CN106529090A - Evaluation method of reliability of aerospace electronic product - Google Patents
Evaluation method of reliability of aerospace electronic product Download PDFInfo
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- G06F30/30—Circuit design
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Abstract
The invention discloses an evaluation method of reliability of an aerospace electronic product based on a failure mechanism. On the basis that an analysis method with a combination of physical simulation and a test is adopted, a physical simulation model of the electronic product is constructed, the means such as sensitivity analysis, Monte Carlo analysis, and stress analysis are adopted, and product key elements are identified and in combination with actual working conditions of the elements, a failure mechanism analysis is conducted. Currently, the failure mechanism analysis aiming at the elements is a means used for analyzing a failure reason after a failure occurs during the test or in the using process of the electronic product, thus pertinent improvement is conducted on the product, the service life of the product is prolonged, and the reliability of the product is improved; the failure mechanism analysis aims at finding key characteristic parameters which affect the service life and the reliability of weak elements as an independent variable of the reliability model.
Description
Technical field
The invention belongs to the fail-safe analysis of aerospace electron product and assessment technology field, and in particular to a kind of based on failure
The aerospace electron Reliability Assessment method of mechanism.
Background technology
Electronic product is widely used in space flight satellite and the rocket product, for example, the power amplifier, calculating in carrier rocket
The unit product such as machine, distributor, frequency modulation transmitter；The unit products such as computer, circuit box, driver, temperature control instrument in satellite
Belong to electronic product.These electronic product majorities are made up of printed circuit board (PCB) (PCB), and circuit board has different type
Components and parts composition, such as resistance, electric capacity etc..
Reliability completes the index of ability as an indirect expression product work and task, it is impossible to directly survey in test
Amount, needs just obtain by the analysis to product and its test data and calculating, and this is accomplished by serviceability assessment technology.
So-called reliability assessment refer to using product development, test, produce, using etc. during the data collected and information estimating
With the reliability for evaluating product.
At present, for reliability assessment, for the electronic product in carrier rocket, due to the working time it is shorter,
It is generally acknowledged that its failure type is random failure, the out-of-service time obeys exponential, by the accumulation test period of statistical product
And failure number, calculate the reliability of product.For satellite class product, it also hold that the failure type of product is random failure,
But as its operation on orbit time is longer, its reliability index requirements cannot be met with ground test data only.Therefore, at present
For the electronic product in satellite mainly using the reliability estimation method based on Bayes (Bayes), by the reliability of product
Property intended result as priori data, using ground test data as supplement information, with bayesian theory by two parts information
COMPREHENSIVE CALCULATING is carried out, so as to obtain the reliability assessment result of product, causing as ground test data is not enough with improvement can
By the problem that property index cannot be verified.
Either carrier rocket or the electronic product of satellite, using the reliability estimation method based on statistics, recognize
Failure type for product is random failure, and the reliability of product is calculated by statistical test time and failure number.For boat
The characteristics of its highly reliable System in Small Sample Situation, the method, have certain limitation：
1) reliability test cycle length, high cost
At present, the projected life of satellite class product is increasingly longer, has gradually lifted 5-8 from 3-5 before, very
To the longer time.It is long-life to require to increased difficulty with reliability demonstration to the life-span of satellite electrical type unit.Due to grinding
The requirement of progress processed, cannot almost carry out 1 on ground：1 test.Additionally, going to verify its longevity by the test of substantial amounts of unit level
Life and reliability, make the cost of test remain high.
2) test data is using insufficient
Two Test Information of service test time and failure number are assessing product reliability, numerous for what is measured in test
Critical performance parameters, cannot be used in reliability assessment, between thereby resulting at the trial during relative deficiency, assessment result
Tend not to reflect product real reliability level.
3) it is not enough with the correlation degree of product
Failure cause and failure machine of the test data adopted due to reliability assessment for test period, with product itself
Reason is not set up directly association.Therefore, under conditions of reliability assessment result is unsatisfactory for index request, pin cannot also be provided
Product Improvement Proposal to property, when failure number is more, illustrates that product needed design is improved, failure number is less or during zero failure,
Reliability assessment value can only be improved by increasing test period, cause experimentation cost and waste of time.
The content of the invention
In view of this, a kind of the characteristics of present invention is directed to highly reliable aerospace electron class product, System in Small Sample Situation, there is provided reliability
The aerospace electron class Reliability Assessment method that physics is combined with intellectual learning.For aerospace electron class product, including boat
It uses PCB and electrical type unit etc., the method combined with intellectual learning using Reliability Physics to make full use of the key of product
Performance parameter information, assesses the reliability of product, to solve the problems, such as highly reliable Small scale product reliability assessment, and by weakness
Link recognition methodss, recognize the key componentses of product, and the design improvement and reliability for product provides support.
A kind of reliability estimation method of aerospace electron product of the present invention, comprises the steps：
Step 1, the weak link to aerospace electron product are identified, and obtain key componentses, specifically include following step
Suddenly：
S11, according to electronic product physical arrangement, using circuit simulation analysis software, build electronic product physical simulation mould
Type, arranges the input and output characteristic parameter of each components and parts in electronic product, tentatively sets up the input and output of the electronic product
Relation；
S12, judge electronic product whether there is incipient fault；If there is incipient fault, after being improved to electronic product
Next step is performed, if there is no incipient fault, S13 is directly performed；
Wherein, electronic product is judged with the presence or absence of incipient fault using Monte Carlo Method, concrete grammar is：
1) each components and parts are generated respectively according to formula (1) and formula (2) according to the distributed constant of each components and parts normal distribution
Performance random number, wherein, the random number generated by i-th components and parts input and output characteristic parameter is designated as Xi；
Xi=μ+σ Xi' (2)
Wherein, UjIt is the random number between [0,1], μ is average, and σ is standard deviation；
2) by each components and parts corresponding random number XiIt is updated in the physical phantom in S11, by simulation calculation, obtains
To electronic product output result；
3) repeat 1) step and 2) step n times, obtain M circuit output result, wherein, M at least takes 50 times；
The M simulation data result to obtaining is analyzed, and obtains the extreme value for exporting, and according to the output of electronic product
Energy index request, judges whether simulation data result meets performance indications requirement, if extreme value is in claimed range, judges to produce
Product meet requirement, there is no incipient fault；Conversely, there is incipient fault；
S13, the weak link for using stress analysis means, recognizing electronic product, as key componentses, specially：
Under conditions of electronic product normal work, with the electronic product physical phantom set up in step S11, count
The working stress of each components and parts in product is calculated, and obtains the working stress of each components and parts and the ratio of setting, and according to ratio
Order from big to small is ranked up to each components and parts；During three below condition will be met, one or more components and parts are sentenced
It is set to weak link：
Condition 1：Components and parts of the ratio more than 1；
Condition 2：By observing ratio, the components and parts of drop volume requirement are unsatisfactory for；
Condition 3：The difference of the ratio of two neighboring components and parts is more than 30% and the forward components and parts ratio that sorts is more than 0.5
When, determine that two components and parts sort forward components and parts for weak link, while determining sequence before the weak link device
All components and parts be weak link；
In the case where 3 conditions of the above are unsatisfactory for, judge to sort the components and parts of first three as weak link；
Step 2, the Primary Component for determining in step 1, with reference to its working condition, with Analysis of Failure Mechanism method,
Determine the failure mechanism of key componentses；Then the failure mechanism according to key componentses, the working environment of bonded products, analyze
And determine the key componentses performance parameter related to failure mechanism and working life, i.e. life characteristics parameter；
Step 3, for key componentses carry out device level test, measure components and parts in process of the test life characteristics ginseng
Number, and record components and parts its time to failure TF；The corresponding relation of time to failure TF and life characteristics parameter is then set up, i.e.,
Obtain Reliable Mathematics model；
Step 4, each life characteristics parameter for for different samples, collecting components and parts in Reliable Mathematics model, for each
The sample variation of parameter, calculates the distribution pattern distributed constant corresponding with distribution pattern of each life characteristics parameter；
Step 5, the reliability assessment based on Latin Hypercube Sampling are calculated, specially：
For each life characteristics parameter, frequency in sampling N is determined according to required precision first, then obtained according to step 4
The distribution pattern and distributed constant of each life characteristics parameter, carries out n times sampling using Latin Hypercube Sampling method, and will sampling
Sample is updated in the Reliable Mathematics model of step 3 foundation respectively, is calculated the value of calculation of N number of time TF；By counting N
Individual TF is calculated, and obtains TF probability-distribution functions F (t), and according to the task time t of productmRequirement, calculate product reliability
R, i.e.,：
R(tm)=P { TF ＞ tm}=1-F (tm)。
Further, after step S11 sets up the input of the electronic product and output relation, by reality test, survey
The actual operating conditions and working condition of each components and parts are measured, the input/output relation of electronic product is treated in checking, to physical simulation mould
In type, the input and output characteristic parameter of each components and parts is adjusted, and after adjustment is finished, then performs S12.
Further, after step S11 obtains input/output relation, based on each yuan in the relation pair electronic product
Device carries out sensitive analysis, and obtaining to export electronic product affects most sensitive components and parts, and according to sensitivity height,
Each components and parts are ranked up；In the step 12, choosing the preceding part components and parts of sensitivity sequence carries out Monte Carlo
Analysis.
Further, after step S11 obtains input/output relation, based on each yuan in the relation pair electronic product
Device carries out sensitive analysis, and obtaining to export electronic product affects most sensitive components and parts, and according to sensitivity height,
Each components and parts are ranked up；In method of the step 12 to product improvement, according to components and parts sensitivity ranking results, choosing
The forward components and parts of sensitivity sequence are taken, is replaced or is retrofited, to realize the improvement to electronic product.
Preferably, in the step 2, adopting for the Analysis of Failure Mechanism method of electronic product：Electric test, micro- shape
Sample preparation analysis method is dissected in looks analysis, microstructure analysis, physical property detection, Microanalysis, stress test.
Preferably, using neutral net or the learning algorithm of support vector machine, setting up TF corresponding with life characteristics parameter
Relation, you can by property mathematical model.
Preferably, in the step 5, the frequency in sampling N meets：In formula, γ is required precision, σ2For sample
This variance, is calculated by step 4；zαFor normal distribution quantile.
The present invention has the advantages that：
(1) use for reference and effectively utilizes worst case analysis (WCA) thinking, recognize electronic product weak link, be reliability
Assessment provides support；Worst case analysis are China's aerospace electron product (usually circuit) Jing one circuit analysis works of normally opened exhibition
Make, Sensitive Apparatuses, and the worst feelings being likely to occur come prediction circuit by Monte-Carlo Simulation are recognized using sensitive analysis
Condition is combined, so as to identification circuit whether there is incipient fault.The present invention is effectively utilized this in weak link identification process
Analytical mathematics.But, on the basis of identification incipient fault, the invention is by stress analysis to the main purpose of circuit analysis
Etc. the most weak device of method identification circuit, and Analysis of Failure Mechanism is carried out to weak device, it is determined that the life-span of weak device is special
Parameter is levied, and builds reliability model, to assess the reliability level of product.
(2) reliability assessment based on failure mechanism is even closer with what the performance of electronic product was combined, is conducive to design
Improve and lifted with reliability；In the analysis method combined with test using physical simulation, the physical simulation of electronic product is built
Model, using means such as sensitive analysis, Monte Carlo Analysis, stress analyses, on the basis of recognizing product key componentses, knot
The actual condition of components and parts is closed, carries out Analysis of Failure Mechanism.At present, it is mainly in electronics for the Analysis of Failure Mechanism of components and parts
After there is failure during product testing or use, for analyzing a kind of means of failure cause, so as to targetedly improve
Product, improves life-span and the reliability of product.
The present invention is to find to affect weak device lifetime special with the key of reliability with the purpose of Analysis of Failure Mechanism
Parameter is levied, is the independent variable as reliability model.Additionally, after reliability assessment, if the reliability of electronic product is not up to
Reliability index requirements, then with Analysis of Failure Mechanism result, be improved to product, to improve life-span and the reliability of product
Property so as to meet reliability index requirements.
(3) test data needed for reliability assessment mostlys come from the performance data of components and parts level, and the whole machine of non-product
Test, experimentation cost are controlled；At present, in order to verify the reliability of aerospace electron product whether index request, generally with list
Machine is that unit carries out overall test, and due to experimentation cost and the restriction of lead time, the product for participating in test is difficult to failure,
Almost 0 failure of test data used by reliability assessment.On the one hand, this kind of reliability test method be not due to accomplishing product failure
On the other hand (Censoring), it is difficult to obtain product real life-span and reliability, the whole unit of this kind of approach application carries out examination
Test, experimentation cost can be increased.Additionally, in reliability assessment, only using the accumulative test period of product with quantity is failed commenting
The reliability of the yield by estimation product, it is not abundant enough that test data is utilized, and causes the waste of data message.
The present invention is directed to problem above, and by the Primary Component for recognizing electronic product, and emphasis is with Primary Component as test
Object, the life characteristics parameter obtained with Analysis of Failure Mechanism carry out different magnitude of bias test to test variable, then with
A small amount of unit test is used as checking.Using this test data as input, using intellectual learning algorithm, fitting builds reliability mould
Type, assesses the reliability of product, can effectively save experimentation cost, compression test time, and obtaining in components and parts process of the test
Each life characteristics parameter and complete lifetime data are obtained, the more accurate, data separate described to the reliability of product is more abundant.
(4) algorithm combined with Latin Hypercube Sampling with intellectual learning, realizes the Reliability modeling of electronic product
With assessment；One important application of intellectual learning algorithm (such as neutral net and support vector machine etc.) is data fitting.Draw
Fourth hypercube sampling (LHS) is a kind of sampling approach that can improve simulation efficiency.
The algorithm that the present invention is combined with Latin Hypercube Sampling with intellectual learning, realizes that the reliability of electronic product is built
Mould and assessment.Using intellectual learning algorithm as Reliability modeling instrument, the test data of product is processed, to build failure
Functional relationship between front time (TF) and life characteristics parameter, and using this model as foundation, by counting each life characteristics ginseng
Several distribution characteristicss, with LHS thoughts, and are applied in reliability assessment, using sampling simulation algorithm assessment product
Reliability.
In sum, integrated use of the present invention circuit simulation analysis, Analysis of Failure Mechanism, intelligent algorithm, Latin
The technical methods such as hypercube sampling, recognize the weak link of aerospace electron product, assess the reliability of product, and can be effective
Electronic product reliability experimentation cost and cycle are reduced, the design improvement and reliability for product provides technical support.
Description of the drawings
Fig. 1 is the reliability estimation method implementing procedure figure that the Reliability Physics of the present invention is combined with intellectual learning；
Fig. 2 is the implementing procedure figure recognized based on virtual test weak link of the present invention；
Fig. 3 is the electronic product Analysis of Failure Mechanism flow process of the present invention；
Fig. 4 is the signal modulation module circuit course of work of certain type safety governor in the embodiment of the present invention；
Fig. 5 is the signal modulation module circuit physical phantom of certain type safety governor in the embodiment of the present invention, wherein
A () (b) (c) is three parts for splitting；
Fig. 6 is key point signal waveform detail view in the embodiment of the present invention；
Fig. 7 is 50 Monte-Carlo Simulation result statistical results charts of DPSKCA signals virtual value in the embodiment of the present invention；
Fig. 8 is neural network structure figure in the embodiment of the present invention；
Fig. 9 is neutral net fitting result chart in the embodiment of the present invention；
Figure 10 is probability-distribution function curve chart in the embodiment of the present invention.
Specific embodiment
Develop simultaneously embodiment below in conjunction with the accompanying drawings, describes the present invention.
The present invention proposes the reliability estimation method that a kind of Reliability Physics is combined with intellectual learning, high for space flight
The reliability assessment of reliable small sample electronic product, implementing procedure are as shown in Figure 1.
Reliability assessment is comprised the following steps：
Step 1, the weak link based on virtual test are recognized
The physical model of electronic product to be analyzed is built using virtual test method, the input and output for describing electronic product are closed
System, and verified using reality, improve accuracy of the physical model to product description.On this basis, with sensitive
The means such as degree analysis, Monte-Carlo Simulation and stress analysis, recognize the weak link of product, that is, restrict the electronic product life-span
Key componentses.The implementing procedure that weak link based on virtual test is recognized is as shown in Figure 2.
S11, physical phantom build, and set up circuit input and output relation
According to electronic product physical arrangement (circuit diagram), using circuit simulation analysis software, physical phantom is built, if
The input and output characteristic parameter of each components and parts is put, input and the output relation of product, execution step S12 is tentatively set up.
S12, simulation parameter amendment, improve simulation result accuracy
By reality test, the actual operating conditions and working condition of each components and parts are measured, the defeated of electronic product is treated in checking
Enter output relation.When deviation occurs in physical phantom, need for real test data, to each in physical phantom
The input and output characteristic parameter of components and parts is adjusted, to improve the standard of the showed product input/output relation of physical phantom
True property.
After the completion of simulation parameter amendment, S13 is performed.
S13, sensitive analysis, recognize sensitive device, provide information for Monte-Carlo Simulation
Sensitivity this concept, as one of index of statement Sensor, refers to the change of sensor output
Measure the ratio with the input variable quantity for causing the variable quantity.It is frequently used for describing the static characteristic of sensor, what it characterized is output
Reflection degree of the amount to input quantity.
For electronic product, output of products, in addition to |input paramete, the input and output feature of each components and parts are affected
Output of the parameter to product can also have a certain impact.The sensitive analysis of electronic product mainly reflect components and parts input and output
Analysis of the characteristic parameter change to electronic product output performance Index Influence degree to be analyzed (including size and Orientation).By spirit
Basis of sensitivity analysis, confirms sensitivity of the properties of product to each composition components and parts input and output characteristic parameter change.By sensitivity point
Analysis is obtained in that the components and parts most sensitive on the electronic product output impact, and according to sensitivity height, to each components and parts
It is ranked up.
The specific implementation method of sensitive analysis refers to GJB/Z 223《Worst-case circuit analysis guide》.
After the completion of sensitive analysis, S14 is performed.
S14, Monte-Carlo Simulation analysis, calculate the ripple of the electronic product output parameter under the influence of each components and parts uncertainty
Dynamic scope, judges that product whether there is incipient fault accordingly
Due to reasons such as production technology, materials, input and output of the same model even with a batch of different components and parts are special
Levy parameter and there is certain diversity, show certain uncertainty, according to historical experience, the uncertainty is usually expressed as
Normal distribution.
It is according to the uncertainty of each components and parts input and output characteristic parameter, with normal distribution as distribution pattern, special using covering
Caro emulates sampling algorithm, realizes propagation of each device performance unascertained information in electronic product, so as to describe each first device
Impact of the fluctuation of part input and output characteristic parameter to electronic product output characteristics, obtains the fluctuation model of electronic product output parameter
Enclose.
In order to improve Monte-Carlo Simulation efficiency, generally choosing M forward components and parts of sensitivity sequence carries out discretization,
The relatively low components and parts of other sensitivity, then take the average of its input-output characteristic parameter.
Electronic product Monte-Carlo Simulation analysis implementation be：
1) distributed constant (mean μ, standard deviation sigma) according to each components and parts normal distribution in circuit, according to formula (1) and public affairs
Formula (2) generates the performance random number of each components and parts respectively.The random number generated by each components and parts input and output characteristic parameter is designated as
(X1,X2,...,XM)。
Xi=μ+σ Xi' (2)
Wherein, UjIt is the random number between [0,1].
2) by (X1,X2,...,XM) be brought in physical phantom, by simulation calculation, obtain electronic product output knot
Really.
3) repeat 1) step and 2) step n times, obtain N number of circuit output result.
Generally, simulation times N are bigger, and the effect of Monte-Carlo Simulation is better, can more reflect that product whether there is
Incipient fault.But simulation times are more, the consumed time is analyzed longer.It is according to the characteristics of space flight Small scale product, general to emulate
Number of times should be no less than 50 times, and N number of normal output bias for assembling the electronic product for completing are simulated with this.
After Monte-Carlo step emulation, the N number of simulation data result to obtaining is analyzed, and obtains the extreme value for exporting
(maximum max, minima min etc.), and according to the output performance index request of electronic product, whether judge simulation data result
Meet performance indications requirement.If extreme value is in claimed range, judges that product meets and require there is no incipient fault, perform
S16；If extreme value is beyond the scope for requiring, judge that the product has incipient fault, need further to analyze, perform S15.
S15, using sensitive analysis result, improve for product design and support be provided
If learning that electronic product has incipient fault by Monte-Carlo Simulation analysis, need to be designed product and change
Enter.
Sensitivity reflects the impact sensitivity that the change of components and parts input and output characteristic parameter is input into electronic product,
If carrying out the type or performance boost to components and parts in higher sensitivity, the improvement to output of products can be more notable.Therefore, in order to
Product improvement efficiency is improved, sensitive analysis result is referred to, the forward components and parts of sensitivity sequence is chosen, is preferentially replaced
Or remodeling, to improve the output characteristics of electronic product.
After the completion of product improvement, S16 is performed.
S16, stress analysis, recognize the weak link of electronic product
Under conditions of electronic product normal work, with electronic product physical phantom, each first device in product is calculated
The actual stress of part, and judge whether the actual working stress of each components and parts has exceeded rated value and whether meet according to rated value
Drop volume is required.Working stress includes the working stress of the working stress and transient condition of limit.
The working stress of each components and parts and the ratio of setting are calculated, and each components and parts is entered according to order from big to small
Row sequence, ranking results are the output of stress analysis.The forward components and parts of sequence are the weak link of product.Weak link
Really fixed condition includes：
Condition 1：Components and parts of the ratio more than 1；
Condition 2：By observing ratio, the components and parts of drop volume requirement are unsatisfactory for；(national army to be asked for an interview to regard to drop volume wherein,
With in standard《Components and parts derating criteria》Regulation)
Condition 3：The difference of the ratio of two neighboring components and parts is more than 30% and the forward components and parts ratio that sorts is more than 0.5
When, determine that two components and parts sort forward components and parts for weak link, while determining sequence before the weak link device
All components and parts be weak link；
In the case where being unsatisfactory for last condition, it is determined that the components and parts of sequence front 3 are weak link.
Step 2, Analysis of Failure Mechanism and determination
For key componentses, with reference to its working condition, with Analysis of Failure Mechanism method, the mistake of key componentses is determined
Effect mechanism.
Mainly include for the Analysis of Failure Mechanism method of electronic product：Electric test, microstructure analysis, microstructure
Analysis, physical property detection, Microanalysis, stress test, dissection sample preparation.Appropriate analysis can be chosen according to products characteristics
Method, determines component failure mechanism.The Analysis of Failure Mechanism principle of components and parts is first to carry out non-destructive analysis, after broken
Bad property is analyzed；First external analysiss, afterwards internal (dissection) analysis；Situation (circuit, the stress bar relevant with failure is investigated first
Part, failure phenomenon etc.), post analysis failure components and parts.According to the analysis method of failure mechanism, binding analysis principle, it is determined that failure
The analysis process of mechanism is as shown in Figure 3.According to the failure mechanism of key componentses, the working environment of bonded products, analysis are simultaneously true
The fixed performance parameter related to failure mechanism and working life, i.e. life characteristics parameter.
Step 3, the Reliable Mathematics model construction based on intellectual learning
Carry out targetedly device level for key componentses to test, the life characteristics parameter during experiment with measuring,
And record its time to failure (TF).Generally, TF has non-linear relation with the life characteristics parameter of components and parts, for
This case, using the intellectual learning algorithm such as neutral net, support vector machine, sets up TF passes corresponding with life characteristics parameter
System, you can by property mathematical model.
By taking multilayer neural network method as an example, reliability mathematical model construction method, but not limited to this method.Multilamellar
The implementing procedure of neutral net is：
1) variable-definition
The input layer number of definition neutral net, hidden layer number, output layer neuron number, and determine initial
Weights ωni、ωij。
2) forward-propagating of working signal
According to hidden layer transmission function and output layer transmission function, with each parameter of input layer as input, output is calculated, and is counted
Calculate network error.
3) back propagation of error signal
Using neural network BP training algorithm, weights are successively reversely adjusted along network.First, hidden layer and output layer are adjusted
Weights ωij, subsequently, error signal propagated forward, to the weights ω between input layer and hidden layerniIt is adjusted.
4) repeat 2) with 3), require until error meets.
By the intellectual learning algorithm such as neutral net, the mapping relations of TF and life characteristics parameter are set up, you can by property number
Model is learned, used as input, TF is used as output for the life characteristics parameter using components and parts.
Step 4, test data statistical analysiss
For different samples, each life characteristics parameter of components and parts in Reliable Mathematics model is collected, for each parameter
Sample variation, is calculated the average and variance of each life characteristics parameter, is calculated dividing for each performance parameter using Statistical Inference
Cloth type and distributed constant etc..
The many factors such as each life characteristics parameter and its processing technique and the materials variancess due to components and parts are related, according to
The general Normal Distribution of each life characteristics parameter of experience components and parts, also needs to be fitted data inspection.Disobeying normal state
During distribution, statistical inference need to be carried out according to data, determine distribution pattern distributed constant corresponding with distribution pattern.
Step 5, the reliability assessment based on Latin Hypercube Sampling are calculated
For the probability-distribution function of each life characteristics parameter, according to Reliable Mathematics model, can with reference to space product height
By the characteristics of, use for reference the thought of Latin Hypercube Sampling (Latin hypercube sampling, LHS), using based on LHS's
Reliability estimation method, to improve the efficiency of Reliability evaluation.
S51, determination sampling dimension
According to components and parts life characteristics number of parameters M that Reliable Mathematics model is included, it is determined that sampling dimension M.
S52, set up hypercube
A hypercube is set up, if hypercube is as follows：The dimension of variable is M,I=1,2 ...,
M；Wherein, xiFor i-th dimension variable,WithRespectively the upper and lower boundary of i-th dimension variable, generally defines
S53, determine frequency in sampling
Required according to precision γ, determine frequency in sampling N.RequireIn formula, σ2For sample variance, step can be passed through
Rapid 4 are calculated；zαFor normal distribution quantile, can pass through to inquire about GB4086.1《Statistical distribution numerical tabular normal distribution》
Arrive；1- α are confidence level, and related to required precision, α typically takes 0.01～0.1.
S54, region division
Will be per one-dimensional variable xiDomain of definition it is intervalN number of equal minizone is divided into, i.e.,：An original hypercube is divided into into M × N number of little thus
Hypercube.
S55, definition matrix
The matrix A of a M × N is produced, every string of A is a random fully intermeshing of array { 1,2 ..., N }.
S56, generation random number
The often row of A is with regard to one selected little hypercube of correspondence, in each selected little hypercube, random to produce
A raw sample, thus selects N samples.
S57, calculating reliability
According to the distribution pattern of each life characteristics parameter in Reliable Mathematics model, respectively N number of sample is brought at random
In number computing formula (computing formula of conventional distribution random numbers is as shown in table 1), the random number of each life characteristics parameter is calculated, and
Each random number for generating is updated in corresponding function, the result of calculation of TF is calculated.
The random number computational methods of 1 typical probability of table distribution
Note：I-th row of the Ui for matrix A, other parameters are the distributed constant of correspondence distribution
Repeat above sample calculation process, after n times sampling, just obtain the result of calculation of N number of TF.It is N number of by counting
TF is calculated, and obtains TF probability-distribution functions F (t), and according to the task time t of productmRequirement, calculate product reliability R,
I.e.：
R(tm)=P { TF ＞ tm}=1-F (tm) (3)
Embodiment：
It is illustrated by taking the signal modulation module circuit of space flight type safety governor as an example.Modulator block is spaceborne distant
The ingredient of measurement equipment, major function are to complete the modulation and switching output of dpsk signal.During the task of the modulator block
Between tmIt is required that reliability requirement was 0.99 for 12 years.
Step 1, the weak link based on virtual test are recognized
S11, physical phantom build
Signal modulation module carries out shaping first to the CFA (differential code) and 2ftA (twice subcarrier signal) of input, then
Phase shift frequency dividing circuit is delivered to, after filtered and modulation, real-time dpsk signal (DPSKCA) is produced, the course of work is as shown in Figure 4.
According to circuit theory diagrams, using circuit analysis software, physical phantom is set up, as shown in Figure 5.In artificial circuit
In model, CFA is differential code signal, and 2ftA is twice subcarrier signal, and Reshape is the waveform through shaping, Phase_
Shift is the waveform that phase shift is produced through monostable flipflop, and Frequency_division is the waveform through frequency divider,
Filtering be after filtering after waveform, DPSKCA is the real-time dpsk signal of A machines after modulation.
S12, simulation parameter amendment
By the actual measurement to circuit board, simulation parameter is corrected, and static work is carried out by simulation software
Make point analysiss, verify the correctness of phantom.In emulation, the selection transient working time is 1ms.After emulation, obtain
The normal working voltage waveform detail view of circuit is as shown in Figure 6.It will be apparent from this figure that at CF code saltus steps, sine wave
The reverse 180 degree of phase place；CF codes, without saltus step, are continuous sine wave, meet the feature of dpsk signal.
S13, sensitive analysis
The main object of the sensitive analysis of the circuit board is that the DPSKCA signal voltages virtual value of output is subject to components and parts
The impact of parameter perturbation change, such as impact of the change in resistance of resistance 3R4A etc..Sensitive analysis result is as shown in table 2.From with
It is resistance that upper sensitive analysis result is can be seen that for DPSKCA signal virtual values size affects more sensitive device
3R11A, 3R8A, 3R9A, 3R7A, electric capacity 3C2A, 3C3A.V2VAR and VAR2V is data transformation interface, and here do not pay attention to.
Table 2, sensitive analysis result
The sensitivity of Fig. 4 DPSKCA signal virtual values
S14, Monte-Carlo Simulation analysis
By the result of upper section sensitive analysis, for the Monte Carlo Analysis of signal modulation module circuit, root is answered first
According to the initial deviation value of each components and parts input and output characteristic parameter, the initial deviation of device is set, and initial deviation obeys normal state
Distribution, the distributed constant of each components and parts are as shown in table 3 with distribution pattern.
Table 3, sensitive component input and output characteristic parameter table
It is 50 times to arrange simulation times, as shown in Figure 7 using monte carlo simulation methodology result of calculation.
Resistance 3R11A, 3R8A, 3R9A, 3R7A, electric capacity 3C2A, 3C3A can be seen that by the simulation result of 50 target practices
Input and output characteristic parameter when changing in the device initial deviation, the virtual value of the DPSKCA signals of output is in 0.7V-1.6V
Interior change, meets product requirement, therefore, judge that product meets and require there is no incipient fault.
S16, stress analysis
It is as shown in table 4 according to the stress data of each components and parts.
Table 4, components and parts stress data table
The stress value of above device is updated in emulation analysis parameter, the device reality under the nominal operating mode of circuit is obtained
Stress intensity, as shown in table 5.
Table 5, device actual stress size
Circuit be can be seen that without overstress device from above-mentioned stress analysis result, but the real work electric current of 54HC14
The critical state of the 50mA of device maximum operating currenbt is reached, according to GJB/Z35-93《Components and parts derating criteria》, 54HC14
Though non-overstress, stress ratio are 100%, the MOS type digital circuit derating criteria one-level drop volume that GJB/Z35-93 specifies is exceeded
80% output current derating level is required, is the Primary Component of the circuit board.
Step 2, Analysis of Failure Mechanism and determination
54HC14 is a kind of high-speed cmos type digital integration chip.Analysis of Failure Mechanism is adjusted in signal according to the chip first
Working condition in circuit processed, carries out simulation test.After the chip failure, using Analysis of Failure Mechanism method, the device is determined
The failure cause and mechanism of part.By technical methods such as electric test, microstructure analysis, with ATE (ATE),
The equipment such as scanning electron microscope (SEM), analyze its failure mechanism and are mainly the threshold value that hot carrier injection effect causes device
The isoparametric drift of voltage, until failure.Life characteristics parameter related to the failure mechanism by analysis mainly includes：Work
Two parameters of temperature T and supply current Icc.
Step 3, the Reliable Mathematics model construction based on intellectual learning
For 54HC14 chips, carry out components and parts test, choose different operating temperatures and carry out test with supply current, receive
Collection correlation test data, including：Supply current Icc when time to failure TF, work temperature and work, partial data such as table 6
It is shown.With multilayer neural network method, the mapping relations of T, Icc and TF of chip are built.
According to the life characteristics parameter and test data of 54HC14 chips, determine that input layer number is 2, export node layer
Number is 1.This example is calculated using BP neural network algorithm.According to input layer number, output layer nodes, while considering
The Generalization Capability of neutral net fitting, determines that the number of hidden nodes is 20.Neural network structure is as shown in Figure 8.
Through neutral net the Fitting Calculation, Reliable Mathematics model is obtained, fitting effect is as shown in Figure 9.Can be with from figure
Find out, fitting correlation coefficient is 0.9646, and fitting effect is preferable.
Step 4, test data statistical analysiss
It is analyzed and monitoring by the circuit board different to polylith, counts 54HC14 chips work temperature in the circuit board
Supply current Icc when degree T and work.Gaussian fitting test, two inspections are carried out using the Jarque-Bera methods of inspection to data
Test result h value and be 0, show the equal Normal Distribution of two life characteristics parameters.Parameter estimation is carried out using statistic algorithm, it is determined that
43.2 DEG C of the average of work temperature is 6.12 with standard deviation；Supply current Icc averages 45.5mA be with standard deviation be 3.16.
Step 5, the reliability assessment based on Latin Hypercube Sampling are calculated
According to the statistic analysis result of life characteristics parameter, with reference to the Reliable Mathematics model of components and parts, using based on drawing
The reliability of the reliability assessment computational methods evaluation circuit board of fourth hypercube sampling.
According to the number of life characteristics parameter, it is determined that sampling dimension is 2, and hypercube is set up accordingly for [x1,x2].Root
According to 0.99 reliability requirement, the reliability assessment required precision γ of the circuit board is 0.01, and takes α for 0.05, according to sampling
Frequency in sampling needed for the computational methods of number of times are calculated should be at least 7530 for 7530, i.e. frequency in sampling, just can guarantee that 0.01
Computational accuracy.For this case, determine that this frequency in sampling for calculating is chosen for 10000.
Random number is generated using Latin Hypercube Sampling method, and is updated in Reliable Mathematics model respectively, calculated
To 10000 TF values.Calculated by counting N number of TF, obtain TF probability distribution graphs as shown in Figure 10, and calculate according to formula (3)
Reliability assessment result is 0.996.
In sum, the present embodiment employs the aerospace electron class product reliability that Reliability Physics is combined with intellectual learning
Property appraisal procedure the signal modulation module circuit of space flight type safety governor has been carried out fail-safe analysis with assessment, by point
Analysis obtains drawing a conclusion：
1) analyzed with Monte-Carlo Simulation by sensitive analysis, the product does not deposit incipient fault；
2) by stress analysis, determine that the Primary Component of signal modulation module circuit is 54HC14 chips；
3) by Analysis of Failure Mechanism, the dominant mechanism of 54HC14 chip failures is caused to imitate for hot carrier in jection
Should, its failure is closely related with operating temperature and supply current；
4) reliability for being calculated signal modulation module circuit by Reliability Evaluation Algorithm is 0.996.
In sum, presently preferred embodiments of the present invention is these are only, is not intended to limit protection scope of the present invention.
All any modification, equivalent substitution and improvements within the spirit and principles in the present invention, made etc., should be included in the present invention's
Within protection domain.
Claims (7)
1. a kind of reliability estimation method of aerospace electron product, it is characterised in that comprise the steps：
Step 1, the weak link to aerospace electron product are identified, and obtain key componentses, specifically include following steps：
S11, according to electronic product physical arrangement, using circuit simulation analysis software, build electronic product physical phantom, if
The input and output characteristic parameter of each components and parts in electronic product is put, input and the output relation of the electronic product is tentatively set up；
S12, judge electronic product whether there is incipient fault；If there is incipient fault, perform after being improved to electronic product
Next step, if there is no incipient fault, directly performs S13；
Wherein, electronic product is judged with the presence or absence of incipient fault using Monte Carlo Method, concrete grammar is：
1) property of each components and parts is generated respectively according to the distributed constant of each components and parts normal distribution according to formula (1) and formula (2)
Can random number, wherein, the random number generated by i-th components and parts input and output characteristic parameter is designated as Xi；
Xi=μ+σ X 'i (2)
Wherein, UjIt is the random number between [0,1], μ is average, and σ is standard deviation；
2) by each components and parts corresponding random number XiIt is updated in the physical phantom in S11, by simulation calculation, obtains electricity
Sub- output of products result；
3) repeat 1) step and 2) step n times, obtain M circuit output result, wherein, M at least takes 50 times；
The M simulation data result to obtaining is analyzed, and obtains the extreme value for exporting, and is referred to according to the output performance of electronic product
Mark requires, judges whether simulation data result meets performance indications requirement, if extreme value is in claimed range, judge that product is full
Foot requires there is no incipient fault；Conversely, there is incipient fault；
S13, the weak link for using stress analysis means, recognizing electronic product, as key componentses, specially：
Under conditions of electronic product normal work, with the electronic product physical phantom set up in step S11, calculate and produce
The working stress of each components and parts in product, and obtain the working stress of each components and parts and the ratio of setting, and according to ratio from big
Each components and parts are ranked up to little order；During three below condition will be met, one or more components and parts are judged to
Weak link：
Condition 1：Components and parts of the ratio more than 1；
Condition 2：By observing ratio, the components and parts of drop volume requirement are unsatisfactory for；
Condition 3：When the components and parts ratio that the difference of the ratio of two neighboring components and parts is more than 30% and sequence is forward is more than 0.5, really
Fixed two components and parts sort forward components and parts for weak link, while determining that sequence is all before the weak link device
Components and parts are weak link；
In the case where 3 conditions of the above are unsatisfactory for, judge to sort the components and parts of first three as weak link；
Step 2, the Primary Component for determining in step 1, with reference to its working condition, with Analysis of Failure Mechanism method, it is determined that
The failure mechanism of key componentses；Then the failure mechanism according to key componentses, the working environment of bonded products, analyze and true
Determine the key componentses performance parameter related to failure mechanism and working life, i.e. life characteristics parameter；
Step 3, for key componentses carry out device level test, measure life characteristics parameter of the components and parts in process of the test,
And record components and parts its time to failure TF；The corresponding relation of time to failure TF and life characteristics parameter is then set up, is obtained final product
To Reliable Mathematics model；
Step 4, each life characteristics parameter for for different samples, collecting components and parts in Reliable Mathematics model, for each parameter
Sample variation, calculate the distribution pattern distributed constant corresponding with distribution pattern of each life characteristics parameter；
Step 5, the reliability assessment based on Latin Hypercube Sampling are calculated, specially：
For each life characteristics parameter, frequency in sampling N is determined according to required precision first, each longevity for then obtaining according to step 4
The distribution pattern and distributed constant of life characteristic parameter, carries out n times sampling using Latin Hypercube Sampling method, and by sampling samples
It is updated in the Reliable Mathematics model of step 3 foundation respectively, is calculated the value of calculation of N number of time TF；By counting N number of TF
Calculate, obtain TF probability-distribution functions F (t), and according to the task time t of productmRequirement, calculate product reliability R,
I.e.：
R(tm)=P { TF ＞ tm}=1-F (tm)。
2. a kind of reliability estimation method of aerospace electron product as claimed in claim 1, it is characterised in that the step 1
In, after step S11 sets up the input of the electronic product and output relation, by reality test, measure the reality of each components and parts
Border working condition and working condition, checking treat the input/output relation of electronic product, to each components and parts in physical phantom
Input and output characteristic parameter be adjusted, after adjustment is finished, then perform S12.
3. a kind of reliability estimation method of aerospace electron product as claimed in claim 1, it is characterised in that in the step
After S11 obtains input/output relation, sensitive analysis are carried out based on each components and parts in the relation pair electronic product, obtain to electricity
Sub- output of products affects most sensitive components and parts, and according to sensitivity height, each components and parts is ranked up；In the step
In rapid 12, choosing the preceding part components and parts of sensitivity sequence carries out Monte Carlo analysis.
4. a kind of reliability estimation method of aerospace electron product as claimed in claim 1, it is characterised in that in the step
After S11 obtains input/output relation, sensitive analysis are carried out based on each components and parts in the relation pair electronic product, obtain to electricity
Sub- output of products affects most sensitive components and parts, and according to sensitivity height, each components and parts is ranked up；In the step
In the method for rapid 12 pairs of product improvements, according to components and parts sensitivity ranking results, the forward components and parts of sensitivity sequence are chosen, is entered
Row is replaced or is retrofited, to realize the improvement to electronic product.
5. a kind of reliability estimation method of aerospace electron product as claimed in claim 1, it is characterised in that the step 2
In, adopt for the Analysis of Failure Mechanism method of electronic product：Electric test, microstructure analysis, microstructure analysis, physics
Sample preparation analysis method is dissected in performance detection, Microanalysis, stress test.
6. a kind of reliability estimation method of aerospace electron product as claimed in claim 1, it is characterised in that the step 3
In, using neutral net or the learning algorithm of support vector machine, set up the corresponding relation of TF and life characteristics parameter, you can by property
Mathematical model.
7. a kind of reliability estimation method of aerospace electron product as claimed in claim 1, it is characterised in that the step 5
In, the frequency in sampling N meets：In formula, γ is required precision, σ2For sample variance, it is calculated by step 4；
zαFor normal distribution quantile.
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