CN108959795B - Test field load spectrum standardization method - Google Patents
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Abstract
The invention provides a test field load spectrum standardization method, which is characterized in that based on the similarity of load distribution of a whole vehicle and parts of the whole vehicle under the test field specification, rain flow cycle counting processing is firstly carried out on actual load time history data; secondly, modeling actual variable range frequency data by adopting at least two distribution models, carrying out parameter estimation, and selecting a probability density function corresponding to theoretical total damage closest to the actual total damage as an optimal distribution function; thirdly, normalizing the actual load time history data based on the static vertical load, and simultaneously performing amplitude analysis and rain flow cycle counting on the relative load time history data; and finally, carrying out relative variable range normalization processing and carrying out parameter estimation on the optimal distribution function so as to obtain a test field standard load spectrum, wherein the test field standard load spectrum can evaluate the reliability and durability of the pre-evaluated motor vehicle and parts thereof, thereby shortening the development period and improving the possibility of passing the whole vehicle road test at one time.
Description
Technical Field
The invention belongs to the technical field of reliability analysis of motor vehicles, and particularly relates to a test field load standardization method.
Background
The reliability and the durability are basic guarantees of safe operation of the motor vehicle and are necessary conditions for guaranteeing the transportation efficiency. In the process of running motor vehicles, sudden fatigue failure of key parts can cause road traffic accidents of vehicle damage and death. The automobile test field is responsible for testing and evaluating the reliability of the finished automobile product.
One of the main ways of checking the reliability and durability of the whole vehicle, each subsystem and parts during the bench test based on the test field load spectrum. However, since the load spectrum must be actually collected after the trial production of the sample car is completed, it is difficult to verify the reliability and durability of the product at the initial stage of development.
Disclosure of Invention
Aiming at the defects or shortcomings of the prior art, the load distribution similarity of the whole vehicle and parts thereof adjusted by different vehicle types and different counter weights under the same test field specification is based on the invention, the reliability and durability of the motor vehicle and subsystems and parts thereof to be evaluated are evaluated through the test field standard load spectrum by processing the actual load time history data of the motor vehicle under the test field specification and obtaining the test field standard load spectrum, so that the development period is shortened, and the possibility of passing the whole vehicle road test at one time is improved. The invention aims to provide a test field load spectrum standardization method.
In order to realize the purpose, the invention adopts the following technical scheme:
the invention provides a test field load spectrum standardization method, which is used for processing actual load time history data of a motor vehicle under a test field standard to obtain a test field standard load spectrum and is characterized by comprising the following steps of:
step one, collecting actual load time history data;
step two, carrying out rain flow circulation counting processing on the actual load time history data to obtain actual variable range frequency data, wherein the actual variable range frequency data comprises a multi-stage actual variable range and actual frequency corresponding to the actual variable range, and calculating actual total circulation frequency according to the actual frequency;
step three, calculating the actual fatigue life corresponding to each level of actual variation according to the S-N curve expression of the material of the motor vehicle, and calculating the actual damage and the actual total damage corresponding to each level of actual variation according to the actual fatigue life and the actual frequency;
step four, modeling the actual variable range frequency data by adopting at least two distribution models, and solving a parameter estimation value of a probability density function corresponding to each distribution model by utilizing a maximum likelihood estimation method;
step five, solving the corresponding theoretical frequency of each stage of actual variable process in each distribution model according to the parameter estimation value, and solving the corresponding theoretical total cycle number of each distribution model, the corresponding theoretical damage of each stage of actual variable process and the corresponding theoretical total damage of each distribution model according to the theoretical frequency;
selecting a probability density function of a distribution model corresponding to the theoretical total damage closest to the actual total damage as an optimal distribution function;
step seven, taking the static vertical load value of the motor vehicle as a reference to carry out normalization processing on actual load time history data to obtain relative load time history data, carrying out amplitude analysis on the relative load time history data to obtain a maximum relative load coefficient and a minimum relative load coefficient, and carrying out rain current cycle counting processing on the relative load time history data to obtain a multi-stage relative variable range, relative frequency corresponding to the relative variable range and a maximum relative variable range;
step eight, normalizing the relative variable with the maximum relative variable to obtain a normalized relative variable, wherein the normalized relative variable is in a value interval of [0,1 ];
and step nine, performing parameter estimation on the optimal distribution function by adopting a maximum likelihood estimation method according to the normalized relative variation and the normalized relative frequency, thereby obtaining a standard load spectrum of the test field.
The test field load spectrum standardization method provided by the invention can also have the following characteristics: wherein the distribution model is normal distribution, lognormal distribution, weibull distribution, exponential distribution, rayleigh distribution or generalized pareto distribution.
The test field load spectrum standardization method provided by the invention can also have the following characteristics: and when the distribution model is normal distribution, lognormal distribution, weibull distribution, exponential distribution or Rayleigh distribution, the parameter estimation values of the probability density function corresponding to the distribution model are obtained by estimation according to the actual variable range and the actual frequency obtained in the second step.
The test field load spectrum standardization method provided by the invention can also have the following characteristics: when the distribution model is generalized pareto distribution, the parameter estimation value of the probability density function corresponding to the distribution model is obtained by estimating according to the actual variation and the actual frequency of the accumulated damage larger than the preset threshold.
The test field load spectrum standardization method provided by the invention can also have the following characteristics: wherein the accumulated damage is obtained according to a linear damage accumulation criterion, and the predetermined threshold is 0.05-0.15.
The test field load spectrum standardization method provided by the invention can also have the following characteristics: in the sixth step, when the theoretical total damage closest to the actual total damage is not unique, the probability density function of the distribution model corresponding to the theoretical total cycle number closest to the actual total cycle number is further selected as the optimal distribution function.
The test field load spectrum standardization method provided by the invention can also have the following characteristics: wherein the motor vehicle is a passenger vehicle or a commercial vehicle.
Action and Effect of the invention
According to the test field load spectrum standardization method, based on the similarity of load distribution of the whole vehicle and all parts of the whole vehicle under test field specifications, firstly, rain flow cycle counting processing is carried out on actual load time history data to obtain actual variable-range frequency data and actual total damage; secondly, modeling actual variable range frequency data by adopting at least two distribution models and performing parameter estimation to further obtain theoretical total damage corresponding to each distribution model, and selecting a probability density function corresponding to the theoretical total damage closest to the actual total damage as an optimal distribution function; normalizing the actual load time history data based on the static vertical load to obtain relative load time history data, and performing amplitude analysis and rain flow cycle counting on the relative load time history data to obtain a maximum relative load coefficient, a minimum relative load coefficient, a relative variation, a relative frequency and a maximum relative variation; and finally, normalizing the relative variation through the maximum relative variation to obtain normalized relative variation, and performing parameter estimation on the optimal distribution function according to the normalized relative variation and the relative variation to obtain a test field standard load spectrum, wherein the test field standard load spectrum can evaluate the reliability and durability of the pre-evaluated motor vehicle and parts thereof, so that the development period is shortened, and the possibility of passing the whole vehicle road test at one time is improved.
Drawings
FIG. 1 is a flow chart of a method of normalization of test field load spectra in an embodiment of the invention;
FIG. 2 is a schematic diagram of the actual load time history in an embodiment of the invention;
FIG. 3 is a histogram illustrating the frequency of the range in an embodiment of the present invention;
FIG. 4 is a schematic illustration of a variable range damage distribution in an embodiment of the present invention; and
fig. 5 is a schematic diagram of cumulative damage distribution in an embodiment of the present invention.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
In the present embodiment, the motor vehicle is a passenger vehicle or a commercial vehicle, and the passenger vehicle includes a bridge, an SUV, an MPV, and the like.
FIG. 1 is a flow chart of a method of normalization of test field load spectra in an embodiment of the invention.
As shown in fig. 1, a test field load spectrum normalization method in an embodiment of the present invention is used for processing actual load time history data of a motor vehicle under a test field specification to obtain a normalized load spectrum, and includes the following steps:
and S1, collecting actual load time history data.
Fig. 2 is a schematic diagram of the actual load time history in an embodiment of the present invention.
Fig. 2 is a schematic diagram of the actual load history corresponding to the actual load history data, where the abscissa in fig. 2 is time (sec) and the ordinate is load (N).
S2, carrying out rain flow circulation counting processing on the actual load time history data to obtain actual variable-range frequency dataThe inter-range frequency data comprises a plurality of stages of actual ranges S R,i And with the actual course S R,i Corresponding actual frequency n R,i And according to the actual frequency n R,i Calculating the actual total cycle number N total 。
N total ＝∑n R,i (1)
Wherein i is a natural number of 1 or more.
FIG. 3 is a histogram illustrating the frequency of the variable interval in the embodiment of the present invention.
The histogram of the range-frequency corresponding to the actual range-frequency data is shown in fig. 3, in which the abscissa of fig. 3 is the actual range and the ordinate is the actual frequency.
Step S3, according to S-N curve expression of motor vehicle
Calculating the actual variable distance S of each stage R,i Corresponding actual fatigue life N f,i C is a material constant of the motor vehicle; and according to the actual fatigue life N f,i And the actual frequency n R,i Obtain the actual variation range S of each stage R,i Corresponding actual damage d i And the actual total damage D.
d i ＝n R,i /N f,i (2)
D＝∑d i (3)
FIG. 4 is a schematic diagram of a variation damage distribution in an embodiment of the present invention.
The distribution of the variable-range damage is shown in fig. 4, in which the abscissa of fig. 4 represents the actual variable range and the ordinate represents the actual damage.
Further, each stage of actual damage d i Proportion D of actual total damage D i , p Is composed of
d i,p ＝d i /D (4)
According to the linear damage accumulation criterion, the ratio of the accumulated damage of the front k-level load to the total damage D is D ACC,k ，
Fig. 5 is a schematic diagram of a cumulative damage distribution in an embodiment of the present invention.
From equation (5), a cumulative damage distribution map corresponding to the loading time history can be obtained, as shown in fig. 5.
S4, modeling the actual variable range frequency data by adopting at least two distribution models, and solving a probability density function f (S) corresponding to each distribution model by utilizing a maximum likelihood estimation method R ) An estimate of the parameter of (1).
In this embodiment, six types of distribution models, such as normal distribution, lognormal distribution, weibull distribution, exponential distribution, rayleigh distribution, and generalized pareto distribution, are used, and fitting is performed with the actual variation as the abscissa and the probability density corresponding to the actual frequency as the ordinate.
And when the distribution model is normal distribution, lognormal distribution, weibull distribution, exponential distribution or Rayleigh distribution, the parameter estimation values of the probability density function corresponding to the distribution model are obtained by estimation according to the actual variable range and the actual frequency obtained in the second step.
When the distribution model is generalized pareto distribution, the parameter estimation value of the probability density function corresponding to the distribution model is obtained by estimating according to the actual variation and the actual frequency of the accumulated damage larger than the preset threshold. The predetermined threshold value is 0.05-0.15, preferably 0.1.
Step S5, solving actual variable distance S of each stage according to the parameter estimation value obtained in the step S4 R,i Corresponding theoretical frequency n 'in each distribution model' R,i And n 'according to the theoretical frequency' R,i Obtaining the theoretical total cycle times N 'corresponding to each distribution model' total Actual range per stage S R,i Corresponding theoretical damage d' i And the theoretical total damage D' corresponding to each distribution model,
n' R,i ＝N total ·f(S R,i )·w S,R (6)
d′ i ＝n R ′ ,i /N f,i (7)
D'＝∑d' i (8)
wherein w S,R For the actual variation S R,i The interval width of (c).
S6, selecting a probability density function of a distribution model corresponding to a theoretical total damage D' which is closest to the actual total damage D as an optimal distribution function; when the theoretical total damage D' closest to the actual total damage D is not unique, further selecting the number N of the actual total cycles total Closest theoretical total number of cycles N' total The probability density function of the corresponding distribution model is the optimal distribution function.
S7, normalizing the actual load time history data by taking the static vertical load value of the motor vehicle as a reference to obtain relative load time history data, and carrying out amplitude analysis on the relative load time history data to obtain a maximum relative load coefficient k d,max And a minimum relative load factor k d,min And carrying out rain flow circulation counting processing on the relative load time course data to obtain a multi-stage relative variation S RR,i A relative frequency n corresponding to the relative variation RR,i And the maximum relative variation S RR,max 。
Wherein the maximum relative load coefficient k d,max For maximum amplitude in the time history of the relative load, minimum relative load factor k d,min Is the minimum amplitude in the time history of the relative load.
Step S8, with the maximum relative variation S RR,max For relative variation S RR,i Normalization processing is carried out to obtain normalized relative variation
So that the normalized relative variation is within the value range of [0,1 ].
Step S9, according to the normalized relative variation
And relative frequency n RR,i And performing parameter estimation on the optimal distribution function obtained in the step S6 by adopting a maximum likelihood estimation method, so as to obtain a test field standard load spectrum for evaluating the reliability and durability of the pre-evaluated motor vehicle and parts thereof.
Examples effects and effects
According to the test field load spectrum standardization method, based on the similarity of load distribution of the whole vehicle and parts of the whole vehicle under the test field specification, firstly, rain flow cycle counting processing is carried out on actual load time history data to obtain actual variable range frequency data and actual total damage; secondly, modeling actual variable range frequency data by adopting at least two distribution models and performing parameter estimation to obtain theoretical total damage corresponding to each distribution model, and selecting a probability density function corresponding to the theoretical total damage closest to the actual total damage as an optimal distribution function; thirdly, normalizing the actual load time history data based on the static vertical load to obtain relative load time history data, and simultaneously performing amplitude analysis and rain current cycle counting on the relative load time history data to obtain a maximum relative load coefficient, a minimum relative load coefficient, a relative variation, a relative frequency and a maximum relative variation; and finally, normalizing the relative variation through the maximum relative variation to obtain normalized relative variation, and performing parameter estimation on the optimal distribution function according to the normalized relative variation and the relative variation to obtain a test field standard load spectrum, wherein the test field standard load spectrum can evaluate the reliability and durability of the pre-evaluated motor vehicle and parts thereof, so that the development period is shortened, and the possibility of passing the whole vehicle road test at one time is improved.
In addition, when the distribution model is in generalized pareto distribution, the parameter estimation value of the probability density function corresponding to the distribution model is obtained by estimation according to the actual variation and the actual frequency of the accumulated damage larger than the preset threshold, so that the actual variation frequency data are effectively screened, and the accuracy of the parameter estimation of the probability density function is further improved.
In addition, when the theoretical total damage closest to the actual total damage is not unique, the probability density function of the distribution model corresponding to the theoretical total cycle closest to the actual total cycle is further selected as the optimal distribution function, and the accuracy of determining the optimal distribution function is further improved.
The above embodiments are preferred examples of the present invention, and are not intended to limit the scope of the present invention.
Claims (7)
1. A test field load spectrum standardization method is used for processing actual load time history data of a motor vehicle under a test field standard to obtain a test field standard load spectrum, and is characterized by comprising the following steps of:
step one, collecting the actual load time history data;
step two, carrying out rain flow circulation counting processing on the actual load time history data to obtain actual variable range frequency data, wherein the actual variable range frequency data comprises a multi-stage actual variable range and actual frequency corresponding to the actual variable range, and calculating actual total circulation frequency according to the actual frequency;
step three, calculating the actual fatigue life corresponding to each stage of actual variation according to the S-N curve expression of the material of the motor vehicle, and calculating the actual damage and the actual total damage corresponding to each stage of actual variation according to the actual fatigue life and the actual frequency;
step four, modeling the actual variable range frequency data by adopting at least two distribution models, and solving a parameter estimation value of a probability density function corresponding to each distribution model by utilizing a maximum likelihood estimation method;
step five, solving the corresponding theoretical frequency of each stage of actual variable range in each distribution model according to the parameter estimation value, and solving the corresponding theoretical total cycle number of each distribution model, the corresponding theoretical damage of each stage of actual variable range and the corresponding theoretical total damage of each distribution model according to the theoretical frequency;
step six, selecting the probability density function of the distribution model corresponding to the theoretical total damage closest to the actual total damage as an optimal distribution function;
performing normalization processing on the actual load time history data by taking a static vertical load value of the motor vehicle as a reference to obtain relative load time history data, performing amplitude analysis on the relative load time history data to obtain a maximum relative load coefficient and a minimum relative load coefficient, and performing rain current cycle counting processing on the relative load time history data to obtain a multi-stage relative variation, relative frequency corresponding to the relative variation and a maximum relative variation;
step eight, normalizing the relative variable range by the maximum relative variable range to obtain a normalized relative variable range, wherein the normalized relative variable range is in a value interval of [0,1 ];
and step nine, performing parameter estimation on the optimal distribution function by adopting a maximum likelihood estimation method according to the normalized relative variation and the relative frequency, so as to obtain the standard load spectrum of the test field.
2. The test field load spectrum normalization method of claim 1, wherein:
wherein the distribution model is normal distribution, lognormal distribution, weibull distribution, exponential distribution, rayleigh distribution or generalized pareto distribution.
3. The test field load spectrum normalization method of claim 2, wherein:
and when the distribution model is the normal distribution, the lognormal distribution, the weibull distribution, the exponential distribution or the rayleigh distribution, the parameter estimation values of the probability density function corresponding to the distribution model are obtained by estimating according to the actual variable range and the actual frequency obtained in the second step.
4. The test field load spectrum normalization method of claim 2, wherein:
when the distribution model is the generalized pareto distribution, the parameter estimation value of the probability density function corresponding to the distribution model is obtained by estimating according to the actual variation and the actual frequency pair of the accumulated damage larger than the preset threshold.
5. The test field load spectrum normalization method of claim 4, wherein:
wherein the accumulated damage is derived according to a linear damage accumulation criterion,
the predetermined threshold is 0.05-0.15.
6. The test field load spectrum normalization method of claim 1, wherein:
wherein, in the sixth step, when the theoretical total damage closest to the actual total damage is not unique, the probability density function of the distribution model corresponding to the theoretical total cycle number closest to the actual total cycle number is further selected as an optimal distribution function.
7. The test field load spectrum normalization method of claim 1, wherein:
wherein the motor vehicle is a passenger vehicle or a commercial vehicle.
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| CN110954338B (en) * | 2019-11-22 | 2021-09-28 | 上海理工大学 | Method for standardizing standard load spectrum of four-column bench test of whole vehicle |
| CN111159906B (en) * | 2019-12-31 | 2023-06-16 | 上海理工大学 | ADAMS secondary development-based automobile suspension dynamic model building method |
| CN111581893B (en) * | 2020-04-03 | 2022-06-17 | 上海理工大学 | Compilation method of reliability test load spectrum of electric drive assembly mechanical system |
| CN112001082B (en) * | 2020-08-25 | 2021-04-16 | 上海理工大学 | I-beam front axle lightweight method |
| CN112051076B (en) * | 2020-09-10 | 2023-01-24 | 重庆理工大学 | New energy automobile power assembly road spectrum loading test method |
| CN113435018A (en) * | 2021-06-09 | 2021-09-24 | 中国汽车技术研究中心有限公司 | Damage calculation method for road load spectrum of automobile user |
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