CN110751406A - Metering value sampling plan method for resubmissed batches - Google Patents

Metering value sampling plan method for resubmissed batches Download PDF

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CN110751406A
CN110751406A CN201911020925.1A CN201911020925A CN110751406A CN 110751406 A CN110751406 A CN 110751406A CN 201911020925 A CN201911020925 A CN 201911020925A CN 110751406 A CN110751406 A CN 110751406A
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sampling
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李涛
赵冬阳
刘牧
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Inspur Cloud Information Technology Co Ltd
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    • G06Q10/06395—Quality analysis or management

Abstract

The invention particularly relates to a metering value sampling planning method for resubmitted batches. The method for planning sampling of the metering value of the resubmitted batch aims at improving the sampling efficiency, and the service life performance index C is used in the planning sampling of the metering value of the resubmitted batchL(ii) a Performance index according to product life CLProduction process obeying Burr XII distribution by constructing product life performance index CLOC curves of operating characteristics with product pass p such that the model parameters satisfy both a given producer risk α and a consumer risk β, according to the Maximum Likelihood Estimate (MLE) of the sampleAnd key acceptance value C0And judging whether the sample is qualified or not according to the comparison result. The method for planning the sampling of the metering values of the resubmissed batches has high accuracy and wide application range, is suitable for the acceptance inspection process of products in the manufacturing industry, can improve the acceptance accuracy of the products and the productThe work efficiency of acceptance check is suitable for popularization and application.

Description

Metering value sampling plan method for resubmissed batches
Technical Field
The invention relates to the technical field of statistical sampling inspection, in particular to a metering value sampling planning method for a resubmissed batch.
Background
In modern industry, many products are produced in a relatively short time, so that 100% inspection is not possible but not possible, and an acceptance sampling plan is generated. Acceptance sampling plans are widely used in statistical quality control to ensure product yield. The acceptance sampling scheme with good design can not only ensure the qualification rate of batch products, but also save the time cost of enterprises and improve the production efficiency of the enterprises.
The parameters of the acceptance sampling plan are determined by two points on the operating characteristic curve, one point being the Acceptable Quality Level (AQL) and the other point being the batch allowable failure rate (LTPD). The probability of rejecting a batch of product, i.e., the producer risk, will be represented here by α (class I errors) and the probability of accepting a batch of rejected product, i.e., the consumer risk, will be represented by β (class II errors). for producers, batch acceptance probabilities are required to be at least 1- α of the Acceptable Quality Level (AQL). for consumers, it is desirable that the batch acceptance probability be less than β of the batch allowable failure rate (LTPD).
The application of acceptance sampling plans focuses mainly on two types of sampling schemes. The count value sampling plan uses only "pass" or "fail" to characterize the quality of the product. The metric sampling plan must know the quality attribute distribution of the product and in some cases have better performance. The metering value sampling plan is more advantageous in product testing in the manufacturing industry, even where the sample size is small, since the metering value data provides more information about the manufacturing process or lot than the count value data. Furthermore, conventional metrology value sampling is single sampling, typically taking one sample for testing. In some cases, when the manufacturer calls into question the results of the first sampling and may require resampling as specified, the lot needs to be resampled. Thus, the resampling scheme provides the manufacturer with the opportunity to review the lot again when the first inspection fails. Often, suppliers are more knowledgeable about the lot of goods and are willing to accept a resampling process.
Metering sampling plans incorporating process capability analysis have been developed in recent years. Process capability analysis is an effective method of numerically measuring the performance and potential capabilities of a production process. When the AQL requirement for the defect part is very lowTime, process capability index CpkIs used by many researchers in acceptance sampling schemes. The above studies are all based on the hypothesis CpkThe life of the product follows Gamma distribution or Weibull distribution. Life performance index CLGenerally for life performance analysis of products with better quality property values. Life performance index CLAre recommended for evaluating the life performance of electronic components.
Based on the above situation, the present invention provides a metering value sampling planning method for resubmitted batches.
Disclosure of Invention
In order to make up for the defects of the prior art, the invention provides a simple and efficient metering value sampling planning method aiming at a resubmissed batch.
The invention is realized by the following technical scheme:
a method of scheduling a sample of metrology values for a resubmitted batch, comprising: to improve sampling efficiency, a life performance index C is used in a batch re-submitted metering value sampling planL(ii) a Performance index according to product life CLProduction process obeying Burr XII distribution by constructing product life performance index CLOC curves of operating characteristics with product pass p such that the model parameters satisfy both a given producer risk α and a consumer risk β, according to the Maximum Likelihood Estimate (MLE) of the sample
Figure BDA0002247177630000025
And key acceptance value C0And judging whether the sample is qualified or not according to the comparison result.
The invention relates to a metering value sampling planning method for resubmitted batches, which comprises the following steps:
first, a product life performance index C is constructedLAnd the product percent of pass PrThe relationship between the batch and the batch is applied to the metering value sampling plan of the resubmissed batch;
secondly, deducing a product life performance index C by using a data transformation methodLMaximum Likelihood Estimation (MLE)
Figure BDA0002247177630000021
Thirdly, determining a key acceptance value C according to the optimal parameters of the sample size required by sampling and two points of an operation characteristic OC curve0；
The fourth step, if the maximum likelihood estimation value (MLE)
Figure BDA0002247177630000022
Not less than the critical acceptance value C0If so, the sample is accepted to be qualified; otherwise, the sample is rejected.
In the first step, the life cycle of the product is modeled by a Burr XII distribution, a random sample X obeys the Burr XII distribution, L is set as a corresponding lower limit, and the quality characteristic of the product is that the longer the life is, the better the product is; if random sample xiStatistical transformation value Y ofiIf the value is greater than the set corresponding lower limit L, the random sample x is considerediIs a qualified product; product life performance index CLAnd the product percent of pass PrThe relationship between them is as follows:
Figure BDA0002247177630000023
wherein the content of the first and second substances,
Figure BDA0002247177630000024
k and C are both shape parameters, product life performance index CL<1。
In the first step, the metrology value sampling plan for a resubmitted lot allows resubmission to occur m-1 times, and the metrology value sampling plan operating characteristics OC function for a resubmitted lot containing m-1 resubmissions is represented as:
wherein, Pa(C) For the simple sampling plan, the operating characteristic function m is a natural number not less than 2.
Product life performance index CLC, the operating characteristic function P of the plan is simply sampleda(C) Expressed as:
Figure BDA0002247177630000032
wherein 2 kY-chi2(2n) making
Figure BDA0002247177630000033
Is x2Lower α quantile of (2n), C0For key acceptance values, n is the sample size.
In the second step, a product life performance index C is obtainedLMaximum Likelihood Estimation (MLE)
Figure BDA0002247177630000034
The following were used:
Figure BDA0002247177630000035
wherein the content of the first and second substances,
Figure BDA0002247177630000036
is an estimate of the shape parameter k, c is the shape parameter, n is the sample size,
in the third step, points (AQL,1- α) and (LTPD, &ltttttranslation = β "&gttttβ &ltt/t &gtt) as two points on the OC curve specifying the metering value sampling plan, it can be found that:
solving the two inequalities to obtain the sample volume n and the key acceptance value C0Where the sample size n is an integer up.
And in the fourth step, when the sample of the resubmissed batch of metering value sampling plan is rejected, returning to the first step, repeating the step for m times, and if m-1 times of samples are accepted, accepting the batch of samples, wherein m is a natural number not less than 2.
The invention has the beneficial effects that: the method for planning the sampling of the metering values of the resubmissed batches has the advantages of high accuracy and wide application range, is suitable for the acceptance inspection process of the products in the manufacturing industry, can improve the acceptance accuracy of the products and the work efficiency of the acceptance inspection of the products, and is suitable for popularization and application.
Drawings
FIG. 1 is a schematic view of the metering value sampling plan operating characteristic OC curve for a resubmitted lot according to the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more apparent, the present invention is described in detail below with reference to the embodiments. It should be noted that the specific embodiments described herein are only for explaining the present invention and are not used to limit the present invention.
The method for planning sampling of the metering value of the resubmitted batch aims at improving the sampling efficiency, and the service life performance index C is used in the planning sampling of the metering value of the resubmitted batchL(ii) a Performance index according to product life CLProduction process obeying Burr XII distribution by constructing product life Performance index CLOC curves of operating characteristics with product pass p such that the model parameters satisfy both a given producer risk α and a consumer risk β, according to the Maximum Likelihood Estimate (MLE) of the sample
Figure BDA0002247177630000041
And key acceptance value C0And judging whether the sample is qualified or not according to the comparison result.
The Burr XII distribution was first proposed by Burr and is of interest due to its potential application scenario. These applications can be found in different fields, including quality control, reliability studies, time-to-failure modeling, and acceptance sampling plans.
The probability density function (p.d.f) of the distribution is given by:
f(x，c，k)＝ckxc-1(1+xc)-(k+1)，x＞0，c＞0，k＞0 (1)
the failure rate function is:
h(x，c，k)＝ckxc-1(1+xc)-1，x＞0，c＞0，k＞0 (2)
where k is the shape parameter but does not affect the shape of h (x, c, k), c is also the shape parameter, and more important for the Burr XII distribution, the general parameters c and k are unknown. The estimation of the Burr XII distribution parameters can be derived by utilizing a maximum likelihood estimation method, and the estimation has uniqueness.
Suppose X1，X2......XnIs a randomly chosen variable from the Burr XII distribution, the probability density function is f (x, c, k), the likelihood function is:
Figure BDA0002247177630000042
the log-likelihood is:
Figure BDA0002247177630000043
parameters c and k are both unknown, then:
Figure BDA0002247177630000044
Figure BDA0002247177630000051
the MLEs for parameters c and k can be found from the above two equations.
Suppose the life cycle of the product is modeled by a Burr XII distribution. Let L be the corresponding lower limit. Assuming that the product quality is characterized by longer life and better life, the life performance of the product is evaluated, where C is consideredLThe definition is as follows:
Figure BDA0002247177630000052
where μ and σ are the mean and standard deviation of the process. X obeys the Burr XII distribution and applies the data transformation Y loge(1+Xc) And c > 0, obtaining a Y obedient exponential distribution, the probability density function (p.d.f) of Y being:
f(y，k)＝ke-ky，y＞0，k＞0 (8)
the failure rate function R (y) is:
the lifetime performance index can be written as:
Figure BDA0002247177630000054
wherein, the process mean value mu is EY 1/k, and the standard deviation isIt can be seen that the smaller k, the failure rate function R (y), and the life performance index CLThe larger. Wherein the original data set X and the converted data set Y are one-to-one, and have the same effect in the aspect of evaluating the service life performance of the product, and the service life performance index CLIs reasonably suitable.
The metering value sampling planning method for the resubmitted batch comprises the following steps of:
first, a product life performance index C is constructedLAnd the product percent of pass PrThe relationship between the batch and the batch is applied to the metering value sampling plan of the resubmissed batch;
secondly, deducing a product life performance index C by using a data transformation methodLMaximum Likelihood Estimation (MLE)
Figure BDA0002247177630000056
Thirdly, according to the optimal parameters of the sample size required by sampling and the operationKey acceptance value C is determined by two points of characteristic OC curve0；
The fourth step, if the maximum likelihood estimation value (MLE)
Figure BDA0002247177630000057
Not less than the critical acceptance value C0If so, the sample is accepted to be qualified; otherwise, the sample is rejected.
In the first step, the life cycle of the product is modeled by a Burr XII distribution, a random sample X obeys the Burr XII distribution, L is set as a corresponding lower limit, and the quality characteristic of the product is that the longer the life is, the better the product is; if random sample xiStatistical transformation value Y ofiIf the value is greater than the set corresponding lower limit L, the random sample x is considerediIs a qualified product; product life performance index CLAnd the product percent of pass PrThe relationship between them is as follows:
Figure BDA0002247177630000061
wherein the content of the first and second substances,
Figure BDA0002247177630000062
k and C are both shape parameters, product life performance index CL＜1。
It can be seen that the product life performance index CLAnd a yield PrThere is a strict incremental correspondence between them. Using product life performance index CLAnd a yield PrRelation between them, product life performance index CLNot only can the product quality be estimated, but also the product percent of pass can be estimated.
In the first step, the metrology value sampling plan for a resubmitted lot allows resubmission to occur m-1 times, and the metrology value sampling plan operating characteristics OC function for a resubmitted lot containing m-1 resubmissions is represented as:
Figure BDA0002247177630000063
wherein, Pa(C) Planned for simple samplingAs the characteristic function, m is a natural number not less than 2.
Product life performance index CLC, the operating characteristic function P of the plan is simply sampleda(C) Expressed as:
Figure BDA0002247177630000064
wherein 2 kY-chi2(2n) making
Figure BDA0002247177630000065
Is x2Lower α quantile of (2n), C0For key acceptance values, n is the sample size.
In the second step, the product life performance index C can be obtained according to the formula (10)LMaximum Likelihood Estimation (MLE)
Figure BDA0002247177630000066
The following were used:
Figure BDA0002247177630000067
wherein the content of the first and second substances,
Figure BDA0002247177630000068
is an estimate of the shape parameter k, c is the shape parameter, n is the sample size,
the estimated value is obtained by equation (6) being 0:
Figure BDA0002247177630000071
at the same time, it is also possible to obtain:
2kYi～χ2(2)，
Figure BDA0002247177630000072
when n → ∞ is reached,
Figure BDA0002247177630000073
maximum likelihood estimation value
Figure BDA0002247177630000074
And is a product life performance index CLUsing the estimated value
Figure BDA0002247177630000075
To construct a metric sampling plan.
In the third step, points (AQL,1- α) and (LTPD, &ltttttranslation = β "&gttttβ &ltt/t &gtt) as two points on the OC curve specifying the metering value sampling plan, it can be found that:
PA(CAQL)＝1-[1-Pa(CAQL)]m≥1-α (17)
PA(CLTPD)＝1-[1-Pa(CLTPD)]m≤β (18)
namely:
Figure BDA0002247177630000076
Figure BDA0002247177630000077
solving the two inequalities (19) and (20) to obtain the sample amount n and the key acceptance value C0Where the sample size n is an integer up.
And in the fourth step, when the sample of the resubmissed batch of metering value sampling plan is rejected, returning to the first step, repeating the step for m times, and if m-1 times of samples are accepted, accepting the batch of samples, wherein m is a natural number not less than 2.
Example 1
Applying the sampling plan presented above to the actual, parameters n and C0Various values are calculated according to (AQL,1- α) and (LTPD, &lTtT translation = β "&gTt β &lTt/T &gTt.) Table 1 and Table 2 give parameters n and C, respectively0Values of (a) are based on the mass basis (C) selected at the same time, the number of resampling times m being 2, 3 and the risk (α) being 0.01, 0.025, 0.05, 0.075, 0.10AQL，CLPTD) (0.93, 0.83), (0.95, 0.85), (0.97, 0.87) and(0.99，0.89)。
production risk α -0.01 consumer risk β -0.05, quality benchmark (C)AQL，CLPTD) The values are set to (0.95, 0.85), and the corresponding sample size and target value (n, C) are set according to the resampling number m being 2L) (54, 0.9398) indicating if 54 products were sampledAll are true, and the batch can be considered qualified. In addition, the batch of goods is only allowed to be resubmitted once and will be rejected when the resubmitted goods are not accepted.
Table 1 according to C when m is 2LSpecified resampling metric sampling plan
Figure BDA0002247177630000081
Table 2 according to C when m is 3LSpecified resampling metric sampling plan
Figure BDA0002247177630000082
As can be seen from tables 1 and 2, the number of samples n tested increases as the risk suffered by the manufacturer and/or customer decreases. The reason for this is intuitive, and if we want to reduce the risk, more samples should be examined to make the correct decision. Further, CAQL，CLPTDAnd α risk at the same level, Table 1 shows the key acceptance value C0Reduction increases from 0.01 to 0.10 with risk of β, but for fixed CAQL，CLPTDAnd risk of β, C0Table 2 and Table 1 reflect the same trend, comparing the metering value sampling plans of different re-extraction times m, and it can be seen that as m increases, the required sample amount n becomes smaller, and the corresponding threshold value becomes smallerReceiving value C0. Becomes larger.
In addition, OC curves of the metering value sampling schemes are drawn according to different values of the number m of re-submissions. Fixed sample size and cut-off (n 20, C)00.8), fig. 1 shows OC curves m of the resubmit sampling plan as 1, 2, 3, and 4. It can be readily seen that with CLIncrease in value, CLThe impact on the probability of acceptance will also increase. Furthermore, when m is 1, the proposed sampling scheme is a common variable sampling scheme. As is clear from fig. 1, the proposed re-submitted sampling scheme OC curve is better than the OC curve of the normal sampling scheme.
Example 2
The time to first failure for a small electric cart for internal transport and delivery in a large manufacturing facility is shown in table 3.
The maximum likelihood estimates of two parameters of the Burr XII distribution are respectively
Figure BDA0002247177630000101
Andp-0.1008 and AIC-4.1757 were obtained using the Kolmogorov-Smirnov test. Therefore, the Burr XII distribution is very suitable for fitting the first fault data of the electric cart. In the contract, a quality level (C) is assumedAQL，CLPTD) Set to (0.97, 0.87), producer risk α -0.01 and consumer risk β -0.01 in this case, the sample size and corresponding key acceptance value (n, C) are spot checkedL) Look-up from table 1 yields (20, 0.9587), and m is 2. It shows that if 20 random inspection products are all satisfied
Figure BDA0002247177630000103
The batch of goods is accepted. Otherwise, a resubmit sampling plan is made and if resubmit sampling is not accepted, we should reject the lot.
The application then randomly drawn 20 samples from the real life dataset for the batch, the dataset being shown in table 3. Compliance parameters based on dataIs composed of
Figure BDA0002247177630000104
And
Figure BDA0002247177630000105
the lower limit of life L of the Burr XII distribution (2). Derived from sample estimation
Figure BDA0002247177630000106
Because of the fact thatSo that the batch is checked for the first time and is not accepted for resubmission, and resubmissions are calculatedIf it is not
Figure BDA0002247177630000109
The batch is received, otherwise the batch is rejected.
TABLE 3.20 Life of the electric go-cart before first failure (unit: month)
Figure BDA00022471776300001010
The above-described embodiment is only one specific embodiment of the present invention, and general changes and substitutions by those skilled in the art within the technical scope of the present invention are included in the protection scope of the present invention.

Claims (8)

1. A method of scheduling a sample of metrology values for a resubmitted batch, comprising: to improve sampling efficiency, a life performance index C is used in a batch re-submitted metering value sampling planL(ii) a Performance index according to product life CLProduction process obeying Burr XII distribution by constructing product life performance index CLAnd a product percent of pass p such that the model parameters satisfy both the given producer risk α and the consumer risk β, based on the sampleMaximum Likelihood Estimate of (MLE)
Figure FDA0002247177620000013
And key acceptance value C0And judging whether the sample is qualified or not according to the comparison result.
2. A metering value sampling planning method for a resubmitted batch according to claim 1, comprising the steps of:
first, a product life performance index C is constructedLAnd the product percent of pass PrThe relationship between the batch and the batch is applied to the metering value sampling plan of the resubmissed batch;
secondly, deducing a product life performance index C by using a data transformation methodLMaximum likelihood estimation value
Figure FDA0002247177620000016
Thirdly, determining a key acceptance value C according to the optimal parameters of the sample size required by sampling and two points of an operation characteristic OC curve0；
A fourth step of, if the maximum likelihood estimation value
Figure FDA0002247177620000014
Not less than the critical acceptance value C0If so, the sample is accepted to be qualified; otherwise, the sample is rejected.
3. A metering value sampling planning method for resubmitted batches as claimed in claim 2, wherein: in the first step, the life cycle of the product is modeled by a Burr XII distribution, a random sample X obeys the Burr XII distribution, L is set as a corresponding lower limit, and the quality characteristic of the product is that the longer the life is, the better the product is; if random sample xiStatistical transformation value Y ofiIf the value is greater than the set corresponding lower limit L, the random sample x is considerediIs a qualified product; product life performance index CLAnd the product percent of pass PrThe relationship between them is as follows:
wherein the content of the first and second substances,
Figure FDA0002247177620000015
k and C are both shape parameters, product life performance index CL<1。
4. A metering value sampling planning method for resubmitted batches as claimed in claim 3, wherein: in the first step, the metrology value sampling plan for a resubmitted lot allows resubmission to occur m-1 times, and the metrology value sampling plan operating characteristics OC function for a resubmitted lot containing m-1 resubmissions is represented as:
wherein, Pa(C) For the simple sampling plan, the operating characteristic function m is a natural number not less than 2.
5. A metering value sampling planning method for resubmitted batches as claimed in claim 4, wherein: product life performance index CLC, the operating characteristic function P of the plan is simply sampleda(C) Expressed as:
wherein 2 kY-chi2(2n) making
Figure FDA0002247177620000026
Is x2Lower α quantile of (2n), C0For key acceptance values, n is the sample size.
6. The metering value sampling planner for resubmitted batches of claim 5The method is characterized in that: in the second step, a product life performance index C is obtainedLMaximum Likelihood Estimation (MLE)
Figure FDA0002247177620000028
The following were used:
Figure FDA0002247177620000022
wherein the content of the first and second substances,
Figure FDA0002247177620000027
is an estimate of the shape parameter k, c is the shape parameter, n is the sample size,
Figure FDA0002247177620000023
7. the metering value sampling plan method for a resubmitted batch as set forth in claim 6, wherein in the third step, points (AQL,1- α) and (LTPD, &lTtTtranslation = β "&gTtβ &lTt/T &gTt) as two points on the OC curve designating the metering value sampling plan can be found:
Figure FDA0002247177620000025
solving the two inequalities to obtain the sample volume n and the key acceptance value C0Where the sample size n is an integer up.
8. A metering value sampling planning method for resubmitted batches as claimed in claim 2, wherein: and in the fourth step, when the sample of the resubmissed batch of metering value sampling plan is rejected, returning to the first step, repeating the step for m times, and if m-1 times of samples are accepted, accepting the batch of samples, wherein m is a natural number not less than 2.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111597511A (en) * 2020-06-02 2020-08-28 国网湖北省电力有限公司电力科学研究院 Glass insulator sampling test acceptance determination method based on mathematical hypothesis test
CN114970958A (en) * 2022-04-20 2022-08-30 北京航空航天大学 Sampling inspection scheme optimization method for storage availability of missile engine

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TW200744003A (en) * 2006-05-16 2007-12-01 Tai-Woei Shiah One kind of an expert system for sampling inspection plans application
US20090192743A1 (en) * 2008-01-24 2009-07-30 Takahiro Ikeda Sampling estimating method, sampling inspection estimating apparatus, and computer readable medium storing sampling inspection estimating program

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TW200744003A (en) * 2006-05-16 2007-12-01 Tai-Woei Shiah One kind of an expert system for sampling inspection plans application
US20090192743A1 (en) * 2008-01-24 2009-07-30 Takahiro Ikeda Sampling estimating method, sampling inspection estimating apparatus, and computer readable medium storing sampling inspection estimating program

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
AHMED A. SOLIMAN: ""Estimation of Parameters of Life From Progressively Censored Data Using Burr-XII Model"", 《IEEE TRANSACTIONS ON RELIABILITY》 *
何倩: ""基于过程能力指数的计量抽样检验方案的研究"", 《中国优秀硕士学位论文全文数据库 经济与管理科学辑》 *
晏爱君 等: ""指数寿命产品的寿命性能评估"", 《系统工程与电子技术》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111597511A (en) * 2020-06-02 2020-08-28 国网湖北省电力有限公司电力科学研究院 Glass insulator sampling test acceptance determination method based on mathematical hypothesis test
CN114970958A (en) * 2022-04-20 2022-08-30 北京航空航天大学 Sampling inspection scheme optimization method for storage availability of missile engine

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