CN111639410B - Reliability enhancement test quantitative evaluation method and device and storage medium - Google Patents
Reliability enhancement test quantitative evaluation method and device and storage medium Download PDFInfo
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Abstract
The application relates to a reliability enhancement test quantitative evaluation method, a reliability enhancement test quantitative evaluation device and a storage medium. The reliability enhancement test quantitative evaluation method comprises the following steps: determining the stress type and the test stage of the reliability strengthening test of the electronic product to be tested, and acquiring a fault processing strategy of the reliability strengthening test; the fault processing strategy comprises a fault processing mode; performing reliability enhancement test on the electronic product to be tested to obtain fault data; based on the fault processing mode and the stress type and the test stage, extrapolating fault data by adopting a corresponding acceleration model to obtain fault data under a normal stress level; and performing reliability evaluation on the fault data under the normal stress level to obtain the reliability index of the electronic product to be tested. The application realizes the quantitative evaluation of reliability test data.
Description
Technical Field
The present disclosure relates to the field of electronic product testing technologies, and in particular, to a method and an apparatus for quantitative evaluation of reliability enhancement tests, and a storage medium.
Background
In the development process of modern weaponry, not only higher requirements are put on the performance indexes of the weaponry, but also the requirements of the weaponry for higher reliability are particularly emphasized. The reliability level of the product is however an ever increasing, spiral-up process.
The reliability increase test plays an important role in guaranteeing the improvement of the reliability level of the weapon equipment, the design defects of the product can be exposed through the reliability increase test, improvement measures can be taken, the purpose of improving the reliability level of the product is achieved, and meanwhile, the reliability index of the product can be quantitatively evaluated. The reliability strengthening test is taken as an excitability test, the thinking of the traditional simulation test is broken through, weak links of products in the aspects of design, process, selection and manufacture of components and parts are accelerated by a method of artificially applying high test stress, and the aim of greatly improving the reliability level of the products in a short time is fulfilled by adopting improvement measures.
In the implementation process, the inventor finds that at least the following problems exist in the conventional technology: the reliability of the product cannot be quantitatively evaluated by the current reliability strengthening test.
Disclosure of Invention
In view of the above, it is desirable to provide a reliability enhancement test quantitative evaluation method, a reliability enhancement test quantitative evaluation device, and a storage medium, which can quantitatively evaluate a product.
In order to achieve the above object, in one aspect, an embodiment of the present invention provides a reliability enhancement test quantitative evaluation method, including:
determining the stress type and the test stage of the reliability strengthening test of the electronic product to be tested, and acquiring a fault processing strategy of the reliability strengthening test; the fault processing strategy comprises a fault processing mode;
performing reliability enhancement test on the electronic product to be tested to obtain fault data;
based on the fault processing mode and the stress type and the test stage, extrapolating fault data by adopting a corresponding acceleration model to obtain fault data under a normal stress level;
and performing reliability evaluation on the fault data under the normal stress level to obtain the reliability index of the electronic product to be tested.
In one embodiment, the step of determining the stress type and the test stage of the reliability enhancement test of the electronic product to be tested includes:
and carrying out sensitive stress analysis on the electronic product to be tested, and determining each stress type and test stage of the reliability strengthening test.
In one embodiment, the reliability indicator includes an MTBF value;
the method comprises the following steps of evaluating the reliability of fault data under a normal stress level to obtain the reliability index of the electronic product to be tested, wherein the steps comprise:
evaluating fault data under normal stress level by adopting a reliability growth evaluation model to obtain the MTBF value of the electronic product to be tested
In one embodiment, the fault data includes failure data; the acceleration model comprises a temperature stress acceleration model, a vibration stress acceleration model and a temperature cycle acceleration model.
In one embodiment, the failure handling mode comprises post-incident correction;
based on the fault processing mode and the stress type and the test stage, the step of extrapolating the fault data by adopting a corresponding acceleration model to obtain the fault data under the normal stress level comprises the following steps:
processing failure data under each temperature stress step based on a temperature stress acceleration model, and sequentially performing failure distribution function and likelihood function processing on the processed failure data to obtain acceleration factors of the electronic product to be tested under each temperature stress step;
processing failure data under each vibration stress step based on a vibration stress acceleration model, and sequentially performing failure distribution function and likelihood function processing on the processed failure data to obtain acceleration factors of the electronic product to be tested under each vibration stress step;
and processing failure data under each temperature cycle stress step based on a temperature cycle acceleration model, and sequentially performing failure distribution function and likelihood function processing on the processed failure data to obtain an acceleration factor of the electronic product to be tested on each temperature cycle stress step.
In one embodiment, the temperature stress acceleration model comprises an arrhenius model; the vibration stress acceleration model comprises an inverse power rate model; the temperature cycle acceleration model comprises a Coffin-Manson failure model;
processing failure data under each temperature stress step based on a temperature stress acceleration model, and sequentially performing failure distribution function and likelihood function processing on the processed failure data to obtain acceleration factors of the electronic product to be tested under each temperature stress step:
the frequency factor and the factor containing the activation energy in the arrhenius model are obtained based on the following formulas:
wherein A is0Is a frequency factor; b is a factor containing activation energy, B ═ Ea/K，EaK is boltzmann constant for activation energy; t is tkSetting the k-th failure data as failure time, wherein k is 1,2 … r, r is failure number, and r is more than or equal to 1; xii-1For converting the stress action time of each step into T according to an acceleration modeliEquivalent time under stress; t isiThe stress level of the ith stress step, i ═ 1,2 … m, m stress steps, m>2; x represents taking one sample in the likelihood function.
In one embodiment, the failure handling mode includes staged timely correction;
based on the fault processing mode and the stress type and the test stage, the step of extrapolating the fault data by adopting a corresponding acceleration model to obtain the fault data under the normal stress level comprises the following steps:
processing failure data to obtain failure probability density functions of all stress steps in a temperature test stage; determining each failure probability density function in the same improvement stage as same-family data, and performing likelihood function taking and Arrhenius model processing on the same-family data to obtain an acceleration factor of the electronic product to be tested under each temperature stress step;
processing failure data to obtain failure probability density functions of stress steps in a vibration test stage; determining each failure probability density function in the same improvement stage as same-family data, and performing likelihood function and inverse power rate model processing on the same-family data to obtain an acceleration factor of the electronic product to be tested under each vibration stress step;
processing failure data to obtain failure probability density functions of all circulation steps in the temperature circulation test stage; and determining each failure probability density function in the same improvement stage as the same family data, and performing likelihood function taking and coffee-Manson failure model processing on the same family data to obtain an acceleration factor of the electronic product to be tested under each temperature cycle stress step.
A reliability enhancement test quantitative evaluation device includes:
the stress analysis module is used for determining the stress type and the test stage of the reliability strengthening test of the electronic product to be tested and acquiring the fault processing strategy of the reliability strengthening test; the fault processing strategy comprises a fault processing mode;
the reliability test module is used for carrying out reliability enhancement test on the electronic product to be tested to obtain fault data;
the data extrapolation module is used for extrapolating fault data by adopting a corresponding acceleration model based on a fault processing mode, a stress type and a test stage to obtain the fault data under a normal stress level;
and the reliability evaluation module is used for evaluating the reliability of the fault data under the normal stress level to obtain the reliability index of the electronic product to be tested.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method when the processor executes the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
One of the above technical solutions has the following advantages and beneficial effects:
the method comprises the steps of confirming a fault processing mode based on a stress type and a test stage of a reliability strengthening test, and further extrapolating reliability strengthening test data to fault data under conventional stress in different fault processing modes, namely extrapolating the reliability strengthening test data to the fault data under a normal environmental stress level through an acceleration model; the method and the device for evaluating the reliability index of the product based on the reliability strengthening test fault data realize quantitative evaluation of the reliability strengthening test data, and provide a method suitable for engineering for rapid improvement and evaluation of the reliability level of modern weaponry.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a schematic flow chart of a quantitative evaluation method of a reliability enhancement test according to an embodiment;
FIG. 2 is a schematic diagram of a "timely correction" test procedure in one embodiment;
FIG. 3 is a schematic diagram of an "after the fact correction" trial process in one embodiment;
FIG. 4 is a schematic illustration of a "staged timely correction" trial process in one embodiment;
FIG. 5 is a flow chart illustrating a method for quantitative evaluation of reliability enhancement tests in another embodiment;
FIG. 6 is a flowchart illustrating a method for quantitative evaluation of reliability enhancement tests according to one embodiment;
FIG. 7 is a block diagram showing the structure of a quantitative evaluation device for a reliability enhancement test according to an embodiment;
FIG. 8 is a diagram illustrating an internal structure of a computer device in one embodiment;
fig. 9 is an internal structural view of a computer device in another embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another.
It will be understood that when an element is referred to as being "connected" to another element, it can be directly connected to the other element or be connected to the other element through intervening elements. Further, "connection" in the following embodiments is understood to mean "electrical connection", "communication connection", or the like, if there is a transfer of electrical signals or data between the connected objects.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Reliability growth tests are tests performed on products under actual or simulated use environment conditions with the purpose of fully exposing potential defects of the products, analyzing causes of failures, taking improvement measures, assessing the reliability level of the products and verifying the effectiveness of the improvement measures. The reliability growth test is a comprehensive process of test, analysis and improvement, is a test means for improving the reliability of products in the development stage, and can realize purposeful and planned growth of the reliability of the products by implementing the reliability growth test. However, the reliability growth test belongs to a simulation test, and needs to simulate the real use condition of a task section as much as possible, and the test Time is at least 5 times of the MTBF (Mean Time Between failures) product, that is, the traditional reliability growth test technology cannot meet the development requirement of a highly reliable and long-life product in terms of test Time and expenditure.
Specifically, with the characteristics of high reliability, low cost and short development period of the development of modern products, the traditional reliability increase test consumes huge resources and time, on one hand, the total time of the reliability increase test is usually 5-25 times of the expected MTBF target value of the product, and the test time is long; on the other hand, the conventional reliability increase test mainly depends on simulating a real environment to implement the test, the failure excitation rate is relatively low, the later statistical analysis needs to be performed based on a large number of failure samples, and the operability in practical engineering needs to be enhanced at present.
The reliability strengthening test is a reliability test technology with a brand new concept proposed by the Boeing company in the United states in the 90 s of the 20 th century, and can effectively solve the contradiction between high reliability, low research and development cost and short development period of modern electronic products. The test principle is that some potential or intermittent faults are quickly excited by improving environmental stress, faults are found, researched and corrected in early product development work based on failure physical analysis, and effective information is timely provided for product design improvement, so that the purpose of quickly improving product reliability is achieved. That is, the product passing or failing the test is not the purpose of the reliability enhancement test, and the main purpose of the reliability enhancement test is to identify potential product defects and obtain more product information to promote improved and robust products. However, the reliability enhancement test mainly aims at exciting product defects, no clear reliability increase target and no effective evaluation method exist at present, and the reliability index of the product cannot be obtained quantitatively.
Specifically, the reliability enhancement test is a test technology with a brand new idea, and aims to make products robust in a short time, and due to the lack of an effective growth model and an evaluation method, the reliability index of the products cannot be quantitatively obtained at present, and most of researches are still in the application aspect of the test technology. The traditional technology provides a method for quantitatively evaluating the reliability of a product according to an interference model of the working stress limit distribution and the use environment stress limit distribution of the product; however, the method needs too large amount of samples in the reliability strengthening test, has high test cost, and is not suitable for the requirements of small-sample weaponry tests. In addition, the traditional technology also provides a method for combining an Arrhenius model and a Dou' an model, but the method needs to carry out an accelerated pre-test before a strengthening test, or requires that the strengthening test is carried out simultaneously under two groups of accelerated stress levels and the failure times are the same, so that the method is difficult to guarantee and implement in engineering and is not suitable for the requirements of small sample weapon equipment tests.
The method and the device can make full use of failure data obtained by a reliability strengthening test (the reliability strengthening test process contains abundant product failure information), quantitatively evaluate the specific effect and the final level of product reliability improvement, and solve the problem of rapid reliability evaluation of products with high reliability and long service life.
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The reliability strengthening test quantitative evaluation method provided by the application can be applied to reliability improvement of modern weaponry. In one embodiment, as shown in fig. 1, a reliability enhancement test quantitative evaluation method is provided, comprising the steps of:
step 102, determining a stress type and a test stage of a reliability strengthening test of an electronic product to be tested, and acquiring a fault processing strategy of the reliability strengthening test; the fault handling strategy comprises a fault handling mode.
Specifically, the stress type and the test stage selected in the reliability strengthening test are determined, wherein the test stage in the application can comprise high-temperature stepping, low-temperature stepping, temperature circulation, vibration stepping and comprehensive environment tests; in a specific example, the test stage can be determined according to the sensitive stress of the product, for example, some products are not sensitive to constant high-temperature stepping and low-temperature stepping, but are sensitive to temperature cycling, so that the test stage in the strengthening test can be directly performed with temperature cycling and other test stages without performing high-temperature stepping and low-temperature stepping.
Further, the reliability enhancement test may include the following five test stages: a low-temperature stepping stress test, a high-temperature stepping stress test, a rapid temperature cycle test, a vibration stepping stress test and a comprehensive environmental stress test; the stress type and the test stage determined in the application are key bases for analyzing fault data by subsequently selecting an acceleration model.
In a specific example, the sensitive stress and the sequence thereof can be determined by analyzing historical Failure data of similar products or products, or analyzing Failure modes, mechanisms and influences (FMMEA) of the products, and finally determining the stress type and the test stage selected in the reliability enhancement test.
Meanwhile, a fault processing strategy of the reliability strengthening test is obtained; the fault processing strategy comprises a fault processing mode; namely, the method for determining the fault handling mode adopted in the reliability strengthening test process is provided. The processing modes of the faults are different, and the subsequent fault data processing modes are also different; the present application proposes that, when performing quantitative evaluation of a reliability enhancement test, it is necessary to first confirm what kind of failure handling method is adopted in the current reliability enhancement test.
It should be noted that, in the present application, the failure handling manner in the reliability enhancement test can be obtained according to engineering practice experience; for example, the failure handling means may include post-incident correction and staged timely correction. For another example, in the reliability strengthening test, according to the engineering practice experience, the fault handling method may include timely correction, post correction, and staged timely correction.
Timely correction: namely, for any fault which needs to be corrected in the test, the test is suspended, root cause analysis is carried out on the fault, correction measures are found and implemented on a tested product, and then the corrected product is continuously tested. Every time a product takes a corrective measure, the technical state and the reliability level of the product can be changed and increased; in one specific example, a trial process of timely correction may be as shown in FIG. 2.
And (4) correcting after the fact: when the product fails at a certain test stage of the reliability enhancement test, the product is not corrected, but the tested product is restored to a normal state, and the test is continued; and (5) after the test stage is finished, intensively taking corrective measures. Under the fault processing mode, the reliability level and the technical state of the product can be changed and increased only after the test stage is finished and corrective measures are intensively taken; in one specific example, the experimental process of post correction may be as shown in FIG. 3.
Timely correcting by stages: the correcting mode is similar to a fault processing mode with delayed correction in a reliability increasing test, namely in a certain test stage of the reliability strengthening test, the test is not paused for each fault and the correcting measures are taken, the correcting measures are not taken intensively in the test stage, but the improving measures are taken by pausing the test in stages according to the condition of the number of faults in the test stage. In this way of failure handling, the state of the art and the reliability level of the product are in a process of changing and growing in stages, as shown in fig. 4.
And 104, performing reliability enhancement test on the electronic product to be tested to obtain fault data.
Specifically, the reliability enhancement test can be performed on the electronic product to be tested to obtain the fault data while or after the stress type and the test stage of the reliability enhancement test of the electronic product to be tested are confirmed. In one particular example, the fault data may include failure data.
And 106, based on the fault processing mode, the stress type and the test stage, extrapolating the fault data by adopting a corresponding acceleration model to obtain the fault data under the normal stress level.
Specifically, the method and the device utilize the corresponding acceleration model to extrapolate fault data according to the test stage and the fault processing mode of each fault, and obtain the fault data under the normal stress level. Reliability enhancement test stresses typically include temperature, vibration, and temperature cycling.
According to the different fault processing modes and the stress types causing the faults, the reliability strengthening test data are extrapolated to obtain equivalent fault data under the normal stress level, namely the reliability strengthening test data are extrapolated to the fault data under the normal environmental stress level through an acceleration model. In a particular embodiment, the acceleration model may include a temperature stress acceleration model, a vibration stress acceleration model, and a temperature cycling acceleration model.
For example, by using the failure data of the reliability strengthening test, the variables in the temperature stress acceleration model can be obtained through the corresponding function model, and further the acceleration factors of the product under different stress steps can be obtained, so that the failure data of the high acceleration environmental stress is converted into the failure data under the same environmental stress condition. For another example, a temperature stress acceleration model, a failure distribution function and a maximum likelihood function can be utilized to extrapolate failure data under temperature stress, so that acceleration factors of the product under different stress steps can be obtained; for another example, according to failure data of a vibration stepping stress test and a rapid temperature cycle stage in a reliability strengthening test, an acceleration model, a failure distribution function and a maximum likelihood function are utilized, so that the failure data of the vibration stepping stress test stage and the rapid temperature cycle stage are converted into the failure data under the conventional stress.
108, performing reliability evaluation on fault data under a normal stress level to obtain a reliability index of the electronic product to be tested;
specifically, after fault data under a normal stress level are obtained, reliability evaluation can be carried out, so that a reliability index of the electronic product to be tested is obtained; in one particular example, the reliability indicator may include an MTBF value.
In a specific embodiment, the step of performing reliability evaluation on the fault data at the normal stress level to obtain a reliability index of the electronic product to be tested may include:
and evaluating the fault data under the normal stress level by adopting a reliability growth evaluation model to obtain the MTBF value of the electronic product to be tested.
Specifically, after the failure data under accelerated stress is converted into the failure data under conventional stress, a suitable growth model can be selected for reliability evaluation. Wherein, the reliability growth evaluation model can comprise a Duane model and an AMSAA model; it should be noted that commonly used growth models may include a Duane model and an AMSAA model, and the reliability growth evaluation model is not particularly limited in the present application.
On one hand, the method is based on a fault processing mode, a stress type and a test stage, and adopts a corresponding acceleration model to extrapolate fault data to obtain the fault data under a normal stress level, so that the reliability increasing process of high-acceleration environmental stress is converted into the reliability increasing problem under the same environmental stress condition, and further, a reliability increasing data analysis method can be utilized to carry out reliability evaluation; on the other hand, reliability evaluation is performed on the data based on a reliability increase test evaluation method (that is, a reliability increase evaluation model is adopted), so that the MTBF value of the product at the end of the reliability enhancement test is obtained.
In the reliability strengthening test quantitative evaluation method, aiming at the fact that a quantitative evaluation method suitable for engineering application is not available in the current reliability strengthening test, by taking the evaluation method and theory in the reliability accelerated life test and the reliability growth test as reference, the reliability strengthening test data is extrapolated to the failure data under the normal environmental stress level through the accelerated model, and a method for evaluating the reliability level of a product based on the reliability strengthening test failure data is provided, so that the quantitative evaluation of the reliability test data is realized, and a method suitable for engineering is provided for the rapid improvement and evaluation of the reliability level of modern weaponry.
In one embodiment, as shown in fig. 5, a reliability enhancement test quantitative evaluation method is provided, comprising the steps of:
502, performing sensitive stress analysis on an electronic product to be tested, and determining each stress type and test stage of a reliability strengthening test;
step 504, acquiring a fault processing mode of the reliability strengthening test;
step 506, performing reliability enhancement test on the electronic product to be tested to obtain fault data;
step 508, based on the fault processing mode and stress type and test stage, extrapolating the fault data by adopting a corresponding acceleration model to obtain the fault data under the normal stress level;
and 510, evaluating fault data under a normal stress level by adopting a reliability growth evaluation model to obtain the MTBF value of the electronic product to be tested.
Specifically, the present application may be implemented by the following process: firstly, sensitive stress analysis is carried out on a product (namely an electronic product to be tested), and the stress type and the test stage selected in the reliability strengthening test are determined; determining a fault handling mode adopted in the reliability strengthening test process; according to the test stage and the fault processing mode of each fault, extrapolating fault data by using a corresponding acceleration model to obtain the fault data under the normal stress level; and based on a reliability growth test evaluation method, performing reliability evaluation on the data to obtain the MTBF value of the product at the end of the reliability enhancement test.
In a specific embodiment, the failure handling manner may include post-incident correction;
based on the fault processing mode and the stress type and the test stage, the step of extrapolating the fault data by adopting a corresponding acceleration model to obtain the fault data under the normal stress level comprises the following steps:
processing failure data under each temperature stress step based on a temperature stress acceleration model, and sequentially performing failure distribution function and likelihood function processing on the processed failure data to obtain acceleration factors of the electronic product to be tested under each temperature stress step;
processing failure data under each vibration stress step based on a vibration stress acceleration model, and sequentially performing failure distribution function and likelihood function processing on the processed failure data to obtain acceleration factors of the electronic product to be tested under each vibration stress step;
and processing failure data under each temperature cycle stress step based on a temperature cycle acceleration model, and sequentially performing failure distribution function and likelihood function processing on the processed failure data to obtain an acceleration factor of the electronic product to be tested on each temperature cycle stress step.
In a particular embodiment, the temperature stress acceleration model may include an Arrhenius model; the vibrational stress acceleration model can include an inverse power rate model; the temperature cycling acceleration model may include a Coffin-Manson failure model;
processing failure data under each temperature stress step based on a temperature stress acceleration model, and sequentially performing failure distribution function and likelihood function processing on the processed failure data to obtain acceleration factors of the electronic product to be tested under each temperature stress step:
the frequency factor and the factor containing the activation energy in the arrhenius model are obtained based on the following formulas:
wherein A is0Is a frequency factor; b is a factor containing activation energy, B ═ Ea/K，EaK is boltzmann constant for activation energy; t is tkThe failure time of the kth failure data is k 1,2 … r, r is the failure number, r is not less than1；ξi-1For converting the stress action time of each step into T according to an acceleration modeliEquivalent time under stress; t isiThe stress level of the ith stress step, i ═ 1,2 … m, m stress steps, m>2; x represents taking one sample in the likelihood function.
In one embodiment, the failure handling mode may include staged timely correction;
based on the fault processing mode and the stress type and the test stage, the step of extrapolating the fault data by adopting a corresponding acceleration model to obtain the fault data under the normal stress level comprises the following steps:
processing failure data to obtain failure probability density functions of all stress steps in a temperature test stage; determining each failure probability density function in the same improvement stage as same-family data, and performing likelihood function taking and Arrhenius model processing on the same-family data to obtain an acceleration factor of the electronic product to be tested under each temperature stress step;
processing failure data to obtain failure probability density functions of stress steps in a vibration test stage; determining each failure probability density function in the same improvement stage as same-family data, and performing likelihood function and inverse power rate model processing on the same-family data to obtain an acceleration factor of the electronic product to be tested under each vibration stress step;
processing failure data to obtain failure probability density functions of all circulation steps in the temperature circulation test stage; and determining each failure probability density function in the same improvement stage as the same family data, and performing likelihood function taking and coffee-Manson failure model processing on the same family data to obtain an acceleration factor of the electronic product to be tested under each temperature cycle stress step.
To further illustrate the concepts of the present application, the following description is made with reference to a specific example; as shown in fig. 6, taking an example that the failure processing manner includes post correction and staged timely correction, the following implementation procedures may be included:
1. defining a reliability strengthening test stage;
the reliability enhancement test in the present application may include the following test stages: the test method comprises a low-temperature step stress test, a high-temperature step stress test, a rapid temperature cycle test, a vibration step stress test and a comprehensive environment stress test. Aiming at specific products, sensitive stress analysis can be carried out on the products;
specifically, based on the most effective stress for exciting product defects and weak links, the feasibility of applying environmental stress in an internal field test is combined, and then the stress type of the reliability strengthening test is determined. The sensitive stress and the sequencing thereof can be determined by analyzing historical fault data of similar products or analyzing fault modes, mechanisms and influences (FMMEA) of the products, and finally the stress type and the test stage selected in the reliability strengthening test are determined. The stress type and the test stage determined in the application are key bases for analyzing fault data by subsequently selecting an acceleration model.
2. Determining a fault processing mode of a reliability strengthening test;
the fault handling mode in the reliability strengthening test can be obtained according to engineering practice experience; the types of the failure handling methods can be referred to the foregoing and shown in fig. 2-4, and are not repeated here.
The method for processing the fault is different, the subsequent fault data processing modes are also different, and the method can firstly confirm which fault processing mode is adopted in the reliability strengthening test when the reliability strengthening test is quantitatively evaluated. Since "timely correction" requires correction of each fault and then continuing the test, the amount of failure data generated at each growth stage is small (i.e., only one data per correction), and analysis is difficult. Therefore, the application proposes to study only two types of data, namely 'after correction' and 'timely correction by stages'.
3. Selecting an applicable acceleration model for different stress phases;
the reliability enhancement test stress in this application may include temperature, vibration, and temperature cycling.
Temperature stress acceleration model
When temperature is taken as the acceleration stress, the relationship of the reaction speed at a certain time point and the temperature satisfies an Arrhenius (Arrhenius) model, that is:
in the formula:
ζ — life characteristics at T temperature stress levels, such as median life, mean life, etc.;
A0-the frequency factor is a constant;
Ea-activation energy in eV;
K-Boltzmann constant, 8.6171X 10-5V/K.
Any accelerated high temperature condition (T) can be deduced from the above formula1) Relative conventional temperature condition (T)0) The following acceleration factors are:
in the above formula, AF is an acceleration factor, T1To accelerate the temperature level under stress, T0Is the temperature level under normal stress.
Secondly, a vibration stress acceleration model;
when the vibration stress is taken as the acceleration stress, the relationship between the service life characteristic of the product and the vibration stress meets an inverse power-law model, namely:
η＝K0v-γ (3)
where η represents a lifetime characteristic at a vibrational stress level, such as median lifetime, average lifetime, etc.; k0Expressed as a normal number; gamma is expressed as a normal number related to activation energy; v represents the vibro-power spectral density.
Any accelerated vibratory stress condition (v) can be derived from equation (3)2) Relative to conventional vibrational stress conditions (v)1) The following acceleration factors AF, namely:
thirdly, a temperature circulation acceleration model;
within certain limits, the degree of failure of low cycle fatigue is related to the temperature difference at high and low temperatures. The increased high and low temperature difference accelerates the occurrence of product failure. The temperature difference can be increased by increasing the high temperature and reducing the low temperature in the test, thereby shortening the time required by the test. For the thermal fatigue failure mechanism of the welding spot caused by temperature circulation, a Coffin-Manson failure model can be used for carrying out damage analysis. The number of cycles to failure (or time to failure) is:
N＝Ψ/(ΔT)Ω (5)
where Δ T represents a temperature change range (temperature difference) in one cycle, and Ψ and Ω are undetermined coefficients related to material properties and a test method. This power-law relationship explains the effect of temperature cycling temperature differences on thermal fatigue cycle life.
Any temperature transformation range Delta T can be deduced from the formula (5)2Δ T relative to the conventional temperature variation range1The acceleration factor AF of (a), namely:
4. calculating fault data under different fault processing modes;
aiming at the different fault processing modes and the stress types causing the faults, the application also provides a method for extrapolating the reliability strengthening test data to obtain equivalent fault data under the normal stress level.
Firstly, the fault processing mode is post correction;
"post correction" is to take corrective action only at the end of each test phase, and only repair during the same test phase. Therefore, for the same test stage, the technical state of the product is not changed, namely, for the same test stage, the parameters of the product, such as activation energy, failure rate and the like, are kept unchanged, and the reliability level is increased in a jumping mode only after the design improvement is completed.
1) Extrapolation of failure data under temperature stress;
for the failure data of the product under each temperature stress step, the failure data of the product under the normal stress level can be extrapolated through the acceleration model of the formula (2), namely:
in the formula (I), the compound is shown in the specification,
the time from the jth failure to the jth failure of the product at the ith stress step is the time from the jth-1 failure of the product;
to extrapolate the time from the j-1 to the j-th failure at normal stress levels, T0At normal stress level, TiThe stress level of the ith step.
Suppose that the product has undergone a temperature step test of m different stress levels (i.e., m stress steps, m)>2) The failure number is r (r is more than or equal to 1), and the failure distribution function under each stress level is F (T, T)i) Test time per step is ti(i ═ 1,2 … m). For electronic products, it is assumed that they are at a normal stress level T0And respective acceleration stress levels TiThe following obeys an exponential distribution with a failure distribution function:
in the formula, thetaiFor each stress level TiAverage life of the following. According to the Arrhenius model, theta is knowniThe following relationships are associated with the respective stress levels:
the formula (9) is brought into the formula (8), and the product is under the stress TiThe horizontal failure distribution function is:
wherein B is ═ Eaand/K. Then the product is at T1The failure distribution function under stress is:
for step T2Stress, since the product is already at T1Under stress experiences t1Time, therefore t needs to be calculated according to the acceleration model1Is equivalent to T2Time under stress is ξ1. According to the Arrhenius model, there are:
thus, T2Failure distribution function of stress level and equivalent action time t ″2Respectively as follows:
t'2＝ξ1+t2 (14)
by analogy, for step TiStress, since the product is already under stress T1,T2…TiExperiences ti-1Time, therefore, t' needs to be transformed according to the acceleration modeli-1Converted to TiTime under stress, i.e.:
in the formula, xii-1The stress action time of all the previous steps is converted to T according to an acceleration modeliEquivalent time under stress, then TiThe failure distribution function of the device under stress is:
Tiequivalent time of action t' of stress leveliComprises the following steps:
t’i＝ξi-1+ti (17)
the failure probability density function at each stress is then:
taking the probability function of failure probability density under each stress to obtain an unknown variable A0Function of B:
wherein A is0Is a frequency factor in the Arrhenius model, B ═ EaK is a factor comprising the activation energy of the product, tkThe k-th failure data has a failure time of 1,2 … r. Taking the logarithm of the above formula:
the above formula is respectively to the unknown variable A0And B, deriving to obtain a likelihood equation as follows:
by using the failure data of the reliability strengthening test and through the two likelihood equation formulas (21) and (22), the variable A can be calculated0B, further obtaining the acceleration factor AF of the product under different stress stepsiTherefore, the reliability increasing process of the high accelerated environmental stress is converted into the reliability increasing problem under the same environmental stress condition, and the reliability is evaluated by using a reliability increasing data analysis method.
2) Extrapolation of failure data under vibration stress and temperature change stress;
similar to the process of extrapolating failure data under temperature stress, according to the failure data of a vibration stepping stress test and a rapid temperature cycle stage in a reliability strengthening test, the values of gamma, v, psi and omega can be obtained by utilizing an acceleration model, a failure distribution function and a maximum likelihood function, so that the failure data of the vibration stepping stress test stage and the rapid temperature cycle stage are converted into the failure data under the conventional stress.
Secondly, the fault processing mode is timely correction by stages;
in this fault handling manner, each time improvement measures are taken on the product, the technical state of the product changes, and the parameters in the acceleration model will change continuously along with the improvement of the product. From the perspective of activation energy, the activation energy required to excite a potential defect of a product into a fault is continuously increased along with the improvement of the inherent reliability of the product, namely, the activation energy is increased in a step-like manner along with the increase of the inherent reliability level of the product, and the faults occurring at each stress level have different activation energies and different acceleration coefficients. It is clear that the time interval (t) from the i-th failure to the i + 1-th failure in the reliability enhancement test1i,t1(i+1)) Time interval (t) between inner and outer extrapolation to normal stress level0i,t0(i+1)) The activation energies within are equal.
Suppose that the product has undergone m temperature steps of different stress levelsTest (i.e. m stress steps, m)>2) The failure number is r (r ≧ 1), wherein n failures are improved by design, each improvement being considered as a stage. Therefore, the test is a strengthening test of n +1 stages under different levels of environmental stress, the number of stress steps experienced in each test stage is m from the beginning of the test to the first improvement to the 1 st stage, the two adjacent improvements are used as one stage, and the nth improvement to the n +1 th stagekThe number of failures per test stage is rk. If the failure rate is expanded from the point of view of failure rate, the failure rate of the kth stage product under the conventional stress level is recorded as lambdakK 1,2 … n +1, each improvement generally results in a reduction in product failure rate, i.e., λ1>λ2>…>λn+1。
The product is tested from stage 1, time 0, i.e. t1,iAt this stage the product has experienced a total of mk1And (4) stress. Noting that the activation energy of the first stage is
In phase 1 co-occurrence of r1Secondary failure, and front r11 failures were repaired only, at the r-th1Design improvements were made at secondary failures. For the first stress step T of the first stage1,1In combination with the arrhenius model, the failure distribution is:
in the formula (I), the compound is shown in the specification,
for stress step T1,2Since the product is already at T1,1Under stress experiences t1,1Time, therefore t needs to be calculated according to the acceleration model1,1Is equivalent to T1,2Time under stress is ξ1,1. According to the Arrhenius model, there are:
thus, T1,2Failure distribution function of stress level and equivalent action time t ″1,2Respectively as follows:
t’1,2＝ξ1,1+t1,2 (26)
by analogy, for the stress step T of the first stage1,iSince the product is already at T1,1，T1,2…T1,i-1Experiences t' under stress1,i-1Time, therefore, t' needs to be transformed according to the acceleration model1,i-1Converted to T1,iTime under stress, i.e.:
in the formula, xi1,i-1The first i-1 step stress action time of the 1 st stage is converted to T according to an acceleration model1,iEquivalent time under stress, then T1,iThe failure distribution function of the device under stress is:
T1,iequivalent time of action t' of stress level1,iComprises the following steps:
t’1,i＝ξ1,i-1+t1,i (29)
the failure probability density function at each stress at stage 1 is:
for stage 2, due to the r-th stage to stage 11The secondary failure is a design improvement that changes the activation energy of the product and reduces the failure rate of the product at normal stress levels. Suppose that the product has experienced m in stage 2 altogether2Stress, activation energy of this stage is Ea2In stage 2, r is co-occurring2Secondary failure, and front r21 failures were repaired only, at the r-th2Design improvement is performed again. Firstly, it is necessary to
Equivalent time of action of stress level
Converted to T2,1At the stress level. Because the activation energies of the two phases are not consistent, first, the two phases are activated in a different manner
Converted to the equivalent time of action t' at conventional stress levels1,0：
Wherein the content of the first and second substances,
is the stress level in stage 1
For a time of action of, then t' is applied1,0Converted to T2,1At the stress level:
wherein
T0At normal stress level, T2,1The first stress step stress level of the second stage. Thus, for the 1 st stress step T of stage 22,1Its failure distribution function and equivalent action time t ″2,1Respectively as follows:
t'2,1＝ξ2,0+t2,1 (34)
for the 2 nd stress step T of stage 22,2Since the product is already at T2,1Experiences t' under stress2,1Time, therefore, t' needs to be transformed according to the acceleration model2,1Is equivalent to T2,2Time under stress is ξ2,1. According to the Arrhenius model, there are:
thus, T2,2Failure distribution function of stress level and equivalent action time t ″2,2Respectively as follows:
t'2,2＝ξ2,1+t2,2 (37)
by analogy, for the stress step T of stage 22,iSince the product is already at T2,1，T2,2…T2,i-1Experiences t' under stress2,i-1Time, therefore, t' needs to be transformed according to the acceleration model2,i-1Converted to T2,iTime under stress, i.e.:
in the formula (I), the compound is shown in the specification,ξ2,i-1the action time of i-1 stress steps before the 2 nd stage is converted into T according to an acceleration model2,iEquivalent time under stress, then T2,iThe failure distribution function of the product under stress is:
T2,iequivalent time of action t' of stress level2,iComprises the following steps:
t'2,i＝ξ2,i-1+t2,i (40)
the failure probability density function at each stress at stage 2 is then:
by analogy, for the reliability strengthening test of the k stage, the stress step level Tk,iThe failure distribution function for the following product is:
Tk,iequivalent time of action t' of stress levelk,iComprises the following steps:
t'k,i＝ξk,i-1+tk,i (43)
the failure probability density function at each stress at the kth stage is:
therefore, the failure probability density function of each test stage can be obtained, as shown in formulas (30), (41) and (44), the failure probability density function of each stage can be regarded as a family, and then the likelihood function of the failure density function family of each stage is solved by a method of 'after correction' data processing, namely, the likelihood function is obtainedThe activation energy E of each stage can be obtainedakSubstituting into formula
The acceleration factor AF of each stress step in each stage can be obtainediTherefore, the reliability strengthening data is converted into the reliability increasing problem under the conventional stress level, and the reliability is evaluated by using a reliability increasing data analysis method.
The extrapolation of the data for the vibration step stress phase is similar to that for the temperature stress described above and will not be described further herein.
5. Evaluation of extrapolated data;
for each stress type in the reliability enhancement test, assume a normal stress level of S0The product has undergone S1,S2…SnStress steps, test duration under each stress step is t1,t2…tnObtaining the acceleration factor AF of each stress step according to the method of section 41,AF2…AFn. Suppose the r-th product of the testiThe fault occurs on the jth step, and the test time on the step is tAjThen the total test time experienced by the fault is: t is t1i＝t1+t2+…+tAjThe test time of the tested product (i.e. the electronic product to be tested) under each acceleration stress level is equivalent to the test time under the normal stress level, and the test time is as follows: AF1×t1，AF2×t2，…，AFj×tAj(ii) a The equivalent test time for the fault at normal stress levels is then: t is t0i＝AF1×t1+AF2×t2+…+AFj×tAj. Thus, the time to failure of the tested product under this type of stress can be translated into the time to failure under normal stress, given as t01,t02…t0i。
After the failure data under the accelerated stress is converted into the failure data under the conventional stress, a proper growth model can be selected for reliability evaluation. Common growth models are the Duane model and the AMSAA model. The AMSAA model considers that in the reliability growth process, the accumulated failure number is an inhomogeneous Poisson process, and the failure strength function of the accumulated failure number may be:
λ(t)＝abtb-1 (45)
wherein a >0 is a scale parameter, b >0 is a shape parameter, and m ═ 1-b is a growth rate, the cumulative MTBF and instantaneous MTBF of the product are respectively
The reliability strengthening test can be regarded as fault truncation data, and the following are provided:
M＝n-1 (48)
ts＝tn (49)
where n is the total number of faults, tnTime when nth fault occurs; m and tsFor alphabetic symbols, M is the total number of faults minus 1, tsEqual to the time at which the nth fault occurred.
When M >1, the point estimates for the two parameters in the AMSAA model are:
after the point estimates of a, b or m are obtained, the MTBF of the product at the end of the test can be estimated by substituting formula (47).
In the application, the reliability strengthening test can be regarded as a reliability accelerated growth process, the acceleration factor calculation principle under different stress levels in different fault processing modes is provided by taking the reliability accelerated life test and the reliability growth test data evaluation method as reference, and the method for extrapolating the reliability strengthening test data to the failure data under the conventional stress is established. Meanwhile, the reliability growth evaluation model is used for evaluating the extrapolation data, so that the quantitative evaluation of reliability strengthening test data is realized, and a method suitable for engineering is provided for the rapid improvement and evaluation of the reliability level of modern weaponry.
The application provides a method for evaluating the reliability index of a product based on failure data of a reliability strengthening test; and a reliability strengthening test acceleration coefficient calculation method aiming at two different fault processing modes of 'timely correcting in stages' and 'correcting after the fact'. Specifically, aiming at the fact that a quantitative evaluation method suitable for engineering application does not exist in the current reliability strengthening test, the application refers to evaluation methods and theories in a reliability accelerated life test and a reliability growth test, and extrapolates reliability strengthening test data to failure data under a normal environmental stress level through an accelerated model, and provides a method for evaluating the reliability level of a product based on the reliability strengthening test failure data; secondly, aiming at the quantitative evaluation method, two different fault processing modes of staged timely correction and after correction are combined, and the application provides two different acceleration coefficient calculation methods based on reliability enhancement test data.
It should be understood that although the various steps in the flow charts of fig. 1-6 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-6 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 7, there is provided a reliability-enhancement-test quantitative evaluation device including:
the stress analysis module 710 is used for determining the stress type and the test stage of the reliability strengthening test of the electronic product to be tested, and acquiring a fault processing strategy of the reliability strengthening test; the fault processing strategy comprises a fault processing mode;
the reliability test module 720 is used for performing reliability enhancement test on the electronic product to be tested to obtain fault data;
the data extrapolation module 730 is used for extrapolating fault data by adopting a corresponding acceleration model based on a fault processing mode, a stress type and a test stage to obtain the fault data under a normal stress level;
and the reliability evaluation module 740 is configured to perform reliability evaluation on the fault data at the normal stress level to obtain a reliability index of the electronic product to be tested.
For the specific definition of the reliability enhancement test quantitative evaluation device, reference may be made to the above definition of the reliability enhancement test quantitative evaluation method, which is not described herein again. All or part of each module in the reliability enhancement test quantitative evaluation device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing data such as stress types, test stages, fault processing modes, fault data, acceleration models and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a reliability enhancement test quantitative evaluation method.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a reliability enhancement test quantitative evaluation method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the configurations shown in fig. 8 and 9 are merely block diagrams of some configurations relevant to the present disclosure, and do not constitute a limitation on the computing devices to which the present disclosure may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory in which a computer program is stored and a processor which, when executing the computer program, carries out the steps of the above-mentioned method.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus DRAM (RDRAM), and interface DRAM (DRDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A reliability enhancement test quantitative evaluation method is characterized by comprising the following steps:
determining the stress type and the test stage of the reliability strengthening test of the electronic product to be tested, and acquiring the fault processing strategy of the reliability strengthening test; the fault processing strategy comprises a fault processing mode; the fault processing mode comprises post correction and staged timely correction;
performing the reliability strengthening test on the electronic product to be tested to obtain fault data;
based on the fault processing mode, the stress type and the test stage, extrapolating the fault data by adopting a corresponding acceleration model to obtain the fault data under the normal stress level;
and performing reliability evaluation on the fault data under the normal stress level to obtain a reliability index of the electronic product to be tested.
2. The quantitative evaluation method for reliability-enhancing tests according to claim 1, wherein the step of determining the stress type and the test stage of the reliability-enhancing test of the electronic product to be tested comprises:
and carrying out sensitive stress analysis on the electronic product to be tested, and determining each stress type and test stage of the reliability strengthening test.
3. The reliability-enhancement test quantitative evaluation method according to claim 1, wherein the reliability index includes an MTBF value;
and evaluating the reliability of the fault data under the normal stress level to obtain the reliability index of the electronic product to be tested, wherein the step comprises the following steps of:
and evaluating the fault data under the normal stress level by adopting a reliability growth evaluation model to obtain the MTBF value of the electronic product to be tested.
4. The reliability-enhancement test quantitative evaluation method according to any one of claims 1 to 3, wherein the failure data includes failure data; the acceleration model comprises a temperature stress acceleration model, a vibration stress acceleration model and a temperature cycle acceleration model.
5. The reliability-enhancement test quantitative evaluation method according to claim 4,
based on the fault processing mode and the stress type and test stage, the step of extrapolating the fault data by adopting a corresponding acceleration model to obtain the fault data under the normal stress level comprises the following steps:
processing failure data under each temperature stress step based on the temperature stress acceleration model, and sequentially performing failure distribution function and likelihood function processing on the processed failure data to obtain an acceleration factor of the electronic product to be tested under each temperature stress step;
processing failure data under each vibration stress step based on the vibration stress acceleration model, and sequentially performing failure distribution function and likelihood function processing on the processed failure data to obtain an acceleration factor of the electronic product to be tested under each vibration stress step;
and processing failure data under each temperature cycle stress step based on the temperature cycle acceleration model, and sequentially performing failure distribution function and likelihood function processing on the processed failure data to obtain an acceleration factor of the electronic product to be tested on each temperature cycle stress step.
6. The reliability-enhancement test quantitative evaluation method according to claim 5, wherein the temperature stress acceleration model includes an Arrhenius model; the vibration stress acceleration model comprises an inverse power rate model; the temperature cycle acceleration model comprises a Coffin-Manson failure model;
processing failure data under each temperature stress step based on the temperature stress acceleration model, and sequentially performing failure distribution function and likelihood function processing on the processed failure data to obtain acceleration factors of the electronic product to be tested under each temperature stress step:
the frequency factor and the factor containing the activation energy in the arrhenius model are obtained based on the following formulas:
wherein A is0Is the frequency factor; b is said factor comprising activation energy, B ═ Ea/K，EaK is boltzmann constant for activation energy; t is tkSetting the k-th failure data as failure time, wherein k is 1,2 … r, r is failure number, and r is more than or equal to 1; xii-1For converting the stress action time of each step into T according to an acceleration modeliEquivalent time under stress; t isiThe stress level of the ith stress step, i ═ 1,2 … m, m stress steps, m>2; x represents taking one sample in the likelihood function.
7. The reliability-enhancement test quantitative evaluation method according to claim 4,
based on the fault processing mode and the stress type and test stage, the step of extrapolating the fault data by adopting a corresponding acceleration model to obtain the fault data under the normal stress level comprises the following steps:
processing the failure data to obtain failure probability density functions of all stress steps in the temperature test stage; determining each failure probability density function in the same improvement stage as same-family data, and performing likelihood function taking and Arrhenius model processing on the same-family data to obtain an acceleration factor of the electronic product to be tested under each temperature stress step;
processing the failure data to obtain failure probability density functions of all stress steps in the vibration test stage; determining each failure probability density function in the same improvement stage as same-family data, and performing likelihood function taking and inverse power rate model processing on the same-family data to obtain an acceleration factor of the electronic product to be tested under each vibration stress step;
processing the failure data to obtain failure probability density functions of all circulation steps in the temperature circulation test stage; and determining each failure probability density function in the same improvement stage as same-family data, and performing likelihood function taking and coffee-Manson failure model processing on the same-family data to obtain an acceleration factor of the electronic product to be tested under each temperature cycle stress step.
8. A reliability enhancement test quantitative evaluation device is characterized by comprising:
the stress analysis module is used for determining the stress type and the test stage of the reliability strengthening test of the electronic product to be tested and acquiring the fault processing strategy of the reliability strengthening test; the fault processing strategy comprises a fault processing mode; the fault processing mode comprises post correction and staged timely correction;
the reliability test module is used for carrying out the reliability strengthening test on the electronic product to be tested to obtain fault data;
the data extrapolation module is used for extrapolating the fault data by adopting a corresponding acceleration model based on the fault processing mode, the stress type and the test stage to obtain the fault data under the normal stress level;
and the reliability evaluation module is used for carrying out reliability evaluation on the fault data under the normal stress level to obtain the reliability index of the electronic product to be tested.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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