CN105138770A - Spaceflight product reliability simulation evaluating method based on indirect reliability characteristic quantity - Google Patents
Spaceflight product reliability simulation evaluating method based on indirect reliability characteristic quantity Download PDFInfo
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Abstract
The invention discloses a spaceflight product reliability simulation evaluating method based on indirect reliability characteristic quantity. A functional relationship between testing measurement parameters and the indirection reliability characteristic quantity is established, and a simulation sampling calculation method is adopted, so that the probability distribution of the indirection reliability characteristic quantity is obtained, and then a generalized stress and strength model is adopted to evaluate the reliability of a product; in the calculation of the indirection reliability characteristic quantity, calculation of single fault mode occurrence probability and reliability evaluation of the product with a multi-fault mode, the sampling simulation method is adopted for three times to carry out calculation; compared with an analyzing method, the method is more suitable for reliability evaluation of large complex spaceflight products, the calculation accuracy is high, and programming realization of computer software is easy.
Description
Technical field
The invention belongs to the reliability assessment technical field of space product, be specifically related to a kind of space product Reliablility simulation appraisal procedure based on indirect characteristic quantities.
Background technology
For reliability assessment, space product belongs to Small scale product.How to carry out the reliability assessment of Small scale product, domestic and international many scholars have carried out large quantifier elimination, also achieve certain achievement.But the reliability of the reliability estimation method assessment product that most of product of current space flight industry still adopts Corpus--based Method to analyze, the test sample amount that this method validation reliability index needs is large, cannot Feedback Design.Reliability estimation method based on mechanism model can be good at addressing this problem.But, based on the reliability estimation method of mechanism model apply in space flight industry reliability assessment very limited, one of reason is exactly, this kind of reliability estimation method needs to carry out deep analysis to product, extract suitable characteristic quantities, and by means such as tests, measurement and statistics analysis is carried out to the characteristic quantities of product, thus realizes the reliability assessment of product.
So-called space product characteristic quantities, be exactly product testing or in-flight detectable, can the variable of concentrated expression product reliability level.For space product, extracting suitable characteristic quantities becomes one of difficult point of the reliability estimation method based on mechanism model.
For many space flight complex products, the parameter that test can be surveyed is restricted sometimes, can only could understand product and occur which kind of problem by having tested the rear decomposition to product, continuous parameter in process of the test cannot directly obtain, and at this moment just there will be: testing the parameter that can survey cannot the reliability level of concentrated expression product; Can concentrated expression product reliability level parameter again cannot by test directly measurement.Such as, for liquid-propellant rocket engine, the structure of its parts and working environment are all very complicated, and test method and testing equipment are all very limited.At present, for the crucial parts such as thrust chamber, turbopump, still its function and performance can only be examined by engine complete machine test run test.In engine complete machine commissioning process, the engine parameter that can measure is also very limited, mainly comprises: pressure, rotating speed, flow, temperature, vibration etc.These parameters can reflect function and the performance characteristic of engine to a certain extent, but any one index measured, all cannot concentrated expression product reliability level.That is, directly cannot be measured the characteristic quantities of these parts by research technique, this brings difficulty to reliability assessment.
Summary of the invention
In view of this, the invention provides a kind of space product Reliablility simulation appraisal procedure based on indirect characteristic quantities, to realize the application of reliability estimation method in complicated space product based on mechanism model.
Space product Reliablility simulation appraisal procedure based on indirect characteristic quantities of the present invention, comprises the steps:
Step 1, by analytic product all fault mode, failure cause and failure mechanism, determine corresponding with each fault mode a kind of by testing the characteristic quantities directly measured or not by testing the direct indirect characteristic quantities measured;
Step 2, combing can survey parameter by testing the test of directly measuring, and according to principle of work and the correlation theory of product, set up the function that CHARACTERISATION TESTS can survey relation between parameter and indirect characteristic quantities;
Step 3, for each indirect characteristic quantities, collect each test in the function of its correspondence and can survey the data message of parameter in each sampling test, calculate probability distribution and distribution parameter sample estimated value that each test can survey parameter;
Step 4, for indirect characteristic quantities: probability distribution and distribution parameter sample estimated value that parameter can be surveyed according to each test, random digit generation method is adopted to generate random number n time, and the random number at every turn generated is updated in corresponding function, after calculating the result of calculation of indirect characteristic quantities, adopt Statistical Inference, calculate probability distribution and the distribution parameter sample estimated value of indirect characteristic quantities; N value is greater than 10000;
For characteristic quantities: directly according to test measurement result, adopt Statistical Inference, calculate the probability distribution of characteristic quantities and the sample estimated value of distribution parameter;
Step 5, utilization generalized stress-strength theory, adopt the emulation methods of sampling, determine the fiduciary level that each fault mode is corresponding, concrete steps are:
S51, according to characteristic quantities corresponding to each fault mode or characteristic quantities is corresponding indirectly probability distribution and distribution parameter sample estimated value, random digit generation method is adopted to generate random number, by method for parameter estimation, calculating characteristic quantities or the overall estimated value of distribution parameter that characteristic quantities is corresponding indirectly; Again according to characteristic quantities or probability distribution that indirectly characteristic quantities is corresponding, the overall estimated value of distribution parameter and the threshold value of characteristic quantities or characteristic quantities indirectly when not breaking down, calculate the fiduciary level that fault mode is corresponding, complete single sample;
S52, for each fault mode, by the method for S51 carry out N time sampling, obtain N number of fiduciary level of fault mode; N value is greater than 10000;
S53, the sequence N number of fiduciary level being carried out from small to large, obtain the probability distribution function of fiduciary level; Finally obtain the fiduciary level of fault mode;
Step 6, according to the logical relation between fault mode, set up reliability block diagram model; Based on the fiduciary level of each fault mode that this reliability block diagram model and step 5 obtain, obtain the reliability assessment result of product.
Preferably, in described step 5 and step 6, obtain the fiduciary level sequence of each fault mode in step 5 after, in the sequence of each fiduciary level, randomly draw a fiduciary level; According to the method for step 6, obtain this time and extract corresponding production reliability; After M time is extracted, obtain the fiduciary level of M product, and by order arrangement from small to large, obtain the probability distribution function of fiduciary level; Finally obtain the fiduciary level of product under given degree of confidence; M value is greater than 10000.
Preferably, in described step 3, determine that the method for probability distribution and distribution parameter sample estimated value that each test can survey parameter comprises Maximum-likelihood estimation, least square method and graphic evaluation.
Preferably, when test can be surveyed parameter and indirectly directly cannot be expressed by funtcional relationship between characteristic quantities, adopt the test of finite element simulation method establishment can survey parameter and the relation indirectly between characteristic quantities, and this relation is called funtcional relationship.
The present invention has following beneficial effect:
(1) the present invention is by setting up the funtcional relationship of test measurement parameter and indirect characteristic quantities, and adopt emulation sampling calculation method, obtain the probability distribution of indirect characteristic quantities, and then adopt generalized stress-strength model, the reliability of assessment product; The calculating of indirect characteristic quantities, single failure pattern probability of happening calculating, have in the reliability assessment of multiple faults Model Products, sampling emulation mode is adopted for three times to calculate, the method is compared with analytic method, be more applicable for the reliability assessment of large complicated space product, and it is high to calculate accuracy, is easy to computer software programming and realizes.
(2) sampling emulation mode of the present invention is compared with analytic method, and under certain accuracy requirement, the complexity of the frequency in sampling of the method and calculated amount and reliability block diagram model has nothing to do, and is comparatively applicable to the reliability assessment of complication system; All unrestricted to the distribution pattern, test figure type etc. of each unit of system, do not need the conversion carrying out distribution pattern and logical relation, enhance the accuracy of assessment result; Actual distribution type for product each in Reliability Evaluation Model generates random number, and the approximate conversion process avoiding analytic method computation process, on the impact of reliability assessment result, makes assessment accuracy higher.
Accompanying drawing explanation
Fig. 1 is implementing procedure figure of the present invention;
Fig. 2 is the embodiment figure of the embodiment of the present invention;
Fig. 3 is the reliability model of the embodiment of the present invention;
Fig. 4 is the stress-strength model emulated computation method of the embodiment of the present invention.
Embodiment
To develop simultaneously embodiment below in conjunction with accompanying drawing, describe the present invention.
The present invention proposes a kind of space product Reliablility simulation appraisal procedure based on indirect characteristic quantities, for assessing the reliability of complicated space product.Current space product is to carry out space product reliability assessment, and the first reliability requirement of Water demand product, specifically comprises following four aspects:
(1) define the task scope of evaluation object and the border of product, thus determine scope and the degree of depth of analysis;
(2) clear and definite product experience in practical work process varying environment condition, task function performance requirement and duration etc.;
(3) clearly for judging the parameter of product whether fault, i.e. failure criterion;
(4) dependability parameter of clear and definite product and index etc., as the index that reliability assessment quantizes.
As shown in Figure 1, appraisal procedure comprises the steps: reliability assessment flow process of the present invention
Step 1, determine characteristic quantities
The chife failure models of analytic product and failure cause, analysis of failure mechanism, the rule that research fault mode occurs.For the chife failure models carrying out reliability Work, analyze and determine characteristic quantities (by testing direct measurement) that fault mode is corresponding and indirect characteristic quantities (not by testing direct measurement).Often kind of fault mode and characteristic quantities or indirectly characteristic quantities one_to_one corresponding, assess to adopt the reliability estimation method based on mechanism model.
May be there is various faults pattern in identical product, can adopt different characteristic quantities or indirect characteristic quantities for each fault mode.Wherein, the corresponding characteristic quantities of a part of fault mode possibility, another part fault mode may corresponding characteristic quantities indirectly.
Step 2, set up indirect characteristic quantities and test the funtcional relationship that can survey between parameter:
Combing, by testing the parameter directly measured, for principle of work and the correlation theory of product, is set up indirect characteristic quantities and tests the funtcional relationship (hereinafter referred to as " function ") can surveyed between parameter.
If the relation directly between measurement parameter and indirect characteristic quantities is comparatively complicated, cannot be expressed by funtcional relationship, also can by the direct measurement parameter of finite element simulation method establishment and the relation indirectly between characteristic quantities, it is called " function " in the lump herein, namely refers to all forms can expressing relation between direct measurement parameter and indirect characteristic quantities.
The statistical study of step 3, test figure:
For each indirect characteristic quantities, collect each test in the function of its correspondence and can survey the data message of parameter in each sampling test, the fault mode of combination product and failure mechanism, analyze and determine that each test can survey the measurement data mode of parameter (such as, the parameter of constantly change in time, the mode measurement parameter of continuous coverage or discontinuous measurement need be adopted over time), and collect the correlation test data of each sample, adopt Maximum-likelihood estimation, least square method, the Statistical Inferences such as graphic evaluation, determine that each test can survey probability distribution and the distribution parameter sample estimated value of parameter.
The probability distribution of step 4, characteristic quantities and indirect characteristic quantities calculates:
The distribution pattern of the probability distribution function of parameter can be surveyed according to test, random digit generation method (the generation method of conventional distribution random numbers is as shown in table 1) is adopted to generate random number, and the random number at every turn generated is updated in corresponding function, calculate the result of calculation of indirect characteristic quantities.
The random-number generating method of table 1 typical probability distribution
Note: U is the random number of (0,1), and other parameter is the distribution parameter of corresponding distribution
Repeat above sample calculation process, after n sampling, just obtain the result of calculation of n indirect characteristic quantities.Adopt Statistical Inference, calculate probability distribution and the distribution parameter sample estimated value of indirect characteristic quantities.For ensureing computational accuracy, n is greater than 10000 usually.
If product has multiple indirect characteristic quantities, need to adopt above method to calculate the probability distribution of each indirect characteristic quantities respectively.
For characteristic quantities, due to by testing the size directly measuring characteristic quantity, therefore directly for test measurement result, Statistical Inference can be adopted, calculates probability distribution and the distribution parameter sample estimated value of characteristic quantities.
Step 5, reliability interval estimation
For a unknown quantity, when measuring or calculate, except obtaining approximate value, also needing to take error into account, namely needing the levels of precision of approximate value.For the target component of reliability assessment, except obtaining its point estimation, also should estimate a scope, thus understand this scope and comprise, the credibility of parameter true value.Such scope provides with the form in interval usually, provides the credibility that this interval comprises reliability objectives parameter simultaneously.The estimation of this form is called interval estimation.
Use generalized stress-strength theory, adopt the emulation methods of sampling, determine the fiduciary level that each fault mode is corresponding, concrete steps are:
S51, according to characteristic quantities corresponding to each fault mode or characteristic quantities is corresponding indirectly probability distribution and distribution parameter sample estimated value, random digit generation method is adopted to generate random number, by method for parameter estimation, calculating characteristic quantities or the overall estimated value of distribution parameter that characteristic quantities is corresponding indirectly; Again according to characteristic quantities or probability distribution that indirectly characteristic quantities is corresponding, the overall estimated value of distribution parameter and when not breaking down characteristic quantities or indirectly characteristic quantities and trouble-proof threshold value (required value), calculate the fiduciary level that fault mode is corresponding, complete single sample;
S52, for each fault mode, by the method for S51 carry out N time sampling, obtain N number of fiduciary level; For ensureing computational accuracy, N is for being greater than 10000;
S53, the sequence N number of fiduciary level being carried out from small to large, obtain the probability distribution function of fiduciary level; Finally obtain the fiduciary level of fault mode;
Step 6, according to the logical relation between fault mode, set up reliability block diagram model; Based on the fiduciary level of the fault mode that this reliability block diagram model and step 5 obtain, obtain the reliability assessment result of product.
Wherein, in step 5 and step 6, obtain the fiduciary level sequence of each fault mode in step 5 after, in the sequence of each fiduciary level, randomly draw a fiduciary level; According to the method for step 6, obtain this time and extract corresponding production reliability; After M time is extracted, obtain the fiduciary level of M product, and by order arrangement from small to large, obtain the probability distribution function of fiduciary level; Finally obtain the fiduciary level of product under given degree of confidence; For ensureing computational accuracy, usual M need be greater than 10000.
This sampling emulation mode, compared with analytic method, has the following advantages:
1) under certain accuracy requirement, the complexity of the frequency in sampling of the method and calculated amount and reliability block diagram model has nothing to do, and is comparatively applicable to the reliability assessment of complication system;
2) all unrestricted to the distribution pattern, test figure type etc. of each unit of system, do not need the conversion carrying out distribution pattern and logical relation, enhance the accuracy of assessment result;
3) the actual distribution type for product each in Reliability Evaluation Model generates random number, and the approximate conversion process avoiding analytic method computation process, on the impact of reliability assessment result, makes assessment accuracy higher.
embodiment
This example, for certain product, utilizes method of the present invention to carry out reliability assessment, and as shown in Figure 2, concrete steps are as follows for embodiment:
Step 1, determine indirect characteristic quantities
Analyze known by weak link recognition methods, product comprises 2 main fault modes altogether, is designated as fault mode A and fault mode B respectively.
Analyze failure mechanism, determine that the characteristic quantities that two fault modes are corresponding is respectively u and v.Wherein, v directly measures by correlation test, but due to product structure reason, u directly cannot be measured by test, and therefore, this characteristic quantity is indirect characteristic quantities.
Step 2, set up indirect characteristic quantities and test the funtcional relationship that can survey between parameter
By the parameter that combing product testing can be surveyed, the funtcional relationship setting up indirect characteristic quantities u and each test measurement parameter is:
Because this product comprises two typical fault patterns, wherein, u is indirect characteristic quantities, and v is characteristic quantities, according to the logical relation of two fault modes, sets up reliability model as shown in Figure 3.Two fault modes are series relationship, and namely wherein any one fault mode occurs, then product failure.
The statistical study of step 3, test figure
Collect the data message of each test measurement parameter in function (1).Statistical analysis technique is adopted to obtain probability distribution and the distribution parameter of each parameter, as shown in table 2.
The statistical study of table 2 test figure
The probability distribution of step 4, indirectly characteristic quantities calculates
According to the test figure statistic analysis result in table 2, generate random number, bring in function (1), calculate the result of calculation that each characteristic quantities u indirectly samples at every turn.Arranging simulation times is 10000 times, and to simulation result, carries out statistical study, the probability distribution of matching u and distribution parameter sample estimated value.
Because characteristic quantities v directly measures acquisition by testing, therefore, adopt the statistical analysis technique of step 3, test figure is processed, the probability distribution of matching v and distribution parameter sample estimated value.
The statistic analysis result of two characteristic quantities is as shown in table 3.
Table 3 characteristic quantities/characteristic quantities statistic analysis result indirectly
Step 5, reliability interval estimation
According to the statistic analysis result of u and v, in conjunction with its performance requirement, use stress-strength model, adopt the emulation methods of sampling, the reliability level of assessment product.Wherein, the required value of u is: [2100,2400], and v required value is: [21, ∞).
Be illustrated with the computation process of v below, implementation method as shown in Figure 4.Be one-sided minimum stress limit-strength model in this figure, namely v is greater than the probability of required value L.This probability is the probability that fault mode B does not occur, and is designated as R.
By sample X
1, X
2... X
n, calculate sample average
with sample variance S
2, obtain distribution parameter sample estimated value;
From i=1, for the random number γ that any given (0,1) interval two are separate
i1, γ
i2, according to the overall estimated value of method of estimation Computation distribution parameter of variance
obtaining
after, according to the overall estimated value μ of method of estimation Computation distribution parameter of average
i;
For given ultimate stress L, corresponding fiduciary level R
iby
calculate;
I=i+1 repeats said process, and 1≤i≤N, N is simulation times;
Sequence R is carried out to sample value is ascending
1≤ R
2≤ ... R
n, obtain the probability distribution function of fiduciary level;
Given degree of confidence γ, in the probability distribution function of fiduciary level, asks the R that the integral part of (1-γ) N is corresponding
i, be the fiduciary level under given degree of confidence γ.
As shown in table 4 by calculating the probability that fault mode A and fault mode B do not occur.
The each fault mode of table 4 not probability of happening result of calculation
Step 6, Reliability Assessment
Because product has two kinds of main fault modes, for assessing the reliability of product, need the result of calculation according to table 4, the reliability model of composition graphs 3, sampling emulation mode is adopted to obtain reliability assessment result: point estimate is the fiduciary level under 0.994287,0.7 degree of confidence is 0.992597.
In sum, these are only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (4)
1., based on a space product Reliablility simulation appraisal procedure for indirect characteristic quantities, it is characterized in that, comprise the steps:
Step 1, by analytic product all fault mode, failure cause and failure mechanism, determine corresponding with each fault mode a kind of by testing the characteristic quantities directly measured or not by testing the direct indirect characteristic quantities measured;
Step 2, combing can survey parameter by testing the test of directly measuring, and according to principle of work and the correlation theory of product, set up the function that CHARACTERISATION TESTS can survey relation between parameter and indirect characteristic quantities;
Step 3, for each indirect characteristic quantities, collect each test in the function of its correspondence and can survey the data message of parameter in each sampling test, calculate probability distribution and distribution parameter sample estimated value that each test can survey parameter;
Step 4, for indirect characteristic quantities: probability distribution and distribution parameter sample estimated value that parameter can be surveyed according to each test, random digit generation method is adopted to generate random number n time, and the random number at every turn generated is updated in corresponding function, after calculating the result of calculation of indirect characteristic quantities, adopt Statistical Inference, calculate probability distribution and the distribution parameter sample estimated value of indirect characteristic quantities; N value is greater than 10000;
For characteristic quantities: directly according to test measurement result, adopt Statistical Inference, calculate the probability distribution of characteristic quantities and the sample estimated value of distribution parameter;
Step 5, utilization generalized stress-strength theory, adopt the emulation methods of sampling, determine the fiduciary level that each fault mode is corresponding, concrete steps are:
S51, according to characteristic quantities corresponding to each fault mode or characteristic quantities is corresponding indirectly probability distribution and distribution parameter sample estimated value, random digit generation method is adopted to generate random number, by method for parameter estimation, calculating characteristic quantities or the overall estimated value of distribution parameter that characteristic quantities is corresponding indirectly; Again according to characteristic quantities or probability distribution that indirectly characteristic quantities is corresponding, the overall estimated value of distribution parameter and the threshold value of characteristic quantities or characteristic quantities indirectly when not breaking down, calculate the fiduciary level that fault mode is corresponding, complete single sample;
S52, for each fault mode, by the method for S51 carry out N time sampling, obtain N number of fiduciary level of fault mode; N value is greater than 10000;
S53, the sequence N number of fiduciary level being carried out from small to large, obtain the probability distribution function of fiduciary level; Finally obtain the fiduciary level of fault mode;
Step 6, according to the logical relation between fault mode, set up reliability block diagram model; Based on the fiduciary level of each fault mode that this reliability block diagram model and step 5 obtain, obtain the reliability assessment result of product.
2. a kind of space product Reliablility simulation appraisal procedure based on indirect characteristic quantities as claimed in claim 1, it is characterized in that, in described step 5 and step 6, obtain the fiduciary level sequence of each fault mode in step 5 after, in the sequence of each fiduciary level, randomly draw a fiduciary level; According to the method for step 6, obtain this time and extract corresponding production reliability; After M time is extracted, obtain the fiduciary level of M product, and by order arrangement from small to large, obtain the probability distribution function of fiduciary level; Finally obtain the fiduciary level of product under given degree of confidence; M value is greater than 10000.
3. a kind of space product Reliablility simulation appraisal procedure based on indirect characteristic quantities as claimed in claim 1, it is characterized in that, in described step 3, determine that the method for probability distribution and distribution parameter sample estimated value that each test can survey parameter comprises Maximum-likelihood estimation, least square method and graphic evaluation.
4. a kind of space product Reliablility simulation appraisal procedure based on indirect characteristic quantities as claimed in claim 1, it is characterized in that, when test can be surveyed parameter and indirectly directly cannot be expressed by funtcional relationship between characteristic quantities, adopt the test of finite element simulation method establishment can survey parameter and the relation indirectly between characteristic quantities, and this relation is called funtcional relationship.
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| CN106169124A (en) * | 2016-07-21 | 2016-11-30 | 中国科学院数学与***科学研究院 | Complex Structural System reliability comprehensive estimation confidence inference method |
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| CN108536972A (en) * | 2018-04-13 | 2018-09-14 | 中国人民解放军陆军装甲兵学院 | Complex System Reliability emulation mode and system based on self-adapting intelligent body |
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| CN109376407A (en) * | 2018-09-30 | 2019-02-22 | 中国人民解放军92942部队 | A kind of Reliability assessment method using weaponry in due order |
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