CN105912842A - Testing method of incipient failure removal for machining center - Google Patents
Testing method of incipient failure removal for machining center Download PDFInfo
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Abstract
The invention discloses a testing method of incipient failure removal for a machining center with the view of solving problems of a current machining center such as long testing time for incipient failure removal and high economic cost. The method comprises following steps: 1, reliability modeling of a reference machining center:(1) selecting the reference machining center to track failure interval time recorded by a test, drawing a frequency histogram, primarily determining a hypothetical distribution model-Weibull distribution; (2) employing a maximum likelihood method to estimate unknown parameters Beta and Alfa in Weibull distribution;(3) carrying out Kolmogorov-Smirnov test to the hypothetical distribution model; 2, solving test time of incipient failure removal for a target machining center: (1) calculating a distribution range of quantile tpi; (2) establishing a prior distribution; (3) establishing a posteriori distribution; (4) optimizing test time; (5) distributing test time; 3. incipient failure removal test: (1) carrying out a incipient failure removal test of an overall machine; (2) carrying out an incipient failure removal test on a critical subsystem.
Description
Technical field
The present invention relates to a kind of for machining center initial failure eliminating test method, more cut and really say, the present invention relates to
A kind of initial failure being applied to vertical machining centre, horizontal Machining centers complete machine and key subsystem thereof gets rid of test method.
Background technology
Now, in the machinery manufacturing industry of China, machining center has been widely used in different mechanic factory workers.It
There is the advantages such as production efficiency is high, processing element precision is high, precision stability is good, be conventional process tools incomparable.
But increasing the raising with performance along with machining center function, its initial failure the most gradually increases.In general, machining center makes
Can only achieve about the 60% of its stationary operational phase by the reliability at initial stage, the most substantial amounts of inside it manufacture and design
Defect and potential faults, immature etc. as of poor quality, the design of error of supporting component or manufacturing process, these problems
Must solve before dispatching from the factory.So the work in-process heart carries out initial failure eliminating work before dispatching from the factory, for mechanic factory workers
The machining center providing reliability high just becomes most important.
China starts late, at present about the research of the initial failure eliminating test of machining center complete machine and key subsystem
Although there being some methods eliminated about Digit Control Machine Tool initial failure, but lack perfect, can allow machining center manufacturing enterprise
The scheme accepted with user.Current test method mainly assumes that fault of numerical control machine tool rate curve is tub curve, then looks for
Go out the flex point of tub curve early fault period and random failure period, get rid of test using this flex point as Digit Control Machine Tool initial failure
Deadline.But time corresponding to this flex point is long, and in actual applications, either machining center manufacturing enterprise still adds
Work central user enterprise all cannot accept the highest Financial cost and time cost.Therefore one must be found the most excellent
Change method, shortens initial failure and gets rid of the test period of test.
Summary of the invention
The technical problem to be solved is to overcome the machining center initial failure eliminating examination that prior art exists
Test the problem that time length, Financial cost and time cost are high, it is provided that a kind of machining center initial failure gets rid of test method.
For solving above-mentioned technical problem, the present invention adopts the following technical scheme that realization: described a kind of machining center
Initial failure is got rid of test method and is comprised the following steps that
1) with reference to machining center Reliability modeling:
(1) choose the time between failures with reference to machining center Field tracing test record, draw frequency histogram, tentatively
Determine that machining center time between failures assumes distributed model-Weibull distribution;
(2) Maximum Likelihood Estimation Method is used to estimate to assume distributed model-Weibull distribution unknown parameter β and α;
(3) to assuming that distributed model carries out Kolmogorov-Smirnov inspection;
2) target machining center initial failure eliminating test period solves:
(1) quantile t is calculatedpiDistributed area;
(2) prior distribution is set up;
(3) Posterior distrbutionp is calculated;
(4) test period optimization;
(5) test period distribution;
3) initial failure eliminating test:
(1) complete machine initial failure gets rid of test;
(2) key subsystem initial failure gets rid of test.
The time between failures choosing reference machining center Field tracing test record described in technical scheme, draws frequency
Rate rectangular histogram, primarily determines that machining center time between failures assumes that distributed model-Weibull distribution refers to: assume machining center
Time between failures obeys Weibull distribution, by drawing the method preliminary judgement of frequency histogram;Assume t1,t2,…,tnIt is
With reference to n time between failures sample observations of machining center Field tracing test record, then draw the step of frequency histogram
Rapid as follows:
(1) t is found out1,t2,…,tnIn minima and maximum, be denoted as t respectively(1)And t(n), choose and be less than or equal to
t(1)Several a, more than or equal to t(n)Several b;
(2) empirically formula (1) determine group number k:
K=[1+3.32lgn] (1)
In formula: n is the total number of machining center time between failures, [] expression rounds;
(3) interval, (a b) is divided into k subinterval (xj-1,xj], xjPlace's fight continuity, it is assumed that the length in each subinterval is equal,
Its group is away from Δ x and branch xjIt is respectively as follows:
xj=a+j Δ x (j=0,1,2 ..., k) (3)
(4) machining center time between failures t is counted1,t2,…,tnFall the frequency in each subinterval i.e. number nj,
Calculate frequency f againj
(5) the estimated value f (x often organizing empirical probability density is calculatedj):
(6) on transverse axis, each branch x is drawnj, with each subinterval (xj-1,xj] it is the end, with f (xj) it is that height makees little rectangle,
Machining center frequency histogram can be drawn out;
There is machining center frequency histogram, drawn the probability density curve of time between failures;When form parameter β≤1
Time, the probability density function curve of Weibull distribution is monotonic decreasing type;During β > 1, in single peak type.If probability density curve with
The probability density function Similar Broken Line of Weibull distribution, i.e. primarily determines that machining center time between failures is obeyed Weibull and divided
Cloth;Formula (6), (7), (8), (9) be respectively Weibull distribution probability density function, cumulative distribution function, Reliability Function and
Failure rate function
In formula (6)-(9): α is scale parameter;β is form parameter;T is stochastic variable.
Utilization Maximum Likelihood Estimation Method described in technical scheme is estimated to assume distributed model-Weibull distribution unknown parameter
β and α refers to: assume t1,t2,…,tnIt is n the time between failures sample sight with reference to machining center Field tracing test record
Measured value, separate and obedience Weibull distribution;
According to formulaUtilize multiplication formula, likelihood function can be obtained as follows;
Formula (10) both sides are taken natural logrithm simultaneously, then has
l(β,α;ti) difference β, α local derviation, its estimated value can be tried to achieveWith
In formula: α, β are scale parameter and the form parameter of Weibull distribution respectively, tiFor i-th time between failures.
Described in technical scheme to assume distributed model carry out Kolmogorov-Smirnov inspection refer to:
1) by β, the estimated value that α is the most correspondingWithBring formula intoIn added
Work central fault assumes distributed model interval timeExpression formula
2) by n machining center time between failures t1,t2,…,tnAscending arrangement, brings formula (14) into and calculates every
Individual data are correspondingAnd by itself and empirical distribution function F (ti) i.e. formula
Compare, wherein the maximum value of the difference i.e. observed value of statistic of test D;
3) by D and marginal value Dn,KS-αCompare, meet formula (17), then machining center time between failures obeys prestige cloth
You are distributed;
D=max (D1,D2,...,Dn)≤Dn,KS-α (17)
KS-α is the significant level of Kolmogorov-Smirnov inspection, according to practical situation from 0.2,0.10,0.05,
0.01 these four significance level is appointed and takes one;Dn,KS-αAccording to sample size n sample range and significance level KS-α, from table 1-
Kolmogorov-Smirnov one-sample test is looked in the tables of critical values of D and takes;
The tables of critical values of D in table 1 Kolmogorov-Smirnov one-sample test
Calculating quantile t described in technical schemepiThe step of distributed area as follows:
1) calculating with reference to machining center probability of malfunction is piTime corresponding machining center time between failures tpi,
Wherein: F-1It it is the inverse function of Weibull distribution cumulative distribution function;
piSpan be (0,1);
2) expert contrasts seven kinds of reliability level influence factors of two class machining centers to provide target machining center and ginseng
Pass the examination the reliability level of the heart:
(1) expert combines calculating quantile tpiDistributed area step in quantile tpiDistributed area, add according to seven kinds
Work center reliability level influence factor, provides target machining center and the reliability level with reference to machining center, and by result
Insert table 2-machining center reliability level grade form:
Table 2 machining center reliability level grade form
3) quantile integrating each expert is interval
If step 2) in target machining center and suitable with reference to the reliability level of machining center, the most each expert gives respectively
Go out quantile tpiThe upper bound and lower bound, and give each expert's accuracy weight according to factors such as the experience of each expert, popularity
Ej%, determines quantile t according to formula (20)piDistributed area [tpi_L,tpi_U], and result is inserted table 3-quantile tpiDistrict
Between expert judgments integrate table;
Table 3 quantile tpiInterval expert judgments integrates table
In formula: Ej% is expert's accuracy weight, tpi_L、tpi_UIt is quantile t respectivelypiLower bound and the upper bound.
Prior distribution of setting up described in technical scheme refers to: by quantile tpiInterval [tpi_L,tpi_U] be converted to reference
Interval [the θ of parameter vector θ=[α, β] in machining center time between failures distribution modelL,θU], at piOn the premise of Gei Ding,
In conjunction with monte carlo method, from [tpi_L,tpi_U] appoint take a tpi, then θi=F (β, α | tpi), thus realize quantile tpiInterval
To the conversion that vector θ is interval;
Concrete calculation procedure is:
(1) from [tpi_L,tpi_U] randomly draw a tpi;
(2) θ is calculatediValue;
(3) repeat (1) step and (2) step N-1 time, and record each θiValue;
(4) θ is found out respectivelyiCorresponding maximum and minima;
(5)[θL,θU]=[min (θi),max(θi)];
Machining center time between failures distribution model is Weibull distribution, it is assumed that the scale parameter α of Weibull distribution and
Form parameter β is separate and obedience is uniformly distributed, then the probability density function of target machining center prior distribution is:
In formula: θ=[α, β];α, β are scale parameter and the form parameter of Weibull distribution respectively;θL、θUIt is vector θ respectively
Lower bound and the upper bound;αL、αUIt is lower bound and the upper bound of α respectively;βL、βUIt is lower bound and the upper bound of β respectively.
Calculating Posterior distrbutionp described in technical scheme refers to:
(1) stochastic variable T is made to represent the time between failures that machining center is complete;Make trRepresent a certain observation of T, its
Middle r=1,2 ... n, make the Posterior distrbutionp that π (θ | t) represents θ, according to Bayes theorem:
Likelihood function is
Sample edge is distributed as
P (t)=∫ π (θ) p (t | θ) d θ (24)
In formula (22)-(24): θ=[α, β], α, β are scale parameter and the form parameter of Weibull distribution respectively;
(2) marginal distribution substitution formula (22) of prior distribution, likelihood function and sample being obtained Posterior distrbutionp expression formula is:
In formula: α, β are scale parameter and the form parameter of Weibull distribution respectively;
(3) excessively complicated due to formula (25), cause unknown parameter α, β, without analytic solutions, uses Markov Monte Carlo
Method solves α, β;
(4) obeying Weibull distribution with reference to machining center time between failures, time between failures corresponding during β=1 is
Target machining center flex point t from early fault period to random failure periodpri。
Test period optimization described in technical scheme refers to: carries out initial failure before dispatching from the factory with machining center and gets rid of examination
The maintenance cost sum minimum that the experimentation cost tested and machining center cause in user enterprise generation initial failure is as optimization
Target, the Optimized model thus setting up machining center initial failure eliminating test period is
MinZ=Ym+Yc
In formula:
Z is that target machining center carries out the experimentation cost of initial failure eliminating test before dispatching from the factory and occurs in user enterprise
Initial failure and the maintenance cost sum that causes, i.e. object function;
YmBe machining center carried out before dispatching from the factory initial failure get rid of test experimentation cost;
YcThe maintenance cost that to be machining center occur initial failure in user enterprise and causes;
k1, k2..., knThe electricity charge in representation unit test period, water rate, the consume of material take the wage with workman;
kcMaintenance cost for average fault every time;
λ (t) is the failure rate function of machining center;
F (t) is the fault probability function of machining center;
R (t) is the Reliability Function of machining center;
In mathematical analysis software, work out corresponding calculation procedure can object function Z be solved, and then obtain mesh
Mark machining center initial failure gets rid of the optimization time t of testopt。
Test period distribution described in technical scheme refers to:
1) the failure-frequency f of each subsystem is calculatedk
In formula: fkIt it is the failure-frequency of machining center kth subsystem;
NkIt it is the fault frequency of machining center kth subsystem;
N is the number of faults that machining center is total;
P is the number of machining center subsystem;
2) machining center key subsystem is determined
The failure-frequency of each for machining center subsystem is sorted from high to low, forward subsystem fault frequency is added,
The failure-frequency sum subsystem more than 60% is referred to as key subsystem;
3) time distribution
Key subsystem test period tj:
tj=topt×fj(j=1,2 ..., q) (28)
Then overall test time tcm:
In formula (28)-(29):
fjIt it is the failure-frequency of jth key subsystem;
tjIt is that jth key subsystem initial failure gets rid of test period;
toptThe optimization time of test is got rid of for target machining center initial failure;
Q is the number of key subsystem.
Initial failure described in technical scheme is got rid of test and is referred to:
1) complete machine initial failure eliminating test:
(1) manual function test;
(2) complete machine continuous dry run accelerated test;
(3) axis system torque power load test;
(4) typical case's test specimen cutting test;
2) key subsystem initial failure gets rid of test.
Compared with prior art the invention has the beneficial effects as follows:
A kind of machining center initial failure the most of the present invention gets rid of test method, by bent to machining center fault rate
Line is analysed in depth, and using seven kinds of influence factors of machining center reliability level as prior information, utilizes Bayes theorem to set up and adds
Work center Posterior distrbutionp also estimates parameter beta, flex point t corresponding when making β=1priIt is more accurate to be worth.
A kind of machining center initial failure the most of the present invention gets rid of test method, is dispatching from the factory to advance with machining center
Row initial failure gets rid of the maintenance cost that the experimentation cost tested and machining center cause in user enterprise generation initial failure
And minimum as optimization aim, shorten initial failure and get rid of test period, save experimentation cost, economic benefit is obvious.
A kind of machining center initial failure the most of the present invention gets rid of test method, devises complete machine and key subsystem
Initial failure gets rid of test specific embodiments, effectively excites machining center latent defect, reduces during using after dispatching from the factory
Fault rate.
Accompanying drawing explanation
The present invention is further illustrated below in conjunction with the accompanying drawings:
Fig. 1 is the FB(flow block) that a kind of machining center initial failure of the present invention gets rid of test method;
Fig. 2 is the machining center system subdivision that a kind of machining center initial failure of the present invention gets rid of test method
Block diagram;
Fig. 3 is that a kind of machining center initial failure of the present invention gets rid of in test method prestige under difformity parameter
Boolean's distribution probability densogram;
Fig. 4 is the axonometric view that a kind of machining center initial failure of the present invention gets rid of the typical part of test method
Figure;
Fig. 5 is the main shaft output torque characteristics song that a kind of machining center initial failure of the present invention gets rid of test method
Line chart;
Fig. 6 is the main shaft characteristics of output power song that a kind of machining center initial failure of the present invention gets rid of test method
Line chart.
Detailed description of the invention
Below in conjunction with the accompanying drawings the present invention is explained in detail:
Refering to Fig. 1, a kind of machining center initial failure of the present invention is got rid of test method and is included with reference to machining center
Reliability modeling, target machining center initial failure are got rid of test period and are solved and initial failure eliminating test three steps.Target
Machining center initial failure eliminating test period solves and includes calculating quantile tpiDistributed area, set up prior distribution, calculating
Posterior distrbutionp, test period optimization, test period five steps of distribution.
A kind of machining center initial failure of the present invention gets rid of specifically comprising the following steps that of test method
1. with reference to machining center Reliability modeling
Described reference machining center Reliability modeling refers to choose the event with reference to machining center Field tracing test record
Hinder interval time, draw frequency histogram, primarily determine that the hypothesis distributed model-Weibull distribution of time between failures, then
Estimate to assume unknown parameter β and α in distributed model-Weibull distribution, finally to assuming that distributed model carries out Kolmogorov-
Smirnov checks, and sets up with reference to machining center time between failures distribution model.
Can be vertical machining centre or the horizontal Machining centers being applied to all trades and professions with reference to machining center, it is also possible to be
It is applied to production line or the manufacturing cell of the system of manufacture.Should be with the function of target machining center, technology water with reference to machining center
Flat, structure complexity, Maturity and actual condition are close.Such as, target machining center is vertical machining centre, with reference to processing
Center also should be vertical machining centre;Target machining center is horizontal Machining centers, also should be horizontal processing with reference to machining center
Center;Target machining center is heavy machining center, also should be heavy machining center with reference to machining center;Target machining center should
For the processing of auto parts and components, also should be the processing being applied to auto parts and components with reference to machining center.With reference to machining center
Time between failures derives from early fault period and random failure period.
It is divided into relevant fault and non-relevant fault with reference to machining center fault.Relevant fault is owing to the quality of product own lacks
Fall into and cause, explaining test or calculating the fault that reliability value must be charged to.Non-relevant fault is due to misuse or dimension
Repair caused by improper and extraneous factor, the fault should got rid of when explaining test or calculating reliability value.Therefore,
During with reference to machining center Reliability modeling, only choose relevant fault, and using time between failures as the sample of Reliability modeling
Point.
Need the construction features according to self with reference to machining center, and lathe manufacturing enterprise is to the type machining center
Definition, according to sanctified by usage, structural integrity, the principle of functional independence, and combines practical situation division subsystems.Refering to figure
2, machining center can divide 11 subsystems: axis system, feed system, automatic tool changer, digital control system, electrical system,
Cooling system, lubricating system, pneumatic system, guard system, chip removal system, basic components.Machining center system subdivision completes
After, add up the fault frequency of each subsystem.
1) choose the time between failures with reference to machining center Field tracing test record, draw frequency histogram, tentatively
Determine the hypothesis distributed model-Weibull distribution of time between failures
In general, machining center time between failures obeys Weibull distribution, can be by drawing frequency histogram
Method preliminary judgement.Assume t1,t2,…,tnIt is n the time between failures sample with reference to machining center Field tracing test record
This observation, then the step drawing frequency histogram is as follows:
(1) t is found out1,t2,…,tnIn minima and maximum, be denoted as t respectively(1)And t(n), choose and be slightly less than or be equal to
t(1)Several a, slightly larger than or equal to t(n)Several b.
(2) empirically formula (1) determine group number k:
K=[1+3.32lgn] (1)
In formula: n is the total number of machining center time between failures, [] expression rounds;
(3) interval, (a b) is divided into k subinterval (xj-1,xj], xjPlace's fight continuity, it is assumed that the length in each subinterval is equal,
Its group is away from Δ x and branch xjIt is respectively as follows:
xj=a+j Δ x (j=0,1,2 ..., k) (3)
(4) machining center time between failures t is counted1,t2,…,tnFall at each subinterval (xj-1,xjFrequency in] is i.e.
Number nj, then calculate frequency fj:
(5) the estimated value f (x often organizing empirical probability density is calculatedj):
(6) on transverse axis, each branch x is drawnj, with each subinterval (xj-1,xj] it is the end, with f (xj) it is that height makees little rectangle,
Machining center frequency histogram can be drawn out.
Refering to Fig. 3, there is machining center frequency histogram, it is possible to the probability density substantially drawing time between failures is bent
Line.When form parameter β≤1, the probability density function curve of Weibull distribution is monotonic decreasing type;During β > 1, in single peak type.
If probability density curve and the probability density function Similar Broken Line of Weibull distribution, can primarily determine that between machining center fault
Interval obeys Weibull distribution.Formula (6), (7), (8), (9) are respectively Weibull distribution probability density function, cumulative distribution letter
Number, Reliability Function and failure rate function.
In formula (6)-(9): α is scale parameter;β is form parameter;T is stochastic variable.
2) Maximum Likelihood Estimation Method is used to estimate to assume unknown parameter β and α in distributed model-Weibull distribution
Assume t1,t2,…,tnIt is n the time between failures sample observation with reference to machining center Field tracing test record
Value, separate and obedience Weibull distribution.According to formula (6), utilize multiplication formula, likelihood function can be obtained as follows
Formula (10) both sides are taken natural logrithm simultaneously, then has
l(β,α;ti) difference β, α local derviation, its estimated value can be tried to achieveWith
In formula (10)-(13): α, β are scale parameter and the form parameter of Weibull distribution respectively, tiFor between i-th fault
Interval.
3) to assuming that distributed model carries out Kolmogorov-Smirnov (K-S) inspection
To assuming that distributed model carries out Kolmogorov-Smirnov inspection and is to ensure that assumed Weibull distribution mould
The type appropriateness to machining center time between failures.By step 2) in obtainWithBring into during formula (7) processed
Heart time between failures assumes distributed modelExpression formula (14).Then, by n machining center time between failures t1,
t2,…,tnAscending arrangement, bringing formula (14) into, to calculate each time between failures correspondingAnd it is divided with experience
Cloth function F (ti) i.e. formula (15) compares, the wherein maximum value of the difference i.e. observed value of statistic of test D.By D with face
Dividing value Dn,KS-αCompare, meet formula (17), then machining center time between failures obeys Weibull distribution.
D=max (D1,D2,...,Dn)≤Dn,KS-α (17)
In formula (14)-(17):
WithIt is the maximum likelihood estimation of Weibull distribution unknown parameter β and α respectively;
I=1,2 ..., n;
KS-α is the significant level of Kolmogorov-Smirnov inspection, according to practical situation from 0.2,0.10,0.05,
0.01 these four significance level is appointed and takes one;Dn,KS-αAccording to sample size n sample range and significance level KS-α, from table 1-
Kolmogorov-Smirnov one-sample test is looked in the tables of critical values of D and takes;
The tables of critical values of D in table 1 Kolmogorov-Smirnov one-sample test
2. target machining center initial failure eliminating test period solves
Assume that distributed model-Weibull distribution model has passed through Kolmogorov-with reference to machining center time between failures
Smirnov checks, then can also obey Weibull distribution by assertive goal machining center time between failures.Refering to Fig. 1, target is processed
Center initial failure eliminating test period solves and includes calculating quantile tpiDistributed area, set up prior distribution, calculate posteriority
Distribution, test period optimization, test period five steps of distribution:
1) quantile t is calculatedpiDistributed area
(1) calculating with reference to machining center probability of malfunction is piTime corresponding machining center time between failures tpi
Wherein: F-1It it is the inverse function of Weibull distribution cumulative distribution function;
piSpan be (0,1).
(2) expert contrasts seven kinds of reliability level influence factors of two class machining centers and provides target machining center and reference
The reliability level at center
Expert combines 1) quantile t in steppiValue of calculation, according to seven kinds of reliability level influence factors of machining center,
Provide target machining center and the reliability level with reference to machining center, and result is inserted table 2-machining center reliability level
Grade form;
Table 2 machining center reliability level grade form
Wherein:
A. seven kinds of machining center reliability level influence factors are respectively machining center functional diversity, manufacturer's technology water
Flat, complete machine cost, structure complexity, technology maturity, user satisfaction, operation performance;
B. expert is according to the significance level of every kind of reliability level influence factor, gives corresponding weight w1%, w2%,
W3% ..., w7%, and must be fulfilled for
C. expert (does not consider the impact on reliability level of the other influences factor) with jth influence factor as standard, recognizes
Make the reliability level of target machining center higher than with reference to machining center for this influence factor, then give target machining center SojCompose
Value 1, gives with reference to machining center SRjAssignment 0;Less than with reference to machining center, to target machining center SojAssignment 0, gives with reference to processing
Center SRjAssignment 1;Equal to reference to machining center, to target machining center SojAssignment 1, gives with reference to machining center SRjAssignment 1;
D. the reliability level weighted scoring computing formula of target machining center jth influence factor is
SOwj=SOj× wj%;
E. the reliability level weighted scoring computing formula with reference to machining center jth influence factor is SRwj=SRj×
Wj%;
If f.Then target process reliability level and reference machining center reliability level phase
When;Then target machining center reliability level is higher than with reference to machining center reliability level;Otherwise,
Target machining center reliability level is less than with reference to machining center reliability level.
(3) quantile t that each expert is given is integratedpiInterval
If the reliability level of target machining center and reference machining center is suitable in (2), the most each expert is given respectively
Quantile tpiLower bound and the upper bound, and give each expert's accuracy weight according to factors such as the experience of each expert, popularity
Ej%, determines quantile t according to formula (20)piDistributed area [tpi_L,tpi_U], and result is inserted table 3-quantile tpiInterval
Expert judgments integrates table.
Wherein:
Table 3-quantile tpiInterval expert judgments integrates table
2) prior distribution is set up
By 1) quantile t required in (3) step of steppiInterval [tpi_L,tpi_U] be converted to reference to machining center event
Interval [the θ of parameter vector θ=[α, β] in barrier distributed model interval timeL,θU].At piOn the premise of Gei Ding, in conjunction with Meng Teka
Lip river method, from [tpi_L,tpi_U] appoint take a tpi, then θi=F (β, α | tpi.Thus realize quantile tpiInterval interval to vector θ
Conversion.
Concrete calculation procedure is:
(1) from [tpi_L,tpi_U] randomly draw a tpi;
(2) θ is calculatediValue;
(3) repeat (1) step and (2) step N-1 time, and record each θiValue;
(4) θ is found out respectivelyiCorresponding maximum and minima;
(5)[θL,θU]=[min (θi),max(θi)]。
Machining center time between failures distribution model is Weibull distribution.Assume Weibull distribution scale parameter α and
Form parameter β is separate and obedience is uniformly distributed, then the probability density function of target machining center prior distribution is:
In formula: θ=[α, β];α, β are scale parameter and the form parameter of Weibull distribution respectively;θL、θUIt is vector θ respectively
Lower bound and the upper bound;αL、αUIt is lower bound and the upper bound of α respectively;βL、βUIt is lower bound and the upper bound of β respectively.
3) Posterior distrbutionp is calculated
(1) stochastic variable T is made to represent the time between failures that machining center is complete;Make trRepresent a certain observation of T, its
Middle r=1,2 ... n.Make the Posterior distrbutionp that π (θ | t) represents θ, according to Bayes theorem:
Likelihood function is
Sample edge is distributed as
P (t)=∫ π (θ) p (t | θ) d θ (24)
In formula: in (22)-(24): θ=[α, β], α, β are scale parameter and the form parameter of Weibull distribution respectively.
(2) marginal distribution substitution formula (22) of prior distribution, likelihood function and sample being obtained Posterior distrbutionp expression formula is:
In formula: α, β are scale parameter and the form parameter of Weibull distribution respectively.
(3) excessively complicated due to formula (25), cause unknown parameter α, β, without analytic solutions, uses Markov Monte Carlo side
Method solves;
(4) obeying Weibull distribution with reference to machining center time between failures, time between failures corresponding during β=1 is
Target machining center flex point t from early fault period to random failure periodpri。
4) test period optimization
Machining center early fault period is the longest.If with tpriWhen getting rid of test cut-off as machining center initial failure
Between, either machining center manufacturing enterprise or user enterprise are all difficult to accept.Therefore, the test period Optimized model set up
Machining center manufacturing enterprise and the interests of user enterprise should be taken into account.According to practical situation, carried out before dispatching from the factory with machining center
Initial failure get rid of the experimentation cost of test and machining center user enterprise there is initial failure and the maintenance cost that causes it
With minimum as optimization aim.The machining center initial failure set up gets rid of the Optimized model of test period
MinZ=Ym+Yc
In formula:
Z is that target machining center carries out the experimentation cost of initial failure eliminating test before dispatching from the factory and occurs in user enterprise
Initial failure and the maintenance cost sum that causes, i.e. object function;
YmBe machining center carried out before dispatching from the factory initial failure get rid of test experimentation cost;
YcThe maintenance cost that to be machining center occur initial failure in user enterprise and causes;
k1, k2..., knThe respectively electricity charge in representation unit test period, water rate, the consume of material take, the wage of workman
Deng;
kcMaintenance cost for average fault every time;
λ (t) is the failure rate function of machining center, i.e. formula (9);
F (t) is the fault probability function of machining center, i.e. formula (6);
R (t) is the Reliability Function of machining center, i.e. formula (8);
In mathematical analysis software, work out corresponding calculation procedure can object function Z be solved, and then obtain mesh
Mark machining center initial failure gets rid of the optimization time t of testopt。
5) test period distribution
In machining center running, the failure-frequency of each subsystem differs greatly.Generally, axis system, feed system
The 60% more than of machine failure rate is accounted for the fault rate sum of automatic tool changer.Therefore, based on each subsystem of machining center
Total initial failure is got rid of test period t by fault rateoptIt is allocated tallying with the actual situation.By subsystem higher for fault rate
It is classified as key subsystem, distributes more test period during failture evacuation test in early days, in order to eliminate in test more
Initial failure.Other each subsystems are relatively low due to fault rate ratio, the most individually distribute test period, carrying out overall test or key
During subsystems test, the initial failure synchronously completing these subsystems gets rid of test.Concrete test period allocation flow is as follows:
(1) the failure-frequency f of each subsystem is calculatedk
In formula:
fkIt it is the failure-frequency of machining center kth subsystem;
NkIt it is the fault frequency of machining center kth subsystem;
N is the number of faults that machining center is total;
P is the number of machining center subsystem.
(2) machining center key subsystem is determined
The failure-frequency of each for machining center subsystem is sorted from high to low, forward subsystem fault frequency is added,
The failure-frequency sum subsystem more than 60% is referred to as key subsystem.
(3) time distribution
Key subsystem test period tj:
tj=topt×fj(j=1,2 ..., q) (28)
Then overall test time tcm:
In formula (28)-(29):
fjIt it is the failure-frequency of jth key subsystem;
tjIt is that jth key subsystem initial failure gets rid of test period;
toptThe optimization time of test is got rid of for target machining center initial failure;
Q is the number of key subsystem.
3. initial failure gets rid of test
Complete machine initial failure is got rid of test and is included manual function test, complete machine continuous dry run accelerated test, main shaft system
System torque power load test and typical case's test specimen cutting test, each test cycle number of times is no less than 1 time, can increase according to practical situation
Add the cycle-index of typical case's test specimen cutting test.
1) complete machine initial failure gets rid of test
(1) manual function test
Test table according to table 4-manual function, machining center complete machine is carried out manual function test.
Table 4 manual function test table
(2) complete machine continuous dry run accelerated test
Refering to Fig. 4, machining center continuous dry run accelerated test is that the simulation of numerical programming program can body under repertoire
The typical part course of processing of existing actual production feature, does and does not cuts continuous dry run accelerated test.Examination is accelerated in dry run continuously
Test and should meet following requirement:
A. main shaft uses middling speed and runs up;
B. increase each feed shaft and quickly move the time with high-speed cruising;
C. full flow injection at coolant simulation cutting to be directed at;
D. the numerical control program of typical part should comprise linear interpolation, circular interpolation, linear interpolation circulation, and circular arc cutting follows
Ring;
E. chip cleaner must not terminate operating in the circulating cycle;
Stop time between the most each circulation must not exceed 1min;
The most whole operation process should not break down, such as natural faults such as power failures, it is allowed to carry out after restarting, run
Time can add up.Breaking down and shut down in all centres, then must re-start continuous dry run accelerated test after fixing a breakdown.
Dry run accelerated test continuously, uses normal orthogonal table, loads.Wherein having five factors is that main shaft turns respectively
Speed A, X-axis feed speed B, Y-axis feed speed C, Z axis feed speed D and interpolation type E, these five factors respectively take 4 levels,
The dry run each factor of accelerated test as continuous in table 5-and water-glass.By orthogonal test analysis, the continuous dry run of table 6-can be obtained and add
Speed test load table.
The continuous each factor of dry run accelerated test of table 5 and water-glass
In table 5:
nS_Max、FX_Max、FY_MaxAnd FZ_MaxBe respectively maximum speed of spindle, the highest feed speed of X feed shaft, Y feed shaft
Roughing feed speed and Z feed shaft maximum feed speed.ΔnS、ΔFX、ΔFYWith Δ FZRepresent speed of mainshaft horizontal increment, X respectively
Feed shaft feed speed horizontal increment, Y feed shaft feed speed horizontal increment and Z feed shaft feed speed horizontal increment.Calculate
Formula is as follows:
The continuous dry run accelerated test load table of table 6
(3) axis system torque power load test
Axis system torque power load test comprises main-shaft torque load test and spindle power load test.
A. axis system moment of torsion load test
Refering to Fig. 5, work in-process heart main shaft low [0, nT_MaxIn the range of], select 5 grades of speeds of mainshaft ni, change feeding step by step
Amount or cutting depth, make machining center main shaft maximum output torque TMax.In figure, nT_MaxRepresent main shaft maximum output torque TMaxTime
Corresponding maximum speed;nMaxRepresent the maximum speed of main shaft output;Tn_MaxRepresent main shaft output maximum speed nMaxTime corresponding
Moment of torsion.With the power of power meter measures main shaft output, tachoscope is measured the speed of mainshaft, is calculated by formula (34) in loading every time, main
Peak torque T of the actual output of axleMax_i, and result of the test is inserted form 7-axis system moment of torsion load test table.
In formula: PiI & lt loads, main shaft peak power output, unit: kW;
P0iI & lt loads, main shaft no-load power, unit: kW;
niI & lt loads, main shaft actual speed, unit: r/min.
Table 7 axis system moment of torsion load test table
B. axis system power load test
Refering to Fig. 6, the rotating speed [n that work in-process heart main axis constant power is correspondingT_Max,nP_MaxIn the range of], 4 grades of main shafts are selected to turn
Speed ni, change the amount of feeding or cutting depth step by step, make machining center main shaft Maximum Power Output PMax.In figure, nT_MaxRepresent main shaft
Maximum Power Output PMaxTime corresponding minimum speed, the moment of torsion of now main shaft output is maximum;nMaxShow that main shaft exports the highest turn
Speed;nP_MaxRepresent main shaft Maximum Power Output PMaxTime corresponding maximum speed.With the power of power meter measures main shaft output, turn
The speed of mainshaft measured by speed table, and result of the test is inserted form 8-axis system power load test table.
Table 8 axis system power load test table
(4) typical case's test specimen cutting test
Typical test specimen in typical case's test specimen cutting test both can be consulted with user enterprise, chooses and processes in actual applications
Typical part, carry out cylinder cap processing as automobile engine manufacturing enterprise commonly uses machining center, carrying out typical case test specimen cutting examination
When testing, cylinder cap can be chosen as typical case's test specimen, it is also possible to select the part in complete machine continuous dry run test as typical case's test specimen.
After typical case's test specimen has cut, according to the accuracy standard of machining center and the tolerance of test specimen, test specimen should be carried out quality inspection.As
Fruit cutting test specimen is overproof, again machining center should be carried out accuracy checking.
2) key subsystem initial failure gets rid of test
After machining center completes the eliminating of complete machine initial failure, for fully exposing machining center initial failure, need impact
The key subsystem of complete machine carries out initial failure and gets rid of test.In general, the crucial son of machining center reliability level is affected
System has axis system, feed system, automatic tool changer, digital control system, electrical system, pneumatic system etc..Wherein main shaft system
System, feed system and automatic tool changer need by programming realization long run test.Digital control system and electrical system are being carried out
Can realize when axis system, feed system simultaneously, therefore need not it is carried out routine tests.Pneumatic system can carried out automatically
Realize during knife-changing system simultaneously.Therefore key subsystem is described as a example by axis system, feed system and automatic tool changer in early days
Failture evacuation test loading scheme.
During test, the speed of mainshaft is loaded from high to low by rotating speed, and feed system loads from fast to slow with feed speed, automatically
Knife-changing system is by the simulation descending loading of tool weight.Axis system rotating speed loaded value is from [0.5nMax,nMax] this scope
In by be uniformly distributed extraction 24 groups, extraction result is sorted from small to large, nMaxIt it is maximum speed of spindle.Feed system feeding speed
Rate loaded value is from [0.5fMax,fMax] in the range of this by being uniformly distributed extraction 24 groups, extraction result is sorted from small to large,
fMaxIt it is maximum feed rate.The simulation cutter quality that ATC system loads is from [0, MMax] this scope is random by being uniformly distributed
Extract 24 groups, extraction result is sorted from big to small, MMaxIt it is the maximum tool weight of tool magazine permission loading.After extraction, will
Result inserts table 9-key subsystem load table, works out corresponding numerical control program and carries out load test.
Table 9 key subsystem load table
Cutter number | Simulation cutter quality (kg) | The speed of mainshaft (m/min) | Feed speed (mm/min) |
1 | |||
2 | |||
… | … | … | … |
24 |
Claims (10)
1. a machining center initial failure gets rid of test method, it is characterised in that described a kind of machining center initial failure
Get rid of test method to comprise the following steps that
1) with reference to machining center Reliability modeling:
(1) choose the time between failures with reference to machining center Field tracing test record, draw frequency histogram, primarily determine that
Machining center time between failures assumes distributed model-Weibull distribution;
(2) Maximum Likelihood Estimation Method is used to estimate to assume distributed model-Weibull distribution unknown parameter β and α;
(3) to assuming that distributed model carries out Kolmogorov-Smirnov inspection;
2) target machining center initial failure eliminating test period solves:
(1) quantile t is calculatedpiDistributed area;
(2) prior distribution is set up;
(3) Posterior distrbutionp is calculated;
(4) test period optimization;
(5) test period distribution;
3) initial failure eliminating test:
(1) complete machine initial failure gets rid of test;
(2) key subsystem initial failure gets rid of test.
2. get rid of test method according to a kind of machining center initial failure described in claim 1, it is characterised in that described choosing
Take the time between failures with reference to machining center Field tracing test record, draw frequency histogram, primarily determine that machining center
Time between failures assumes that distributed model-Weibull distribution refers to:
Assume that machining center time between failures obeys Weibull distribution, by drawing the method preliminary judgement of frequency histogram;
Assume t1,t2,…,tnIt is n the time between failures sample observations with reference to machining center Field tracing test record, then paints
The step of frequency histogram processed is as follows:
(1) t is found out1,t2,…,tnIn minima and maximum, be denoted as t respectively(1)And t(n), choose less than or equal to t(1)'s
Number a, more than or equal to t(n)Several b;
(2) empirically formula (1) determine group number k:
K=[1+3.32lgn] (1)
In formula: n is the total number of machining center time between failures, [] expression rounds;
(3) interval, (a b) is divided into k subinterval (xj-1,xj|, xjPlace's fight continuity, it is assumed that the length in each subinterval is equal, its group
Away from Δ x and branch xjIt is respectively as follows:
xj=a+j Δ x (j=0,1,2 ..., k) (3)
(4) machining center time between failures t is counted1,t2,…,tnFall at each subinterval (xj-1,xjFrequency in] i.e. number
nj, then calculate frequency fj:
(5) the estimated value f (x often organizing empirical probability density is calculatedj):
(6) on transverse axis, each branch x is drawnj, with each subinterval (xj-1,xj] it is the end, with f (xj) it is that height makees little rectangle,
Draw out machining center frequency histogram.
There is machining center frequency histogram, drawn the probability density curve of time between failures;When form parameter β≤1,
The probability density function curve of Weibull distribution is monotonic decreasing type;During β > 1, in single peak type.If time between failures is general
Rate density curve and the probability density function Similar Broken Line of Weibull distribution, i.e. primarily determine that machining center time between failures takes
From Weibull distribution;Formula (6), (7), (8), (9) be respectively Weibull distribution probability density function, cumulative distribution function, can
By degree function and failure rate function
In formula (6)-(9): α is scale parameter;β is form parameter;T is stochastic variable.
3. get rid of test method according to a kind of machining center initial failure described in claim 1, it is characterised in that described fortune
Estimate to assume that distributed model-Weibull distribution unknown parameter β and α refers to Maximum Likelihood Estimation Method:
Assume t1,t2,…,tnIt is n the time between failures sample observations with reference to machining center Field tracing test record,
Separate and obey Weibull distribution;
According to formulaUtilize multiplication formula, likelihood function can be obtained as follows;
Formula (10) both sides are taken natural logrithm simultaneously, then has
l(β,α;ti) respectively β, α are sought local derviation, its estimated value can be tried to achieveWith
In formula (10)-(13): α, β are scale parameter and the form parameter of Weibull distribution respectively, tiDuring for i-th between-failures
Between.
4. get rid of test method according to a kind of machining center initial failure described in claim 1, it is characterised in that described is right
Assume that distributed model carries out Kolmogorov-Smirnov inspection and refers to:
1) by β, the estimated value that α is the most correspondingWithBring formula intoIn obtain machining center therefore
Barrier assumes distributed model interval timeExpression formula
2) by n machining center time between failures t1,t2,…,tnAscending arrangement, brings formula (14) into and calculates each event
Barrier is corresponding for interval timeAnd by itself and empirical distribution function F (ti) i.e. formula
Compare, wherein the maximum value of the difference i.e. observed value of statistic of test D;
3) by D and marginal value Dn,KS-αCompare, meet formula (17), then machining center time between failures distribution model is obeyed
Weibull distribution;
D=max (D1,D2,...,Dn)≤Dn,KS-α (17)
KS-α be Kolmogorov-Smirnov inspection significant level, according to practical situation from 0.2,0.10,0.05,0.01 this
Four kinds of significance levels are appointed and takes one;Dn,KS-αAccording to sample size n sample range and significance level KS-α, from table 1-Kolmogorov-
Smirnov one-sample test is looked in the tables of critical values of D and takes;
The tables of critical values of D in table 1 Kolmogorov-Smirnov one-sample test
5. get rid of test method according to a kind of machining center initial failure described in claim 1, it is characterised in that described meter
Point counting figure place tpiThe step of distributed area as follows:
1) calculating with reference to machining center probability of malfunction is piTime corresponding machining center time between failures tpi,
Wherein: F-1It it is the inverse function of Weibull distribution cumulative distribution function;
piSpan be (0,1);
2) expert contrasts seven kinds of reliability level influence factors of two class machining centers to provide in target machining center and reference
The reliability level of the heart:
(1) expert combines calculating quantile tpiDistributed area step in quantile tpiDistributed area, according to seven kinds processing in
Heart reliability level influence factor, provides target machining center and the reliability level with reference to machining center, and result is inserted
Table 2-machining center reliability level grade form:
Table 2 machining center reliability level grade form
3) quantile integrating each expert is interval
If step 2) in target machining center and suitable with reference to the reliability level of machining center, the most each expert is given point respectively
Figure place tpiThe upper bound and lower bound, and give each expert's accuracy weight Ej% according to the experience of each expert, popularity two factor,
Quantile t is determined according to formula (20)piDistributed area [tpi_L,tpi_U], and result is inserted table 3-quantile tpiInterval special
Family judges to integrate table;
Table 3 quantile tpiInterval expert judgments integrates table
In formula: Ej% is expert's accuracy weight, tpi_L、tpi_UIt is quantile t respectivelypiLower bound and the upper bound.
6. get rid of test method according to the machining center initial failure described in claim 1, it is characterised in that described foundation is first
Test distribution to refer to:
By quantile tpiInterval [tpi_L,tpi_U] be converted to reference to parameter vector θ in machining center time between failures distribution model
Interval [the θ of=[α, β]L,θU], at piOn the premise of Gei Ding, in conjunction with monte carlo method, from [tpi_L,tpi_U] appoint take one
tpi, then θi=F (β, α | tpi), thus realize quantile tpiThe interval conversion interval to vector θ;
Concrete calculation procedure is:
(1) from [tpi_L,tpi_U] randomly draw a tpi;
(2) θ is calculatediValue;
(3) repeat (1) step and (2) step N-1 time, and record each θiValue;
(4) θ is found out respectivelyiCorresponding maximum and minima;
(5)[θL,θU]=[min (θi),max(θi)];
Machining center time between failures distribution model is Weibull distribution, it is assumed that the scale parameter α of Weibull distribution and shape
Parameter beta is separate and obedience is uniformly distributed, then the probability density function of target machining center prior distribution is:
In formula: θ=[α, β];α, β are scale parameter and the form parameter of Weibull distribution respectively;θL、θURespectively under vector θ
Boundary and the upper bound;αL、αUIt is lower bound and the upper bound of α respectively;βL、βUIt is lower bound and the upper bound of β respectively.
7. get rid of test method according to a kind of machining center initial failure described in claim 1, it is characterised in that described meter
Calculation Posterior distrbutionp refers to:
(1) stochastic variable T is made to represent the time between failures that machining center is complete;Make trRepresent a certain observation of T, wherein r=
1,2 ..., n, make the Posterior distrbutionp that π (θ | t) represents θ, according to Bayes theorem:
Likelihood function is
Sample edge is distributed as
P (t)=∫ π (θ) p (t | θ) d θ (24)
In formula (22)-(24): θ=[α, β], α, β are scale parameter and the form parameter of Weibull distribution respectively;
(2) marginal distribution substitution formula (22) of prior distribution, likelihood function and sample being obtained Posterior distrbutionp expression formula is:
In formula: α, β are scale parameter and the form parameter of Weibull distribution respectively;
(3) excessively complicated due to formula (25), cause unknown parameter α, β, without analytic solutions, uses Markov monte carlo method
Solve α, β;
(4) obeying Weibull distribution with reference to machining center time between failures, time between failures corresponding during β=1 is target
Machining center flex point t from early fault period to random failure periodpri。
8. get rid of test method according to a kind of machining center initial failure described in claim 1, it is characterised in that described examination
Test time-optimized referring to:
Before dispatching from the factory, carry out initial failure with machining center and get rid of the experimentation cost tested and machining center in the generation of user enterprise
Initial failure and the maintenance cost sum that causes is minimum as optimization aim, thus set up machining center initial failure and get rid of test
The Optimized model of time is
MinZ=Ym+Yc
In formula:
Z is that target machining center carries out the experimentation cost of initial failure eliminating test before dispatching from the factory and occurs in early days in user enterprise
Fault and the maintenance cost sum that causes, i.e. object function;
YmBe machining center carried out before dispatching from the factory initial failure get rid of test experimentation cost;
YcThe maintenance cost that to be machining center occur initial failure in user enterprise and causes;
k1, k2..., knThe electricity charge in representation unit test period, water rate, the consume of material take the wage with workman;
kcMaintenance cost for average fault every time;
λ (t) is the failure rate function of machining center;
F (t) is the fault probability function of machining center;
R (t) is the Reliability Function of machining center;
In mathematical analysis software, work out corresponding calculation procedure can object function Z be solved, and then obtain target and add
Work center initial failure gets rid of the optimization time t of testopt。
9. get rid of test method according to a kind of machining center initial failure described in claim 1, it is characterised in that described examination
Test time distribution to refer to:
1) the failure-frequency f of each subsystem is calculatedk
In formula: fkIt it is the failure-frequency of machining center kth subsystem;
NkIt it is the fault frequency of machining center kth subsystem;
N is the number of faults that machining center is total;
P is the number of machining center subsystem;
2) machining center key subsystem is determined
The failure-frequency of each for machining center subsystem is sorted from high to low, forward subsystem fault frequency is added, fault
The frequency sum subsystem more than 60% is referred to as key subsystem;
3) time distribution
Key subsystem test period tj:
tj=topt×fj(j=1,2 ..., q) (28)
Then overall test time tcm:
In formula (28)-(29):
fjIt it is the failure-frequency of jth key subsystem;
tjIt is that jth key subsystem initial failure gets rid of test period;
toptThe optimization time of test is got rid of for target machining center initial failure;
Q is the number of key subsystem.
10. get rid of test method according to the machining center initial failure described in claim 1, it is characterised in that described early stage
Failture evacuation test refers to:
1) complete machine initial failure eliminating test:
(1) manual function test;
(2) complete machine continuous dry run accelerated test;
(3) axis system torque power load test;
(4) typical case's test specimen cutting test;
2) key subsystem initial failure gets rid of test.
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109684713A (en) * | 2018-12-21 | 2019-04-26 | 中国人民解放军陆军工程大学 | Bayes-based complex system reliability analysis method |
CN114076592A (en) * | 2020-08-19 | 2022-02-22 | 湖北省电力勘测设计院有限公司 | Bayesian-based tunnel radial deformation monitoring error reduction method |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101963548A (en) * | 2010-09-21 | 2011-02-02 | 吉林大学 | Numerical control capstan turret reliability testing system with electro-hydraulic servo loading device |
CN103592148A (en) * | 2013-11-08 | 2014-02-19 | 吉林大学 | Reliability testing table for multistage tandem chip removing devices |
CN104515920A (en) * | 2015-01-14 | 2015-04-15 | 吉林大学 | Numerically-controlled machine tool electric cabinet reliability test device with environment loading function |
CN205120389U (en) * | 2015-10-30 | 2016-03-30 | 吉林大学 | Heavy machine tool tailstock reliability test platform |
-
2016
- 2016-04-05 CN CN201610207315.2A patent/CN105912842A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101963548A (en) * | 2010-09-21 | 2011-02-02 | 吉林大学 | Numerical control capstan turret reliability testing system with electro-hydraulic servo loading device |
CN103592148A (en) * | 2013-11-08 | 2014-02-19 | 吉林大学 | Reliability testing table for multistage tandem chip removing devices |
CN104515920A (en) * | 2015-01-14 | 2015-04-15 | 吉林大学 | Numerically-controlled machine tool electric cabinet reliability test device with environment loading function |
CN205120389U (en) * | 2015-10-30 | 2016-03-30 | 吉林大学 | Heavy machine tool tailstock reliability test platform |
Non-Patent Citations (2)
Title |
---|
李小兵: "加工中心时间动态可靠性建模", 《中国博士学位论文全文数据库 工程科技I辑》 * |
高威: "加工中心早期故障排除试验技术研究", 《中国优秀硕士学位论文全文数据库 工程科技I辑》 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109684713A (en) * | 2018-12-21 | 2019-04-26 | 中国人民解放军陆军工程大学 | Bayes-based complex system reliability analysis method |
CN109684713B (en) * | 2018-12-21 | 2023-05-23 | 中国人民解放军陆军工程大学 | Bayesian-based complex system reliability analysis method |
CN114076592A (en) * | 2020-08-19 | 2022-02-22 | 湖北省电力勘测设计院有限公司 | Bayesian-based tunnel radial deformation monitoring error reduction method |
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