CN105354402A - Reliability evaluation method for wear-out failure of vehicle gearbox - Google Patents
Reliability evaluation method for wear-out failure of vehicle gearbox Download PDFInfo
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- CN105354402A CN105354402A CN201410404106.8A CN201410404106A CN105354402A CN 105354402 A CN105354402 A CN 105354402A CN 201410404106 A CN201410404106 A CN 201410404106A CN 105354402 A CN105354402 A CN 105354402A
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Abstract
The invention discloses a reliability evaluation method for wear-out failure of a vehicle gearbox, and belongs to the technical field of vehicle gearbox research. According to the method, reliability evaluation of wear-out failure is performed by comprehensively utilizing information contained in test time and lubrication oil wear particle concentration during a vehicle gearbox test. For test time data, reliability evaluation is performed by utilizing Weibull distribution of the test time; and for lubrication oil wear particle concentration data, reliability evaluation is performed by utilizing Weibull distribution of pseudo failure life corresponding to a failure threshold. Further, two evaluation results are converted into equivalent binary data, and reliability comprehensive evaluation is performed by utilizing Bayesian data fusion in Beta distribution. The wear-out failure information contained in the lubrication oil wear particle concentration during the vehicle gearbox test process is considered and effectively fused with the wear-out failure information contained in the test time, so that the reliability evaluation result of wear-out failure of the vehicle gearbox is relatively high in credibility and the information utilization rate is high.
Description
Technical field
The present invention relates to vehicle gear box studying technological domain, particularly relate to the method that reliability assessment is carried out in the inefficacy of a kind of consume type to vehicle gear box.
Background technology
Vehicle gear box is the vitals of vehicle, is generally made up of products such as transmission shaft, gear, bearing, clutch coupling, lubrication and cooling systems, and major function transmits power and realizes speed change.
It is the topmost fault mode of vehicle gear box that the consume type of the parts such as gear, bearing and clutch abrasion lost efficacy.The general reliability adopting test period information evaluation consume type to lose efficacy in current vehicle gear box development, specific embodiments is:
(1) identical with state to multiple stage model vehicle gear box carries out the Censoring bench test under same load section;
(2) lost efficacy if there is non-consuming type in process of the test, continue test after then fixing a breakdown, fault correction time, not included within test period, lost efficacy if there is consume type in process of the test, then record the test period of carrying out, in sampling test here;
(3) Weibull parameter estimation is carried out to the test period of each sample, obtain the Weibull distribution of test period;
(4) is substituted into the Weibull Function expression formula that (3) step obtains the nominal operation time, carry out the reliability assessment that vehicle gear box consume type lost efficacy.
There is following defect in such scheme:
(1) consume type lost efficacy and mostly occurred after vehicle gear box uses the long period, by the impact of the factor such as financial cost and time cost, the test sample amount of vehicle gear box is generally less, truncated time is shorter, utilizes test period to carry out the confidence level of consume type inefficacy reliability assessment lower;
(2) in vehicle gear box process of the test, except test period, consume type lost efficacy also closely related with the metal particle concentration in lubricating oil, along with the wearing and tearing of gear, bearing and clutch coupling, a large amount of metal worn particle enters lubricating oil, wear and tear more serious, in lubricating oil, the concentration of metallic particles is higher.When carrying out vehicle gear box reliability assessment, only utilize information fault-time in process of the test, and a large amount of and consume type not considering to comprise in lubricating oil lost efficacy relevant reliability information, will cause significant information waste.
Therefore, how to design a kind of can fully utilize vehicle gear box test test period and lubricating oil wear particle concentration carry out the method for reliability assessment, to improve the confidence level of vehicle gear box consume type inefficacy reliability assessment, become technical matters urgently to be resolved hurrily.
Summary of the invention
(1) technical matters that will solve
The technical problem to be solved in the present invention is: for defect of the prior art, there is provided a kind of can fully utilize vehicle gear box test test period and lubricating oil wear particle concentration carry out the method for consume type inefficacy reliability assessment, to improve confidence level and the information utilization of vehicle gear box consume type inefficacy reliability assessment.
(2) technical scheme
In order to solve the problems of the technologies described above, the invention provides the reliability comprehensive estimation method that a kind of vehicle gear box consume type lost efficacy, comprising the following steps:
S1, the vehicle gear box identical with state to multiple stage model carry out the Censoring bench test under same load section, if there is non-consuming type in process of the test to lose efficacy, test is continued after then fixing a breakdown, fault correction time is not included within test period, if there is consume type in process of the test to lose efficacy, then record the test period of carrying out, in sampling test here;
S2, carry out in process in each sampling test, at set intervals, in vehicle gear box, get a samples of lubricant oil, measure metal worn particle concentration;
S3, Weibull parameter estimation is carried out to each sample test period, obtain the Weibull distribution of test period;
S4, utilize the Weibull Function of the test period obtained in S3, calculate fiduciary level and the Reliability confidence lower limit of vehicle gear box under the nominal operation time;
S5, the fiduciary level obtained in S4 and Reliability confidence lower limit are converted to equivalent success failure type data, and build the Beta distribution of vehicle gear box fiduciary level;
S6, lubricating oil wear particle concentration information to be analyzed, for the detection data of same test sample, if comparatively last time, detected value significantly reduced a certain moment wear particle concentration detected value, then show the operation having carried out replacement oil between twice detection, utilize the principle of " vehicle gear box fluid metal particle concentration rate of growth is basicly stable " to revise detection data;
S7, time series data is detected to the revised lubricating oil wear particle concentration of each sample carry out linear fit, obtain the change function of each sample wear particle concentration with test period;
S8, each sample lubricating oil wear particle concentration-function of time failure threshold of lubricating oil wear particle concentration substituted in S7, obtain the pseudo-burn-out life of each test sample;
S9, Weibull parameter estimation is carried out to the pseudo-burn-out life of each test sample obtained in S8, obtain the Weibull distribution of pseudo-burn-out life;
S10, utilize the Weibull Function of the pseudo-burn-out life obtained in S9, calculate fiduciary level and the Reliability confidence lower limit of vehicle gear box under the nominal operation time;
S11, the fiduciary level obtained in S10 and Reliability confidence lower limit are converted to equivalent success failure type data;
S12, using the Beta distribution that obtains in S5 as fiduciary level prior distribation, using the equivalent success failure type data that obtains in S11 as field data, carry out bayesian data fusion, the rear Beta that tests obtaining fiduciary level distributes;
S13, utilize the fiduciary level obtained in S12 test rear Beta distribute assessment the nominal operation time under vehicle gear box consume type lost efficacy reliability.
Preferably, in step S1, the sample size of vehicle gear box test is greater than 4, and total time on test is more than or equal to the nominal operation time;
Preferably, in step S2, the interval of lubricating oil sample time is even, samples number of times and be greater than 10 times in whole process of the test, and sampling is greater than 1/3 of total time on test T.T.;
Preferably, in step S3 and S9, maximum likelihood function method is utilized to carry out the parameter estimation of Weibull distribution;
Preferably, in step S4 and S10, Reliability confidence lower limit is the one-sided confidence lower limit of degree of confidence 0.9;
Preferably, in step S4 and S10, utilize bootstrap to calculate Reliability confidence lower limit, Bootstrap sampling number of times is the integral multiple of 10, and is more than or equal to 1000 times;
Preferably, in step S5 and S11, utilize the definition of moments method and confidence lower limit that fiduciary level and Reliability confidence lower limit are converted to equivalent success failure type data.
(3) beneficial effect
The present invention has fully utilized the reliability assessment that information that in vehicle gear box test, test period and lubricating oil wear particle concentration comprise carries out the inefficacy of consume type.For test period data, utilize the Weibull distribution of test period to carry out reliability assessment, and assessment result is converted to equivalent success failure type data; For lubricating oil wear particle concentration data, reliability assessment is carried out in the Weibull distribution utilizing concentration to reach the pseudo-burn-out life of failure threshold, and assessment result is converted to equivalent success failure type data.Further, the equivalent success failure type data that two kinds of data obtain is carried out Beta and divides the bayesian data fusion planted, obtain the reliability comprehensive estimation result that vehicle gear box consume type lost efficacy.Owing to considering the consume type fail message that lubricating oil wear particle concentration in vehicle gear box process of the test comprises, and the consume type fail message effective integration that itself and test period are comprised, therefore higher to the reliability assessment credible result degree of vehicle gear box consume type inefficacy, and information utilization is high.
Accompanying drawing explanation
Fig. 1 is method flow diagram of the present invention;
Fig. 2 is the fluid metal particle concentration timing curve modification method schematic diagram comprising the operation of replacement oil utilizing the method for the embodiment of the present invention to obtain.
Embodiment
Below in conjunction with drawings and Examples, the specific embodiment of the present invention is described in further detail.Following examples for illustration of the present invention, but are not used for limiting the scope of the invention.
As shown in Figure 1, the invention provides the reliability estimation method that a kind of vehicle gear box consume type lost efficacy, comprise the following steps:
S1, the vehicle gear box identical with state to more than 4 models carry out the Censoring bench test under same load section, total time on test is more than or equal to the nominal operation time, if there is non-consuming type in process of the test to lose efficacy, test is continued after then fixing a breakdown, fault correction time is not included within test period, if occur in process of the test consume type lost efficacy, then record the test period of carrying out, in sampling test here;
S2, carry out in process in each sampling test, at set intervals, in vehicle gear box, get a samples of lubricant oil, measure metal worn particle concentration, the interval of lubricating oil sample time is even, samples number of times and be greater than 10 times in whole process of the test, and sampling is greater than 1/3 of total time on test T.T.;
S3, Weibull parameter estimation is carried out to each sample test period, obtain the Weibull distribution of test period; In the present embodiment, maximum likelihood function method is utilized to carry out the parameter estimation of Weibull distribution:
S4, utilize the Weibull Function F of the test period obtained in S3
1(t), the fiduciary level R of vehicle gear box under calculating nominal operation time T
1and degree of confidence is the one-sided confidence lower limit R of fiduciary level of 0.9
l1; In the present embodiment, utilize relational expression R
1(T)=1-F
1(T) fiduciary level R is calculated
1; Bootstrap is utilized to calculate the one-sided confidence lower limit R of fiduciary level
l1, Bootstrap sampling frequency n
1be the multiple of 10, and be more than or equal to 1000 times, by the n that self-service sample calculates
1individual reliability calculating result arranges from small to large, gets 0.1*n
1individual reliability calculating result is the one-sided confidence lower limit R of fiduciary level of 0.9 as degree of confidence
l1;
S5, the fiduciary level R will obtained in S4
1and Reliability confidence lower limit R
l1be converted to equivalent success failure type data (s
1, f
1), the Beta (s and the Beta building vehicle gear box fiduciary level distributes
1, f
1); In the present embodiment, utilize the definition of moments method and confidence lower limit by R
1and R
l1try to achieve (s
1, f
1), shown in (1), wherein B represents Beta function;
S6, lubricating oil wear particle concentration information to be analyzed, for the detection data of same test sample, if comparatively last time, detected value significantly reduced a certain moment wear particle concentration detected value, then show the operation having carried out replacement oil between twice detection, utilize the principle of " vehicle gear box fluid metal particle concentration rate of growth is basicly stable " to revise detection data; Fig. 2 is the lubricating oil metal particle concentration timing curve schematic diagram comprising the operation of replacement oil, and AB section and EF section are the actual lubricant oil metal granule density timing curve recorded, at t
1to t
2time period has carried out repairing, therefore t
2the metal concentration E in moment is significantly lower than t
1the metal particle concentration B in moment, does not so consider that the detection curve of replacement oil operation should be made up of AB section and BC section;
S7, time series data is detected to the revised lubricating oil wear particle concentration of each sample carry out linear fit, obtain the change function x=at+b of each sample wear particle concentration with test period, wherein x represents lubricant oil metal granule density, and t is test period, a and b is undetermined parameter;
S8, each sample lubricating oil wear particle concentration-function of time failure threshold of lubricating oil wear particle concentration substituted in S7, obtain the pseudo-burn-out life of each test sample;
S9, Weibull parameter estimation is carried out to the pseudo-burn-out life of each test sample obtained in S8, obtain the Weibull distribution of pseudo-burn-out life; In the present embodiment, maximum likelihood function method is utilized to carry out the parameter estimation of Weibull distribution;
S10, utilize the Weibull Function F of the pseudo-burn-out life obtained in S9
2(t), the fiduciary level R of vehicle gear box under calculating nominal operation time T
2and Information Meter is the one-sided confidence lower limit R of fiduciary level of 0.9
l2; In the present embodiment, utilize relational expression R
2(T)=1-F
2(T) fiduciary level R is calculated
2; Bootstrap is utilized to calculate the one-sided confidence lower limit R of fiduciary level
l2, Bootstrap sampling frequency n
2be the multiple of 10, and be more than or equal to 1000 times, by the n that self-service sample calculates
2individual reliability calculating result arranges from small to large, gets 0.1*n
2individual reliability calculating result is the one-sided confidence lower limit R of fiduciary level of 0.9 as degree of confidence
l2;
S11, the fiduciary level R will obtained in S10
2and Reliability confidence lower limit R
l2be converted to equivalent success failure type data (s
2, f
2); In the present embodiment, utilize the definition of moments method and confidence lower limit by R
2and R
l2try to achieve (s
2, f
2), shown in (2), wherein B represents Beta function;
S12, the Beta distribution Beta (s will obtained in S5
1, f
1) as fiduciary level prior distribation, by the equivalent success failure type data (s obtained in S11
2, f
2) as field data, carry out bayesian data fusion, what obtain fiduciary level tests rear Beta distribution Beta (s
1+ s
2, f
1+ f
2);
S13, utilize the fiduciary level obtained in S12 to test rear Beta to distribute Beta (s
1+ s
2, f
1+ f
2) assessment nominal operation time T under vehicle gear box consume type lost efficacy fiduciary level R, shown in (3).
Below for certain type vehicle gear box, the solution of the present invention is further described.
Have 6 samples and carry out 400 hours fixed time tests, the nominal operation time is 400 hours.Detect the lubricant oil metal wear particle concentration of each sample 0-165 hour, sampling number of times is 30 times.
No. 3 little consume types that occur constantly of sampling test to 295 lost efficacy, and all the other 5 samples consume type did not occur in fixed time test process at 400 hours and lost efficacy.Then test period data are as shown in table 1.
The test period data that certain type vehicle gear box consume type of table 1 lost efficacy
| Sample number | 1 | 2 | 3 | 4 | 5 | 6 |
| Test period/hour | 400 | 400 | 295 | 400 | 400 | 400 |
| Data type | Right censored data | Right censored data | Complete data | Right censored data | Right censored data | Right censored data |
Utilize the test period data in maximum likelihood function method his-and-hers watches 1 to carry out Weibull parameter estimation, obtain Weibull Function such as formula shown in (4).
The 400 hours nominal operation time fiduciary level of correspondence is:
R
1(400)＝1-F
1(400)＝0.8239(5)
Utilize the data in bootstrap his-and-hers watches 1 to carry out 1000 Bootstrap samplings, 1000 the reliability calculating results calculated by self-service sample arrange from small to large, get the one-sided confidence lower limit of fiduciary level that the 100th reliability calculating result as degree of confidence is 0.9:
R
L1(400)＝0.6492(6)
Formula (5) and formula (6) are substituted into formula (1), and solving equivalent success failure type data is (s
1, f
1)=(13.43,2.76).Obtaining fiduciary level prior distribation is further:
Beta(s
1，f
1)＝Beta(13.43，2.76)(7)
Carry out replacement oil to the time-series rules data of each sample lubricant oil metal granule density to revise, revised data are as shown in table 2.
The correction data of certain type vehicle gear box lubricant oil metal granule density of table 2
Lubricant oil metal granule density time series data in his-and-hers watches 2 carries out linear fit, obtains each sample time-series degenrate function as shown in table 3.
Certain type vehicle gear box lubricant oil metal granule density of table 3
| Sample number | 1 | 2 | 3 | 4 | 5 | 6 |
| Sequential degenrate function | x＝0.783t+28 | x＝0.477t+37.5 | x＝0.516t+34.6 | x＝0.633t+34.9 | x＝0.595t+38.3 | x＝0.65t+28.3 |
The failure threshold of this kind of vehicle gear box lubricant oil metal granule density is 350 μ g*L
-3, substitute into the sequential degenrate function of each sample in table 3, obtain each sample pseudo-burn-out life as shown in table 4.
The pseudo-burn-out life that certain type vehicle gear box consume type of table 4 lost efficacy
| Sample number | 1 | 2 | 3 | 4 | 5 | 6 |
| Pseudo-burn-out life/hour | 411.24 | 655.14 | 611.24 | 497.79 | 523.87 | 494.92 |
Utilize the pseudo-burn-out life of each sample in maximum likelihood function method his-and-hers watches 4 to carry out the parameter estimation of Weibull distribution, obtain Weibull Function such as formula shown in (8).
The 400 hours nominal operation time fiduciary level of correspondence is:
R
2(400)＝1-F
2(400)＝0.9171(9)
Utilize the data in bootstrap his-and-hers watches 2 to carry out 1000 Bootstrap samplings, 1000 the reliability calculating results calculated by self-service sample arrange from small to large, get the one-sided confidence lower limit of fiduciary level that the 100th reliability calculating result as degree of confidence is 0.9:
R
L2(400)＝0.8315(10)
Formula (9) and formula (10) are substituted into formula (2), and solving equivalent success failure type data is (s
2, f
2)=(16.97,1.53).
By the Beta (s obtained in formula (7)
1, f
1)=Beta (13.43,2.76) as fiduciary level prior distribation, by (s
2, f
2)=(16.97,1.53) as field data, carry out bayesian data fusion, what obtain fiduciary level tests rear Beta distribution Beta (s
1+ s
2, f
1+ f
2)=Beta (30.4,4.29).
Utilize Beta (s
1+ s
2, f
1+ f
2) assessment nominal operation time T under vehicle gear box consume type lost efficacy fiduciary level R:
As can be seen from the above embodiments, the present invention has fully utilized the reliability assessment that information that in vehicle gear box test, test period and lubricating oil wear particle concentration comprise carries out the inefficacy of consume type.For test period data, utilize the Weibull distribution of test period to carry out reliability assessment, and assessment result is converted to equivalent success failure type data; For lubricating oil wear particle concentration data, reliability assessment is carried out in the Weibull distribution utilizing concentration to reach the pseudo-burn-out life of failure threshold, and assessment result is converted to equivalent success failure type data.Further, the equivalent success failure type data that two kinds of data obtain is carried out Beta and divides the bayesian data fusion planted, obtain the reliability comprehensive estimation result that vehicle gear box consume type lost efficacy.Owing to considering the consume type fail message that lubricating oil wear particle concentration in vehicle gear box process of the test comprises, and the consume type fail message effective integration that itself and test period are comprised, therefore higher to the reliability assessment credible result degree of vehicle gear box consume type inefficacy, and information utilization is high.Utilize maximum likelihood function method to carry out Weibull distribution parameters estimation, utilize the mathematical methods such as bootstrap calculating Reliability confidence lower limit more ripe; Extract samples of lubricant oil in system of vehicle transmission case process of the test convenient, metal particle concentration detection technique cost is lower, and the method that therefore the present invention proposes also has good practicality.
The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the prerequisite not departing from the technology of the present invention principle; can also make some improvement and replacement, these improve and replace and also should be considered as protection scope of the present invention.
Claims (7)
1. a reliability estimation method for vehicle gear box consume type inefficacy, is characterized in that, comprise the following steps:
S1, the vehicle gear box identical with state to multiple stage model carry out the Censoring bench test under same load section, if there is non-consuming type in process of the test to lose efficacy, test is continued after then fixing a breakdown, fault correction time is not included within test period, if there is consume type in process of the test to lose efficacy, then record the test period of carrying out, in sampling test here;
S2, carry out in process in each sampling test, at set intervals, in vehicle gear box, get a samples of lubricant oil, measure metal worn particle concentration;
S3, Weibull parameter estimation is carried out to each sample test period, obtain the Weibull distribution of test period;
S4, utilize the Weibull Function of the test period obtained in S3, calculate fiduciary level and the Reliability confidence lower limit of vehicle gear box under the nominal operation time;
S5, the fiduciary level obtained in S4 and Reliability confidence lower limit are converted to equivalent success failure type data, and build the Beta distribution of vehicle gear box fiduciary level;
S6, lubricating oil wear particle concentration information to be analyzed, for the detection data of same test sample, if comparatively last time, detected value significantly reduced a certain moment wear particle concentration detected value, then show the operation having carried out replacement oil between twice detection, utilize the principle of " vehicle gear box fluid metal particle concentration rate of growth is basicly stable " to revise detection data;
S7, time series data is detected to the revised lubricating oil wear particle concentration of each sample carry out linear fit, obtain the change function of each sample wear particle concentration with test period;
S8, each sample lubricating oil wear particle concentration-function of time failure threshold of lubricating oil wear particle concentration substituted in S7, obtain the pseudo-burn-out life of each test sample;
S9, Weibull parameter estimation is carried out to the pseudo-burn-out life of each test sample obtained in S8, obtain the Weibull distribution of pseudo-burn-out life;
S10, utilize the Weibull Function of the pseudo-burn-out life obtained in S9, calculate fiduciary level and the Reliability confidence lower limit of vehicle gear box under the nominal operation time;
S11, the fiduciary level obtained in S10 and Reliability confidence lower limit are converted to equivalent success failure type data;
S12, using the Beta distribution that obtains in S5 as fiduciary level prior distribation, using the equivalent success failure type data that obtains in S11 as field data, carry out bayesian data fusion, the rear Beta that tests obtaining fiduciary level distributes;
S13, utilize the fiduciary level obtained in S12 test rear Beta distribute assessment the nominal operation time under vehicle gear box consume type lost efficacy reliability.
2. the reliability estimation method of vehicle gear box consume type inefficacy as claimed in claim 1, is characterized in that, in step S1, the sample size of vehicle gear box test is greater than 4, and total time on test is more than or equal to the nominal operation time.
3. the reliability estimation method of vehicle gear box consume type inefficacy as claimed in claim 1, it is characterized in that, in step S2, the interval of lubricating oil sample time is even, sample number of times in whole process of the test and be greater than 10 times, sampling is greater than 1/3 of total time on test T.T..
4. the reliability estimation method of vehicle gear box consume type inefficacy as claimed in claim 1, is characterized in that, in step S3 and S9, utilize maximum likelihood function method to carry out the parameter estimation of Weibull distribution.
5. the reliability estimation method of vehicle gear box consume type inefficacy as claimed in claim 1, it is characterized in that, in step S4 and S10, Reliability confidence lower limit is the one-sided confidence lower limit of degree of confidence 0.9.
6. as reliability estimation method that claim 1 and vehicle gear box consume type according to claim 5 lost efficacy, it is characterized in that, in step S4 and S10, utilize bootstrap to calculate Reliability confidence lower limit, Bootstrap sampling number of times is the integral multiple of 10, and is more than or equal to 1000 times.
7. as reliability estimation method that claim 1 and vehicle gear box consume type according to claim 5 lost efficacy, it is characterized in that, in step S5 and S11, utilize the definition of moments method and confidence lower limit that fiduciary level and Reliability confidence lower limit are converted to equivalent success failure type data.
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| CN107871418A (en) * | 2017-12-27 | 2018-04-03 | 吉林大学 | It is a kind of to be used to evaluate the man-machine experiment porch for driving reliability altogether |
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| CN109376407B (en) * | 2018-09-30 | 2023-06-20 | 中国人民解放军92942部队 | Reliability evaluation method for carrier-based aircraft catapult |
| CN110263472A (en) * | 2019-06-27 | 2019-09-20 | 中国人民解放军国防科技大学 | Electromechanical product reliability evaluation method based on regression method comprehensive life test data |
| CN110263472B (en) * | 2019-06-27 | 2021-01-29 | 中国人民解放军国防科技大学 | Electromechanical product reliability evaluation method based on regression method comprehensive life test data |
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