A method for predicting and optimizing the fatigue life of gearbox gears

Abstract

The present invention discloses a method for predicting and optimizing the fatigue life of gears in a gearbox, and relates to the technical field of gear fatigue life prediction. The prediction method comprises: establishing a reference model: establishing a virtual gearbox model, simulating the virtual gearbox model with a computer to obtain virtual data, and obtaining a physical gear to establish a laboratory model. The present invention introduces the linear cumulative damage theory and the nonlinear cumulative damage theory, and establishes corresponding mathematical models No. 1 and No. 2, which can more comprehensively consider the effects of stress, speed and time on gear damage during the shifting process, merge the mathematical model No. 1 and the mathematical model No. 2 through a merging strategy, and combine the actual transmission operation data to correct the theoretical S-N curve, obtain an actual S-N curve that is more in line with the actual working conditions, and achieve effective prediction of fatigue life, further improving the accuracy of fatigue life prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of gear fatigue life prediction, and in particular to a gearbox gear fatigue life prediction and optimization method. Background Art

[0002] The gearbox is a mechanism used to change the speed and torque from the engine. It can change the transmission ratio of the output shaft and the input shaft in a fixed or step-by-step manner. The fatigue life of the gears inside the gearbox refers to the number of stress cycles or time that the gears experience under cyclic loads from the beginning of use until fatigue failure occurs. By predicting the fatigue life of the gears, potential failure risks can be discovered in a timely manner, and necessary maintenance or replacement measures can be taken to ensure the normal operation of the equipment.

[0003] For example, the patent publication number is "CN116628440A", and the name is "A method and system for predicting the remaining life of a gearbox". S1. Obtain the driving data of the current vehicle, which includes the gearbox model, driving scene category, and the number of gear cycles in each gear of the gearbox; S2. Obtain the standard value of the number of gear cycles that matches the gearbox model, driving scene category, and gear in each gear; S3. Input the number of gear cycles and the standard value of the number of gear cycles corresponding to each gear into the remaining life prediction model to obtain the remaining mileage in the corresponding gear; S4. Send an alarm signal when the remaining mileage in any gear is less than or equal to zero; The number of gear cycles in different gears of the current vehicle is predicted by the remaining life prediction model, and reflected in the form of remaining mileage, which can improve the accuracy of the remaining life of the gearbox.

[0004] When making predictions, the above method fails to fully consider the complex effects of stress changes on gears during the gear shifting process. Not all gears inside the gearbox are in a loaded state during the transmission process. The gears of the remaining transmission ratios are in a rotating state and mesh with each other, but are idling. When the gearbox is shifting, the originally idling gears are converted into a loaded state. The transition from idling to loaded state and the sudden increase in load will have an impact on the gears, which may cause stress concentration on the gear surface, thereby affecting their fatigue life. Therefore, the above method cannot effectively predict the fatigue life of the gears inside the gearbox. For this purpose, a gearbox gear fatigue life prediction and optimization method is invented. Summary of the invention

[0005] The purpose of the present invention is to provide a method for predicting and optimizing the fatigue life of gearbox gears to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for predicting the fatigue life of gearbox gears, the prediction method comprising:

[0007] Establish reference model: establish a virtual gearbox model, simulate the virtual gearbox model by computer to obtain virtual data, obtain physical gears to establish laboratory model, and obtain laboratory data of gear life through laboratory testing;

[0008] Obtain reference life: Use virtual data and laboratory data of gear life to establish the theoretical SN curve of the gears in the gearbox;

[0009] Introducing fatigue accumulation: Introducing fatigue accumulation damage theory, the fatigue accumulation damage theory includes linear accumulation damage theory and nonlinear accumulation damage theory, in which a mathematical model is established for calculating the damage caused to the gear by each gear shift and calculating the total damage amount cumulatively, and the gear shift process time, load and speed during the gear shift process are included in the calculation of the mathematical model, and the mathematical model includes a No. 1 mathematical model for calculating linear accumulation damage and a No. 2 mathematical model for calculating nonlinear accumulation damage;

[0010] Merge: Merge the mathematical model No. 1 and the mathematical model No. 2 through a merging strategy to obtain a merged model;

[0011] Input parameters to obtain results: The theoretical SN curve and the combined model are combined to establish a parameter input terminal. Based on the operating data of the actual transmission, the operating data is input into the parameter input terminal. The combined model obtains the calculation result of the combined model through a calculation method based on the operating data. The coordinate value of the SN curve is modified by the calculation result to obtain the actual SN curve.

[0012] The calculation method includes establishing a correlation between the conversion coefficient and the stress, establishing a conversion coefficient for the influence of the damage amount on the fatigue life of the gear at different stress levels based on the correlation, multiplying the calculation result of the combined model and the conversion coefficient to obtain the number of loss cycles, and obtaining the actual SN curve on the theoretical SN curve according to the number of loss cycles based on different stress reactions;

[0013] The mathematical model No. 1 includes: setting the gear in the gear shifting process, when the i-th gear shift, the stress on the gear is , the speed is The shifting time is , establish the damage volume ;

[0014] Establish the total damage amount of mathematical model No. 1 = α, β, and θ are the indices of stress, time, and speed on damage. The specific values ​​of α, β, and θ are obtained by simulating the shifting of the virtual gearbox model using a computer. It represents the number of gear shifting and speed changing in mathematical model No. 1;

[0015] The second mathematical model includes setting the gear in the gear shifting process, when the jth gear shifts, the stress on the gear is , the speed is The shifting time is , establish the damage volume ;

[0016] Establish the total damage of mathematical model No. 2 = , The value range is 1.001-1.003. It represents the number of gear shifting in mathematical model No. 2.

[0017] Furthermore, the operating data includes the gear shift sequence number and specific parameters of the stress, speed and gear shifting process time of the gear shift;

[0018] The calculation result includes establishing a shift parameter set according to the shift sequence number in the operation data, obtaining the specific parameters of the stress, speed and shift process time of the gear in the corresponding shift through the shift sequence number in the shift parameter set, selecting a parameter No. 1 from the shift parameter set by a random selection method, the random selection must follow the time sequence, the parameter No. 1 is established according to the time sequence. The parameter set is input into the combined model, and the combined model is used to calculate the total damage amount , the total damage Multiply by the conversion factor to get the number of cycles lost.

[0019] Furthermore, the correlation between the conversion coefficient and stress is established, and the fatigue life data of the gears at different stress levels in the simulation values ​​are collected. The fatigue life data of the gears include the stress level, number of cycles and the damage data of the gears. The correlation between the number of cycles and the damage data of the gears at different stress levels is calculated through a regression algorithm. Based on the correlation, the correlation between the conversion coefficient and stress at different stress levels is established, and the correlation between the conversion coefficient and stress is obtained.

[0020] Furthermore, establishing a virtual gearbox model includes obtaining gearbox size data and gearbox internal gear arrangement data and size data, and testing the physical gearbox and physical gears at different stress levels based on laboratory internal equipment to obtain laboratory data.

[0021] Furthermore, the merging strategy includes simulating the gear shifting of the virtual gearbox model by a computer, recording and marking the rotation angle of the gear when the load on the gear suddenly increases during the gear shifting process, obtaining the number of times the gear load increases, calculating how many number 1 angles are required to obtain an angle value greater than or equal to 360 degrees, obtaining the number 1 number, dividing the number of times the gear load increases by the number 1 number and obtaining the number 1 integer through the progressive principle , the total damage is established based on the minimum failure principle , = + , =1, = -1, the calculation method of area 1 includes obtaining the gear speed and the gear shifting process time, and calculating the rotation angle of the gear when the load on the gear suddenly increases through the gear speed and the gear shifting process time.

[0022] Furthermore, the merging strategy includes obtaining the number of tooth surfaces for meshing in the gear, obtaining the number of times the gear load increases, dividing the number of times the gear load increases by the number of tooth surfaces for meshing in the gear and obtaining a second integer by the progressive principle. , the total damage is established based on the minimum failure principle , = + , =1, = -1.

[0023] Furthermore, the merging strategy includes simulating the gear shifting of the virtual gearbox model by a computer, recording and marking the rotation angle of the gear when the load on the gear suddenly increases during the gear shifting process, obtaining the number of times the gear load increases, calculating how many second angles are required to obtain an angle value greater than or equal to 360 degrees, obtaining the second number, dividing the number of times the gear load increases by the second number, and obtaining the third integer through the progressive principle , set weighting coefficients X and Y, X+Y=1, and establish the total damage amount based on the minimum failure principle , =X +Y , = = .

[0024] A method for optimizing the fatigue life of a gearbox gear adopts the above-mentioned method for predicting the fatigue life of a gearbox gear. The optimization method includes: based on the actual SN curve and the total damage amount , analysis affects the total damage The number of parameters is obtained, and the point parameters that affect the specific value of the second parameter are analyzed through the virtual gearbox model. The point parameters in the gearbox model are adjusted. The adjusted point parameters reduce the total damage amount The specific value of .

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] This gearbox gear fatigue life prediction and optimization method introduces linear cumulative damage theory and nonlinear cumulative damage theory, and establishes corresponding mathematical model No. 1 and mathematical model No. 2. It can more comprehensively consider the influence of stress, speed and time on gear damage during the shifting process. The mathematical model No. 1 and the mathematical model No. 2 are merged through a merging strategy, and the theoretical SN curve is corrected in combination with the actual transmission operation data to obtain an actual SN curve that is more in line with actual working conditions, thereby achieving effective prediction of fatigue life and further improving the accuracy of fatigue life prediction.

[0027] At the same time, by establishing a virtual gearbox model and a laboratory model, this method can make full use of the data from computer simulation and physical testing to accurately construct the theoretical SN curve of the gears in the gearbox. This curve is the basis for predicting the fatigue life of the gears. The construction of the theoretical SN curve ensures the reliability of the prediction of the fatigue life of the gears.

[0028] The linear cumulative damage model and the nonlinear cumulative damage model have different characteristics and applicable scopes. The combined model combines two different types of cumulative damage models through reasonable algorithms and strategies, thereby providing a comprehensive fatigue life assessment method. The use of the combined model optimizes the prediction process and improves calculation efficiency. By combining the two models, the fatigue cumulative damage process of gears under complex working conditions is more comprehensively considered, thereby improving the accuracy and reliability of the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a schematic diagram of the prediction method of the present invention;

[0030] Figure 2 A flow chart for calculating the number of lost cycles for the present invention;

[0031] Figure 3 It is a schematic diagram of the actual SN curve and the theoretical SN curve of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] like Figure 1 - Figure 3 As shown, the present invention provides a technical solution: a method for predicting the fatigue life of a gearbox gear, the prediction method comprising:

[0034] Establish reference model: establish a virtual gearbox model, simulate the virtual gearbox model by computer to obtain virtual data, obtain physical gears to establish laboratory model, and obtain laboratory data of gear life through laboratory testing;

[0035] Obtain reference life: Use virtual data and laboratory data of gear life to establish the theoretical SN curve of the gears in the gearbox;

[0036] Introducing fatigue accumulation: Introducing fatigue accumulation damage theory, which includes linear accumulation damage theory and nonlinear accumulation damage theory. In the fatigue accumulation damage theory, a mathematical model is established to calculate the damage caused to the gears by each gear shift and calculate the total damage amount cumulatively. The gear shift process time, load and speed during the gear shift process are included in the calculation of the mathematical model. The mathematical model includes mathematical model No. 1 for calculating linear accumulation damage and mathematical model No. 2 for calculating nonlinear accumulation damage.

[0037] Merge: Merge the mathematical model No. 1 and the mathematical model No. 2 through a merging strategy to obtain a merged model;

[0038] Input parameters to obtain results: The theoretical SN curve and the combined model are combined to establish a parameter input terminal. Based on the operating data of the actual transmission, the operating data is input into the parameter input terminal. The combined model obtains the calculation result of the combined model through a calculation method based on the operating data. The coordinate value of the SN curve is modified by the calculation result to obtain the actual SN curve.

[0039] The calculation method includes establishing a correlation between the conversion coefficient and the stress, establishing a conversion coefficient for the influence of the damage amount on the gear fatigue life at different stress levels based on the correlation, multiplying the calculation result of the combined model and the conversion coefficient to obtain the loss cycle number, and obtaining the actual SN curve on the theoretical SN curve according to the different stress responses;

[0040] Mathematical model No. 1 includes: setting the gear in the gear shifting process, when the i-th gear shift, the stress on the gear is , the speed is The shifting time is , establish the damage volume ;

[0041] Establish the total damage amount of mathematical model No. 1 = α, β, and θ are the indices of stress, time, and speed on damage. The specific values ​​of α, β, and θ are obtained by simulating the shifting of the virtual gearbox model using a computer. It represents the number of gear shifting and speed changing in mathematical model No. 1;

[0042] The second mathematical model includes setting the gear in the gear shifting process. When the jth gear shifts, the stress on the gear is , the speed is The shifting time is , establish the damage volume ;

[0043] Establish the total damage of mathematical model No. 2 = , The value range is 1.001-1.003. It represents the number of gear shifting in mathematical model No. 2.

[0044] The operation data includes the gear shift sequence number and the specific parameters of the stress, speed and shift process time of the gears during the gear shift;

[0045] The calculation results include establishing a shift parameter set according to the shift sequence number in the operating data, obtaining the specific parameters of the stress, speed and shift process time of the gear in the corresponding shift through the shift sequence number in the shift parameter set, selecting parameter No. 1 from the shift parameter set by random selection, and the random selection must follow the time sequence. The parameter No. 1 is used to establish a parameter set according to the time sequence, and the parameter set is input into the combined model. The combined model calculates the total damage amount , the total damage Multiply by the conversion factor to get the number of cycles lost.

[0046] Establish the correlation between the conversion coefficient and stress, collect the fatigue life data of the gear at different stress levels in the simulation values, the fatigue life data of the gear includes the stress level, number of cycles and the damage data of the gear, calculate the correlation between the number of cycles and the damage data of the gear at different stress levels through the regression algorithm, establish the correlation between the conversion coefficient and stress at different stress levels based on the correlation, and obtain the correlation between the conversion coefficient and stress.

[0047] Establishing a virtual gearbox model includes obtaining gearbox size data and gear arrangement and size data inside the gearbox, and testing the physical gearbox and physical gears at different stress levels based on the laboratory's internal equipment to obtain laboratory data.

[0048] The merging strategy includes simulating the gear shifting of the virtual gearbox model by computer, recording and marking the rotation angle of the gear when the load on the gear suddenly increases during the gear shifting process, obtaining the number of times the gear load increases, calculating the minimum number of first angles required to obtain an angle value greater than or equal to 360 degrees, obtaining the first number, dividing the number of times the gear load increases by the first number, and obtaining the first integer through the progressive principle , the total damage is established based on the minimum failure principle , = + , =1, = -1, the calculation method of area 1 includes obtaining the gear speed and the gear shifting process time, and calculating the rotation angle of the gear when the load on the gear suddenly increases through the gear speed and the gear shifting process time.

[0049] The merging strategy includes obtaining the number of tooth surfaces used for meshing in the gear, obtaining the number of times the gear load increases, dividing the number of times the gear load increases by the number of tooth surfaces used for meshing in the gear and obtaining the second integer by the progressive principle , the total damage is established based on the minimum failure principle , = + , =1, = -1.

[0050] The merging strategy includes simulating the gear shifting of the virtual gearbox model by computer, recording and marking the rotation angle of the gear when the load on the gear suddenly increases during the gear shifting process, obtaining the number of times the gear load increases, calculating the number of second angles required to obtain an angle value greater than or equal to 360 degrees, obtaining the second number, dividing the number of times the gear load increases by the second number, and obtaining the third integer through the progressive principle , set weighting coefficients X and Y, X+Y=1, and establish the total damage amount based on the minimum failure principle , =X +Y , = = .

[0051] A gearbox gear fatigue life optimization method adopts the above gearbox gear fatigue life prediction method. The optimization method includes: based on the actual SN curve and the total damage amount , analysis affects the total damage The number of parameters is obtained, and the point parameters that affect the specific value of the second parameter are analyzed through the virtual gearbox model. The point parameters in the gearbox model are adjusted. The adjusted point parameters reduce the total damage amount The specific value of .

[0052] The computer simulates that the gears inside the gearbox can mesh with each other. By meshing with each other and analyzing the force simulation of the gears under different transmission ratios and under different loads, virtual data can be obtained. The virtual data can be obtained through finite element analysis. The finite element analysis software is used to create a virtual gearbox model, and the parameters of the simulation environment are set, including boundary conditions, loads, motion laws, etc., to simulate the operating state of the gearbox in actual work. The virtual gearbox model is simulated, and key data such as stress, strain, speed, and load of the gears during the simulation process are collected as the basis for subsequent analysis.

[0053] Disassemble the gears from the actual gearbox, or process the physical gears that are the same as the actual gears according to the design drawings, build a laboratory test bench, install sensors, data acquisition systems and other equipment to ensure that the test environment can simulate the working conditions of the gearbox in actual work, such as load and speed, install the physical gears on the test bench, conduct laboratory tests according to the predetermined test plan, record the life data such as the time of crack occurrence and the number of cycles when the gears break during the test, and perform fatigue tests on multiple gears on one device at the same time. Different gears have different actual cyclic stresses, and a series of test data under different mean cyclic stresses can be obtained. By performing regression analysis on a series of data, laboratory data can be obtained.

[0054] The virtual data output by the simulation software is sorted out, and the key parameters such as stress, strain, speed, load, etc. of the gear under different working conditions are extracted. The gear life data recorded in the laboratory test is sorted out, including the crack appearance time under different cyclic stresses, the number of cycles at fracture, etc., and the sorted virtual data and laboratory data are regressed or fitted using statistical software or data analysis tools. Through regression analysis or fitting, the theoretical SN curve of the gear in the gearbox is obtained. In actual situations, it is inconvenient to conduct laboratory tests on physical gears under all different cyclic stresses. Therefore, virtual data is needed to assist the physical gears so that the obtained theoretical SN curve can cover the life under comprehensive different cyclic stresses. By establishing a virtual gearbox model and a laboratory model, this method can make full use of the data from computer simulation and physical tests to accurately construct the theoretical SN curve of the gear in the gearbox. This curve is the basis for predicting the fatigue life of the gear. The construction of the theoretical SN curve ensures the reliability of the prediction of the fatigue life of the gear.

[0055] During the actual operation of the gearbox, when shifting gears, the transmission ratio inside the gearbox will change the speed of the output end. During the gear shifting process, the originally idling gear will enter a loaded rotation state. In this process, the load change will affect the tooth contact surface on the gear. The damage caused to the gear during each gear shifting process is accumulated, and a corresponding mathematical model is established to describe the total damage of the gear during multiple gear shifting processes. The model needs to comprehensively consider the fatigue characteristics of the gear material, the complex load conditions during the gear shifting process, and the cumulative effect of damage.

[0056] Nonlinear cumulative damage theory is a fatigue life prediction method that takes into account the interaction between damages. Unlike linear cumulative damage theory, nonlinear cumulative damage theory believes that when a material or structure is subjected to multiple cyclic loads, the damage caused by each cycle is not independent, but will interfere with each other, thereby affecting the overall damage accumulation process and the final fatigue life. In nonlinear cumulative damage theory, the damage caused by each cyclic load depends not only on the current load level, but also on the history of the previous loads. This means that even if the stress levels of the two loads are the same, the damage they cause to the material or structure may be different due to the different order of the loads. Generally, the damage caused by the later load is more serious than the damage caused by the previous load due to damage accumulation.

[0057] The linear cumulative damage theory assumes that each damage is independent and the total damage is a simple accumulation of each damage. The damage caused by each load action is independent of the previous or subsequent load action. Each damage event is independent and will not affect each other. The linear cumulative damage theory simplifies the analysis process of damage accumulation, but because it does not need to consider the interaction between damages or the complexity of the cumulative effect, the linear cumulative damage theory has limitations. It ignores factors such as the interaction between damages, the degradation of material properties, and the randomness of load effects.

[0058] Only the increase in load on the gear is calculated, and the stress is expressed as the load on the gear. Compared with the reduction in load on the gear, this process has little effect on the life of the gear, so it is not considered. During the gear shifting process, considering the gear meshing, the load is mainly reflected in the tooth shape on the gear. The following situation exists, that is, the tooth shape in this area of ​​the gear is affected by the load change after the gear shifting, but there will be another part of the tooth shape that does not participate in the meshing of the gear shifting, and the next gear shifting may cause the tooth shape that did not participate in the meshing of the gear shifting before. Therefore, the gear shifting between the previous and next two gear shifting If there is no overlap in the area on the gear, the linear cumulative damage theory can be used. However, when the areas on the front and rear gears that have undergone gear shifting overlap, the nonlinear cumulative damage theory will be used. Considering that they are all on the same gear, the linear cumulative damage theory and the nonlinear cumulative damage theory need to be effectively combined, that is, the mathematical model No. 1 and the mathematical model No. 2 are merged through a merging strategy to obtain a merged model. The stress in the mathematical model No. 1 and the mathematical model No. 2 refers to the stress on the output end of the gearbox. Under theoretical conditions, the stress on the output end of the gearbox will not change during continuous operation.

[0059] In the first embodiment, during the gear shifting process, the contact areas formed by all gears during the gear shifting process will rotate, resulting in overlapping of all tooth areas on the outer wall of the gear. When the gear is in use, as long as one tooth is damaged, the gear will fail, but the teeth at other positions can be used in theory, but the entire gear is still failed. Therefore, the life of the gear is calculated based on the minimum failure principle. The minimum failure principle means that when a single gear fails, the remaining life of the entire gear is considered to be zero. At the same time, the linear cumulative damage theory and the nonlinear cumulative damage theory need to take the above situation into account. Therefore, the linear cumulative damage theory and the nonlinear cumulative damage theory establish corresponding mathematical models No. 1 and No. 2 for the above situation.

[0060] Mathematical model No. 1 adopts the linear cumulative damage theory. When calculating, the single damage amount is related to the stress S, speed N and the time T of the shifting process, and the total damage amount is constructed. = , α, β, θ are indexes of the influence of stress, time and speed on damage, which need to be determined through experiments or experience. The computer simulates the gear shifting of the virtual gearbox model to obtain the values. The α, β, θ in mathematical model No. 1 and mathematical model No. 2 are the same values. Since the influence of gear shifting on damage is considered, the α, β, θ in mathematical model No. 1 and the α, β, θ in mathematical model No. 2 are the same values.

[0061] The second mathematical model adopts the nonlinear cumulative damage theory. , j is a positive integer, This means that the damage to the gears increases during the subsequent gear shifting process due to the influence of the previous gear shifting process. is always greater than 1, The selected value range is 1.001-1.003.

[0062] In order to facilitate computer calculations, some parameters in the mathematical model can be standardized, such as the shifting process time, load changes and speed during the shifting process. The above parameters can be standardized and unified, and the unification is represented by the same numerical value for subsequent calculations. The laboratory data on gear life is tested in the laboratory. The gears are tested in the laboratory by equipment to obtain laboratory data on gear life. It is inconvenient to mesh and test the gears inside the gearbox in the laboratory. Only a single gear can be tested. Therefore, the virtual data and the laboratory data of the gear life are used to establish the theoretical SN curve of the gears in the gearbox. The virtual data is a simulation of the interaction and meshing of the gears inside the transmission, while the laboratory data is a test of a single gear.

[0063] The progressive principle means that when two positive integers are divided to obtain an integer with a decimal, as long as there is a decimal value, the integer is increased by one to eliminate the decimal and obtain an integer without a decimal value, thereby ensuring the stability of the operation. A conversion coefficient L is set, which is a function of stress S. The relationship between the conversion coefficient and stress can be expressed by this function. This function reflects the degree of influence of the damage amount on the fatigue life of the material under different stress levels. Therefore, the total damage amount in the calculation result of the combined model is Multiply by the coefficient L to get the number of loss cycles , L = τ (S), L = , τ represents the correlation between the conversion coefficient and stress. Considering the gap between the ideal model and the actual gear, and the fact that the ideal model is superior to the actual gear and the influence of other external factors in actual use, it is inconvenient to calculate other external factors, so the safety factor is established. , The optimal value range of is 1.04-1.20. Applied in the life SN curve, its value is greater than 1.

[0064] The linear cumulative damage theory and nonlinear cumulative damage theory were introduced, and the corresponding mathematical model No. 1 and mathematical model No. 2 were established, which can more comprehensively consider the influence of stress, speed and time on gear damage during the gear shifting process. The mathematical model No. 1 and the mathematical model No. 2 were merged through a merging strategy, and the theoretical SN curve was corrected in combination with the actual transmission operating data to obtain an actual SN curve that is more in line with actual working conditions, thereby achieving effective prediction of fatigue life and further improving the accuracy of fatigue life prediction.

[0065] Embodiment 1: Embodiment 1 is based on all tooth profile areas that participate in the increase of load during the gear shifting process. This area is regarded as the No. 1 area, that is, the angle of rotation of the gear during the gear shifting process is regarded as the No. 1 angle, and the number of No. 1 angles required is calculated to obtain an angle value just greater than or equal to 360 degrees, and then the No. 1 number is obtained. In this way, it is possible to calculate how many times of gear shifting is required in theory to achieve full coverage of the gears, and the number of times the gear load increases is divided by the No. 1 number and the No. 1 integer is obtained through the progressive principle. , integer number one It indicates the number of times the gear load increases to achieve the number of layers of coverage on the gear, and the integer The total damage is established based on the principle of minimum failure by rounding up according to the progressive principle. , = + , =1, = -1.

[0066] Based on the actual input data, the actual input data includes the stress, rotational speed and shifting process time of the gears in each gear shift. According to the specific parameters of the stress, rotational speed and shifting process time of the gears in each gear shift, a gear shift parameter set is established. The gear shift parameter set is selected by random selection. After the selected gear shift parameter is selected, it will not be selected again. The number of random selections is equal to the integer No. 1 in the merged model. The randomly selected parameters are substituted into the actual calculation process. At the same time, the randomly selected parameters will have a sequence and are arranged according to the sequence. Considering that the damage caused by the subsequent gear shift is affected by the damage caused by the previous gear shift, it is necessary to sort them in sequence. In the calculation process, the integer No. 1 is in chronological order. The specific value of is equal to the number of No. 1 parameters in the corresponding parameter set. The parameter set is substituted into the calculation in sequence. First, it is substituted into the No. 1 mathematical model for calculation, and then the subsequent No. 1 parameters are substituted into the No. 2 mathematical model for calculation. The minimum failure principle is used to predict the fatigue life of the gear. The minimum failure principle means that when any meshing tooth surface area on the gear reaches the damage state, the entire gear is regarded as failed.

[0067] Embodiment 2: Embodiment 2 is based on the process of gear shifting and speed change. Due to the speed angle in the process of gear shifting and speed change, when the time of gear shifting and speed change is short, considering that the stress mutation acts on the meshing tooth surface during the gear shifting process, the number of meshing tooth surfaces in the gear is obtained, and it is considered that when a single tooth surface is meshed, the gear shifting and speed change is realized. The number of times the gear load increases is divided by the number of meshing tooth surfaces in the gear and the second integer is obtained by the progressive principle. , the total damage is established based on the minimum failure principle , = + , =1, = -1, use parameter No. 1 from the parameter set, substitute the parameter set into the calculation in order, first substitute it into mathematical model No. 1 for calculation, and then substitute the subsequent parameter No. 1 into mathematical model No. 2 for calculation to obtain the total damage .

[0068] Embodiment 3: Considering the standardization and unification of gear shifting, the method for obtaining the second angle is the same as the method for obtaining the first angle, and the third integer is obtained. , set weighting coefficients X and Y, X+Y=1, and establish the total damage amount based on the minimum failure principle , =X +Y , = = , through the virtual gearbox model to simulate the accumulation process of damage during gear shifting, the simulation data is used to ensure the specific values ​​of X and Y. By introducing the weighting coefficient, the contribution of mathematical model No. 1 and mathematical model No. 2 in the calculation of the total damage can be more flexibly adjusted, which helps to more accurately reflect the fatigue damage of gears under actual working conditions, thereby improving the accuracy of the prediction. Different gears in the gearbox may have different fatigue damage characteristics. By adjusting the weighting coefficient, the prediction method can be better adapted to different gears, improving the versatility and applicability of the method. The introduction of the weighting coefficient can simplify the calculation process of the merging strategy.

[0069] The random selection method of embodiment 1, embodiment 2 and embodiment 3 is the same, and the selected parameters must follow the time sequence to participate in the calculation. At the same time, when predicting the life of the gear, all three can be used at the same time, and three actual SN curves are displayed at the same time so that users can comprehensively analyze the gear life and remaining life, total damage The number of loss cycles under different stresses is obtained by multiplying the conversion coefficient. The linear cumulative damage model and the nonlinear cumulative damage model have different characteristics and applicable scopes. The combined model combines two different types of cumulative damage models through reasonable algorithms and strategies, thereby providing a comprehensive fatigue life assessment method. The use of the combined model optimizes the prediction process and improves the calculation efficiency. By combining the two models, the fatigue cumulative damage process of gears under complex working conditions is more comprehensively considered, thereby improving the accuracy and reliability of the prediction results.

[0070] like Figure 3 As shown, the total damage Multiply it by the conversion coefficient to get the number of loss cycles. The theoretical SN curve is adjusted by the number of loss cycles to get the actual SN curve. The difference between △N1 and △N2 is different at different stress levels. The number of loss cycles reduced by the same amount of damage is different at different stress levels. When the stress level decreases, the material can withstand more stress cycles without being damaged. This is because at lower stress levels, the stress and strain inside the material are smaller, and the formation and expansion of microcracks are slower. Therefore, at the same amount of damage, more cycles are required for low stress levels, while fewer cycles are required for high stress levels.

[0071] The calculated damage amount is converted into the number of loss cycles at a specific stress level through a conversion coefficient. The theoretical SN curve obtained may not be expressed by an equation, but can only be expressed by an image. The total damage amount is calculated by inputting operating data, including the number of gear shifts, the gears involved in the gear shift, the stress of the gearbox output shaft, and the number of times the gear has rotated, etc. , the total damage The number of cycles lost under different stresses is obtained through the conversion coefficient, and the number of cycles lost is reflected on the theoretical SN curve. The effects on gears under different stresses are different, so a conversion factor is set based on the total damage amount. By modifying the theoretical SN curve and combining it with the number of cycles the gear has gone through, the fatigue life of the gear can be obtained.

[0072] Point parameters include basic parameters of gears such as module, number of teeth, and pressure angle. The point parameters are adjusted on the virtual gearbox model. After the adjustment, the specific value of parameter No. 2 is affected. By adjusting back and forth, the point parameters that affect the specific value of parameter No. 2 are analyzed through the virtual gearbox model, and the point parameters in the gearbox model are adjusted. The adjusted point parameters reduce the total damage amount. , thereby optimizing the gear.

[0073] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. A method for predicting gearbox gear fatigue life, characterized in that: The prediction method comprises: Establish reference model: establish a virtual gearbox model, simulate the virtual gearbox model by computer to obtain virtual data, obtain physical gears to establish laboratory model, and obtain laboratory data of gear life through laboratory testing; Obtain reference life: Use virtual data and laboratory data of gear life to establish the theoretical SN curve of the gears in the gearbox; Introducing fatigue accumulation: Introducing fatigue accumulation damage theory, the fatigue accumulation damage theory includes linear accumulation damage theory and nonlinear accumulation damage theory, in which a mathematical model is established for calculating the damage caused to the gear by each gear shift and calculating the total damage amount cumulatively, and the gear shift process time, load and speed during the gear shift process are included in the calculation of the mathematical model, and the mathematical model includes a No. 1 mathematical model for calculating linear accumulation damage and a No. 2 mathematical model for calculating nonlinear accumulation damage; Merge: Merge the mathematical model No. 1 and the mathematical model No. 2 through a merging strategy to obtain a merged model; Input parameters to obtain results: The theoretical SN curve and the combined model are combined to establish a parameter input terminal. Based on the operating data of the actual transmission, the operating data is input into the parameter input terminal. The combined model obtains the calculation result of the combined model through a calculation method based on the operating data. The coordinate value of the SN curve is modified by the calculation result to obtain the actual SN curve. The calculation method includes establishing a correlation between the conversion coefficient and the stress, establishing a conversion coefficient for the influence of the damage amount on the fatigue life of the gear at different stress levels based on the correlation, multiplying the calculation result of the combined model and the conversion coefficient to obtain the number of loss cycles, and obtaining the actual SN curve on the theoretical SN curve according to the number of loss cycles based on different stress reactions; The mathematical model No. 1 includes: setting the gear in the gear shifting process, when the i-th gear shift, the stress on the gear is , the speed is The shifting time is , establish the damage volume ; Establish the total damage amount of mathematical model No. 1 = α, β, and θ are the indices of stress, time, and speed on damage. The specific values ​​of α, β, and θ are obtained by simulating the shifting of the virtual gearbox model using a computer. It represents the number of gear shifting and speed changing in mathematical model No. 1; The second mathematical model includes setting the gear in the gear shifting process, when the jth gear shifts, the stress on the gear is , the speed is The shifting time is , establish the damage volume ; Establish the total damage of mathematical model No. 2 = , The value range is 1.001-1.

003. It represents the number of gear shifting in mathematical model No.

2.

2. A gearbox gear fatigue life prediction method according to claim 1, characterized in that: The operating data includes the gear shift sequence number and specific parameters of the stress, speed and gear shifting process time during the gear shifting; The calculation result includes establishing a shift parameter set according to the shift sequence number in the operation data, obtaining the specific parameters of the stress, speed and shift process time of the gear in the corresponding shift through the shift sequence number in the shift parameter set, selecting a parameter No. 1 from the shift parameter set by a random selection method, the random selection must follow the time sequence, the parameter No. 1 is established according to the time sequence. The parameter set is input into the combined model, and the combined model is used to calculate the total damage amount , total damage Multiply by the conversion factor to get the number of cycles lost.

3. A gearbox gear fatigue life prediction method according to claim 1, characterized in that: Establish the correlation between the conversion coefficient and stress, collect the fatigue life data of the gear at different stress levels in the simulation values, the fatigue life data of the gear includes the stress level, number of cycles and the damage data of the gear, calculate the correlation between the number of cycles and the damage data of the gear at different stress levels through the regression algorithm, establish the correlation between the conversion coefficient and stress at different stress levels based on the correlation, and obtain the correlation between the conversion coefficient and stress.

4. A gearbox gear fatigue life prediction method according to claim 1, characterized in that: Establishing a virtual gearbox model includes obtaining gearbox size data and gear arrangement and size data inside the gearbox, and testing the physical gearbox and physical gears at different stress levels based on the laboratory's internal equipment to obtain laboratory data.

5. The method for predicting gearbox gear fatigue life according to claim 1, characterized in that: The merging strategy includes simulating the gear shifting of the virtual gearbox model by computer, recording and marking the rotation angle of the gear when the load on the gear suddenly increases during the gear shifting process, obtaining the number of times the gear load increases, calculating how many number 1 angles are required to obtain an angle value greater than or equal to 360 degrees, obtaining the number 1 number, dividing the number of times the gear load increases by the number 1 number, and obtaining the number 1 integer through the progressive principle , the total damage is established based on the minimum failure principle , = + , =1, = -1, the calculation method of area 1 includes obtaining the gear speed and the gear shifting process time, and calculating the rotation angle of the gear when the load on the gear suddenly increases through the gear speed and the gear shifting process time.

6. A gearbox gear fatigue life prediction method according to claim 1, characterized in that: The merging strategy includes obtaining the number of tooth surfaces for meshing in the gear, obtaining the number of times the gear load increases, dividing the number of times the gear load increases by the number of tooth surfaces for meshing in the gear, and obtaining a second integer by the progressive principle. , the total damage is established based on the minimum failure principle , = + , =1, = -1.

7. A gearbox gear fatigue life prediction method according to claim 1, characterized in that: The merging strategy includes simulating the gear shifting of the virtual gearbox model by computer, recording and marking the rotation angle of the gear when the load on the gear suddenly increases during the gear shifting process, obtaining the number of times the gear load increases, calculating how many second angles are required to obtain an angle value greater than or equal to 360 degrees, obtaining the second number, dividing the number of times the gear load increases by the second number, and obtaining the third integer through the progressive principle , set weighting coefficients X and Y, X+Y=1, and establish the total damage amount based on the minimum failure principle , =X +Y , = = .

8. A method for optimizing the fatigue life of a gearbox gear, using the gearbox gear fatigue life prediction method according to any one of claims 1 to 7, characterized in that: The optimization method includes: based on the actual SN curve and the total damage , analysis affects the total damage The number of parameters is obtained, and the point parameters that affect the specific value of the second parameter are analyzed through the virtual gearbox model. The point parameters in the gearbox model are adjusted. The adjusted point parameters reduce the total damage amount The specific value of .

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