nonlinear wiener process, obtaining a first unreliable function of the target product when the multi-element performance is degraded and invalid, and obtaining a second unreliable function of the target product when the target product is suddenly invalid.

Wherein, the multiple performance degradation failure refers to gradual degradation of multiple performances of the target product and finally exceeds a specified range. A catastrophic failure refers to a sudden loss of a specified function of the target product at a point in time, also known as a hard failure.

Optionally, the server obtains parameters according to the solutionσ、ηand σ _η Can obtain the parameterμAnd parameter(s)σAndμthe values of (2) are substituted into the nonlinear wiener process to obtain a first non-characteristic when multiple performances of the target product are gradually degraded and finally exceed a specified rangeReliability function F _d (x)=1-C(1-F ₁ (x),1-F ₂ (x),…,1-F _k (x),θ)，θFor intermediate unknown parameters, C (. Cndot.) represents 1-F ₁ (x),1-F ₂ (x),…,1-F ₂ (x) Is used as a function of the connection function of (a),F _k (x) And representing the unreliable degree sub-function corresponding to the kth performance of the target product. The server also obtains a second uncertainty function F when the target product suddenly loses the specified function at a certain moment _s (x)。

And step 208, constructing a coupling competition failure model based on the coupling competition relation between the multiple performance degradation failure and the burst failure, the first unreliable degree function and the second unreliable degree function, and determining a target reliability function of a target product according to the coupling competition failure model.

The coupling competition relationship refers to an interaction and mutual influence relationship between two things.

Optionally, the server constructs a coupling competition failure model of the target product by considering the interaction and the coupling competition relation of the interaction between the multi-element performance degradation failure and the burst failure of the target product and based on the obtained first unreliable function in the multi-element performance degradation failure and the obtained second unreliable function in the burst failure, and obtains the target reliability function of the target product by solving unknown parameters in the coupling competition failure model.

And 210, obtaining a reliability evaluation result of the target product under the acceleration test according to the target reliability function.

The reliability evaluation result refers to the capability evaluation result of the element or the system for continuously realizing the function of the element or the system under the specified conditions within the given time interval.

Optionally, the server solves the average interval time of the faults of the target product by using the target reliability function, and evaluates the reliability of the target product under the acceleration test according to the average interval time of the faults, so as to obtain the capability evaluation result of the target product for continuously realizing the functions of the target product under the specified conditions within the given time interval.

According to the reliability evaluation method based on the coupling competition failure model, the target conversion function is obtained from the plurality of linear conversion functions according to the obtained target correlation coefficient, and the nonlinear wiener process of the target product is established according to the target conversion function, so that the optimization of the conversion function and the description of nonlinear characteristics of performance degradation of the target product can be realized, and the problem of low accuracy of the traditional reliability evaluation method is solved; by considering the coupling competition relation between the multiple performance degradation failure and the burst failure and establishing a coupling competition failure model and a target reliability function of the target product according to the first unreliable degree function and the second unreliable degree function, the accuracy of the reliability evaluation result of the target product can be further improved.

In one embodiment, obtaining a target correlation coefficient of a target product under an acceleration test, and determining a target linear transformation function to which the target correlation coefficient belongs, includes:

and acquiring performance degradation data and a plurality of linear conversion functions of a plurality of target products under an acceleration test.

And substituting the performance degradation data of a plurality of target products into a function formed by the linear conversion functions aiming at each linear conversion function to obtain a correlation coefficient corresponding to the linear conversion function.

And determining a target correlation coefficient meeting a target condition in the correlation coefficients based on the numerical value of each correlation coefficient.

A target linear transformation function for establishing a nonlinear wiener process is determined based on the target correlation coefficient.

The performance degradation data are obtained by testing the target product in the test process, and rich and key information can be provided for life prediction and reliability evaluation of the target product.

The linear transformation function mainly comprises

Etc., xTime of acceleration test +.>

Is time ofxIs a function of (a) and (b),aandbis an unknown parameter.

Optionally, the server obtains performance degradation data obtained by multiple performance tests on the target product under the acceleration test x _i ,y _i )，x _i For the time of the ith test,y _i the test performance value of the ith time. The server also obtains a common linear conversion function

. The server obtains performance degradation data [ ]x _i ,y _i ) Substituting into a function composed of linear conversion function as parameter to obtain

（1）

In the formula (1),x _i for the time of the ith test,y _i for the test performance value of the i-th time,

mean value of all performance test values, +.>

Indicating all->

Average value of>

Represents a linear conversion function substituted into the ith test data, and r represents a correlation coefficient. The server calculates the correlation coefficients of all target products under the acceleration test and solves the average value of the correlation coefficients of all target products under each linear conversion function, thereby obtaining the linear conversion function corresponding to the maximum average value, namelyAnd the target linear conversion function of the target correlation function meeting the target condition. Maximum average->

The calculation formula of (2) is as follows:

（2）

in the formula (2)mFor the total number of products to be targeted,r _j and the correlation coefficient representing the performance degradation of the jth target product.

In this embodiment, by selecting the linear conversion function corresponding to the correlation coefficient of the maximum average value as the target conversion function, the relationship between the performance degradation of the target product and time can be more sufficiently represented.

In one embodiment, determining a target linear transformation function for establishing a nonlinear wiener process based on a target correlation coefficient includes:

and determining a maximum likelihood function corresponding to the function item of the target correlation coefficient based on the value of the target correlation coefficient.

And solving the maximum likelihood function corresponding to the function item of the target correlation coefficient to obtain the value of each parameter in the target linear conversion function to which the target correlation coefficient belongs.

A target linear transformation function for establishing a nonlinear wiener process is determined based on the value of each parameter.

The probability of occurrence of the event is estimated by adopting an approximate mathematical mode, and the maximum likelihood function has mathematical reasoning and rigor compared with the probability estimated according to the proportion.

Optionally, the server takes the logarithm of a function item composed by taking the target linear conversion function to which the target correlation coefficient belongs as a parameter, and obtains a maximum likelihood function corresponding to the function item based on the numerical value of the target correlation coefficient. And the server solves each parameter in the maximum likelihood function corresponding to the function item according to the solving mode of the maximum likelihood function, so as to obtain the value of each unknown parameter in the target linear conversion function. The server substitutes the solved parameter values into the target linear conversion function again, so that the target linear conversion function for constructing the nonlinear wiener process can be obtained.

In this embodiment, the unknown parameters in the target linear transformation function are solved by using the maximum likelihood function, so that the solving process of the unknown parameters can be simplified, and the speed of obtaining the solving result of the unknown parameters is improved.

In one embodiment, after establishing the nonlinear wiener process for the target product based on the target linear transfer function, the method comprises:

and constructing a probability density function of the target product when the multiple performance is degraded and invalid.

And determining a maximum likelihood function of a nonlinear wiener process of the target product based on the probability density function.

Wherein the probability density function is a function describing the likelihood that the output value of the random variable is near a certain point of value, and the probability that the value of the random variable falls within a certain region is the integral of the probability density function over that region.

Optionally, the server constructs a probability density function of the target product in the multiple performance degradation failure according to the condition of the target product in the multiple performance degradation failure under the acceleration test f(x;σ,η,σ _η ) Thus, the maximum likelihood function of the nonlinear wiener process of the target product when the multiple performance is degraded and invalid can be obtained as follows:

（3）

in the formula (3)x _ji For the performance value of the ith test of the jth target product, m represents the total number of target products, n represents the number of performance tests,σ、ηandσ _η Is an intermediate unknown parameter that needs to be solved. The server solves the equation set

Obtaining intermediate unknown parametersσ,η,σ _η Is a value of (2).

In this embodiment, the unknown parameters in the nonlinear wiener process are solved by using the maximum likelihood function, so that the solving process of each parameter in the nonlinear wiener process can be simplified, and the speed of obtaining the solving result of each parameter in the nonlinear wiener process is improved.

In one embodiment, according to a result of solving parameters in a nonlinear wiener process, obtaining a first unreliable function of a target product when multiple performance degradation fails, and obtaining a second unreliable function of the target product when the target product fails suddenly, including:

step 302, obtaining a solution result of each parameter in the nonlinear wiener process, and constructing an unreliable degree sub-function corresponding to each performance of the target product.

The unreliability sub-function refers to a function which is respectively established according to a plurality of performances of a target product. For example, for performance 1 of the target product, a corresponding uncertainty subfunction B is established ₁ Aiming at the performance 2 of the target product, a corresponding unreliable degree function B is established ₂ 。

Optionally, after the server establishes the maximum likelihood function of the nonlinear wiener process, the value of each unknown parameter in the nonlinear wiener process can be obtained by solving. The server also builds a corresponding unreliability sub-function for each performance of the target product.

And 304, substituting the solving result into the unreliable degree sub-functions corresponding to each performance to obtain a first unreliable degree function of the target product when the multi-element performance is degraded and invalid.

The first unreliability function is obtained according to a coupling competition relation when multiple performances of the target product are degraded and invalid. For example, the target product has k properties, the k properties correspond to the propertiesThe unreliability subfunctions of degradation failure are F ₁ (x),F ₂ (x),…,F _k (x) The first unreliable degree function of the target product when the multiple performance degradation fails is thatF _d (x)=1-C(1-F ₁ (x),1-F ₂ (x),…,1-F _k (x),θ) Parameters (parameters)θIs an intermediate unknown parameter.

Optionally, after obtaining the solution result of each unknown parameter in the nonlinear wiener process, the server substitutes the solution result into each unreliable degree sub-function corresponding to the performance, and then obtains the unreliable degree sub-function corresponding to the performance k

. Wherein D is the critical value of performance degradation, A and B are intermediate parameters, +.>

，

. The server combines all the unreliable degree sub-functions corresponding to the performance of the target product, so as to obtain a first unreliable degree function of the target product when the multiple performance is degraded and disabledF _d (x)=1-C(1-F ₁ (x),1-F ₂ (x),…,1-F _k (x),θ) Parameters (parameters)θIs an intermediate unknown parameter. The server adopts the maximum likelihood function to obtain parametersθValues of (2)。

Step 306, establishing a burst unreliability function of the target product in case of burst failure, and obtaining the time of the burst failure of the target product.

Wherein, the burst failure is a spontaneous unpredictable failure of the target product.

Optionally, the server establishes a burst uncertainty function of the target product in case of burst failure aiming at the burst failure of the target product

Which is provided withIn (a)α、βand x ₀ Is an intermediate unknown parameter. The server also obtains the time of each occurrence of the unexpected failure of the target product.

Step 308, performing parameter solving processing on parameters in the maximum likelihood function corresponding to the burst unreliable function based on the burst unreliable function and the burst failure time, so as to obtain a second unreliable function of the target product in the burst failure.

Optionally, the server generates a burst uncertainty function F according to the burst failure of the target product under the acceleration test _t (x) And the time of occurrence of burst failure of p target productsx ₁ ,x ₂ ,…,x _p Obtaining a maximum likelihood function lnL corresponding to the burst unreliability function:

(4)

in the formula (4)α、βand x ₀ As an intermediate unknown parameter, x _q The time when the q-th target product has burst failure. The server can obtain intermediate unknown parameters by solving the maximum likelihood function lnLα、βand x ₀ The server substitutes the solved parameter values into the burst uncertainty function F _t (x) Then, a second unreliable degree function of the sudden failure of the target product under the acceleration test is obtainedF _s (x)。

In this embodiment, the unknown parameters are added to the burst uncertainty function when the target product fails suddenlyx ₀ The application range of the unreliable degree function of the sudden failure can be enlarged, so that the application range of the constructed second unreliable degree function is wider.

In one embodiment, determining the target reliability function for the target product based on the coupling competition failure model includes:

and establishing a maximum likelihood function corresponding to the coupling competition failure model to calculate a parameter value in the coupling competition failure model, so as to obtain a parameter solving result.

The coupling competition failure model is a model which is constructed according to the coupling competition relation among various performance failure modes of a target product and an unreliable degree function corresponding to each performance failure mode and is used for calculating the reliability of the product.

The target reliability function is a function obtained after the unknown parameters in the coupling competition model are solved.

Optionally, the server uses a first unreliability function F when the multiple performance fails according to the coupling competition relationship between the multiple performance failure and the burst failure of the target product _d (x) And a second uncertainty function F in the event of a sudden failure _s (x) Based on the method, a coupling competition failure model of a target product under an acceleration test is established:

R(x)=C ₁ (1-F _d (x),1-F _s (x),θ ₁ ) (5)

in the formula (5)θ ₁ As unknown parameters, C ₁ (. Cndot.) is 1-F _d (x),1-F _s (x) Is a function of the connection of (a) to (b). The server adopts a maximum likelihood function to log unknown parameters in the coupling competition model by taking logarithm of functions at two ends of the equal sign of the coupling competition failure modelθ ₁ Solving to obtain parametersθ ₁ Is a result of the parameter solving. The server will pair the parametersθ ₁ And substituting the parameter solving result into the coupling competition failure model, thereby obtaining a target reliability function R (x) without unknown parameters.

In this embodiment, by determining the target reliability function by considering the coupling competition relationship between the multiple performance degradation failure and the burst failure, the problem of low evaluation accuracy based on the analysis method in which the single failure mode or the multiple failure modes are independent of each other can be solved.

In one embodiment, obtaining a reliability evaluation result of the target product under the acceleration test according to the target reliability function includes:

and integrating the function term of the target reliability function to obtain a first average fault interval time of the target product under the acceleration test.

And multiplying the first average fault interval time and the acceleration factor of the target product to obtain the second average fault interval time of the target product under the normal test.

The first average fault interval time and the second average fault interval time refer to average time of the target product working correctly in two adjacent fault interval periods, namely average fault-free working time, and are the quantities for marking how long the target product can work averagely. The first average fault interval is the amount of average working time of the target product under the acceleration test, and the second average fault interval is the amount of average working time of the target product under the normal test.

The acceleration factor is an acceleration effect reflecting the acceleration stress level in the accelerated life test, i.e., is a function of the acceleration stress.

The reliability evaluation result is determined according to the duration of the first average fault interval time and the second average fault interval time, and the longer the interval time is, the higher the reliability of the target product is.

Optionally, the server integrates the function term of the target reliability function R (x) to obtain a first average fault interval time of the target product representing the average time of correct operation in two adjacent fault intervals under the acceleration test

. The server multiplies the first Mean Time Between Failures (MTBF) by an acceleration factor M reflecting the acceleration effect of the acceleration stress level in the accelerated life test, so as to obtain a second Mean Time Between Failures (MTBF) representing the mean time of correct operation of the target product in two adjacent fault intervals under the normal test ₀ =m×mtbf. The server obtains the time length of the first average fault interval time and the time length of the second average fault interval time according to the time length of the first average fault interval timeAnd (5) obtaining a reliability evaluation result of the target product.

In this embodiment, the average time of the target product in the correct working period between two adjacent faults can be obtained by obtaining the average fault interval time of the target product under the acceleration test and the normal test respectively, so as to obtain the accurate reliability evaluation result of the target product.

The application scenario also provides an application scenario, and the application scenario applies the reliability evaluation method based on the coupling competition failure model. Specifically, the reliability evaluation method based on the coupling competition failure model is applied to the application scene as follows: analyzing the target product by using the performance 1 and the performance 2 of the target product, firstly fitting and optimizing the performance 1 by using a common linear conversion function to obtain

The correlation coefficient of the linear conversion function is the largest, and the fitting result is the best. Select->

The linear transformation function is used as a target linear transformation function and the values of unknown parameters a and b are solved, so that the target linear transformation function with the performance of 1 is obtained as +.>

. According to the target linear transfer function->

The established nonlinear wiener process is +.>

. Solving to obtain unknown parameters in the nonlinear wiener process through a maximum likelihood functionμ、σand σ _η To obtain the unreliable degree subfunction of the performance degradation failure of the target product performance 1 under the acceleration testF ₁ (x). Obtaining the unreliable degree subfunction of the performance 2 of the target product under the accelerated stressF ₂ (x). According to the property 1 and the property of the target productCan obtain the first unreliable degree function of the target product multi-element performance degradation failure by the coupling competition relation between 2 F _d (x)=1-C(1-F ₁ (x),1-F ₂ (x),θ) Solving to obtain parametersθ1.231, substituted parametersθValues of 1.231 to F _d (x) Then, a first unreliable degree function of the multiple performance degradation failure of the target product under the acceleration test is obtainedF _d (x)=1-C(1-F ₁ (x),1-F ₁ (x),1.231)。

Modeling the sudden failure of the target product in the acceleration test to obtain a second unreliable degree function of the sudden failure of the product under the acceleration test of the target product

. Taking into account the coupling competition relationship between the multiple performance degradation failure and the burst failure, and performing a first unreliability functionF _d (x) And a second uncertainty functionF _s (x) Based on the method, a coupling competition failure model of a target product under an acceleration test is established:R(x)=C ₁ (1-F _d (x),1-F _s (x),θ ₁ ). Solving unknown parameters in the coupling competition failure model by using a maximum likelihood function to obtainθ ₁ 1.351. Finally, the target reliability function of the target product under the acceleration test is obtainedR(x)=C ₁ (1-F _d (x),1-F _s (x),1.351). Integrating function items of the target reliability function to obtain first average fault interval time +_of the target product under the acceleration stress test>

. The acceleration factor of the target product under the acceleration test is 9.8, and the second Mean Time Between Failure (MTBF) of the target product under the normal stress test ₀ =9.8×3462h= 33927.6h. According to a first average time between failures and a second average time between failures And obtaining the reliability evaluation result of the target product by the time duration.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a reliability evaluation device based on the coupling competition failure model for realizing the reliability evaluation method based on the coupling competition failure model. The implementation scheme of the device for solving the problem is similar to that described in the above method, so specific limitations in the embodiments of the device for evaluating reliability based on the coupling competition failure model provided below can be referred to above for limitations of the method for evaluating reliability based on the coupling competition failure model, and will not be described herein.

In one embodiment, as shown in fig. 4, there is provided a reliability evaluation device based on a coupling competition failure model, including:

the target linear conversion function determining module 402 is configured to obtain a target correlation coefficient of a target product under an acceleration test, and determine a target linear conversion function to which the target correlation coefficient belongs; the target correlation coefficient represents a linear relationship between performance degradation and time of the target product.

A nonlinear wiener process construction module 404 for establishing a nonlinear wiener process for the target product based on the target linear transfer function.

The unreliable function determining module 406 is configured to obtain a first unreliable function of the target product when the multiple performance degradation fails, and obtain a second unreliable function of the target product when the target product fails suddenly according to a result of solving parameters in the nonlinear wiener process.

The target reliability function determining module 408 is configured to construct a coupling competition failure model based on the coupling competition relationship between the multiple performance degradation failure and the burst failure, the first unreliability function, and the second unreliability function, and determine a target reliability function of the target product according to the coupling competition failure model.

And the reliability evaluation result acquisition module 410 is configured to obtain a reliability evaluation result of the target product under the acceleration test according to the target reliability function.

In one embodiment, the target linear transfer function determination module includes:

and the data acquisition unit is used for acquiring performance degradation data of a plurality of target products under the acceleration test and a plurality of linear conversion functions.

And the correlation coefficient acquisition unit is used for substituting the performance degradation data of the plurality of target products into the function formed by the linear conversion functions for each linear conversion function to obtain the correlation coefficient corresponding to the linear conversion function.

And a target correlation coefficient acquisition unit configured to determine a target correlation coefficient satisfying a target condition among the correlation coefficients, based on the numerical value of each correlation coefficient.

And the target linear conversion function determining unit is used for determining a target linear conversion function for establishing a nonlinear wiener process according to the target correlation coefficient.

In one embodiment, the target linear transfer function determining unit includes:

and the maximum likelihood function determining subunit is used for determining the maximum likelihood function corresponding to the function item of the target correlation coefficient based on the numerical value of the target correlation coefficient.

And the parameter solving subunit is used for solving the maximum likelihood function corresponding to the function item of the target correlation coefficient to obtain the value of each parameter in the target linear conversion function to which the target correlation coefficient belongs.

And the target linear conversion function determining subunit is used for determining a target linear conversion function for establishing a nonlinear wiener process according to the value of each parameter.

In one embodiment, the nonlinear wiener process building block comprises:

the probability density function construction unit is used for constructing a probability density function of the target product when the multiple performance degradation fails.

And the maximum likelihood function determining unit is used for determining the maximum likelihood function of the nonlinear wiener process of the target product based on the probability density function.

And the first parameter solving unit is used for solving the maximum likelihood function of the nonlinear wiener process of the target product to obtain a solving result of each parameter in the nonlinear wiener process.

In one embodiment, the unreliability function determination module includes:

the unreliable degree sub-function construction unit is used for obtaining the solving result of each parameter in the nonlinear wiener process and constructing an unreliable degree sub-function corresponding to each performance of the target product.

And the first unreliable degree function acquisition unit is used for substituting the solving result into the unreliable degree sub-functions corresponding to each performance to obtain the first unreliable degree function of the target product when the multi-element performance is degraded and invalid.

The burst unreliable degree function construction unit is used for establishing a burst unreliable degree function of the target product in the case of burst failure and obtaining the time of the burst failure of the target product.

The second unreliable degree function obtaining unit is used for carrying out parameter solving processing on parameters in the maximum likelihood function corresponding to the burst unreliable degree function based on the burst unreliable degree function and the burst failure time to obtain a second unreliable degree function of the target product in the burst failure.

In one embodiment, the target reliability function determination module includes:

and the second parameter solving unit is used for establishing a maximum likelihood function corresponding to the coupling competition failure model so as to calculate a parameter value in the coupling competition failure model and obtain a parameter solving result.

And the target reliability function acquisition unit is used for substituting the parameter solving result into the coupling competition failure model to obtain the target reliability function.

In one embodiment, the reliability evaluation result acquisition module includes:

The first average fault interval time acquisition unit is used for carrying out integral processing on the function item of the target reliability function to obtain the first average fault interval time of the target product under the acceleration test.

And the second average fault interval time acquisition unit is used for multiplying the first average fault interval time and the acceleration factor of the target product to obtain the second average fault interval time of the target product under the normal test.

And the reliability evaluation result acquisition unit is used for acquiring the reliability evaluation result of the target product according to the first average fault interval time and the second average fault interval time.

The above-described respective modules in the reliability evaluation device based on the coupling competition failure model may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing target correlation coefficients, target linear conversion functions, nonlinear wiener processes of target products, solving results of parameters in the nonlinear wiener processes, first unreliable degree functions, second unreliable degree functions, target reliable degree functions and reliability evaluation result data of the target products under an acceleration test. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a reliability evaluation method based on a coupling competition failure model.

It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:

acquiring a target correlation coefficient of a target product under an acceleration test, and determining a target linear conversion function to which the target correlation coefficient belongs; the target correlation coefficient represents a linear relationship between performance degradation and time of the target product. And establishing a nonlinear wiener process of the target product based on the target linear conversion function. According to the solving result of the parameters in the nonlinear wiener process, a first unreliable degree function of the target product when the multiple performance is degraded and invalid is obtained, and a second unreliable degree function of the target product when the target product is suddenly invalid is obtained. Based on the coupling competition relation between the multiple performance degradation failure and the burst failure, the first unreliable degree function and the second unreliable degree function, a coupling competition failure model is constructed, and the target reliable degree function of the target product is determined according to the coupling competition failure model. And obtaining a reliability evaluation result of the target product under the acceleration test according to the target reliability function.

In one embodiment, the processor when executing the computer program further performs the steps of:

and acquiring performance degradation data and a plurality of linear conversion functions of a plurality of target products under an acceleration test. And substituting the performance degradation data of a plurality of target products into a function formed by the linear conversion functions aiming at each linear conversion function to obtain a correlation coefficient corresponding to the linear conversion function. And determining a target correlation coefficient meeting a target condition in the correlation coefficients based on the numerical value of each correlation coefficient. A target linear transformation function for establishing a nonlinear wiener process is determined based on the target correlation coefficient.

and determining a maximum likelihood function corresponding to the function item of the target correlation coefficient based on the value of the target correlation coefficient. And solving the maximum likelihood function corresponding to the function item of the target correlation coefficient to obtain the value of each parameter in the target linear conversion function to which the target correlation coefficient belongs. A target linear transformation function for establishing a nonlinear wiener process is determined based on the value of each parameter.

And constructing a probability density function of the target product when the multiple performance is degraded and invalid. And determining a maximum likelihood function of a nonlinear wiener process of the target product based on the probability density function. And solving the maximum likelihood function of the nonlinear wiener process of the target product to obtain a solving result of each parameter in the nonlinear wiener process.

and obtaining a solving result of each parameter in the nonlinear wiener process, and constructing an unreliable degree sub-function corresponding to each performance of the target product. Substituting the solving result into the unreliable degree sub-function corresponding to each performance to obtain a first unreliable degree function of the target product when the multi-element performance is degraded and invalid. And establishing a burst unreliability function of the target product in case of burst failure, and acquiring the time of the burst failure of the target product. And carrying out parameter solving processing on parameters in the maximum likelihood function corresponding to the burst unreliable function based on the burst unreliable function and the burst failure time to obtain a second unreliable function of the target product in the burst failure.

And establishing a maximum likelihood function corresponding to the coupling competition failure model to calculate a parameter value in the coupling competition failure model, so as to obtain a parameter solving result. Substituting the parameter solving result into the coupling competition failure model to obtain the target reliability function.

and integrating the function term of the target reliability function to obtain a first average fault interval time of the target product under the acceleration test. And multiplying the first average fault interval time and the acceleration factor of the target product to obtain the second average fault interval time of the target product under the normal test. And obtaining a reliability evaluation result of the target product according to the first average fault interval time and the second average fault interval time.

In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:

In one embodiment, the computer program when executed by the processor further performs the steps of:

In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:

acquiring a target correlation coefficient of a target product under an acceleration test, and determining a target linear conversion function to which the target correlation coefficient belongs; the target correlation coefficient represents a linear relationship between performance degradation and time of the target product. And establishing a nonlinear wiener process of the target product based on the target linear conversion function. According to the solving result of the parameters in the nonlinear wiener process, a first unreliable degree function of the target product when the multiple performance is degraded and invalid is obtained, and a second unreliable degree function of the target product when the target product is suddenly invalid is obtained. The target reliability function of the target product is determined based on the coupling competition relationship between the multiple performance degradation failure and the burst failure, the first unreliability function, and the second unreliability function. And obtaining a reliability evaluation result of the target product under the acceleration test according to the target reliability function.

It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to comply with the related laws and regulations and standards of the related countries and regions.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile memory may include Read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high density embedded nonvolatile memory, resistive random access memory (ReRAM), magnetic random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (FerroelectricRandom Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, and the like. Volatile memory can include random access memory (RandomAccess Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (DynamicRandom Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.

Claims

1. The reliability evaluation method based on the coupling competition failure model is characterized by comprising the following steps of:

acquiring a target correlation coefficient of a target product under an acceleration test, and determining a target linear conversion function to which the target correlation coefficient belongs; the target correlation coefficient represents a linear relationship between performance degradation and time of the target product; the formula of the target correlation coefficient comprises the time x _i As a function of parameters

Sum function->

The method comprises the steps of carrying out a first treatment on the surface of the The target linear conversion function is determined by the average value of correlation coefficients of a plurality of target products under each linear conversion function; the function is

Is a function->

Average value of (2), said function->

For the accelerated test time ofxIs a function of (2);

according to the solving result of the parameters in the nonlinear wiener process, a first unreliable function of the target product in the case of multiple performance degradation failure is obtained, and a second unreliable function of the target product in the case of sudden failure is obtained; the second unreliability function is composed of a burst unreliability function based on the target product in case of burst failure

And time of burst failure, determining the result of parameter solving of the parameters in the maximum likelihood function corresponding to the burst unreliability function, wherein x represents the time of acceleration test,α、βandx ₀ Is an intermediate unknown parameter;

constructing a coupling competition failure model based on a coupling competition relation between the multi-element performance degradation failure and the burst failure, the first unreliable degree function and the second unreliable degree function, and determining a target reliable degree function of the target product according to the coupling competition failure model; the coupling competition failure model is determined by the result of connecting the first unreliable degree function and the second unreliable degree function by a connecting function;

2. The method according to claim 1, wherein the obtaining the target correlation coefficient of the target product under the acceleration test and determining the target linear transformation function to which the target correlation coefficient belongs comprises:

3. The method of claim 2, wherein determining a target linear transformation function for establishing a nonlinear wiener process based on the target correlation coefficient comprises:

4. The method of claim 1, wherein after establishing the nonlinear wiener process for the target product based on the target linear transfer function, comprising:

5. The method according to claim 1, wherein the obtaining a first unreliable function of the target product when the multiple performance degradation fails according to the solving result of the parameters in the nonlinear wiener process, and obtaining a second unreliable function of the target product when the target product fails suddenly includes:

6. The method of claim 1, wherein said determining a target reliability function for the target product based on the coupling competition failure model comprises:

7. The method according to claim 1, wherein the obtaining the reliability evaluation result of the target product under the acceleration test according to the target reliability function includes:

8. A reliability evaluation device based on a coupling competition failure model, the device comprising:

the target linear conversion function determining module is used for acquiring a target correlation coefficient of a target product under an acceleration test and determining a target linear conversion function to which the target correlation coefficient belongs; the formula of the target correlation coefficient comprises the time x _i As a function of parameters

Sum function->

The method comprises the steps of carrying out a first treatment on the surface of the The target linear conversion function is determined by the average value of correlation coefficients of a plurality of target products under each linear conversion function; said function- >

Is a function->

Average value of (2), said function->

For the accelerated test time ofxIs a function of (2);

unreliability functionThe determining module is used for obtaining a first unreliable degree function of the target product when the multiple performance is degraded and invalid according to the solving result of the parameters in the nonlinear wiener process, and obtaining a second unreliable degree function of the target product when the target product is suddenly invalid; the second unreliability function is composed of a burst unreliability function based on the target product in case of burst failure

the target reliability function determining module is used for constructing a coupling competition failure model based on the coupling competition relation between the multi-element performance degradation failure and the burst failure, the first unreliable degree function and the second unreliable degree function, and determining a target reliability function of the target product according to the coupling competition failure model; the coupling competition failure model is determined by the result of connecting the first unreliable degree function and the second unreliable degree function by a connecting function;

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.