CN107301285A - Non-electronic product Sequential Compliance Method based on predicting residual useful life - Google Patents
Non-electronic product Sequential Compliance Method based on predicting residual useful life Download PDFInfo
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Abstract
The present invention discloses a kind of non-electronic product Sequential Compliance Method based on predicting residual useful life, first with the Monitoring Data of test products, sets up the wiener degradation model of product, the parameter of degradation estimation model；Secondly the method for predicting residual useful life based on Wiener-Hopf equation, the residual life T for the sample tested probability density function f are utilizedT|X(τ)(t | X (τ)) and Cumulative Distribution Function FT|X(τ)(t | X (τ)), and calculate the desired value E (T | X (τ)) for obtaining residual life, and the entire life value E of the product (T | X (τ))+τ, as a new fault data, is made into judgement in advance；Due to making judgement in advance producer, user can be made to increase certain risk, fixed value-at-risk (wherein Production venture α, consumer's risk β) at the beginning of so as to change producer, user, it is constant in order to keep producer, user to undertake total risk value, α ', β ' are adjusted to respectively by value-at-risk is determined at the beginning of producer, user；Finally, by all fault datas comprising new fault data, and corresponding α ', β ' value etc. substitutes into and receives and reject equation, and then makes judgement.
Description
Technical field：
The present invention relates to a kind of non-electronic product Sequential Compliance Method based on predicting residual useful life.
Background technology：
The likelihood ratio sequential sampling testing program that national military standard GJB899A-2009, American army mark MIL-STD-781D are provided is all
It is that exponential distribution is obeyed based on the product bug time it is assumed that this assumes to be to set up to most of electronic products.But it is most
The non-electronic product bug time disobeys exponential distribution, but obeys or approximate Follow Weibull Distribution, therefore,《Reliability and
Maintainability engineering manual》The Probability Rate of Sequence Test of Weibull distribution is given in (volume two).It is non-yet with the long-life
The test period of required by electronic product is very long, so it is the focus paid close attention at present to reduce the checking test time.Accelerated aging
Experiment can reduce test period, but accelerated life test requires high to testing equipment, and at present in terms of accelerated life model
Research and imperfection.So, a large amount of monitoring informations of the generation during product testing are made full use of, it is pre- using residual life
The thought of survey, the fault time of prediction product is an effective way for reducing test period.Practice have shown that in many occasions
The state of product and behavior are non-monotone variations, and the degradation model based on Wiener-Hopf equation can describe non-monotonic move back well
Change process, therefore, the predicting residual useful life based on Wiener-Hopf equation are widely studied application.
Not yet there is document to consider the predicting residual useful life based on Wiener-Hopf equation and the likelihood ratio sequence of Weibull distribution at present
Testing program combination is passed through, to reach the purpose for shortening the long-life non-electronic product testing time.
The content of the invention：
The present invention is to provide a kind of based on the non-of predicting residual useful life to solve the problem of above-mentioned prior art is present
Electronic product Sequential Compliance Method, it can solve the problem that《Reliability and maintainability engineering manual》It is non-electronic that (volume two) is provided
Do not have to utilize the residual life information implied in product surveillance data in the sequential checking test scheme of product so that Long Life Products
The problem of test period is long.
The technical solution adopted in the present invention has：A kind of sequential checking test of non-electronic product based on predicting residual useful life
Method, comprises the following steps：
First, the Performance Degradation Data monitored according to completed test specimen and ongoing test specimen,
Set up the wiener degradation model of product and estimate model parameter, according to the method for predicting residual useful life based on Wiener-Hopf equation, obtain
To just test specimen residual life T probability density function fT|X(τ)(t | X (τ)) and Cumulative Distribution Function FT|X(τ)(t|X
(τ))；
Secondly, calculating desired remaining lifetime value is：
Obtain the entire life value of the sample：
tn=E (T | X (τ))+τ (2)
And calculate and make the value-at-risk brought to producer and user of judgement in advance and be respectively：
Rrisk_product=1-FT|X(τ)(E(T|X(τ))|X(τ)) (3)
Rrisk_customer=FT|X(τ)(E(T|X(τ))|X(τ)) (4)
Then, in order to ensure that producer and the respective overall risk of user are constant, the producer wind of judgement formula will be substituted into
Danger and User venture are adjusted to respectively：
Finally, the n-1 sample fault datas t of experiment will have been completedi(i=1,2 ..., n-1), and tested
The expectation entire life value t of samplen=E (T | X (τ))+τ described points on Weibull probability paper, form parameter m is estimated, according to public affairs
Formula (7) calculates and obtains dimensionless number：
According to receiving equation (8) and rejecting equation (9), the K obtained under n sample failure is calculated respectivelyaAnd KnValue
Wherein：
θ0The upper limit is examined for the mean time between failures (MFBF) of product；
θ1Lower limit is examined for the mean time between failures (MFBF) of product；
D is discrimination ratio, i.e. d=θ0/θ1；
If K ＜ Kn, then rejection judgement is made；If K ＞ Ka, then reception judgement is made；Otherwise continue to test.
The present invention has the advantages that：The present invention replaces traditional Weibull distribution Probability Rate of Sequence Test,
Method for predicting residual useful life is employed, by controlling the overall risk of producer and user, reception is made or rejects judgement, so that
Test period is saved.
Brief description of the drawings：
Fig. 1 is the non-electronic product Sequential Compliance Method schematic diagram based on predicting residual useful life.
Embodiment：
The present invention is further illustrated below in conjunction with the accompanying drawings.
The present invention discloses a kind of non-electronic product Sequential Compliance Method based on predicting residual useful life, including following step
Suddenly：
First, the Performance Degradation Data monitored according to completed test specimen and ongoing test specimen,
Set up the wiener degradation model of product and estimate model parameter (drift parameter λ, diffusion parameter σ), according to based on Wiener-Hopf equation
Method for predicting residual useful life, obtain just test specimen residual life T probability density function fT|X(τ)(t | X (τ)) and it is tired
Count distribution function FT|X(τ)(t|X(τ))；
Secondly, calculating desired remaining lifetime value is：
Obtain the entire life value of the sample：
tn=E (T | X (τ))+τ (2)
And calculate and make the value-at-risk brought to producer and user of judgement in advance and be respectively：
Rrisk_product=1-FT|X(τ)(E(T|X(τ))|X(τ)) (3)
Rrisk_customer=FT|X(τ)(E(T|X(τ))|X(τ)) (4)
Then, in order to ensure that producer and the respective overall risk of user are constant, the producer wind of judgement formula will be substituted into
Danger and User venture are adjusted to respectively：
Finally, the n-1 sample fault datas t of experiment will have been completedi(i=1,2 ..., n-1), and tested
The expectation entire life value t of samplen=E (T | X (τ))+τ described points on Weibull probability paper, estimate form parameter m.According to public affairs
Formula (7) calculates and obtains dimensionless number：
According to receiving equation (8) and rejecting equation (9), the K obtained under n sample failure is calculated respectivelyaAnd KnValue.
Wherein：
θ0The upper limit is examined for the mean time between failures (MFBF) of product；
θ1Lower limit is examined for the mean time between failures (MFBF) of product；
D is discrimination ratio, i.e. d=θ0/θ1。
If K ＜ Kn, then rejection judgement is made；If K ＞ Ka, then reception judgement is made；Otherwise continue to test.
Illustrate the sequential checking examination of non-electronic product of the present invention based on predicting residual useful life below by specific embodiment
Proved recipe method：
1) parameter Estimation of Wiener-Hopf equation
Assuming that having completed the experiment of n-1 sample, n-th of sampling test is currently carried out, each sample i (i=
1,2 ..., n) in initial time ti0Performance degradation amount be X (ti0)=0；At the momentThe properties of sample amount of degradation measured
RespectivelyRemember Δ xij=X (tij)-X(ti(j-1)) it is sample i in moment (ti(j-1),tij) between performance move back
Change amount, Δ x is understood by the property of Wiener-Hopf equationijNormal Distribution：
Δxij~N (λ Δs tij,σ2Δtij)
Wherein Δ tij=tij-ti(j-1), i=1,2 ..., n, j=1,2 ..., mi。
Likelihood function is set up with the Performance Degradation Data of monitoring：
Parameter lambda, σ can directly be tried to achieve by formula (10)2Maximum likelihood estimation be：
2) predicting residual useful life of n-th of sample
It is assumed that at the τ moment, the amount of degradation X (τ) of n-th of sample is xr, therefore residual life T can be expressed as：
T=inf t | X (t+ τ) >=L, X (τ)=xr,t≥0} (13)
Because the increment of Wiener-Hopf equation is separate, and possess homogeneous Markov property, can be obtained by formula (13)：
So, residual life T probability density function and Cumulative Distribution Function are respectively：
Desired remaining lifetime value is：
Therefore, the entire life value of n-th of sample is：
tn=E (T | X (τ))+τ (18)
3) producer User venture is calculated
The residual life by predicting sample is can be seen that from risk angle, judgement is made in advance or certain wind is produced
Danger, i.e. sample are possible to not reach desired remaining lifetime value E (T | X (τ)), and this is equivalent to adding User venture.By formula
(16) understand, the value-at-risk brought to producer and user of judgement is made in advance is respectively：
Rrisk_product=1-FT|X(τ)(E(T|X(τ))|X(τ)) (19)
Rrisk_customer=FT|X(τ)(E(T|X(τ))|X(τ)) (20)
So the overall risk that user undertakes is：
βall=β (1+Rrisk_customer) (21)
Corresponding Production venture is：
αall=α (1+Rrisk_product) (22)
In order to ensure that producer and the respective overall risk of user are constant, i.e., as in contract require as, institute for
The Production venture and User venture of judgement are adjusted to respectively：
4) receive and adjudicated with rejecting
N-1 sample fault data t of experiment will be completedi(i=1,2 ..., n-1), and the n-th sample expectation total longevity
Order tn=E (T | X (τ))+τ described points on Weibull probability paper, estimate form parameter m.
Calculated according to formula (25) and obtain dimensionless number：
According to receiving equation (26) and rejecting equation (27), the K obtained under n sample failure is calculated respectivelyaAnd KnValue.
Wherein：
θ0The upper limit is examined for the mean time between failures (MFBF) of product；
θ1Lower limit is examined for the mean time between failures (MFBF) of product；
D is discrimination ratio, i.e. d=θ0/θ1。
If K ＜ Kn, then rejection judgement is made；If K ＞ Ka, then reception judgement is made；Otherwise continue to test.
Described above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, some improvement can also be made under the premise without departing from the principles of the invention, and these improvement also should be regarded as the present invention's
Protection domain.
Claims (1)
1. a kind of non-electronic product Sequential Compliance Method based on predicting residual useful life, it is characterised in that：Including following step
Suddenly：
First, the Performance Degradation Data monitored according to completed test specimen and ongoing test specimen, sets up
The wiener degradation model of product simultaneously estimates model parameter, according to the method for predicting residual useful life based on Wiener-Hopf equation, obtains just
In the residual life T of test specimen probability density function fT|X(τ)(t | X (τ)) and Cumulative Distribution Function FT|X(τ)(t|X(τ))；
Secondly, calculating desired remaining lifetime value is：
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Obtain the entire life value of the sample：
tn=E (T | X (τ))+τ (2)
And calculate and make the value-at-risk brought to producer and user of judgement in advance and be respectively：
Rrisk_product=1-FT|X(τ)(E(T|X(τ))|X(τ)) (3)
Rrisk_customer=FT|X(τ)(E(T|X(τ))|X(τ)) (4)
Then, in order to ensure that producer and the respective overall risk of user are constant, by substitute into adjudicate formula Production venture and
User venture is adjusted to respectively：
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Wherein：
θ0The upper limit is examined for the mean time between failures (MFBF) of product；
θ1Lower limit is examined for the mean time between failures (MFBF) of product；
D is discrimination ratio, i.e. d=θ0/θ1；
If K ＜ Kn, then rejection judgement is made；If K ＞ Ka, then reception judgement is made；Otherwise continue to test.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114897349A (en) * | 2022-05-09 | 2022-08-12 | 中国人民解放军海军工程大学 | Success-failure type sequential sampling test scheme determining system and method |
| CN114970120A (en) * | 2022-05-09 | 2022-08-30 | 中国人民解放军海军工程大学 | System and method for determining service life test scheme of exponential life type electronic part |
| CN114969658A (en) * | 2022-05-09 | 2022-08-30 | 中国人民解放军海军工程大学 | Grouping sequential test method for exponential life type product |
| CN115906544A (en) * | 2023-03-09 | 2023-04-04 | 中国人民解放军国防科技大学 | Reliability identification test method based on reliability growth process |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011000888A1 (en) * | 2009-07-01 | 2011-01-06 | European Aeronautic Defence And Space Company Eads France | Method for estimating the lifespan of a deep-sub-micron integrated electronic circuit |
| CN102707255A (en) * | 2012-06-19 | 2012-10-03 | 华北电网有限公司计量中心 | Quick sampling and detecting method for intelligent ammeter |
| CN104462757A (en) * | 2014-11-03 | 2015-03-25 | 南京航空航天大学 | Sequential verification test method of Weibull distribution reliability based on monitoring data |
| CN105760605A (en) * | 2015-12-17 | 2016-07-13 | 南京航空航天大学 | Prediction method for fatigue life of complex braided structure ceramic-based composite material |
-
2017
- 2017-06-16 CN CN201710456326.9A patent/CN107301285B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011000888A1 (en) * | 2009-07-01 | 2011-01-06 | European Aeronautic Defence And Space Company Eads France | Method for estimating the lifespan of a deep-sub-micron integrated electronic circuit |
| CN102707255A (en) * | 2012-06-19 | 2012-10-03 | 华北电网有限公司计量中心 | Quick sampling and detecting method for intelligent ammeter |
| CN104462757A (en) * | 2014-11-03 | 2015-03-25 | 南京航空航天大学 | Sequential verification test method of Weibull distribution reliability based on monitoring data |
| CN105760605A (en) * | 2015-12-17 | 2016-07-13 | 南京航空航天大学 | Prediction method for fatigue life of complex braided structure ceramic-based composite material |
Non-Patent Citations (2)
| Title |
|---|
| JING CAI 等: "Optimization of Aeroengine Shop Visit Decisions Based on Remaining Useful Life and Stochastic Repair Time", 《MATHEMATICAL PROBLEMS IN ENGINEERING》 * |
| 李鑫 等: "Weibull分布可靠性序贯验证试验方案选择及分析", 《工程与试验》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114897349A (en) * | 2022-05-09 | 2022-08-12 | 中国人民解放军海军工程大学 | Success-failure type sequential sampling test scheme determining system and method |
| CN114970120A (en) * | 2022-05-09 | 2022-08-30 | 中国人民解放军海军工程大学 | System and method for determining service life test scheme of exponential life type electronic part |
| CN114969658A (en) * | 2022-05-09 | 2022-08-30 | 中国人民解放军海军工程大学 | Grouping sequential test method for exponential life type product |
| CN114897349B (en) * | 2022-05-09 | 2023-09-05 | 中国人民解放军海军工程大学 | Success-failure type sequential sampling test scheme determining system and method |
| CN114970120B (en) * | 2022-05-09 | 2023-10-24 | 中国人民解放军海军工程大学 | System and method for determining life test scheme of exponential life type electronic part |
| CN115906544A (en) * | 2023-03-09 | 2023-04-04 | 中国人民解放军国防科技大学 | Reliability identification test method based on reliability growth process |
| CN115906544B (en) * | 2023-03-09 | 2023-05-05 | 中国人民解放军国防科技大学 | Reliability identification test method based on reliability growth process |
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