CN116990021B - Fatigue life assessment method and device for hub bearing - Google Patents
Fatigue life assessment method and device for hub bearing Download PDFInfo
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Abstract
The invention provides a fatigue life assessment method and device of a hub bearing, wherein vibration test data of the target hub bearing are obtained by carrying out a loading test of a first preset number of times and a vibration test of a second preset number of times on the target hub bearing; and obtaining a fatigue life evaluation value of the target hub bearing by using a fatigue life prediction model according to the vibration test data of the target hub bearing. According to the invention, the fatigue life assessment value of the target hub bearing is obtained through solving the fatigue life prediction model, the input data of the model is only the vibration test data of the target hub bearing, and the vibration test data of the target hub bearing does not need to be all the data obtained by the fatigue life tester for the hub bearing after the fatigue life tester for the target hub bearing is tested until failure, so that compared with the prior art, the efficiency is improved, and meanwhile, the test cost is reduced.
Description
Technical Field
The application relates to the technical field of bearings, in particular to a fatigue life assessment method and device for a hub bearing.
Background
Since the life and performance of a vehicle hub bearing are directly related to the safety, steering smoothness and comfort of the vehicle, simulation tests of various conditions must be performed on the vehicle hub bearing. In production practice, a vehicle main engine plant and a hub bearing manufacturer typically simulate the use conditions of a hub bearing on a vehicle by using a hub bearing fatigue life tester, and perform measurement of relevant data in a test process, so that the fatigue life of the hub bearing is estimated by using the data obtained by the test.
In the prior art, when the fatigue life of the hub bearing is evaluated, the hub bearing fatigue life testing machine usually needs to be operated until the hub bearing fails to finish a single test, the whole test time is often hundreds of hours, the time is too long, and the data obtained by the test is more, so that the complexity of data processing for evaluating the fatigue life of the hub bearing is improved, and the production and manufacturing costs are increased to a certain extent.
Disclosure of Invention
In view of the above, one of the technical problems to be solved by the embodiments of the present invention is to provide a method and an apparatus for evaluating fatigue life of a hub bearing, which are used for solving the problems of long test time and high complexity of data processing when evaluating the fatigue life of the hub bearing in the prior art.
An embodiment of the present application discloses a fatigue life assessment method for a hub bearing, the method comprising:
the method comprises the steps that a hub bearing fatigue life testing machine carries out loading tests of a first preset number of times and vibration tests of a second preset number of times on a target hub bearing according to preset loading rules, and vibration test data of the target hub bearing are obtained; the hub bearing fatigue life testing machine performs multiple loading tests on the target hub bearing between two adjacent vibration tests; the vibration test data are used for representing the corresponding relation between the vibration measured value and the test duration;
and determining a fatigue life evaluation value of the target hub bearing by using a fatigue life prediction model according to the vibration test data of the target hub bearing.
A second aspect of an embodiment of the present application discloses a fatigue life assessment device for a hub bearing, the device comprising:
the data acquisition module is used for carrying out a first preset number of loading tests and a second preset number of vibration tests on a target hub bearing by using the hub bearing fatigue life testing machine according to a preset loading rule to obtain vibration test data of the target hub bearing; the hub bearing fatigue life testing machine performs multiple loading tests on the target hub bearing between two adjacent vibration tests; the vibration test data are used for representing the corresponding relation between the vibration measured value and the test duration;
and the fatigue life evaluation module is used for determining a fatigue life evaluation value of the target hub bearing by utilizing a fatigue life prediction model according to the vibration test data of the target hub bearing.
In the embodiment of the invention, firstly, the vibration test data of a target hub bearing is obtained by carrying out a loading test for a first preset number of times and a vibration test for a second preset number of times on the target hub bearing; and then obtaining a fatigue life evaluation value of the target hub bearing by using a fatigue life prediction model according to vibration test data of the target hub bearing. According to the embodiment of the invention, the fatigue life assessment value of the target hub bearing is obtained through solving the fatigue life prediction model, the input data of the model is only the vibration test data of the target hub bearing, and the vibration test data of the target hub bearing are not required to be all the data obtained by the fatigue life tester for the hub bearing after the target hub bearing is tested until failure, so that compared with the prior art, the fatigue life assessment method for the target hub bearing has the advantages that the data processing amount is less, the time consumption for testing the target hub bearing by the fatigue life tester for the hub bearing is less, the efficiency is improved, and meanwhile, the test cost is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method of fatigue life assessment for a hub bearing as disclosed in example one of the present application;
FIG. 2 is a flow chart of a method of fatigue life assessment for a hub bearing as disclosed in example two of the present application;
FIG. 3 is a schematic block diagram of a fatigue life assessment device for a hub bearing as disclosed in example III of the present application.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
It should be noted that the terms "first," "second," "third," and "fourth," etc. in the description and claims of the present application are used for distinguishing between different objects and not for describing a particular sequential order. The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or device that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or device.
Example one
As shown in fig. 1, fig. 1 is a schematic flowchart of a fatigue life assessment method of a hub bearing according to an example of the present application, the fatigue life assessment method of the hub bearing includes:
step S101, the hub bearing fatigue life testing machine performs loading tests of a first preset number of times and vibration tests of a second preset number of times on a target hub bearing according to preset loading rules to obtain vibration test data of the target hub bearing.
In this embodiment, the hub bearing fatigue life testing machine is used for simulating the use condition of the hub bearing on the vehicle, and measuring the relevant data in the testing process. During the test, parameters controlled by the hub bearing fatigue life tester may generally include rotational speed r (rpm), radial force (kN), axial force (kN), and the like. The set rotating speed is controlled by the hub bearing fatigue life testing machine and is mainly used for simulating the rotating speed of a wheel; the set radial force is mainly used for simulating the supporting force provided by the hub bearing in the vertical direction; the set axial force Fa is used to simulate the lateral force applied to the hub bearing during left and right cornering of the vehicle. Parameters that may be obtained and monitored in real time during the test may include rotational speed (rpm), radial force (kN), axial force (kN), radial displacement (mm), axial displacement (mm), acceleration vibration (g), temperature (c), time(s), and the like.
In this embodiment, the preset loading rules are mainly determined according to the driving path spectrum of the vehicle, and different automobile manufacturers may set different preset loading rules. The preset loading rules may include a plurality of loading tests and a plurality of vibration tests, and the number and order of the loading tests and the vibration tests are set in the preset loading rules.
In this embodiment, a loading test includes a plurality of steps, and each step includes parameters such as a corresponding rotational speed value, a radial force value, an axial force value, a number of turns, and a time, and N loading tests are performed in a cycle of N steps.
In this embodiment, the primary vibration test mainly refers to a special vibration measurement step, and the vibration test is usually not performed continuously, that is, the fatigue life tester of the hub bearing performs multiple loading tests on the target hub bearing between two adjacent vibration tests. In multiple vibration tests, the fatigue life test of the hub bearing is carried out by applying the same and proper rotation speed and radial force to the hub bearing, so as to acquire and obtain vibration measurement values of the hub bearing in the same stress simulation environment and different test time periods.
In this embodiment, the number of times of loading test performed on the target hub bearing by the hub bearing fatigue life testing machine in step S101 is a first preset number of times, the number of times of performing vibration test is a second preset number of times, and specific values of the first preset number of times and the second preset number of times are not limited, and can be reasonably set according to practical application requirements, but generally speaking, the second predicted number of times will be smaller than the first predicted number of times, and the first preset number of times and the second preset number of times will be respectively smaller than the normal number of times of loading test and the normal number of times of vibration test corresponding to the time of performing test on the hub bearing until failure. In addition, the number of loading tests and the number of vibration tests included in the preset loading rule may be generally greater than the first preset number of times and the second preset number of times.
In this embodiment, the vibration test data is used to characterize the correspondence between the vibration measurement value and the test duration. The specific characterization mode is not limited, and reasonable design can be carried out according to actual application requirements. For example, the test duration may be characterized by the value of the interval between the point in time when the test begins and the point in time when each vibration test begins. As another example, since the time of each load test is substantially uniform, the test duration may also be characterized using the number of load tests performed after the start of the test, before each vibration test.
When the fatigue life tester for the hub bearing is used for applying different loads to the target hub bearing and carrying out multiple loading tests, the vibration measured value obtained by detection is always in a dynamic change state because the rotating speed and the force load of each step in the loading tests are different, and in order to obtain the vibration measured value which is convenient to count and analyze, the vibration test can be added once at intervals to obtain the vibration measured value of the target hub bearing in the same stress simulation environment and in different test time periods, and vibration test data of the target hub bearing are obtained according to the vibration measured value.
Step S102, determining a fatigue life evaluation value of the target hub bearing by using a fatigue life prediction model according to vibration test data of the target hub bearing.
In this embodiment, the fatigue life prediction model is used to calculate and obtain a fatigue life evaluation value according to vibration test data, that is, the input of the fatigue life prediction model is vibration test data, and the output is the fatigue life evaluation value. The specific construction method of the fatigue life prediction model is not limited, and reasonable selection can be performed according to actual application requirements.
In this embodiment, the fatigue life assessment value is used to characterize the service time of the hub bearing in a conventional operating environment and the failure mode is a conventional failure mode. Conventional failure modes may include failure due to fatigue flaking or wear of steel balls, race tracks inside the hub bearing, among others.
As can be seen from the above embodiments, in the embodiments, vibration test data of a target hub bearing is obtained by performing a loading test for a first preset number of times and a vibration test for a second preset number of times on the target hub bearing; and then obtaining a fatigue life evaluation value of the target hub bearing by using a fatigue life prediction model according to vibration test data of the target hub bearing. According to the embodiment of the invention, the fatigue life assessment value of the target hub bearing is obtained through solving the fatigue life prediction model, the input data of the model is only the vibration test data of the target hub bearing, and the vibration test data of the target hub bearing are not required to be all the data obtained by the fatigue life tester for the hub bearing after the target hub bearing is tested until failure, so that compared with the prior art, the fatigue life assessment method for the target hub bearing has the advantages that the data processing amount is less, the time consumption for testing the target hub bearing by the fatigue life tester for the hub bearing is less, the efficiency is improved, and meanwhile, the test cost is reduced.
Example two
As shown in fig. 2, fig. 2 is a schematic flowchart of a fatigue life assessment method of a hub bearing disclosed in example two of the present application, where the fatigue life assessment method of a hub bearing includes:
and S201, carrying out multiple loading tests and multiple vibration tests on a plurality of test hub bearings by the hub bearing fatigue life testing machine according to a preset loading rule until each test hub bearing fails, and obtaining vibration test data and failure time data of all the test hub bearings.
In this embodiment, in order to make the overall error between the fatigue life evaluation value of the target hub bearing calculated by using the fatigue life prediction model and the actual fatigue life value small, it is preferable that the model numbers of the test hub bearing and the target hub bearing be identical or that the relevant parameter values be very similar.
In this embodiment, the preset loading rule is the same as the preset loading rule in the first embodiment, and the failure mode of each test bearing may be the same or may be different, but the failure time data may be used to represent the corresponding test duration when the hub bearing fails.
In this embodiment, a plurality of test hub bearings with the same model as the target hub bearing may be preferably selected, and multiple loading tests and multiple vibration tests are performed by the hub bearing fatigue life tester according to a preset loading rule, until each test hub bearing fails, at least a vibration measurement value and a test duration corresponding to each vibration test and a test duration corresponding to the failure of the test hub bearing may be recorded in the test process, so that vibration test data and failure time data of all test hub bearings may be obtained.
In this embodiment, the judging manner of whether the test hub bearing fails is not limited, and can be reasonably selected according to actual application requirements. For example, the alarm parameter of the hub bearing fatigue life tester can be preset, if the vibration measured value of the tested hub bearing obtained by the first measurement in the test is A, the alarm parameter of the hub bearing fatigue life tester can be set to be 5A, when the vibration measured value obtained by the measurement in the test process reaches 5A, the hub bearing fatigue life tester can be stopped, at the moment, the test can be checked, and when the tool fixture and the like are determined to be free from damage and abnormality, the tested hub bearing can be determined to be invalid.
And S202, constructing a fatigue life prediction model according to vibration test data and failure time data of all the tested hub bearings.
In this embodiment, vibration test data and failure time data of all the test hub bearings may be selected, or a fatigue life prediction model may be constructed by screening out portions from the vibration test data and failure time data of all the test hub bearings. The selection method of the specific data is not limited, and reasonable selection can be performed according to actual application requirements.
In this embodiment, when the fatigue life prediction model is constructed, vibration test data may be used as input, failure time data may be used as output, and the specific model construction method is not limited, and may be calculated reasonably according to actual application requirements. For example, regression analysis methods, curve fitting methods, or machine learning algorithms may be used.
Optionally, in order to make the overall error between the fatigue life evaluation value of the target hub bearing calculated by using the fatigue life prediction model and the actual fatigue life value smaller, after the fatigue life prediction model is preferably initially constructed, vibration test data and failure time data of a part of test hub bearings may be selected to verify the initially constructed fatigue life prediction model, and when the accuracy of the verification and the display calculation result is lower, relevant parameters of the initially constructed fatigue life prediction model may be adjusted to obtain an optimized fatigue life prediction model.
Alternatively, since the number of hub bearings whose failure mode belongs to the abnormal failure mode is relatively small in general, and the abnormal failure mode is difficult to regularly follow, the fatigue life prediction model is mainly used for evaluating the actual fatigue life value corresponding to the target hub bearing in the normal working environment and in the failure mode. However, when the test hub bearing is tested, a small amount of failure modes of the test hub bearing are possible to be abnormal failure modes, so that vibration test data and failure time data of the test bearing with the failure modes being conventional failure modes can be screened out for improving the accuracy of output data of the fatigue life prediction model, and the test hub bearing is further used for constructing the fatigue life prediction model.
Specifically, step S202 may include the following substeps S202 a-b:
sub-step S202a, the sample hub-bearing is screened from all the test hub-bearings.
Sub-step S202b, constructing a fatigue life prediction model according to vibration test data and failure time data of all sample hub bearings.
Wherein the failure mode of the sample hub bearing is a conventional failure mode. The method for screening the sample bearings from all the test hub bearings is not limited and can be selected according to actual conditions.
Further, in order to ensure accuracy of the calculation result of the fatigue life prediction model, it may be preferable that the number of sample hub bearings is 20 or more.
Further, the substep S202b may further include the following steps A-C:
and step A, obtaining curve fitting data of each sample hub bearing according to vibration test data and failure time data of each sample hub bearing.
And step B, obtaining a curve fitting equation according to curve fitting data of all the sample hub bearings.
And C, constructing a fatigue life prediction model according to a curve fitting equation.
The curve fitting data are used for representing the corresponding relation between the vibration measured value and the test duration.
The curve fitting equation is used for calculating and obtaining corresponding test duration according to the vibration measured value. The specific type of curve fitting equation is not limited and can be selected according to practical conditions. For example, logarithmic equations, exponential equations, and the like may be selected.
In the step a and the step B, one or more candidate fitting equations may be predetermined, and then curve fitting is performed on vibration test data and failure time data of the plurality of sample hub bearings by using the candidate curve fitting equations, and one of the candidate fitting equations in the step B is determined according to the accuracy of fitting, and corresponding coefficient values in the curve fitting equations are determined.
In the step C, the fatigue life prediction model is constructed by utilizing the characteristics of the curve fitting equation, so that the overall error between the fatigue life evaluation value of the target hub bearing calculated by utilizing the fatigue life prediction model and the actual fatigue life value is smaller, the calculation process is relatively simpler, and the data processing efficiency is improved.
Furthermore, considering that in the fatigue life test of the hub bearing, the vibration measurement value obtained by measurement when the hub bearing starts to perform the vibration test is usually smaller, as the test is continuously performed, the steel ball and the raceway of the hub bearing deform or wear under the action of the load, so that the vibration measurement value obtained by measurement when the subsequent vibration test is performed becomes larger gradually, and the difference value between the vibration measurement values corresponding to the two vibration tests is also larger and larger. When the steel balls and the raceways of the hub bearing wear or deform to a certain extent, particularly when the skin of the steel balls or the raceways has a tiny peeling phenomenon, the vibration measurement value obtained by carrying out vibration test on the hub bearing can be suddenly increased.
In the practical application process, the corresponding relation between the vibration test value and the test duration of the hub bearing shows an approximately weak exponential growth trend through analyzing curve fitting data of all sample hub bearings. Therefore, in order to more accurately characterize the correspondence between the vibration measurement value and the test duration of the hub bearing, it may be preferable that the curve fitting equation be:
；
wherein,for the test duration->For vibration measurement, +.>And->To fit the parameter values.
Furthermore, in the practical application process, the analysis of curve fitting data of all the sample hub bearings finds that the test duration when the vibration measurement value of the hub bearing is suddenly changed has a certain association with the corresponding test duration when the hub bearing is invalid, so that in order to make the overall error between the fatigue life evaluation value of the target hub bearing calculated by using the fatigue life prediction model and the actual fatigue life value smaller, the fatigue life prediction model may be preferably:
；
wherein,an evaluation value for fatigue life; />And->Fitting parameter values; />Measuring an obtained vibration test value when the hub bearing fatigue life testing machine performs a first vibration test according to a preset loading rule; />And->Is a preset constant value.
Wherein,the value range is more than 0 and less than 100; />The range of the value of (2) is more than 1./>And->The specific value of (2) is not limited, and can be set according to actual conditions.
Further, in the practical application process, it is found that by analyzing the curve fitting data of all the sample hub bearings, it is preferable to make the overall error between the calculated fatigue life evaluation value of the target hub bearing and the actual fatigue life value smallerThe value of (2) is greater than or equal to 93 and less than or equal to 95; />Greater than or equal to 1.7 and less than or equal to 1.9.
Further, it may be further preferable in terms of practical application and verification results94 @ of>1.8, so that the overall error between the fatigue life evaluation value of the target hub bearing calculated by using the fatigue life prediction model and the actual fatigue life value is minimized.
Step S203, the hub bearing fatigue life testing machine performs a first preset number of loading tests and a second preset number of vibration tests on the target hub bearing according to a preset loading rule to obtain vibration test data of the target hub bearing.
In this embodiment, the step S203 is substantially the same as or similar to the step S101 in the first embodiment, and will not be described herein. However, to reduce the duration of the test performed on the target hub bearing, it may be preferable that the second preset number of times is less than the number of vibration tests experienced before all of the sample hub bearings fail.
Step S204, determining a fatigue life evaluation value of the target hub bearing by using a fatigue life prediction model according to vibration test data of the target hub bearing.
Alternatively, in order to make the overall error between the calculated fatigue life evaluation value of the target hub bearing and the actual fatigue life value smaller, a corresponding fatigue life prediction model may be obtained from the vibration test data of each target hub bearing, that is, the fatigue life prediction model used may be different when performing the fatigue life evaluation value calculation for different target hub bearings. Specifically, step S204 may include the following substeps S204 a-c:
and step S204a, performing curve fitting processing by utilizing a curve fitting equation according to vibration test data of the target hub bearing to obtain fitting parameter values corresponding to the target hub bearing.
And step S204b, obtaining a fatigue life prediction model corresponding to the target hub bearing according to the fitting parameter value corresponding to the target hub bearing and the curve fitting equation.
And step S204c, determining a fatigue life evaluation value of the target hub bearing by utilizing a fatigue life prediction model corresponding to the target hub bearing according to vibration test data of the target hub bearing.
The curve fitting equation in the substep S204a is the same as the curve fitting equation in the step S202, and the fitting parameter value in the fitting equation is determined according to the vibration test data of the target hub bearing.
Further, considering that when the value of the second preset number of times is set to be too small, that is, the number of times of vibration testing is performed on the target hub bearing is too small, the obtained vibration measurement value and the number of corresponding test durations are small, so that when curve fitting is performed in the sub-step S204a, the determined fitting parameter value may not accurately characterize the corresponding relationship between the vibration measurement value and the test duration of the target hub bearing, in order to avoid this phenomenon, it may be preferable that the second preset number of times is greater than or equal to 8.
Further, considering the problem that the duration of the test performed on the target hub bearing is long when the value of the second preset number of times is set too large, the test time is reduced while the fitting parameter value is ensured to accurately characterize the corresponding relationship between the vibration measured value and the test duration of the target hub bearing, so that the efficiency of determining the fatigue life estimated value of the target hub bearing is improved, and the second preset number of times can be preferably equal to 8.
As can be seen from the above embodiments, in the present example, a fatigue life tester for hub bearings performs multiple loading tests and multiple vibration tests on multiple test hub bearings according to a preset loading rule, until each test hub bearing fails, and obtains vibration test data and failure time data of all test hub bearings; and then constructing a fatigue life prediction model according to vibration test data and failure time data of all the tested hub bearings. Because the model of the test hub bearing is the same as that of the target hub bearing, the overall error between the fatigue life evaluation value of the target hub bearing calculated by using the fatigue life prediction model and the actual fatigue life value is smaller.
Example three
As shown in fig. 3, fig. 3 is a schematic structural diagram of a fatigue life assessment device for a hub bearing according to a third embodiment of the present application, where the device includes:
the data acquisition module is used for carrying out a first preset number of loading tests and a second preset number of vibration tests on the target hub bearing by using the hub bearing fatigue life testing machine according to a preset loading rule to obtain vibration test data of the target hub bearing. And the hub bearing fatigue life testing machine performs multiple loading tests on the target hub bearing between two adjacent vibration tests. The vibration test data is used for representing the corresponding relation between the vibration measured value and the test duration.
And the fatigue life evaluation module is used for determining a fatigue life evaluation value of the target hub bearing by utilizing the fatigue life prediction model according to the vibration test data of the target hub bearing.
Optionally, the embodiment may include a fatigue life prediction model building module, where the module may be configured to perform multiple loading tests and multiple vibration tests on each of the multiple test hub bearings using the hub bearing fatigue life tester according to a preset loading rule, until each test hub bearing fails, and obtain vibration test data and failure time data for all of the test hub bearings. The model of the test hub bearing is the same as that of the target hub bearing. The failure time data is used for representing the corresponding test time length when the hub bearing fails.
And constructing a fatigue life prediction model according to vibration test data and failure time data of all the tested hub bearings.
Further, the fatigue life prediction model building module may also be used to screen out sample hub bearings from all test hub bearings. Wherein the failure mode of the sample hub bearing is a conventional failure mode.
And constructing a fatigue life prediction model according to vibration test data and failure time data of all the sample hub bearings. Wherein the second preset number of times is less than the number of vibration tests experienced before failure of all sample hub bearings.
Still further, the fatigue life prediction model building module may be further configured to obtain curve fit data for each sample hub bearing based on the vibration test data and the time to failure data for each sample hub bearing. The curve fitting data are used for representing the corresponding relation between the vibration measured value and the test duration.
And obtaining a curve fitting equation according to curve fitting data of all the sample hub bearings. The curve fitting equation is used for calculating and obtaining corresponding test duration according to the vibration measured value.
And constructing a fatigue life prediction model according to the curve fitting equation.
Optionally, the fatigue life evaluation module may be configured to perform curve fitting processing according to vibration test data of the target hub bearing by using a curve fitting equation, so as to obtain a fitting parameter value corresponding to the target hub bearing.
And obtaining a fatigue life prediction model corresponding to the target hub bearing according to the fitting parameter value corresponding to the target hub bearing and the curve fitting equation.
And determining a fatigue life evaluation value of the target hub bearing by utilizing a fatigue life prediction model corresponding to the target hub bearing according to the vibration test data of the target hub bearing.
Further, the curve fitting equation is:
；
wherein,for the test duration->For vibration measurement, +.>And->To fit the parameter values.
Further, the fatigue life prediction model is:
；
wherein,an evaluation value for fatigue life; />And->Fitting parameter values; />Measuring an obtained vibration test value when the hub bearing fatigue life testing machine performs a first vibration test according to a preset loading rule; />And->Is a preset constant value.
Still further, the method further comprises the steps of,greater than or equal to 93 and less than or equal to 95; />Greater than or equal to 1.7 and less than or equal to 1.9.
Further, the second preset number of times is greater than or equal to 8.
The fatigue life assessment device for the hub bearing of the embodiment can achieve the fatigue life assessment method for the hub bearing corresponding to the method embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein.
Thus far, specific embodiments of the present application have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as methods, apparatus. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer storage media (including, but not limited to, magnetic disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (5)
1. A method of fatigue life assessment of a hub bearing, the method comprising:
the hub bearing fatigue life testing machine carries out multiple loading tests and multiple vibration tests on a plurality of test hub bearings according to a preset loading rule until each test hub bearing fails, and vibration test data and failure time data of all the test hub bearings are obtained; the model of the test hub bearing is the same as that of the target hub bearing; the failure time data are used for representing the corresponding test duration when the hub bearing fails;
screening sample hub bearings from all of the test hub bearings; wherein the failure mode of the sample hub bearing is a conventional failure mode;
obtaining curve fitting data of each sample hub bearing according to the vibration test data and the failure time data of each sample hub bearing; the curve fitting data are used for representing the corresponding relation between the vibration measured value and the test duration;
obtaining a curve fitting equation according to the curve fitting data of all the sample hub bearings; the evaluation mathematical model curve fitting equation is used for calculating and obtaining corresponding test duration according to the vibration measured value; the curve fitting equation is:
；
wherein,for the test duration->For vibration measurement, +.>And->Fitting parameter values;
constructing a fatigue life prediction model according to the curve fitting equation; wherein a second preset number of times is less than the number of times of the vibration test experienced before all of the sample hub bearings failed; the fatigue life prediction model is as follows:
；
wherein,an evaluation value for fatigue life; />And->Fitting parameter values; />Measuring an obtained vibration test value when the hub bearing fatigue life testing machine performs a first vibration test according to a preset loading rule; />And->Is a preset constant value;
the hub bearing fatigue life testing machine performs the loading test of a first preset number of times and the vibration test of a second preset number of times on the target hub bearing according to the preset loading rule to obtain vibration test data of the target hub bearing; the hub bearing fatigue life testing machine performs multiple loading tests on the target hub bearing between two adjacent vibration tests; the vibration test data are used for representing the corresponding relation between the vibration measured value and the test duration;
and determining a fatigue life evaluation value of the target hub bearing by utilizing the fatigue life prediction model according to the vibration test data of the target hub bearing.
2. The method of claim 1, wherein said determining a fatigue life assessment value for said target hub bearing using a fatigue life prediction model based on said vibration test data for said target hub bearing comprises:
performing curve fitting processing by utilizing the curve fitting equation according to the vibration test data of the target hub bearing to obtain the fitting parameter value corresponding to the target hub bearing;
obtaining the fatigue life prediction model corresponding to the target hub bearing according to the fitting parameter value corresponding to the target hub bearing and the curve fitting equation;
and determining the fatigue life evaluation value of the target hub bearing by utilizing the fatigue life prediction model corresponding to the target hub bearing according to the vibration test data of the target hub bearing.
3. The method of claim 2, wherein the second predetermined number of times is greater than or equal to 8.
4. The method of claim 1, wherein the step of determining the position of the substrate comprises,greater than or equal to 93 and less than or equal to 95; />Greater than or equal to 1.7 and less than or equal to 1.9.
5. A fatigue life assessment device for a hub bearing, the device comprising:
the data acquisition module is used for carrying out a first preset number of loading tests and a second preset number of vibration tests on a target hub bearing by using the hub bearing fatigue life testing machine according to a preset loading rule to obtain vibration test data of the target hub bearing; the hub bearing fatigue life testing machine performs multiple loading tests on the target hub bearing between two adjacent vibration tests; the vibration test data are used for representing the corresponding relation between the vibration measured value and the test duration;
the fatigue life evaluation module is used for determining a fatigue life evaluation value of the target hub bearing by utilizing a fatigue life prediction model according to the vibration test data of the target hub bearing;
wherein the device further comprises a fatigue life prediction model construction module;
the fatigue life prediction model construction module is used for carrying out multiple loading tests and vibration tests on multiple test hub bearings by using a hub bearing fatigue life testing machine according to a preset loading rule until each test hub bearing fails, and obtaining vibration test data and failure time data of all the test hub bearings; the model of the test hub bearing is the same as that of the target hub bearing; the failure time data are used for representing the corresponding test duration when the hub bearing fails;
screening sample hub bearings from all of the test hub bearings; wherein the failure mode of the sample hub bearing is a conventional failure mode;
obtaining curve fitting data of each sample hub bearing according to the vibration test data and the failure time data of each sample hub bearing; the curve fitting data are used for representing the corresponding relation between the vibration measured value and the test duration;
obtaining a curve fitting equation according to the curve fitting data of all the sample hub bearings; the curve fitting equation is used for calculating and obtaining corresponding test duration according to the vibration measured value; the curve fitting equation is:
；
wherein,for the test duration->For vibration measurement, +.>And->Fitting parameter values;
constructing the fatigue life prediction model according to the curve fitting equation; wherein a second preset number of times is less than the number of times of the vibration test experienced before all of the sample hub bearings failed; the fatigue life prediction model is as follows:
；
wherein,an evaluation value for fatigue life; />And->Fitting parameter values; />Measuring an obtained vibration test value when the hub bearing fatigue life testing machine performs a first vibration test according to a preset loading rule; />And->Is a preset constant value.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104503952A (en) * | 2015-01-15 | 2015-04-08 | 中国科学院光电技术研究所 | Fitting single-exponential decay weighted linear least square method for cavity ring-down |
| DE102018210470A1 (en) * | 2018-06-27 | 2020-01-02 | Robert Bosch Gmbh | Process for early damage detection, as well as program and control unit for executing the process |
| CN110705181A (en) * | 2019-10-13 | 2020-01-17 | 重庆交通大学 | Rolling bearing residual life prediction method based on convolution length-time memory cyclic neural network |
| CN113947017A (en) * | 2021-10-18 | 2022-01-18 | 北京化工大学 | Method for predicting residual service life of rolling bearing |
| CN115438443A (en) * | 2022-09-08 | 2022-12-06 | 上海电气风电集团股份有限公司 | Bearing residual life prediction method, device and system and readable storage medium |
-
2023
- 2023-09-22 CN CN202311228045.XA patent/CN116990021B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104503952A (en) * | 2015-01-15 | 2015-04-08 | 中国科学院光电技术研究所 | Fitting single-exponential decay weighted linear least square method for cavity ring-down |
| DE102018210470A1 (en) * | 2018-06-27 | 2020-01-02 | Robert Bosch Gmbh | Process for early damage detection, as well as program and control unit for executing the process |
| CN110705181A (en) * | 2019-10-13 | 2020-01-17 | 重庆交通大学 | Rolling bearing residual life prediction method based on convolution length-time memory cyclic neural network |
| CN113947017A (en) * | 2021-10-18 | 2022-01-18 | 北京化工大学 | Method for predicting residual service life of rolling bearing |
| CN115438443A (en) * | 2022-09-08 | 2022-12-06 | 上海电气风电集团股份有限公司 | Bearing residual life prediction method, device and system and readable storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 动车组轴箱轴承状态评估与剩余寿命预测方法;李阳;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》(第02（2023）期);正文第27-55页 * |
| 李阳.动车组轴箱轴承状态评估与剩余寿命预测方法.《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》.2023,(第02（2023）期),正文第27-55页. * |
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