A gear single tooth impact fatigue test method and system

Abstract

The present invention relates to a gear single-tooth impact fatigue test method and system, the method comprising: Q1. placing the gear to be tested on a gear impact fatigue endurance tester for testing, acquiring the force value data information of the gear impact specimen in real time based on an airborne force sensor, and acquiring the image data information of the gear to be tested in real time based on an airborne camera; Q2. inputting the image data information of the gear to be tested and the force value data information of the gear impact specimen into a trained multivariate nonlinear regression model based on the interaction effect of independent variables, predicting the relationship between the image of the gear and the force value of the gear impact specimen, and obtaining the data information of the relationship between the image of the gear and the force value of the impact specimen. The present invention can not only update the force value of the gear impact specimen in real time according to the image data information of the gear to be tested, thereby improving the accuracy of the impact fatigue test of the gear to be tested, but also the force variable can be monitored, and the test efficiency is high.

Description

Technical Field

[0001] The invention relates to the technical field of gear single tooth impact fatigue testing, and in particular to a gear single tooth impact fatigue testing method and system. Background Art

[0002] In the flywheel's automated control production system, the control layer is the core component. It is directly involved in the actual control and regulation of the production process, and monitors various physical parameters (such as temperature, pressure, flow, speed, position, etc.) in the production process in real time. It adjusts and controls these parameters according to preset control strategies to ensure that the production process remains in an ideal operating state.

[0003] Among them, the principle of the single-tooth impact fatigue test of the flywheel is to place the gear on a gear impact fatigue endurance testing machine, and under the condition of energy conservation, calculate the number of impacts according to the force value of the impact specimen, so as to estimate the single-tooth impact fatigue of the gear. Among them, the force value of the impact specimen cannot be updated in real time according to the changing state of the single tooth of the gear, resulting in large final structural errors and low accuracy of the impact fatigue test. Summary of the invention

[0004] In view of the above problems, the present invention provides a method and system for a single-tooth impact fatigue test of a gear, which can not only update the force value of the gear impact specimen in real time according to the image data information of the gear to be tested, thereby improving the accuracy of the impact fatigue test of the gear to be tested, but also the force variable can be monitored, and the test efficiency is high.

[0005] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows:

[0006] A gear single tooth impact fatigue test method, the method comprising:

[0007] Q1. Place the gear to be tested on a gear impact fatigue endurance testing machine for testing, obtain the force value data information of the gear impact specimen in real time based on the onboard force sensor, and obtain the image data information of the gear to be tested in real time based on the onboard camera;

[0008] Q2. Input the image data information of the gear to be tested and the force value data information of the gear impact specimen into the trained multivariate nonlinear regression model based on the interaction effect of independent variables, predict the relationship between the image of the gear and the force value of the gear impact specimen, and obtain the relationship data information between the image of the gear and the force value of the impact specimen;

[0009] Q3. Based on the data information of the relationship between the image of the gear and the force value of the impact specimen, the force value of the gear impact specimen is optimized by using a sine-cosine algorithm based on an adaptive feedback adjustment factor to obtain the optimized force value data information of the gear impact specimen;

[0010] Q4. Based on the force data information of the optimized gear impact specimen, a gear single tooth impact fatigue test function P is established to characterize the gear single tooth impact fatigue durability, and the test data information of the gear single tooth impact fatigue durability is obtained.

[0011] Furthermore, the gear single tooth impact fatigue test function P is:

[0012] ,

[0013] Among them, z is the force data information of the optimized gear impact specimen, and ω1, ω2 and ω3 are the determining factors of the single tooth impact fatigue test of the gear.

[0014] Furthermore, the constraints of the determining factors ω1, ω2 and ω3 of the gear single tooth impact fatigue test are:

[0015] .

[0016] Furthermore, in step Q2, the trained multivariate nonlinear regression model based on the interaction effect of independent variables includes:

[0017] Q21. Based on the image data information of the gear to be tested and the force value data information of the gear impact specimen, establish the gear independent variable interaction function G,

[0018] ,

[0019] Among them, x1 is the image data information of the gear to be tested, x2 is the force value data information of the gear impact specimen, α1 and α2 are the independent variable interaction weight coefficients of the gear, and the interaction data of the gear image and the force value of the impact specimen are characterized to obtain the gear image and the force value interaction matrix data information of the impact specimen;

[0020] Q22. Based on the gear image and the force value interaction matrix data information of the impact specimen, a multivariate nonlinear regression function H of the gear is established.

[0021] ,

[0022] Among them, x i is the data information of the force value interaction matrix of the gear image and the impact specimen, n is the sample size of the force value interaction matrix of the gear image and the impact specimen, β 1i , β 2i and β 3i is the relationship factor between the gear image and the force value of the impact specimen;

[0023] Q23. Based on the multivariate nonlinear regression function H of the gear, the relationship between the image of the gear and the force value of the gear impacting the specimen is predicted to obtain data information on the relationship between the image of the gear and the force value of the gear impacting the specimen.

[0024] Furthermore, the relationship factor β between the gear image and the force value of the impact specimen is 1i , β 2i and β 3i The constraints are:

[0025] ,

[0026] Where n is the sample size of the interaction matrix of the gear image and the force value of the impact specimen.

[0027] Furthermore, in step Q3, the optimization of the force value of the gear impact specimen using the sine-cosine algorithm based on the adaptive feedback adjustment factor includes:

[0028] Q31. Based on the data information of the relationship between the image of the gear and the force value of the impact specimen, a gear impact fatigue test data set is constructed, and initialized, and the number of iterations is determined to obtain data information of the initialized gear impact fatigue test data set;

[0029] Q32. Based on the data information of the initialized gear impact fatigue test data set, establish the fitness function S of the gear impact fatigue test,

[0030] ,

[0031] Among them, y is the data information of the initialized gear impact fatigue test data set, λ1, λ2 and λ3 are the covariance factors of the gear impact fatigue test, and the fitness value of each data in the gear impact fatigue test data set is calculated to obtain the fitness value data information of each data in the gear impact fatigue test data set;

[0032] Q33. Based on the fitness value data information of each data of the gear impact fatigue test data set, establish an optimization function W of the force value of the gear impact specimen,

[0033] ,

[0034] Among them, r is the fitness value data information of each data in the gear impact fatigue test data set, η1, η2 and η3 are the adaptive feedback adjustment factors of the gear impact fatigue test data set, and the force value of the gear impact specimen is optimized to obtain the optimized force value data information of the gear impact specimen.

[0035] Furthermore, the adaptive feedback adjustment factors η1, η2 and η3 of the gear impact fatigue test data set are:

[0036] ,

[0037] ,

[0038] ,

[0039] Among them, r is the fitness value data information of each data in the gear impact fatigue test data set.

[0040] Furthermore, the constraint function f of the covariance factors λ1, λ2 and λ3 of the gear impact fatigue test is,

[0041] ,

[0042] The value range of f is (0,1).

[0043] Furthermore, the method further comprises:

[0044] Q5. Based on the test data information of the impact fatigue durability of a single tooth of the gear, a preset threshold is set. If the test data of the impact fatigue durability of a single tooth of the gear is greater than the preset threshold, the gear is qualified; if the test data of the impact fatigue durability of a single tooth of the gear is less than the preset threshold, the gear is unqualified.

[0045] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a gear single tooth impact fatigue test system, including a computer device, which is programmed or configured to execute the steps of any one of the gear single tooth impact fatigue test methods.

[0046] The present invention has the following positive effects:

[0047] 1. The present invention predicts the relationship between the image of the gear and the force value of the gear impact specimen through a trained multivariate nonlinear regression model based on the interaction effect of independent variables, and optimizes the force value of the gear impact specimen by combining a sine-cosine algorithm based on an adaptive feedback adjustment factor. Not only can the force value of the gear impact specimen be updated in real time according to the relationship between the image of the gear and the force value of the gear impact specimen, thereby improving the accuracy of the impact fatigue test of the gear to be tested, but the force variable can be monitored, and the test efficiency is high.

[0048] 2. The present invention characterizes the impact fatigue durability of a single gear tooth by establishing a gear single tooth impact fatigue test function P. This can not only accurately display and characterize the gear single tooth impact fatigue and facilitate the screening of gears, but also reduce labor input, save labor costs, reduce human errors, and improve the production efficiency of the flywheel. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0050] Figure 2 It is a flow chart of the multivariate linear regression model based on the interaction effect of independent variables trained in the present invention;

[0051] Figure 3 It is a flow chart of the sine-cosine algorithm based on the adaptive feedback adjustment factor of the present invention;

[0052] Figure 4 It is a structural schematic diagram of the test device of the present invention;

[0053] Figure 5 It is a schematic diagram of the 3D structure of the test device of the present invention.

[0054] Explanation of the numbers in the figure: 1- gear to be tested, 2- impact head, 3- force sensor, 4- actuator cylinder. DETAILED DESCRIPTION

[0055] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0056] Example 1: Figure 1 or Figure 4 or Figure 5 As shown, a gear single tooth impact fatigue test method, the method comprising:

[0057] Q1. The gear 1 to be tested is placed in a gear impact fatigue endurance testing machine for testing. The actuator 4 drives the impact head 2 to move. The force value data information of the gear impact specimen is obtained in real time based on the airborne force sensor 3. The image data information of the gear to be tested is obtained in real time based on the airborne camera.

[0058] Q2. Input the image data information of the gear to be tested and the force value data information of the gear impact specimen into the trained multivariate nonlinear regression model based on the interaction effect of independent variables, predict the relationship between the image of the gear and the force value of the gear impact specimen, and obtain the relationship data information between the image of the gear and the force value of the impact specimen;

[0059] Q3. Based on the data information of the relationship between the image of the gear and the force value of the impact specimen, the force value of the gear impact specimen is optimized by using a sine-cosine algorithm based on an adaptive feedback adjustment factor to obtain the optimized force value data information of the gear impact specimen;

[0060] Q4. Based on the force data information of the optimized gear impact specimen, a gear single tooth impact fatigue test function P is established to characterize the gear single tooth impact fatigue durability, and the test data information of the gear single tooth impact fatigue durability is obtained.

[0061] In this embodiment, the gear single tooth impact fatigue test function P is:

[0062] ,

[0063] Among them, z is the force data information of the optimized gear impact specimen, and ω1, ω2 and ω3 are the determining factors of the single tooth impact fatigue test of the gear.

[0064] In this embodiment, the constraints of the determining factors ω1, ω2 and ω3 of the gear single tooth impact fatigue test are:

[0065] .

[0066] In this embodiment, if Figure 2 As shown, in step Q2, the trained multivariate nonlinear regression model based on the interaction effect of independent variables includes:

[0067] Q21. Based on the image data information of the gear to be tested and the force value data information of the gear impact specimen, establish the gear independent variable interaction function G,

[0068] ,

[0069] Among them, x1 is the image data information of the gear to be tested, x2 is the force value data information of the gear impact specimen, α1 and α2 are the independent variable interaction weight coefficients of the gear, and the interaction data of the gear image and the force value of the impact specimen are characterized to obtain the gear image and the force value interaction matrix data information of the impact specimen;

[0070] Q22. Based on the gear image and the force value interaction matrix data information of the impact specimen, a multivariate nonlinear regression function H of the gear is established.

[0071] ,

[0072] Among them, x i is the data information of the force value interaction matrix of the gear image and the impact specimen, n is the sample size of the force value interaction matrix of the gear image and the impact specimen, β 1i , β 2i and β 3i is the relationship factor between the gear image and the force value of the impact specimen;

[0073] Q23. Based on the multivariate nonlinear regression function H of the gear, the relationship between the image of the gear and the force value of the gear impacting the specimen is predicted to obtain data information on the relationship between the image of the gear and the force value of the gear impacting the specimen.

[0074] In this embodiment, the relationship factor β between the gear image and the force value of the impact specimen is 1i , β 2i and β 3i The constraints are:

[0075] ,

[0076] Where n is the sample size of the interaction matrix of the gear image and the force value of the impact specimen.

[0077] Example 2: Based on the gear single tooth impact fatigue test method in Example 1, the present invention is further illustrated and described below.

[0078] like Figure 1 or Figure 4 or Figure 5 As shown, a gear single tooth impact fatigue test method, the method comprising:

[0079] Q1. The gear 1 to be tested is placed in a gear impact fatigue endurance testing machine for testing. The actuator 4 drives the impact head 2 to move. The force value data information of the gear impact specimen is obtained in real time based on the airborne force sensor 3. The image data information of the gear to be tested is obtained in real time based on the airborne camera.

[0080] Q2. Input the image data information of the gear to be tested and the force value data information of the gear impact specimen into the trained multivariate nonlinear regression model based on the interaction effect of independent variables, predict the relationship between the image of the gear and the force value of the gear impact specimen, and obtain the relationship data information between the image of the gear and the force value of the impact specimen;

[0081] Q3. Based on the data information of the relationship between the image of the gear and the force value of the impact specimen, the force value of the gear impact specimen is optimized by using a sine-cosine algorithm based on an adaptive feedback adjustment factor to obtain the optimized force value data information of the gear impact specimen;

[0082] Q4. Based on the force data information of the optimized gear impact specimen, a gear single tooth impact fatigue test function P is established to characterize the gear single tooth impact fatigue durability, and the test data information of the gear single tooth impact fatigue durability is obtained.

[0083] In this embodiment, if Figure 3 As shown, in step Q3, the optimization of the force value of the gear impact specimen using the sine-cosine algorithm based on the adaptive feedback adjustment factor includes:

[0084] Q31. Based on the data information of the relationship between the image of the gear and the force value of the impact specimen, a gear impact fatigue test data set is constructed, and initialized, and the number of iterations is determined to obtain data information of the initialized gear impact fatigue test data set;

[0085] Q32. Based on the data information of the initialized gear impact fatigue test data set, establish the fitness function S of the gear impact fatigue test,

[0086] ,

[0087] Among them, y is the data information of the initialized gear impact fatigue test data set, λ1, λ2 and λ3 are the covariance factors of the gear impact fatigue test, and the fitness value of each data in the gear impact fatigue test data set is calculated to obtain the fitness value data information of each data in the gear impact fatigue test data set;

[0088] Q33. Based on the fitness value data information of each data of the gear impact fatigue test data set, establish an optimization function W of the force value of the gear impact specimen,

[0089] ,

[0090] Among them, r is the fitness value data information of each data in the gear impact fatigue test data set, η1, η2 and η3 are the adaptive feedback adjustment factors of the gear impact fatigue test data set, and the force value of the gear impact specimen is optimized to obtain the optimized force value data information of the gear impact specimen.

[0091] In this embodiment, the adaptive feedback adjustment factors η1, η2 and η3 of the gear impact fatigue test data set are:

[0092] ,

[0093] ,

[0094] ,

[0095] Among them, r is the fitness value data information of each data in the gear impact fatigue test data set.

[0096] In this embodiment, the constraint function f of the covariance factors λ1, λ2 and λ3 of the gear impact fatigue test is,

[0097] ,

[0098] The value range of f is (0,1).

[0099] In this embodiment, the method further includes:

[0100] Q5. Based on the test data information of the impact fatigue durability of a single tooth of the gear, a preset threshold is set. If the test data of the impact fatigue durability of a single tooth of the gear is greater than the preset threshold, the gear is qualified; if the test data of the impact fatigue durability of a single tooth of the gear is less than the preset threshold, the gear is unqualified.

[0101] In this embodiment, the present invention provides a gear single tooth impact fatigue test system, including a computer device, which is programmed or configured to execute the steps of any one of the gear single tooth impact fatigue test methods.

[0102] In this embodiment, the present invention provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the improved Yolov5 pedestrian detection methods described above.

[0103] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0104] In summary, the present invention can not only update the force value of the gear impact test specimen in real time according to the image data information of the gear to be tested, thereby improving the accuracy of the impact fatigue test of the gear to be tested, but also the force variable can be monitored, and the test efficiency is high.

[0105] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A gear single tooth impact fatigue test method, characterized in that: The method comprises: Q1. Place the gear to be tested on a gear impact fatigue endurance testing machine for testing, obtain the force value data information of the gear impact specimen in real time based on the onboard force sensor, and obtain the image data information of the gear to be tested in real time based on the onboard camera; Q2. Input the image data information of the gear to be tested and the force value data information of the gear impact specimen into the trained multivariate nonlinear regression model based on the interaction effect of independent variables, predict the relationship between the image of the gear and the force value of the gear impact specimen, and obtain the relationship data information between the image of the gear and the force value of the impact specimen; Q3. Based on the data information of the relationship between the image of the gear and the force value of the impact specimen, the force value of the gear impact specimen is optimized by using a sine-cosine algorithm based on an adaptive feedback adjustment factor to obtain the optimized force value data information of the gear impact specimen; Q4. Based on the force data information of the optimized gear impact specimen, a gear single tooth impact fatigue test function P is established to characterize the gear single tooth impact fatigue durability, and the test data information of the gear single tooth impact fatigue durability is obtained.

2. The gear single tooth impact fatigue test method according to claim 1, characterized in that: The gear single tooth impact fatigue test function P is: ， Among them, z is the force data information of the optimized gear impact specimen, and ω1, ω2 and ω3 are the determining factors of the single tooth impact fatigue test of the gear.

3. The gear single tooth impact fatigue test method according to claim 2, characterized in that: The constraints of the determining factors ω1, ω2 and ω3 of the gear single tooth impact fatigue test are: 。 4. The gear single tooth impact fatigue test method according to claim 1, characterized in that: In step Q2, the trained multivariate nonlinear regression model based on the interaction effect of independent variables includes: Q21. Based on the image data information of the gear to be tested and the force value data information of the gear impact specimen, establish the gear independent variable interaction function G, ， Among them, x1 is the image data information of the gear to be tested, x2 is the force value data information of the gear impact specimen, α1 and α2 are the independent variable interaction weight coefficients of the gear, and the interaction data of the gear image and the force value of the impact specimen are characterized to obtain the gear image and the force value interaction matrix data information of the impact specimen; Q22. Based on the gear image and the force value interaction matrix data information of the impact specimen, a multivariate nonlinear regression function H of the gear is established. ， Among them, x i is the data information of the force value interaction matrix of the gear image and the impact specimen, n is the sample size of the force value interaction matrix of the gear image and the impact specimen, β 1i , β 2i and β 3i is the relationship factor between the gear image and the force value of the impact specimen; Q23. Based on the multivariate nonlinear regression function H of the gear, the relationship between the image of the gear and the force value of the gear impacting the specimen is predicted to obtain data information on the relationship between the image of the gear and the force value of the gear impacting the specimen.

5. The gear single tooth impact fatigue test method according to claim 4, characterized in that: The relationship factor β between the gear image and the force value of the impact specimen 1i , β 2i and β 3i The constraints are: ， Where n is the sample size of the interaction matrix of the gear image and the force value of the impact specimen.

6. The gear single tooth impact fatigue test method according to claim 1, characterized in that: In step Q3, the optimization of the force value of the gear impact specimen using the sine-cosine algorithm based on the adaptive feedback adjustment factor includes: Q31. Based on the data information of the relationship between the image of the gear and the force value of the impact specimen, a gear impact fatigue test data set is constructed, and initialized, and the number of iterations is determined to obtain data information of the initialized gear impact fatigue test data set; Q32. Based on the data information of the initialized gear impact fatigue test data set, establish the fitness function S of the gear impact fatigue test, ， Among them, y is the data information of the initialized gear impact fatigue test data set, λ1, λ2 and λ3 are the covariance factors of the gear impact fatigue test, and the fitness value of each data in the gear impact fatigue test data set is calculated to obtain the fitness value data information of each data in the gear impact fatigue test data set; Q33. Based on the fitness value data information of each data of the gear impact fatigue test data set, establish an optimization function W of the force value of the gear impact specimen, ， Among them, r is the fitness value data information of each data in the gear impact fatigue test data set, η1, η2 and η3 are the adaptive feedback adjustment factors of the gear impact fatigue test data set, and the force value of the gear impact specimen is optimized to obtain the optimized force value data information of the gear impact specimen.

7. The gear single tooth impact fatigue test method according to claim 6, characterized in that: The adaptive feedback adjustment factors η1, η2 and η3 of the gear impact fatigue test data set are: ， ， ， Among them, r is the fitness value data information of each data in the gear impact fatigue test data set.

8. The gear single tooth impact fatigue test method according to claim 6, characterized in that: The constraint function f of the covariance factors λ1, λ2 and λ3 of the gear impact fatigue test is, ， The value range of f is (0,1).

9. The gear single tooth impact fatigue test method according to claim 1, characterized in that: The method further comprises: Q5. Based on the test data information of the impact fatigue durability of a single tooth of the gear, a preset threshold is set. If the test data of the impact fatigue durability of a single tooth of the gear is greater than the preset threshold, the gear is qualified; if the test data of the impact fatigue durability of a single tooth of the gear is less than the preset threshold, the gear is unqualified.

10. A gear single tooth impact fatigue test system, comprising a computer device, characterized in that: The computer device is programmed or configured to execute the steps of the gear single tooth impact fatigue test method as claimed in any one of claims 1 to 9.

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