CN102789528A - Storage reliability analysis method on basis of multi-mechanism competition degeneration - Google Patents
Storage reliability analysis method on basis of multi-mechanism competition degeneration Download PDFInfo
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Abstract
The invention relates to a storage reliability analysis method on the basis of the multi-mechanism competition degeneration. The method comprises the following steps of: 1, storage section analysis and storage data detection; 2, storage test data randomness analysis; 3, storage test data dispersibility analysis; and 4, storage reliability analysis estimation on the basis of the multi-mechanism competition degeneration. According to the invention, a moving standard deviation method is adopted to analyze the variation rule of the stability level of each key part under the influence of the randomness so as to solve the problems that under the long-term storage condition, each characteristic performance parameter storage data has no obvious degeneration trend and the storage service life showing the randomness variation characteristics is difficult to analyze; and a distributional hypothesis test method is adopted to analyze the distribution rule of the storage service life of each key part under the influence of the dispersibility to obtain a storage reliability model of each key part so as to solve the problems that under the long-term storage condition, the storage service life of each key part has high dispersibility between different products and the storage reliability is difficult to analyze.
Description
Technical field
The present invention provides a kind of storage reliability analytical approach that competition is degenerated based on the multimachine reason, and it relates to a kind of storage reliability analytical approach of considering that randomness, dispersiveness and the competition of multimachine reason are degenerated, belongs to the storage reliability technical field.
Background technology
The storage product often has the characteristics of long term storage, and in its cycle life-cycle, the time of the overwhelming majority is to be in stored condition, so its storage reliability level becomes the key factor of its war preparedness integrity of restriction.
Storage reliability is defined as " under the storage requirement of regulation, in the period of storage of regulation, product keeps the ability of fixed function ".Current; Countries such as the United States, Russia pay much attention to the research of storage reliability; At first carry out discriminance analysis, quicken storage test, then the storage test data are carried out the storage reliability that analyses and prediction go out the storage product to the single mechanism of carrying out of weakest link parts to storing weak link and mechanism thereof; Through comparing analysis, obtain the storage reliability assessment result at last with natural storage test result.But; The storage reliability of often many storage products is multimachine reason problems of a complicacy; Under multimachine reason competition degeneration; Each mechanism characteristic parameter often has randomness and dispersed variation characteristics, and the combined influence of these factors makes current domestic and international storage reliability analytical approach based on single weak link and single forecast model have certain limitation.
Summary of the invention
The objective of the invention is for a kind of storage reliability analytical approach that competition is degenerated based on the multimachine reason is provided; Remedy prior art and do not consider that multimachine reason competition degenerates and can not analyze the defectives such as storage reliability assessment that characteristic parameter under the multimachine reason competition degeneration has randomness, dispersed variation characteristics.A kind of storage reliability analytical approach that competition is degenerated based on the multimachine reason that the present invention relates to is through considering the storage life of storage product under the randomness influence; With each the critical component storage life characteristic distributions under the dispersiveness influence; And according to each critical component storage reliability Changing Pattern of distribution pattern calculating; The difference that obtains storage product storage life under the multimachine reason competition degenerate case is considered situation; With the multimachine reason competitive properties in different storage life stage reliability, the present invention can provide fundamental basis and technical support for storage reliability assessment, the research of storage maintenance scheme of storage product under the long term storage condition.
The present invention is achieved by the following technical solutions, at first stores the section characteristics by clear and definite with analysis, regularly detects the characteristic performance supplemental characteristic that obtains each critical component of storage product under the long term storage condition; The characteristics that change to characteristic performance supplemental characteristic randomness adopt the bin stability level that the standard deviation method has been described each critical component characteristic performance parameter that moves then; By to the match of the horizontal deterioration law model of bin stability; With according to each critical component characteristic performance parametric stability level requirement, obtain the storage life values of 10 cover certain storage product corresponding critical components; Secondly be directed against the dispersed characteristics of each sample storage test data; Each critical component storage life is carried out distributional assumption check and estimation of distribution parameters; And, can get the storage reliability model of considering each dispersed critical component under the long term storage condition according to the relation of the conversion between different distributions type and the formula of reliability; Consider situation according to the difference of storage product storage life under the multimachine reason competition degenerate case at last; Obtain each critical component reliability Changing Pattern under the long term storage condition by MATLAB emulation; Analysis-by-synthesis has obtained under the long term storage condition, and certain storage product storage reliability is stored the analysis and evaluation result in stage at each.Realized the storage reliability analytical approach that competition is degenerated based on the multimachine reason, its operating process and step are as shown in Figure 1.
A kind of storage reliability analytical approach that competition is degenerated based on the multimachine reason of the present invention, its step is following:
Step 1: store profile analysis and store Data Detection
At first; Confirm to store section, certain the storage product among the present invention is stored section and is meant the whole incidents except that " use ", after promptly paying from equipment; To equipping use or equipping the whole process between scrapping, wherein mainly comprise transportation loading and unloading, storehouse 2 stages of storage;
Secondly; Store Data Detection; (assay intervals is generally more fixing through the regular detection scheme in the storage section; Be made as 6 months), regularly detect the storage data of critical component characteristic performance parameter wherein, statistics obtains in 10 certain storage product of cover above-mentioned 5 critical components in difference characteristic performance value constantly;
Step 2: storage test data randomness is analyzed
At first, draw, obtain critical component 1, critical component 2, critical component 3, critical component 4 and the critical component 5 characteristic performance parameter storage data actual change track rule of totally 5 critical components through the storage test data being carried out the actual change track;
Then, adopt mobile standard deviation method to analyze the randomness characteristic that each critical component regularly detects data, mobile standard deviation methods analyst step is: 1) each moment characteristic performance measured value is deducted the design standards value, obtain the characteristic performance deviate; 2) adopt mobile standard deviation method analysis to obtain the mobile standard deviation sequence of each critical component characteristic performance deviate, i.e. the bin stability horizontal data of each critical component;
At last; Store constantly is that the bin stability level of abscissa, each critical component characteristic performance parameter is an ordinate with each; Bin stability horizontal data to each critical component characteristic performance parameter is drawn; Obtain critical component 1, critical component 2, critical component 3, critical component 4 and the horizontal degradation with time rule of critical component 5 characteristic performance parameter bin stabilities model respectively, and require to obtain successively the storage life value of 10 certain storage product critical component 1 of cover, critical component 2, critical component 3, critical component 4 and critical component 5 based on each critical component bin stability level;
Step 3: the storage test data are dispersed to be analyzed
To the dispersed characteristics of each storage test data, adopt the probability graph distribution inspection method in the MINITAB software that each critical component storage life value is carried out the distributional assumption check, portray the dispersed characteristic of storage test data;
At first; Adopt the probability graph distribution inspection method in the MINITAB software that each critical component storage life value is carried out the distributional assumption check; Obtain the Life Distribution type and the estimation of distribution parameters value thereof of critical component 1, critical component 2, critical component 3, critical component 4 and critical component 5, i.e. the dispersed characteristic of storage test data;
Then; Calculate principle according to fiduciary level; And calculation of reliability model between the different distributions type, in each critical component Life Distribution estimates of parameters substitution Reliability Calculation Model, can get the storage reliability model of considering each dispersed critical component under the long term storage condition;
Step 4: the storage reliability analysis and evaluation that competition is degenerated based on the multimachine reason
At first; According to multimachine reason competition degeneration modeling characteristics; Adopt MATLAB software that each the critical component storage reliability model in the step 3 is carried out emulation; Promptly calculate different each critical component fiduciary level value size constantly, obtain each critical component fiduciary level change with time under the long term storage condition respectively;
Then; Considering under the independent condition of degenerating of multimachine reason; The storage reliability analysis and evaluation that competition is degenerated based on the multimachine reason is the process of a complicacy; The storage reliability analysis and evaluation principle that competition is degenerated based on the multimachine reason among the present invention is: under multimachine reason competition degenerative conditions; The storage reliability level of product is the result who competes by between the different degradation modes of these mechanism, and therefore in the assay process of its storage reliability, the reliability of products level is by the reliability degree decision of the easiest degeneration critical component.
Wherein,, be meant in the long term storage process, whenever each device feature parameter measured clear and definite its residing stored condition at a distance from 6 months at the regular detection scheme described in the step 1.
Wherein, the mobile standard deviation method described in the step 2 is meant for one group of data x
1, x
2..., x
n, at first, to data sequence x
1, x
2, x
3, x
4, x
5Ask standard deviation, obtain moving standard difference y
1Then, remove x
1, add x
6, obtain new data sequence x
2, x
3, x
4, x
5, x
6, ask standard deviation to obtain moving standard difference y to it equally
2, and the like, obtain data sequence x
N-4, x
N-3, x
N-2, x
N-1, x
nMobile standard deviation value y
N-4At last, obtain moving mark sequence of differences y
1, y
2..., y
N-4, said method is the standard deviation method that moves.
A kind of storage reliability analytical approach that competition is degenerated based on the multimachine reason of the present invention has the following advantages:
1. adopt and move the standard deviation methods analyst randomness influence Changing Pattern of each critical component level of stability down; Solved under the long term storage condition; Each characteristic performance parameter is stored data and is not had obvious degradation trend, and shows the difficult problem of analyzing of storage life of randomness variation characteristic.
2. adopt the distributional assumption method of inspection to analyze the dispersed influence regularity of distribution of each critical component storage life down; Get the storage reliability model of each critical component; It is bigger to have solved under the long term storage condition each critical component storage life dispersiveness between different product, the difficult problem of analyzing of storage reliability.
3. according to multimachine reason competition degeneration modeling characteristics, obtain each critical component fiduciary level change with time under the long term storage condition through MATLAB emulation, and storage life under the different aspiration level and the balance between the storage reliability have been carried out analysis and evaluation.
Description of drawings
Fig. 1 is the inventive method process flow diagram.
Fig. 2 is typical storage life sectional view.
Fig. 3 is that critical component regularly detects data actual change geometric locus figure.
Fig. 4 is that each critical component fiduciary level is competed Changing Pattern figure in time under the long term storage condition.
Embodiment
To combine accompanying drawing and instantiation that the present invention is done further detailed description below.
According to implementing procedure shown in the accompanying drawing 1, from storing profile analysis and storage Data Detection, the analysis of storage test data randomness, the dispersed analysis of storage test data, the present invention being set forth in detail based on 4 aspects such as storage reliability analysis and evaluation of multimachine reason competition degeneration.Under storage requirement, influence certain storage product long term storage reliability critical component and be followed successively by critical component
1, critical component 2, critical component 3, critical component 4 and critical component 5.
A kind of storage reliability analytical approach that competition is degenerated based on the multimachine reason of the present invention, its step is following:
Step 1: store profile analysis and store Data Detection
At first, store profile analysis, the storage among the present invention is meant the whole incidents except that " use "; After promptly handing over dress from equipment; To equipping use or equipping the whole process between scrapping, wherein mainly comprise transportation loading and unloading and storehouse 2 stages of storage, typical storage life section is as shown in Figure 2.
Then; Store Data Detection; Critical component 1, critical component 2, critical component 3, critical component 4 and critical component 5 are current 5 critical components that certain stores the product storage reliability that influence; Its characteristic performance design standards value be followed successively by 27V ,-9.9V, 1.95V, 6V and 36V, under the long term storage condition, require its characteristic performance parameter to have certain level of stability, be followed successively by: 3,1,0.2,2.5 and 3.6; Obtain in the 10 cover products above-mentioned 5 critical components in difference characteristic performance value constantly through regular detection (assay intervals is 6 months), from calendar year 2001 to 2010 totally 20 data year.Wherein 1
#The characteristic performance parameter of above-mentioned 5 critical components of cover product is as shown in table 1.
Table 11
#Overlap each critical component characteristic performance parameter
| Sequence number | Period of storage (year) | Critical component 1 (V) | Critical component 2 (V) | Critical component 3 (V) | Critical component 4 (V) | Critical component 5 (V) |
| 1 | 0.5 | 27.153 | -9.6052 | 1.88 | 5.49 | 36.563 |
| 2 | 1 | 27.058 | -9.5052 | 1.876 | 5.375 | 36.593 |
| 3 | 1.5 | 27.048 | -9.5756 | 1.88 | 5.37 | 36.602 |
| 4 | 2 | 27.279 | -9.656 | 1.781 | 5.25 | 36.582 |
| 5 | 2.5 | 27.069 | -9.5856 | 1.785 | 5.37 | 36.577 |
| 6 | 3 | 27.838 | -9.5656 | 1.854 | 5.475 | 36.562 |
| 7 | 3.5 | 27.967 | -9.5465 | 1.791 | 5.34 | 36.707 |
| 8 | 4 | 27.438 | -9.5065 | 1.782 | 5.31 | 36.727 |
| 9 | 4.5 | 27.75 | -9.4465 | 1.83 | 5.28 | 36.772 |
| 10 | 5 | 27.259 | -9.6465 | 1.764 | 5.37 | 36.807 |
| 11 | 5.5 | 27.859 | -9.466 | 1.762 | 5.365 | 36.833 |
| 12 | 6 | 27.927 | -9.4465 | 1.85 | 5.355 | 36.831 |
| 13 | 6.5 | 27.053 | -9.4877 | 1.759 | 5.315 | 36.847 |
| 14 | 7 | 26.058 | -9.4573 | 1.784 | 5.305 | 36.833 |
| 15 | 7.5 | 26.853 | -9.4673 | 1.74 | 5.31 | 36.837 |
| 16 | 8 | 26.121 | -9.4567 | 1.78 | 5.295 | 36.817 |
| 17 | 8.5 | 27.89 | -9.455 | 1.783 | 5.325 | 36.846 |
| 18 | 9 | 26.602 | -9.466 | 1.856 | 5.295 | 36.853 |
| 19 | 9.5 | 27.952 | -9.471 | 1.742 | 5.286 | 36.863 |
| 20 | 10 | 26.492 | -9.42 | 1.733 | 5.32 | 36.855 |
Step 2: storage test data randomness is analyzed
The first, draw through the storage test data being carried out the actual change track, obtain that 5 critical component characteristic performance parameters regularly detect data actual change geometric locus as shown in Figure 3 in the table 1.
Second; Adopt and move the randomness that each critical component of standard deviation analysis regularly detects data; After soon each moment characteristic performance measured value and design standards value will be done difference, adopt mobile standard deviation method to analyze each stage randomness fluctuating characteristic again, mobile standard deviation calculation formula is following.
In the formula, m representes moving step length, is taken as 5 in the present invention, and i=1 ~ 5 are the critical component 1 shown in the table 1, critical component 2, critical component 3, critical component 4 and critical component 5, and j=5 ~ 20 are sequence number shown in table 1 the 1st row, θ
IkRepresent the different characteristic performance measured values constantly of storing of i critical component,
The characteristic performance parameter designing standard value of representing each critical component.
The 3rd, the mobile standard deviation result who calculates through formula (1) is the bin stability level of each critical component, and wherein 1
#The bin stability level of each critical component characteristic performance parameter of cover product is as shown in table 2.
Table 21
#Cover critical component characteristic performance parameter bin stability level
| Sequence number | Period of storage (year) | Critical component 1 (V) | Critical component 2 (V) | Critical component 3 (V) | Critical component 4 (V) | Critical component 5 (V) |
| 1 | 2.5 | 0.1117 | 0.50639 | 0.0711 | 2.0070 | 1.7027 |
| 2 | 3 | 0.7905 | 0.53131 | 0.0754 | 2.0226 | 1.7016 |
| 3 | 3.5 | 1.7222 | 0.50040 | 0.0952 | 2.0675 | 1.8497 |
| 4 | 4 | 1.9118 | 0.55001 | 0.1185 | 2.1467 | 2.0159 |
| 5 | 4.5 | 2.3964 | 0.69614 | 0.1043 | 2.1026 | 2.2731 |
| 6 | 5 | 2.4588 | 0.66155 | 0.1117 | 2.1026 | 2.5915 |
| 7 | 5.5 | 2.4944 | 0.73809 | 0.1378 | 2.2302 | 2.9695 |
| 8 | 6 | 2.4186 | 0.81879 | 0.1226 | 2.2107 | 3.1602 |
| 9 | 6.5 | 2.2296 | 0.83393 | 0.1308 | 2.2038 | 3.3491 |
| 10 | 7 | 2.5545 | 0.82426 | 0.1440 | 2.1684 | 3.4470 |
| 11 | 7.5 | 2.5090 | 0.94722 | 0.1535 | 2.2476 | 3.4963 |
| 12 | 8 | 2.5438 | 0.95538 | 0.1470 | 2.3414 | 3.4699 |
| 13 | 8.5 | 2.4765 | 0.94774 | 0.1649 | 2.3810 | 3.4951 |
| 14 | 9 | 2.6321 | 0.96611 | 0.1373 | 2.4088 | 3.5053 |
| 15 | 9.5 | 2.6511 | 0.95417 | 0.1530 | 2.4356 | 3.5562 |
| 16 | 10 | 2.8875 | 0.99734 | 0.1560 | 2.4219 | 3.5866 |
The 4th; On the basis of considering storage test data randomness; The bin stability level of each the critical component characteristic performance parameter in the his-and-hers watches 2 is carried out the variation model match, obtains critical component 1, critical component 2, critical component 3, critical component 4 and the horizontal degradation with time rule of critical component 5 characteristic performance parameter bin stabilities model respectively to be:
Critical component 1 variation model:
y
1=0.8721n{(t-2)/0.5}+0.502,r=0.9327 （2）
y
2=0.03688×{(t-2)/0.5}+0.4633，r=0.9654 （3）
Critical component 3 variation models are:
y
3=0.068×{(t-2)/0.5}
0.308,r=0.9560 （4）
y
4=0.028×{(t-2)/0.5}+11.972,r=0.9602 （5）
Critical component 5 variation models are:
y
5=1.432×{(t-2)/0.5}
0.346,r=0.9545 （6）
The 5th, require to be followed successively by according to each critical component bin stability level: 3,1,0.2,2.5 and 3.6, can get 1
#The storage life of cover critical component 1, critical component 2, critical component 3, critical component 4 and critical component 5 is followed successively by: 10.77,9.27,18.6,11.43 and 9.18.In like manner, it is as shown in table 3 to obtain the storage life value of 10 cover product critical components 1, critical component 2, critical component 3, critical component 4 and critical component 5 successively.
Table 3 10 cover critical component storage life values
Step 3: the storage test data are dispersed to be analyzed
At first, can find out from table 3 that the dispersiveness of storage life value in different product of same critical component is very big, the present invention adopts distributional assumption to check and describes this dispersed degree.
Then, through the distributional assumption assay, obtain distribution pattern and estimation of distribution parameters value that critical component 1, critical component 2, critical component 3, critical component 4 and critical component 5 storage lives are obeyed.
At last, according to the relation of the conversion between different distributions type and the formula of reliability, it is as shown in table 4 to get the storage reliability computing formula of considering each dispersed critical component under the long term storage condition.
Table 4 storage test data are dispersed to be analyzed
Step 4: the storage reliability analysis and evaluation that competition is degenerated based on the multimachine reason
At first,, the storage reliability model of each critical component is carried out emulation, obtain under the long term storage condition each critical component fiduciary level respectively and compete Changing Pattern in time, as shown in Figure 4 through MATLAB according to multimachine reason competition degeneration modeling characteristics.
Then, associative list 3 obtains the storage reliability analysis and evaluation result in multimachine reason competition degeneration with Fig. 4, and it is the process of a complicacy, and concrete manifestation is following:
1) under the most pessimistic expectation value, storage life is to determine by the minimum life value in all critical components of considering under randomness and the dispersed combined influence in the associative list 3 there be data:
T=min (8.82,8.67,17.07,10.32,8,79)=8.67 years (7)
Be that storage life mainly is by 3
#The storage life decision of the critical component 2 of product can in 8.67, fiduciary level also mainly be determined by the fiduciary level of critical component 2 according to Fig. 4, and fiduciary level presents the exponential distribution deterioration law.
2) under the most optimistic expectation value, storage life is to determine by the minimum value in all maximum life value parts of considering under randomness and the dispersed combined influence in the associative list 3 there be data:
T=min (13.37,14.77,21.2,13.18,11.28)=11.28 (8)
Be that storage life mainly is by 10
#The storage life decision of the critical component 5 of product; Can in 11.28, fiduciary level consider in two stages according to Fig. 4; Fiduciary level by critical component 2 in 0 ~ 10 year determines; Present the exponential distribution deterioration law, in 10 ~ 11.28 years,, present the normal distribution deterioration law by the fiduciary level decision of critical component 5.
3) under other expectation values, storage life is between the most pessimistic expectation value and the most optimistic expectation value, is 8.67 ~ 11.28 years, and the storage reliability that can get during this period of time according to Fig. 4 is all little by 0.5, needs to be serviced scheme.
Claims (3)
1. storage reliability analytical approach of degenerating based on multimachine reason competition, it is characterized in that: these method concrete steps are following:
Step 1: store profile analysis and store Data Detection
At first, confirm to store section, said storage section is meant the whole incidents except that " uses ", after promptly paying from equipment, to the equipment use or equip the whole process between scrapping, wherein mainly comprises transportation loading and unloading, storehouse 2 stages of storage;
Secondly, store Data Detection, through storing the regular detection scheme in the section, regularly detect the storage data of critical component characteristic performance parameter wherein, statistics obtains in the 10 cover storage products above-mentioned 5 critical components in difference characteristic performance value constantly;
Step 2: storage test data randomness is analyzed
At first, draw, obtain critical component 1, critical component 2, critical component 3, critical component 4 and the critical component 5 characteristic performance parameter storage data actual change track rule of totally 5 critical components through the storage test data being carried out the actual change track;
Then, adopt mobile standard deviation method to analyze the randomness characteristic that each critical component regularly detects data, mobile standard deviation methods analyst step is: 1) each moment characteristic performance measured value is deducted the design standards value, obtain the characteristic performance deviate; 2) adopt mobile standard deviation method analysis to obtain the mobile standard deviation sequence of each critical component characteristic performance deviate, i.e. the bin stability horizontal data of each critical component;
At last; Store constantly is that the bin stability level of abscissa, each critical component characteristic performance parameter is an ordinate with each; Bin stability horizontal data to each critical component characteristic performance parameter is drawn; Obtain critical component 1, critical component 2, critical component 3, critical component 4 and the horizontal degradation with time rule of critical component 5 characteristic performance parameter bin stabilities model respectively, and require to obtain successively the 10 storage life values of overlapping storage product critical component 1, critical component 2, critical component 3, critical component 4 and critical component 5 based on each critical component bin stability level;
Step 3: the storage test data are dispersed to be analyzed
To the dispersed characteristics of each storage test data, adopt the probability graph distribution inspection method in the MINITAB software that each critical component storage life value is carried out the distributional assumption check, portray the dispersed characteristic of storage test data;
At first; Adopt the probability graph distribution inspection method in the MINITAB software that each critical component storage life value is carried out the distributional assumption check; Obtain the Life Distribution type and the estimation of distribution parameters value thereof of critical component 1, critical component 2, critical component 3, critical component 4 and critical component 5, i.e. the dispersed characteristic of storage test data;
Then; Calculate principle according to fiduciary level; And calculation of reliability model between the different distributions type, in each critical component Life Distribution estimates of parameters substitution Reliability Calculation Model, can get the storage reliability model of considering each dispersed critical component under the long term storage condition;
Step 4: the storage reliability analysis and evaluation that competition is degenerated based on the multimachine reason
At first; According to multimachine reason competition degeneration modeling characteristics; Adopt MATLAB software that each the critical component storage reliability model in the step 3 is carried out emulation; Promptly calculate different each critical component fiduciary level value size constantly, obtain each critical component fiduciary level change with time under the long term storage condition respectively;
Then; Considering under the independent condition of degenerating of multimachine reason; The storage reliability analysis and evaluation principle that competition is degenerated based on the multimachine reason is: under multimachine reason competition degenerative conditions; The storage reliability level of product is the result who competes by between the different degradation modes of these mechanism, and therefore in the assay process of its storage reliability, the reliability of products level is by the reliability degree decision of the easiest degeneration critical component.
2. a kind of storage reliability analytical approach that competition is degenerated based on the multimachine reason according to claim 1; It is characterized in that: at the regular detection scheme described in the step 1; Be meant in the long term storage process; Whenever each device feature parameter is measured clear and definite its residing stored condition at a distance from 6 months.
3. a kind of storage reliability analytical approach that competition is degenerated based on the multimachine reason according to claim 1, it is characterized in that: the mobile standard deviation method described in the step 2 is meant for one group of data x
1, x
2..., x
n, at first, to data sequence x
1, x
2, x
3, x
4, x
5Ask standard deviation, obtain moving standard difference y
1Then, remove x
1, add x
6, obtain new data sequence x
2, x
3, x
4, x
5, x
6, ask standard deviation to obtain moving standard difference y to it equally
2, and the like, obtain data sequence x
N-4, x
N-3, x
N-2, x
N-1, x
nMobile standard deviation value y
N-4At last, obtain moving mark sequence of differences y
1, y
2..., y
N-4, said method is the standard deviation method that moves.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1967573A (en) * | 2005-11-17 | 2007-05-23 | 鸿富锦精密工业（深圳）有限公司 | System and method for reliability analysis of electron products |
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|---|---|---|---|---|
| CN1967573A (en) * | 2005-11-17 | 2007-05-23 | 鸿富锦精密工业（深圳）有限公司 | System and method for reliability analysis of electron products |
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| Title |
|---|
| 徐真红: "武器系统的贮存寿命预测方法研究", 《 中国优秀硕士学位论文全文数据库》, 30 June 2011 (2011-06-30) * |
Cited By (1)
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|---|---|---|---|---|
| CN109033556A (en) * | 2018-07-04 | 2018-12-18 | 哈尔滨工业大学 | The relay class single machine Estimation of The Storage Reliability method of combined process and reliability block diagram |
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