CN108460221A - A kind of quantile self_consistent model method of fuel regulator system storage life assessment - Google Patents
A kind of quantile self_consistent model method of fuel regulator system storage life assessment Download PDFInfo
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Abstract
The present invention provides a kind of quantile self_consistent model method of fuel regulator system storage life assessment, assumes at three kinds, implementation step is as follows：One：Fuel servo valve performance degenerates to model to be assessed with storage life；Two：Compression spring performance degradation is modeled to be assessed with storage life；Three：Fuel regulator system storage life is assessed；Four：Fuel regulator system storage reliability is assessed；By above step, the present invention has carried out performance degradation modeling and shelf-life analysis for two typical weak links of fuel regulator system, i.e. fuel servo valve and compression spring；On this basis, consider each element correlation, the storage life and storage reliability of system are assessed by quantile self_consistent model；Achieve the effect that comprehensively utilize component degradation information with accurate evaluation fuel regulator system storage life, solves engineering existing data large dispersion during fuel regulator system evaluation, the problems such as Evaluation accuracy is low in practice.
Description
Technical field
In aero-engine, fuel regulator is machine fuel feeding being adjusted reasonably to coordinate engine to work
Structure, internal structure is complicated, is made up of multiple elements.During storage, it is influenced by environment such as temperature, humidity, fuel oil is adjusted
Two elements of device internal fuel servo valve and compression spring are easy to happen performance degradation, are influenced on fuel regulator storage life notable.
Therefore, storage life assessment is carried out to aero-engine fuel regulator system, needs comprehensive fuel servo valve and compression spring two
The performance degradation information of typical weakness elements.The present invention is a kind of fuel regulator system storage considering correlation between each element
Lifetime estimation method is deposited, each element storage reliability information is based on, fuel regulator system is provided using quantile self_consistent model
Storage life assessment result is suitable for the fields such as aero-engine storage life assessment.
Background technology
Engineering in practice, assess aero-engine fuel regulator system storage life generally use competitiveness failure mould
Type.In competitive fault model hypothesis system between each element independently of each other, any element failure can cause thrashing, i.e.,
The thrashing time is：
T=min { T1,T2,L,Tm} (1)
In formula：Element number in m expression systems, T indicate thrashing time, TiI-th of component failure in expression system
Time, i=1,2, L, m.
Under competitive fault model hypothesis, system dependability function is：
In formula：R (t) indicates t moment system dependability function, Ri(t) i-th of element reliability in t moment system is indicated
Function.
Competitive fault model is although easy to operate, but does not account for the correlation between each element.Under normal conditions, this
One assumes not to be consistent actually with engineering.For example, when element number is more in system, though each element have it is high can
By degree, the reliability of system is also very low, this is clearly unreasonable.
Therefore, when assessing fuel regulator system storage life, the correlation between each element in consideration system is needed.
Copula function methods and joint probability density function method can solve relativity problem, but both methods is to test data
It is more demanding, when data large dispersion, it is difficult to calculate accurate result.In conclusion to solve fuel regulator system
Storage life evaluation problem, urgent need establishes a kind of each element correlation of consideration, and is store to the adaptable system of test data
Deposit lifetime estimation method.
Invention content
(1) purpose of the present invention：Exist in storage life evaluation process for aero-engine fuel regulator system
The problems such as not independent between data large dispersion, each element, makes full use of each element storage reliability information, provides a kind of fuel oil
The quantile self_consistent model method of regulator system storage life assessment, improves fuel regulator system storage life Evaluation accuracy,
Theoretical foundation is provided for maintenance decision.
(2) technical solution：
The present invention need to establish following basic setup：
It is typical weak link, combustion in aero-engine fuel regulator storage process that 1 fuel servo valve and compression spring, which is arranged,
Oil conditioner system storage life is determined by two elements of fuel servo valve and compression spring；
Two element function degenerative processes of 2 fuel servo valves and compression spring are arranged, and there are correlations；
The performance degradation amount initial value Normal Distribution of two elements of 3 fuel servo valves and compression spring is set；
The invention firstly uses fuel servo valves and compression spring performance degradation information, carry out element storage life appraisal；Then,
Element storage life appraisal result is subjected to system synthesis using quantile self_consistent model, to provide the storage of fuel regulator system
Deposit life appraisal result；
Based on above-mentioned hypothesis and thinking, a kind of quantile of fuel regulator system storage life assessment of the present invention is from being in harmony mould
Type method, is mainly achieved by the steps of：
Step 1：Fuel servo valve performance degenerates to model to be assessed with storage life
Under normal circumstances, common Performance Degradation Model includes random variance model, edge distribution model, accumulated damage mould
Type, Wiener-Hopf equation model, gamma process model and inverse Gaussian process model etc.；Under particular characteristic degradation model hypothesis, root
Model parameter can be estimated according to Maximum Likelihood Estimation Method；Since fuel servo valve performance degenerative process is that have increase simultaneously
With reduce trend nonmonotonic random process, therefore select Wiener-Hopf equation model its test data is handled；
For the degraded data feature of fuel servo valve, the Wiener-Hopf equation model of foundation is as follows：
X1(t)=μ1t+σ1W(t)+Z1 (3)
In formula：X1(t) the degeneration magnitude after fuel servo valve storage t is indicated；Parameter μ1Join for the drift of the degenerative process
Number, for describing deterioration velocity；Parameter σ1For diffusion parameter, fluctuated for describing to degenerate；W (t) indicates standard Brownian movement
Journey；Z1Indicate that fuel servo valve amount of degradation initial value, obedience parameter are θ1And ε1Normal distribution, i.e. Z1~N (θ1,ε1 2)；
According to the property of Wiener-Hopf equation, amount of degradation X1(t) Normal Distribution, i.e.,：
X1(t)~N (θ1+μ1t,ε1 2+σ1 2t) (4)
Based on formula (4), it is as follows log-likelihood function can be established：
In formula：x1(ti) indicate amount of degradation X1(t) in tiThe value at moment；Accordingly, it can be obtained based on Maximum Likelihood Estimation Method
To unknown parameter μ1, σ1, θ1, ε1Estimated value；
Given fuel servo valve degradation failure threshold value l1, then its out-of-service time T1It is represented by：
T1=inf t >=0 | X1(t)≥l1} (6)
Based on formula (6), fuel servo defective valve time T can be obtained1Dead wind area is obeyed, Reliability Function is：
In formula：
μ 1 is drift parameter, σ1For diffusion parameter, l1For fuel servo valve degradation failure threshold value, θ1And ε1Respectively normal distribution Z1Phase
Prestige and variance；
Step 2：Compression spring performance degradation is modeled to be assessed with storage life
When carrying out storage life assessment to compression spring, using the value of elastic of compression spring as performance degradation amount；According to the physics of compression spring
Property establishes following one-dimensional viscoelastic stress relaxation model：
X2(t)=Aexp (- t/B)+Z2 (8)
In formula：X2(t) the degeneration magnitude after compression spring storage t is indicated；Parameter A indicates elasticity modulus；Parameter B indicates the time
Coefficient of relaxation；Z2Indicate that the amount of degradation initial value of compression spring, obedience parameter are θ2And ε2Normal distribution, i.e.,
According to assumed above, the performance degradation amount X of compression spring2(t) Normal Distribution, i.e.,：
Based on formula (9), it is as follows log-likelihood function can be established：
In formula：x2(ti) indicate amount of degradation X2(t) in tiThe value at moment；Accordingly, it can be obtained based on Maximum Likelihood Estimation Method
To unknown parameter A, B, θ2, ε2Estimated value；
The degradation failure threshold value l of given compression spring2, then its out-of-service time T2Cumulative failure distribution function can be expressed as：
In formula：A is elasticity modulus, and B is time coefficient of relaxation, l2For degradation failure threshold value, θ2And ε2Respectively normal distribution
Z2Expectation and variance；
Step 3：Fuel regulator system storage life is assessed
Based on step 1 and step 2 assessment result, quantile self_consistent model comprehensive assessment fuel regulator system can be passed through
Reliable storage life；Quantile self_consistent model be it is a kind of by way of probability weight to the reliability of each element of system believe
Breath is integrated, the method to be assessed system reliability；When element is larger in the failure probability of some period of storage
When, the Q-percentile life of the element is affected to the Q-percentile life of system, otherwise smaller；
The concrete form of quantile self_consistent model is：
In formula：TjIt indicates to give period of storage tjThe Q-percentile life of fuel regulator system when year；T1And T2Combustion is indicated respectively
The Q-percentile life of oily servo valve and compression spring；ω1j(tj) and ω2j(tj) quantile is indicated from weight is in harmony, value is storage tjYear
When fuel servo valve and compression spring failure probability (unreliable degree) normalization magnitude, calculation formula is as follows：
In formula：ωij(tj) indicate quantile corresponding to i-th of component from being in harmony weight, Ri(tj) indicate that i-th of component exists
Store tjReliability when year；
As can be seen that giving any period of storage t from quantile self_consistent modelj, a system reliable longevity can be obtained
Order estimated value Tj；Therefore, by changing period of storage tj, system Life estimating value can be obtained with period of storage tjVariation
Curve；Due to when element fails in system, thrashing, therefore take Tj=tjWhen system Life estimating value (system
The curve that Life estimating value changes with period of storage and curve Tj=tjIntersection point) as system reliable life assessment most
Terminate fruit；
Step 4：Fuel regulator system storage reliability is assessed
Fuel regulator system storage reliability needs are calculated on the basis of quantile self_consistent model, such as formula
(14) shown in：
R=f (Tj) (14)
In formula：TjExpression system period of storage, R indicate that the storage reliability of system, f () are TjFunction, implicit function
Form can be expressed as：
It is identical as formula (12), in formula (15), TjIndicate the Q-percentile life of system when reliability is R, T1(R) and T2(R)
It is indicated respectively when reliability is R, the Q-percentile life of fuel servo valve and compression spring；ω1jAnd ω2jIt is quantile from being in harmony weight, it
Be period of storage be tjThe normalized magnitude of fuel servo valve and compression spring failure probability (unreliable degree), t when yearjTo select at random
The row period of storage value taken；
For the system dependability R for calculating when period of storage is T, system dependability R can be taken on section [0,1]
Value；According to formula (15), for any given system reliability R, a system Q-percentile life T can be calculatedj, will calculate
The T arrivedjValue is compared with given period of storage T, with the immediate T of TjThe corresponding R values of value are period of storage when being T
System dependability estimated value.
By above step, the present invention is for two typical weak links of fuel regulator system, i.e. fuel servo valve
And compression spring, carry out performance degradation modeling and shelf-life analysis；On this basis, each element correlation is considered, by dividing position
Point self_consistent model assesses the storage life and storage reliability of system；Reached comprehensive utilization component degradation information with
The effect of accurate evaluation fuel regulator system storage life solves engineering in practice during fuel regulator system evaluation
Existing data large dispersion, the problems such as Evaluation accuracy is low.
Description of the drawings
Fig. 1 is the method for the invention flow chart.
Fig. 2 is the system shelf-life analysis flow chart based on quantile self_consistent model.
Specific implementation mode
With reference to example, the present invention is described in detail.
In order to carry out storage life assessment to certain model fanjet fuel regulator system, it is necessary first in conjunction with experiment
Data carry out storage life assessment to fuel servo valve and the typical weakness elements of compression spring two, then utilize quantile self_consistent model
Carry out system storage life comprehensive assessment.
A kind of quantile self_consistent model method of fuel regulator system storage life assessment of the present invention, as shown in Figure 1, mainly
It is achieved by the steps of：
Step 1：Fuel servo valve performance degenerates to model to be assessed with storage life
Table 1 is the Performance Degradation Data of fuel servo valve, i.e., the fuel oil output stream of fuel servo valve under different periods of storage
Measure difference.
1 fuel servo valve performance degraded data of table
| Period of storage (year) | 5.3 | 6.1 | 6.2 | 6.3 | 6.4 | 6.6 | 6.9 | 7 |
| Fuel oil output flow difference (L/h) | -18.2 | -28.0 | -47.0 | -35.5 | 5.00 | -10.8 | -13.0 | -11.5 |
| Period of storage (year) | 7.1 | 7.2 | 7.3 | 7.4 | 7.5 | 8.1 | 8.2 | \ |
| Fuel oil output flow difference (L/h) | -19.6 | -7.25 | -15.0 | -28.5 | -29.3 | -3.33 | -7.50 | \ |
Wherein：L/h expressions l/h.
Based on Wiener-Hopf equation model shown in formula (3), performance degradation modeling and storage longevity are carried out to fuel servo valve components
Life analysis.According to method shown in step 1 in technical solution, model parameter is estimated using Maximum Likelihood Estimation Method, parameter
Estimated result is：
In conjunction with fuel servo defective valve threshold value l1=38.51L/h, the Reliability Function that can obtain fuel servo valve are：
In formula：
Step 2：Compression spring performance degradation is modeled to be assessed with storage life
Table 2 is the Performance Degradation Data of compression spring, the i.e. value of elastic of compression spring.
2 compression spring Performance Degradation Data of table
| Period of storage (year) | 9.1 | 9.2 | 9.3 | 9.4 | 9.5 | 9.7 | 9.8 | 9.9 |
| Value of elastic (N) | 296.7 | 295.4 | 299.5 | 293.6 | 291.0 | 293.6 | 291.5 | 279.0 |
| Period of storage (year) | 103.1 | 108.3 | 100.8 | 115.1 | 110.5 | 119.6 | 123.7 | 0\ |
| Value of elastic (N) | 279.6 | 269.3 | 267.0 | 295.0 | 263.0 | 275.0 | 260.0 | \ |
Wherein：N indicates newton.
According to method shown in step 2 in technical solution, model parameter is estimated using Maximum Likelihood Estimation Method, is joined
Counting estimated result is：
In conjunction with fuel servo defective valve threshold value l2=260N, the Reliability Function that can obtain compression spring are：
Step 3：Fuel regulator system storage life is assessed
After the Reliability Function for calculating two elements of fuel servo valve and compression spring, calculated based on quantile self_consistent model
System storage reliability, as shown in Figure 2.
First, according to method shown in step 3 in technical solution, two elements of fuel servo valve and compression spring are calculated reliable
Q-percentile life when degree is respectively 0.9,0.95 and 0.99, i.e. t0.90, t0.95With t0.99.Result of calculation is as follows：
3 fuel servo valve of table and compression spring Life estimating value
| Q-percentile life (year) | t0.90 | t0.95 | t0.99 |
| Fuel servo valve | 5.11 | 3.99 | 2.53 |
| Compression spring | 13.79 | 11.98 | 8.59 |
Secondly, different period of storage t are takenj, the unreliable degree of fuel servo valve and compression spring is calculated, can be obtained after normalization
ω1j(tj) and ω2j(tj) value.Wherein, tjValue and its corresponding element unreliable angle value it is as follows：
4 t of tablejThe unreliable angle value of value and element
| tj(year) | 9 | 12 | 14 | 16 | 18 | 20 | 22 | 25 | 30 |
| Fuel servo valve | 0.32 | 0.47 | 0.55 | 0.62 | 0.68 | 0.73 | 0.77 | 0.83 | 0.88 |
| Compression spring | 0.01 | 0.05 | 0.11 | 0.20 | 0.33 | 0.49 | 0.64 | 0.83 | 0.98 |
Finally, Q-percentile life when fuel regulator system dependability is 0.9 is calculated.According to step 3, T1And T2Respectively
Q-percentile life of two elements of fuel servo valve and compression spring when reliability is 0.9, for different period of storage tj, can count
Calculation obtains a system Q-percentile life TjValue, work as tj=TjWhen, corresponding TjSystem Life estimating exactly to be asked
Value.According to calculating, it is 5.221 years to obtain Q-percentile life of the fuel regulator system when reliability is 0.9.
Step 4：Fuel regulator system storage reliability is assessed
Assuming that find out the storage reliability of fuel regulator system when period of storage is 10 years.It is walked according in technical solution
Rapid four the method, value is carried out to reliability R, and the system Q-percentile life under Different Reliability is calculated using formula (15).R's
Value and corresponding Q-percentile life value are shown in Table 5.
System Q-percentile life under 5 Different Reliability value of table
| Reliability | 0.600 | 0.650 | 0.655 | 0.656 | 0.660 | 0.670 | 0.700 | 0.800 |
| System Q-percentile life (year) | 11.259 | 10.113 | 10.006 | 9.987 | 9.899 | 9.688 | 9.066 | 7.144 |
As can be seen from Table 5, when reliability R is 0.655, system Q-percentile life was closest to 10 years.Therefore, work as storage
When time is 10 years, system dependability 0.655.The result shows that the storage life and storage reliability of fuel regulator system
Assessment result with engineering is practical is consistent.
The thought of dichotomy may be used when carrying out value to reliability R.It is being store for example, calculating fuel regulator system
Reliability when depositing 10 years, when R takes 0.6, system Q-percentile life is more than 10 years, and when taking 0.7, system dependability is less than 10 years.
Therefore only need to R between 0.6 and 0.7 value, without calculating the system Q-percentile life under remaining R.Similarly, it calculates
R be 0.65 when system Q-percentile life be more than 10 years, therefore it is required that reliability between 0.65 and 0.7.So move in circles,
The higher system dependability of precision can be found out.
In conclusion the present invention relates to a kind of quantile self_consistent model methods of fuel regulator system storage life assessment.
It has carried out property for two typical weak links of fanjet fuel regulator system, i.e. fuel servo valve and compression spring
Can degenerate modeling and shelf-life analysis.On this basis, the performance degradation information of comprehensive two typical weakness elements, by dividing
Site self_consistent model calculates the storage life and storage reliability of system.The present invention is suitable for similar model fuel oil tune
The storage life assessment for saving device system, has stronger operability.
Claims (1)
1. a kind of quantile self_consistent model method of fuel regulator system storage life assessment, it is assumed that situation is as follows：
It is typical weak link, fuel oil tune in aero-engine fuel regulator storage process that 1 fuel servo valve and compression spring, which is arranged,
Section device system storage life is determined by two elements of fuel servo valve and compression spring；
Two element function degenerative processes of 2 fuel servo valves and compression spring are arranged, and there are correlations；
The performance degradation amount initial value Normal Distribution of two elements of 3 fuel servo valves and compression spring is set；
The invention firstly uses fuel servo valves and compression spring performance degradation information, carry out element storage life appraisal；Then, it utilizes
Element storage life appraisal result is carried out system synthesis by quantile self_consistent model, and the longevity is stored to provide fuel regulator system
Order assessment result；
It is characterized in that：
Based on above-mentioned hypothesis situation, a kind of quantile self_consistent model method of fuel regulator system storage life assessment of the present invention,
It is achieved by the steps of：
Step 1：Fuel servo valve performance degenerates to model to be assessed with storage life
Under normal circumstances, common Performance Degradation Model include random variance model, edge distribution model, Modelling of Cumulative Damage,
Wiener-Hopf equation model, gamma process model and inverse Gaussian process model；Under particular characteristic degradation model hypothesis, according to very big
Possibility predication method estimates model parameter；Since fuel servo valve performance degenerative process is that have increase simultaneously and reduce
The nonmonotonic random process of gesture, therefore Wiener-Hopf equation model is selected to handle its test data；
For the degraded data feature of fuel servo valve, the Wiener-Hopf equation model of foundation is as follows：
X1(t)=μ1t+σ1W(t)+Z1···········(1)
In formula：X1(t) the degeneration magnitude after fuel servo valve storage t is indicated；Parameter μ1For the drift parameter of the degenerative process,
For describing deterioration velocity；Parameter σ1For diffusion parameter, fluctuated for describing to degenerate；W (t) indicates standard Brownian motion process；Z1
Indicate that fuel servo valve amount of degradation initial value, obedience parameter are θ1And ε1Normal distribution, i.e. Z1~N (θ1,ε1 2)；
According to the property of Wiener-Hopf equation, amount of degradation X1(t) Normal Distribution, i.e.,：
X1(t)~N (θ1+μ1t,ε1 2+σ1 2t)··········(2)
Based on formula (4), it is as follows to establish log-likelihood function：
In formula：x1(ti) indicate amount of degradation X1(t) in tiThe value at moment；Accordingly, it can be obtained based on Maximum Likelihood Estimation Method unknown
Parameter μ1, σ1, θ1, ε1Estimated value；
Given fuel servo valve degradation failure threshold value l1, then its out-of-service time T1It is expressed as：
T1=inf t >=0 | X1(t)≥l1}···········(4)
Based on formula (6), fuel servo defective valve time T can be obtained1Dead wind area is obeyed, Reliability Function is：
In formula：C (u)=σ1ε1u3/2,μ1For drift
Shifting parameter, σ1For diffusion parameter, l1For fuel servo valve degradation failure threshold value, θ1And ε1Respectively normal distribution Z1Expectation and side
Difference；
Step 2：Compression spring performance degradation is modeled to be assessed with storage life
When carrying out storage life assessment to compression spring, using the value of elastic of compression spring as performance degradation amount；According to the physical property of compression spring,
Establish following one-dimensional viscoelastic stress relaxation model：
X2(t)=Aexp (- t/B)+Z2···········(6)
In formula：X2(t) the degeneration magnitude after compression spring storage t is indicated；Parameter A indicates elasticity modulus；Parameter B indicates time relaxation
Coefficient；Z2Indicate that the amount of degradation initial value of compression spring, obedience parameter are θ2And ε2Normal distribution, i.e.,
According to assumed above, the performance degradation amount X of compression spring2(t) Normal Distribution, i.e.,：
Based on formula (9), it is as follows log-likelihood function to be established：
In formula：x2(ti) indicate amount of degradation X2(t) in tiThe value at moment；Accordingly, it can be obtained based on Maximum Likelihood Estimation Method unknown
Parameter A, B, θ2, ε2Estimated value；
The degradation failure threshold value l of given compression spring2, then its out-of-service time T2Cumulative failure distribution function be expressed as：
In formula：A is elasticity modulus, and B is time coefficient of relaxation, l2For degradation failure threshold value, θ2And ε2Respectively normal distribution Z2's
It is expected that and variance；
Step 3：Fuel regulator system storage life is assessed
Based on step 1 and step 2 assessment result, can be by quantile self_consistent model comprehensive assessment fuel regulator system can
By storage life；Quantile self_consistent model be it is a kind of by way of probability weight to the reliability information of each element of system into
Row synthesis, the method to be assessed system reliability；It, should when element is when the failure probability of some period of storage is larger
The Q-percentile life of element influences greatly the Q-percentile life of system, otherwise small；
The concrete form of quantile self_consistent model is：
In formula：TjIt indicates to give period of storage tjThe Q-percentile life of fuel regulator system when year；T1And T2Indicate that fuel oil is watched respectively
Take the Q-percentile life of valve and compression spring；ω1j(tj) and ω2j(tj) quantile is indicated from weight is in harmony, value is storage tjIt is fired when year
The normalization magnitude of oily servo valve and compression spring failure probability, that is, unreliable degree, calculation formula are as follows：
In formula：ωij(tj) indicate quantile corresponding to i-th of component from being in harmony weight, Ri(tj) indicate that i-th of component is being stored
tjReliability when year；
It can find out from quantile self_consistent model, give any period of storage tj, can obtain a system Life estimating value
Tj；Therefore, by changing period of storage tj, system Life estimating value is obtained with period of storage tjThe curve of variation；Due to being
When element fails in system, thrashing, therefore take Tj=tjWhen system Life estimating value, that is, system Life estimating
It is worth the curve changed with period of storage and curve Tj=tjFinal result of the intersection point as system reliable life assessment；
Step 4：Fuel regulator system storage reliability is assessed
Fuel regulator system storage reliability needs are calculated on the basis of quantile self_consistent model, such as formula (14) institute
Show：
R=f (Tj) (12)
In formula：TjExpression system period of storage, R indicate that the storage reliability of system, f () are TjFunction, implicit function form
It is expressed as：
It is identical as formula (12), in formula (15), TjIndicate the Q-percentile life of system when reliability is R, T1(R) and T2(R) respectively
It indicates when reliability is R, the Q-percentile life of fuel servo valve and compression spring；ω1jAnd ω2jIt is quantile from weight is in harmony, it is storage
It is t to deposit the timejThe normalized magnitude of fuel servo valve and compression spring failure probability, that is, unreliable degree, t when yearjIt randomly selects
One row period of storage value；
For the system dependability R for calculating when period of storage is T, value is carried out to system dependability R on section [0,1]；Root
According to formula (15), for any given system reliability R, a system Q-percentile life T can be calculatedj, the T that will be calculatedj
Value is compared with given period of storage T, with the immediate T of TjThe system that the corresponding R values of value are period of storage when being T
Reliablity estimation value；
By above step, the present invention is for two typical weak links of fuel regulator system, i.e. fuel servo valve and pressure
Spring has carried out performance degradation modeling and shelf-life analysis；On this basis, consider each element correlation, certainly by quantile
It is in harmony model and the storage life and storage reliability of system is assessed；Comprehensive utilization component degradation information is reached with accurate
The effect for assessing fuel regulator system storage life, solving engineering, fuel regulator system evaluation exists in the process in practice
Data large dispersion, the problems such as Evaluation accuracy is low.
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