CN102184292B - Method for updating electronic product reliability prediction model complying with exponential distribution - Google Patents

Method for updating electronic product reliability prediction model complying with exponential distribution Download PDF

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CN102184292B
CN102184292B CN 201110118578 CN201110118578A CN102184292B CN 102184292 B CN102184292 B CN 102184292B CN 201110118578 CN201110118578 CN 201110118578 CN 201110118578 A CN201110118578 A CN 201110118578A CN 102184292 B CN102184292 B CN 102184292B
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stress
reliability
mean
refers
reliability prediction
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CN102184292A (en
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胡薇薇
丁潇雪
孙宇锋
祁邦彦
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北京航空航天大学
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Abstract

The invention provides a method for updating an electronic product reliability prediction model complying with exponential distribution, which comprises the following steps of: 1, analyzing a use region of products; 2, acquiring calendar year meteorological information of each province and city in the use region, and calculating a year average value of temperature stress and humidity stress in each region; 3, respectively constructing a temperature and humidity probability density distribution function, and solving a year average value of temperature stress and humidity stress in the use region; 4, analyzing each stress borne in a product use process; 5, selecting a reliability prediction manual, and calculating an element work failure rate according to a stress analysis model; 6, drawing a product mission reliability block diagram and compressively calculating a product work failure rate; 7, carrying out an acceleration life test and recording test data; 8, product mean life to failure under a test condition and work failure rate under a normal work condition; and 9, comparing product work failure rates obtained by reliability prediction and the accelerated life test, calculating errors generated by the humidity stress, and offering a reliability prediction correction model.

Description

The electronic product reliability Prediction Model modification method of obeys index distribution

Affiliated technical field

The present invention relates to a kind of reliability prediction model modification method of electronic product, specifically, relate to the electronic product reliability Prediction Model modification method of obeys index distribution, belong to systems engineering system reliability technical field.

Background technology

In recent years, China's electronic product has obtained swift and violent development.But as a complete unit, the technology content of domestic electronic product, reliability etc. still have a certain distance with external similar advanced product.The height of electronic product reliability is to be determined by the work quality in each stage such as development and design, the manufacturing, test verification, working service.Improve the reliability of electronic product, must get hold of each relevant link such as design, production, management, grasp the crucial reliability engineerings such as the related reliability design of links, reliability prediction, fail-test evaluation.

Electronic product is comprised of a series of electronic devices and components, and the inefficacy of electronic devices and components can directly cause product not move.Along with the hyundai electronics industrial expansion, towards the future development of high-performance, high precision and high integration, its function is more and more gradually for electronic product, and self structure becomes increasingly complex, and the electronic devices and components of use are more and more, and the fault of initiation also increases thereupon.

Reliability prediction is in the design phase system reliability to be carried out quantitative estimation, is that the factors such as working environment of the formation of historical reliability data, product according to product and design feature, product estimate to form parts and the product reliability of product.At present, the reliability prediction technology of electronic product has been tending towards ripe.But traditional method for predicting reliability often can not reflect the actual conditions of product work, can not satisfy the needs of producing and using.In addition, traditional method for predicting reliability is owing to not considering that humidity stress exists larger error.Therefore, need to explore a kind of reliability prediction model modification method that is applicable to the electronic product of obeys index distribution.Accelerated test is under the prerequisite of not introducing new failure mechanism, impels sample to lose efficacy in a short time by adopting the method that strengthens stress (temperature, humidity), with the test of the reliability of prediction product under normal running conditions or condition of storage.The result of product accelerated life test can be used as the foundation of the reliability prediction model of revising the obeys index distribution electronic product.

Summary of the invention

The objective of the invention is: a kind of electronic product reliability Prediction Model modification method of obeys index distribution is provided, can accurately and objectively makes correction to the reliability prediction model of product.

Technical scheme of the present invention: the analytical electron product uses the weather information in zone, constructs respectively temperature probability density function and humidity probability density function and averages; Stress Analysis Method is carried out reliability prediction to product, the theoretical value of counting yield operational failure rate among the employing GJB299C-2006; Carry out the product accelerated life test; The analytical test data, the trial value of counting yield operational failure rate; More above-mentioned two results analyze the error that humidity stress produces, and reliability prediction model is revised.

The electronic product reliability Prediction Model modification method of a kind of obeys index distribution of the present invention, its step is as follows:

Step 1, the use of analytic product zone is also divided by province, municipality directly under the Central Government, autonomous region;

Step 2 gathers and uses each province, municipality directly under the Central Government, municipal weather information over the years in the zone, calculates the annual mean of each department temperature stress and humidity stress;

Step 3, annual mean to each department temperature stress, humidity stress divides into groups, draw respectively histogram, according to histogrammic distribution trend, construct respectively temperature probability density function and humidity probability density function, ask for the annual mean that uses regional temperature stress and humidity stress;

Step 4 is fully being understood on the basis of component information the every stress that bears in the analytic product use procedure;

Step 5 is selected the reliability prediction handbook, determines that all kinds of components and parts carry out the model of stress analysis, Computing Meta device operational failure rate;

Step 6 is drawn product mission reliability block diagram, COMPREHENSIVE CALCULATING product work crash rate;

Step 7 is carried out constant stress, Censoring accelerated life test, the record test figure;

Step 8 is calculated the front time of product mean failure rate under the test condition, calculates front time of product mean failure rate and operational failure rate under the normal running conditions;

Step 9, the product work crash rate that the expectation of reliable property and accelerated life test obtain is calculated the error that humidity stress produces, and proposes the reliability prediction correction model.

Wherein, the product described in the step 1 uses the zone to refer to the provinces, autonomous regions and municipalities that the China's Mainland is all.

Wherein, the described weather information over the years of step 2 refers to the proce's-verbal's of China Meteorological department year after year each monthly mean temperature, the relative humidity in nearly ten years.

Wherein, the described annual mean of step 2 refers to the year after year average of each monthly mean temperature, humidity.

Wherein, the histogram described in the step 3 refers to frequency histogram.

Wherein, the annual mean of the temperature stress described in the step 3 refers to use the temperature stress mean value in zone, and this value can be considered as the temperature stress value under the product normal running conditions.

Wherein, the annual mean of the humidity stress described in the step 3 refers to use the humidity stress mean value in zone, and this value can be considered as the humidity stress value under the product normal running conditions.

Wherein, the component information described in the step 4 comprises manufacturer, grown place, material, quality grade, performance parameter of components and parts etc.

Wherein, the stress described in the step 4 refers to temperature stress, electric stress, vibrations stress etc.

Wherein, the reliability prediction handbook described in the step 5 refers to the reliability of electronic equipment expectation handbook of latest edition, and " reliability of electronic equipment is estimated handbook generally to commonly use GJB299C-2006 for homemade goods.

Wherein, the mission reliability block diagram described in the step 6 refers to estimate that product finishes the probability of predetermined function in the process of executing the task, and describes and finishes the predetermined action of each unit of product in the task process and measure a kind of reliability model of work validity.

Wherein, the stress described in the step 7 refers to temperature stress and the humidity stress under the test condition, should be greater than the stress level under the normal running conditions.

Wherein, the Censoring described in the step 7 refers to that total time on test is definite value.

Wherein, the test figure described in the step 7 refers to time and the order of test specimen generation hardware fault.

Wherein, the time refers to all samples mean value in the time interval breaking down from starting working to before the mean failure rate described in the step 8.

Wherein, the operational failure rate described in the step 8 refers to the mean failure rate inverse of front time.

Wherein, the result described in the step 9 refers to the product work crash rate that product work crash rate that reliability prediction obtains and accelerated life test obtain, and the two is under the normal running conditions.

The present invention compared with prior art has the following advantages:

The first, the method upward and on the space has been carried out more deep discussion to operating ambient temperature from the time, and the temperature and humidity stress value that obtains has reflected the average level of temperature, humidity stress under the product normal running conditions more accurately.

The second, the present invention has considered design, the environmental factor of product, can accurately and objectively make assessment to the result of use of product.

The 3rd, the present invention revises method for predicting reliability and model from the angle of test.

The 4th, this method can be made into software, and the input by parameter obtains components and parts, assembly, parts, subsystem, system's operational failure rate at different levels information.

Description of drawings

Fig. 1 is product work environment temperature, humidity stress mean value calculation process flow diagram;

Fig. 2 is the PRE-CALCULATING FOR RELIABILITY OF PRODUCTS process flow diagram;

Fig. 3 is reliability prediction model correction process flow diagram.

Fig. 4 is modification method process flow diagram of the present invention

Embodiment

The electronic product reliability Prediction Model modification method of a kind of obeys index distribution of the present invention as shown in Figure 4, its step is as follows:

Step 1, the use of analytic product zone is also divided by province, municipality directly under the Central Government, autonomous region;

Step 2 gathers each province, municipality directly under the Central Government, municipal weather information over the years, calculates the annual mean of each department temperature stress and humidity stress, and as shown in Figure 1, its detailed step is as follows:

Step 201 gathers and uses each province, municipality directly under the Central Government, municipal weather information over the years in the zone.

Step 202 is utilized each province, municipality directly under the Central Government, municipal year after year each monthly mean temperature, humidity, calculates the annual mean of each department temperature stress, humidity stress.

(1) year-round average temperature:

(2) mean annual humidity:

Step 3, each department temperature stress, humidity stress annual mean are divided into groups, draw respectively histogram, according to histogrammic distribution trend, construct respectively temperature probability density function and humidity probability density function, ask for the annual mean that uses regional temperature stress and humidity stress, as shown in Figure 1, its detailed step is as follows:

Step 301 is divided into groups to the annual mean of each department temperature stress, humidity stress respectively, draws histogram.

Step 302, the distribution trend of observed data is constructed respectively temperature probability density function and humidity probability density function.

Step 303 is asked for expectation value to the function that obtains in the step 302 respectively, and this expectation value is the annual mean that uses regional temperature stress and humidity stress.

Take certain type Video Codec as example, the perform region spreads all over the whole nation, and working environment difference is very large.Its working range is from north to three provinces in the northeast of China, and Guangdong, Fujian are arrived in south, to Tibet Autonomous Region.The perform region of Video Codec can be divided into roughly 20 provinces, municipality directly under the Central Government, autonomous region in the table 1, the result of calculation in each province and city is seen shown in the lower tabulation 1.

Table 1-Chinese Typical Representative province ,city and area year-round average temperature, humidity

??No. Economize ??T u(℃) ??RH u(%) ??No. Economize ??T u(℃) ??RH u(%) ??1 Heilungkiang ??2.5 ??43.9% ??11 Beijing ??17.8 ??57.8% ??2 Gansu ??5.1 ??45.6% ??12 Anhui ??18.1 ??58.1% ??3 Jilin ??5.6 ??46.2% ??13 Hebei ??18.6 ??58.6% ??4 Tibet ??6.2 ??40.5% ??14 Hubei ??18.6 ??51.3% ??5 Qinghai ??7.6 ??47.6% ??15 Jiangsu ??19.8 ??59.8% ??6 Liaoning ??11.4 ??51.4% ??16 Zhejiang ??20.1 ??60.0% ??7 Shanxi ??11.5 ??51.5% ??17 The Hunan ??21.3 ??61.3% ??8 Shaanxi ??11.8 ??51.8% ??18 Fujian ??23.5 ??63.5% ??9 Henan ??13 ??53.0% ??19 Hainan ??24.9 ??74.5% ??10 Shandong ??14.1 ??54.1% ??20 Guangdong ??25.6 ??64.9%

According to the temperature data in the table 1, we can find: the Heilongjiang Province has reached the minimum (T of temperature probability density function Umin) 2.5 ℃, Guangdong Province has reached mxm. (T Umax) 25.6 ℃.Product uses the temperature probability density function in the zone to see formula (3):

Formula (3) should satisfy:

∫ 2.5 25.6 f ( T u ) dT u = 1 - - - ( 4 )

The annual mean T of temperature stress On average=15.05 ℃, computation process is seen formula (5):

∫ 2.5 25.6 T u f ( T u ) d T u = 15.05 - - - ( 5 )

According to the humidity data in the table 1, we can find: Tibet Autonomous Region has reached the minimum (RH of humidity probability density function Umin) 40.5%, Hainan Province has reached mxm. (RH Umax) 74.5%.Product uses the humidity probability density function in the zone to see formula (6):

Formula (6) should satisfy:

∫ 40.5 % 74.5 % g ( RH u ) d RH u = 1 - - - ( 7 )

The annual mean RH of humidity stress On average=57.5%, computation process is seen formula (8):

∫ 40.5 % 74.5 % RH u g ( RH u ) d RH u = 57.5 % - - - ( 8 )

Step 4 is fully being understood on the basis of component information, the every stress that bears in the analytic product use procedure, and as shown in Figure 2, its detailed step is as follows:

Step 401, the components and parts inventory of analytical electron product, investigation components and parts source.

Step 402 is classified to electronic devices and components.

Step 5 is selected the reliability prediction handbook, determines that all kinds of components and parts carry out the model of stress analysis, Computing Meta device operational failure rate, and as shown in Figure 2, its detailed step is as follows:

Step 501 is selected the reliability prediction handbook.

Step 502 determines that all kinds of components and parts carry out the model of stress analysis, the statistics component information.

Step 503, the every stress that bears in the analytic product use procedure.

Step 504 is according to stress analysis model Computing Meta device operational failure rate.

Step 6 is drawn product mission reliability block diagram, COMPREHENSIVE CALCULATING product work crash rate, and as shown in Figure 2, its detailed step is as follows:

Step 601 is drawn product mission reliability block diagram.

Step 602, COMPREHENSIVE CALCULATING product work crash rate.

In the Video Codec, the component quality grade is the I level; Environmental baseline is that ground is good; Environment temperature is 15.05 ℃; The electric stress ratio is 0.2; Envirment factor π ESelect 1.0.Adopt GJB299C-2006 to carry out reliability prediction.It is 9.0805 (10 that the summation of the operational failure rate of all components and parts is obtained the product work crash rate -61/h), being scaled MTTF is 110113 hours or 12.57.

Step 7 is carried out constant stress, the accelerated life test of Censoring product, the record test figure, and as shown in Figure 3, its detailed step is as follows:

Step 701 is determined temperature stress and the humidity stress of accelerated life test to carry out constant stress, Censoring accelerated life test.

Step 702, record product accelerated life test data are rejected software fault.

Step 8 is calculated the front time of product mean failure rate under the test condition, calculates front time of product mean failure rate and operational failure rate under the normal running conditions, and as shown in Figure 3, its detailed step is as follows:

Step 801 is calculated the front time of product mean failure rate under the test condition.

Step 802 is calculated front time of product mean failure rate and operational failure rate under the normal running conditions, and normal operation stress is 15 ℃ of temperature, relative humidity 57.5%.

Step 9, the product work crash rate that the expectation of reliable property and accelerated life test obtain is calculated the error that humidity stress produces, and proposes the reliability prediction correction model, and as shown in Figure 3, its detailed step is as follows:

Step 901, the product work crash rate that the expectation of reliable property and accelerated life test obtain is calculated 57.5% humidity stress error coefficient π according to formula (9) 575%

λ Estimate(1+ π 57.5%)=λ Test(9)

Step 902 is according to π 575%Calculate other humidity stress error coefficients.

π ( RH ) = ( 1 + π 57.5 % ) ( 57.5 % RH ) - 3 - 1 - - - ( 10 )

Step 903 proposes the reliability prediction correction model:

Certain type Video Codec accelerated life test data sees Table 2.According to the warm and humid model of Peck-, at proof stress T S=85 ℃, RH S=95%, normal running conditions stress T u=15 ℃, RH uUnder=57.5% condition, the operational failure rate that test figure analysis is obtained this type Video Codec under the normal running conditions is 1.0359 (10 -51/h), time MTTF is about 11.02 before the mean failure rate.Obtain humidity stress error coefficient π according to formula (9) 575%Be 0.14, the reliability prediction correction model is:

Table 2-type Video Codec accelerated life test data

Claims (9)

1. the electronic product reliability Prediction Model modification method of an obeys index distribution, it is characterized in that: the concrete steps of the method are as follows:
Step 1, the use of analytic product zone is also divided by province, municipality directly under the Central Government, autonomous region;
Step 2 gathers and uses each province, municipality directly under the Central Government, municipal weather information over the years in the zone, calculates the annual mean of each department temperature stress and relative humidity stress;
Step 3, annual mean to each department temperature stress, relative humidity stress divides into groups, draw respectively histogram, according to histogrammic distribution trend, construct respectively temperature probability density function and relative humidity probability density function, ask for the annual mean that uses regional temperature stress and relative humidity stress;
Step 4 is fully being understood on the basis of component information the every stress that bears in the analytic product use procedure;
Step 5 is selected the reliability prediction handbook, determines that all kinds of components and parts carry out the model of stress analysis, Computing Meta device operational failure rate;
Step 6 is drawn product mission reliability block diagram, COMPREHENSIVE CALCULATING product work crash rate;
Step 7 is carried out constant stress, Censoring accelerated life test, the record test figure;
Step 8 is calculated the front time of product mean failure rate under the test condition, front time of product mean failure rate and operational failure rate under the extrapolation normal running conditions;
Step 9, the product work crash rate that the expectation of reliable property and accelerated life test obtain is calculated the error that relative humidity stress produces, and proposes the reliability prediction correction model.
2. the electronic product reliability Prediction Model modification method of obeys index distribution according to claim 1 is characterized in that: the product described in the step 1 uses the zone to refer to the provinces, autonomous regions and municipalities that the China's Mainland is all.
3. the electronic product reliability Prediction Model modification method of obeys index distribution according to claim 1, it is characterized in that: the described weather information over the years of step 2 refers to the proce's-verbal's of China Meteorological department year after year each monthly mean temperature, the relative humidity in nearly ten years; Described annual mean refers to the year after year average of each monthly mean temperature, relative humidity.
4. the electronic product reliability Prediction Model modification method of obeys index distribution according to claim 1, it is characterized in that: the histogram described in the step 3 refers to frequency histogram; The annual mean of described temperature stress refers to use the temperature stress mean value in zone, and this value is considered as the temperature stress value under the product normal running conditions; The annual mean of described relative humidity stress refers to use the relative humidity stress mean value in zone, and this value is considered as the relative humidity stress value under the product normal running conditions.
5. the electronic product reliability Prediction Model modification method of obeys index distribution according to claim 1, it is characterized in that: the component information described in the step 4 comprises manufacturer, grown place, material, quality grade, the performance parameter of components and parts; Described stress refers to temperature stress, electric stress, vibrations stress and relative humidity stress.
6. the electronic product reliability Prediction Model modification method of obeys index distribution according to claim 1 is characterized in that: the reliability prediction handbook described in the step 5 refers to that " reliability of electronic equipment is estimated handbook to GJB299C-2006.
7. the electronic product reliability Prediction Model modification method of obeys index distribution according to claim 1, it is characterized in that: the mission reliability block diagram described in the step 6 refers to estimate that product finishes the probability of predetermined function in the process of executing the task, and describes and finishes the predetermined action of each unit of product in the task process and measure a kind of reliability model of work validity.
8. the electronic product reliability Prediction Model modification method of obeys index distribution according to claim 1, it is characterized in that: the stress described in the step 7 refers to temperature stress and the relative humidity stress under the test condition, should be greater than the stress level under the normal running conditions; Described Censoring refers to that total time on test is definite value; Described test figure refers to time and the order of test specimen generation hardware fault.
9. the electronic product reliability Prediction Model modification method of obeys index distribution according to claim 1 is characterized in that: the time refers to all samples mean value in the time interval breaking down from starting working to before the mean failure rate described in the step 8; Described operational failure rate refers to the mean failure rate inverse of front time.
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