A fatigue life prediction method for PTO assembly of paddy field power machinery

Abstract

The present invention discloses a method for predicting fatigue life of a PTO assembly of a paddy field power machinery, comprising the following steps: establishing a PTO assembly model; designing a time function load test for the assembly model; analyzing multiple dangerous node positions and their stress curve states; designing a load collection experiment for multiple dangerous parts of the PTO assembly based on a DAQ strain test system; load-time history spectrum analysis based on linear elastic deformation; applying stress gradient coefficient correction to the P-S-N curve of the PTO assembly material; calculating multiple dangerous node stress concentration coefficients and surface state coefficients; establishing the S-N curve of PTO assembly parts; and predicting fatigue life based on Miner linear theory. The present invention simplifies and optimizes the reliability analysis process of the transmission structure, applies field measured load data to finite element dynamics analysis, corrects the fatigue life calculation coefficient, ensures the life requirements of the PTO assembly in later actual work, and provides a scientific theoretical basis for fatigue life prediction of complex overall structures.

Description

Technical Field

[0001] The invention relates to the technical field of overall fatigue life prediction and analysis of a transmission mechanism, and in particular to a fatigue life prediction method for a PTO assembly of a paddy field power machinery. Background Art

[0002] There are many mountains and hills in southern China, especially in Lingnan. Most rice planting patterns are block-based and strip-based. Large-scale rice planting areas have not yet been formed. With the continuous development of agricultural mechanization and product replacement, paddy field light power machinery is more suitable for the current planting environment, and the multi-purpose and simple operability of agricultural machinery are highly appreciated by the majority of farmers. Therefore, in order to achieve the multi-purpose of paddy field power machinery, its power take-off device (Power TakeOff, referred to as PTO assembly) needs to meet the fatigue life requirements of different machines under different actual working conditions, to prevent the damage of mechanical core parts during the operation of the whole machine, the machine stopping during the direct seeding of paddy fields, and the occurrence of safety accidents. The transmission meshing gear is the core component in the transmission process of the PTO assembly. Its fatigue deformation determines the efficiency and reliability of the entire PTO assembly power transmission. Therefore, in order to ensure that the PTO assembly structure of paddy field power machinery meets the fatigue life requirements of the actual working conditions of the whole machine, it is necessary to fully and scientifically predict and verify the life of structures such as transmission shafts and transmission gears, and it is necessary to develop a standardized fatigue life analysis technology for the overall structure of the PTO assembly of paddy field power machinery.

[0003] The input torque of the PTO assembly is the torque output by the engine through the gearbox, and it is also subject to the vibration excitation of the chassis structure. This vibration excitation includes the external load influence of the PTO assembly from external complex environments such as rough terrain, bumpy roads, and uneven muddy paddy fields. In order to accurately analyze the fatigue life under high-speed transmission of meshing gears, it is also necessary to consider the influence of inertia force and damping on the PTO. In this way, it is necessary to carry out transient dynamic analysis of the PTO. The transient dynamic analysis of the assembly structure under this complex dynamic load input can fully simulate the stress magnitude and stress distribution state it is subjected to. At present, many scholars at home and abroad have conducted in-depth research on structural fatigue based on theory and experiments for core components, and established a large number of fatigue life prediction methods. Zhao Xueyan optimized the fatigue analysis method of parts based on the traditional stress field intensity method, and conducted experimental verification and comparison between the optimized stress field intensity method and the traditional stress field intensity method, nominal stress method and local strain method, and concluded the applicability of the optimized fatigue analysis method. In view of the limitations of rain flow counting and rain flow domain extrapolation methods in the compilation of traditional transmission system load spectrum, Yang Zihan proposed a time domain extrapolation method for high-power tractor transmission shaft load based on POT model. In the compilation of load spectrum, the load time series of any course can be obtained, and the order of measured load cycle can be retained to a great extent. These fatigue analysis methods and load spectrum determination methods based on actual test experiments have optimized theoretical methods, but lack targeted test design and fatigue analysis steps for integral transmission systems, and lack consideration of the influence of material structure size, stress concentration, surface state and temperature on the overall structure during fatigue analysis. In addition, the load data measured by the field test test system in the process of fatigue life prediction of integral structures such as PTO assembly is critical to the position determination of key parts of PTO assembly and fixed-point fatigue analysis. Therefore, such indoor bench experiments often fail to achieve the expected results to some extent, and thus cannot make accurate analysis of the fatigue life of structures or machinery.

[0004] In summary, the existing fatigue life analysis methods for the overall structure of the PTO assembly still have certain limitations. From the initial force data collection and load spectrum compilation to the optimization of fatigue analysis methods, there are few comprehensive considerations of stress level, stress concentration, surface state, temperature and other influencing factors. With the further improvement and development of fatigue life analysis theory from zero position to integral structure, fatigue life analysis of the overall PTO assembly structure will be a key issue that needs to be solved urgently, which is also of great significance to the life analysis and structural integrity of paddy field power machinery in engineering practice. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies of the above prior art and provide a method for predicting the fatigue life of a PTO assembly of a paddy field power machinery.

[0006] The purpose of the present invention is achieved through the following technical solution: A method for predicting fatigue life of a paddy field power machinery PTO assembly, comprising the following steps:

[0007] S1. Simplify the structure of the PTO assembly and form a three-dimensional model, wherein the three-dimensional model is used to establish a PTO assembly model using finite element analysis software;

[0008] S2. Performing a time function load test and a transient dynamics analysis on the PTO assembly model to obtain a transient dynamics analysis result;

[0009] S3. According to the transient dynamics analysis results, a stress gradient analysis is performed on the PTO assembly model to obtain multiple dangerous node positions and stress curve states thereof;

[0010] S4. According to the positions of multiple dangerous nodes and their stress curve states, multiple dangerous parts are selected in the PTO assembly based on the DAQ strain test system, and load collection experiments are performed on the multiple dangerous parts to obtain a load-time history spectrum;

[0011] S5. Based on linear elastic deformation, select the load-time history of multiple dangerous node positions, translate the load-time history spectrum, and obtain measured stress spectrum data;

[0012] S6. Perform stress gradient correction on the PSN curve of the material of the PTO assembly to obtain a material fatigue life curve;

[0013] S7, calculating according to the plurality of dangerous parts in step S4, obtaining a stress concentration factor and a surface state factor;

[0014] S8, establishing a part SN curve of the PTO assembly according to the PSN curve in step S6, the stress concentration factor and the surface condition coefficient in step S7;

[0015] S9. According to the measured stress spectrum data in step S5 and the SN curve in step S8, the PTO assembly is subjected to stress fatigue analysis, and the fatigue life of the PTO assembly is predicted based on Miner linear damage accumulation.

[0016] A better option is that step S2 includes the following steps:

[0017] S201, performing modal analysis on the PTO assembly model to obtain modal analysis results;

[0018] S202, determining a fixed frequency of the PTO assembly model according to the modal analysis result, and limiting a translation range of the PTO assembly model by a time step under transient dynamics analysis according to the fixed frequency;

[0019] S203, the PTO assembly structure is equipped with a torque sensor, and the signal collected by the torque sensor is processed by a data processing module to obtain a torque-time history load spectrum;

[0020] S204. Input the torque-time history load spectrum into transient dynamics analysis to obtain the transient dynamics analysis result.

[0021] A better option is that step S4 includes the following steps:

[0022] S401, the DAQ strain test system includes a wireless strain sensor system, a data acquisition module, a data acquisition card and a data analysis system, and the wireless strain sensor system is installed at a plurality of dangerous locations;

[0023] S402, the wireless strain sensor system transmits the signal data to the data acquisition module, and the data acquisition module corrects and amplifies the signal data and then transmits it to the data acquisition card;

[0024] S403: The data acquisition card transmits the signal data to the data analysis system, and the data analysis system processes the signal data to obtain the load-time history spectrum.

[0025] A better option is that the wireless strain sensor system in step S401 includes a micro power supply, a strain sensor and a signal transmitter, the strain sensor and the signal transmitter are both connected to the micro power supply, the strain sensor is installed in multiple dangerous locations, and the strain sensor is connected to the data acquisition module through the signal transmitter.

[0026] A better choice is that the formula of the PSN curve in step S6 is:

[0027] lgN p =α p -b p lg(1+K)σ)

[0028] Wherein, Np is the material fatigue life of the PTO assembly when the survival rate is P, and α p and the b p is a material constant of the PTO assembly related to the survival rate, K is an adjustment coefficient, and σ is a stress value after considering the stress gradient correction coefficient of the dangerous part.

[0029] A more preferred option is that the number of the dangerous parts in step S4 is more than 4.

[0030] For a better choice, the SN curve in step S8 conforms to the formula:

[0031]

[0032] Wherein, Kt is the stress concentration coefficient in step S7, p is the error coefficient, β is the surface state coefficient in step S7, and a is the introduced surface state adjustment coefficient.

[0033] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0034] The present invention optimizes the structural fatigue reliability analysis process through a fatigue life prediction method for the PTO assembly of paddy field power machinery, introduces field measured load data into the fatigue analysis of the integral structure power transmission, and actually measures the internal stress and strain of the structure by building a DAQ strain testing system. The measurement results are applied to the finite element dynamics analysis to ensure that the results are reliable and the fatigue life is referenceable, which can ensure the life requirements of the PTO assembly in later actual work. The present invention also optimizes the fatigue life prediction coefficient to make the original life prediction value closer to the actual value, which can provide a scientific theoretical basis for fatigue life prediction of complex integral structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of a method for predicting fatigue life of a PTO assembly of a paddy field power machinery according to the present invention;

[0036] Figure 2 It is a schematic diagram of a three-dimensional model of a PTO assembly structure for a method for predicting fatigue life of a PTO assembly of a paddy field power machinery according to the present invention;

[0037] Figure 3 It is a partial diagram of grid division of a fixed pawl shift gear (with 15 teeth) and an intermediate shaft shift gear (with 34 teeth) in a method for predicting fatigue life of a paddy field power machinery PTO assembly according to the present invention;

[0038] Figure 4 It is a schematic diagram of a method for predicting fatigue life of a PTO assembly of a paddy field power machinery according to the present invention, in which strain sensors are attached to seven dangerous nodes;

[0039] Figure 5 It is a schematic diagram of a PSN curve after stress gradient correction for a fatigue life prediction method of a paddy field power machinery PTO assembly according to the present invention;

[0040] Figure 6 A fatigue life analysis flow chart of key transmission gears of a fatigue life prediction method for a paddy field power machinery PTO assembly according to the present invention;

[0041] Figure numerals: 1. Input gear shaft; 2. Fixed pawl shift gear; 3. Clutch fixed pawl; 4. Clutch shaft; 5. Intermediate shaft shift gear; 6. Intermediate gear shaft; 7-13. Dangerous node positions. DETAILED DESCRIPTION

[0042] The purpose of the present invention is further described in detail below with reference to the accompanying drawings and specific examples. The examples cannot be described one by one here, but the implementation methods of the present invention are not therefore limited to the following examples.

[0043] like Figure 1 As shown, a method for predicting fatigue life of a PTO assembly of a paddy field power machinery comprises the following steps:

[0044] S1. In this embodiment, the meshing gear with a hole distance of 16 cm is taken as an example, and the force of the gear not involved in the transmission on the shaft is ignored. The gear teeth contact is matched by the universal function method, and the PTO assembly structure is simplified and assembled into a three-dimensional model, as shown in FIG. Figure 2 As shown in the figure, the finite element (ANSYS Workbench) software is used to perform meshing, define unit properties, unit real constants and material property parameters, and complete the establishment of the PTO assembly model. The specific steps are as follows: First, define the unit type as solid type, the material type as linear elastic material, and the elastic modulus as 2.09×10 11 g / cm^2, Poisson's ratio is 0.269, density is 7.85g / cm3, tetrahedral units are selected for meshing, mesh encryption and optimization are performed at the meshing points of the gears and the junctions of the gears and shafts, and the mesh precision of the dangerous parts of the structure is controlled as much as possible. Figure 3 The local diagram of mesh division for the fixed pawl shift gear and the intermediate shaft shift gear. According to the shape characteristics of the model and the possible large stress area, the mesh is optimized in a targeted manner, and the mesh size is selected as 2mm. The rest of the areas remain default, and the number of meshes is controlled to about 150,000 as much as possible to ensure the accuracy of finite element analysis while also having a faster calculation speed.

[0045] S2. Perform time function load test measurement and time domain analysis on the input gear shaft of the PTO assembly model to obtain transient dynamic analysis results.

[0046] A time function load test is designed to determine the dynamic torque-time history of the input gear shaft, including the construction of a field measured load test platform and data processing, installing a torque sensor on the gearbox output shaft of the PTO assembly model and the PTO input shaft near the PTO end, transmitting the signal collected during the test to the data processing module after noise reduction filtering and low-pass filtering signal processing, and using the laboratory virtual instrument engineering platform (LabView) data analysis software to process the collected data to obtain a torque-time history load spectrum for load input under transient dynamic analysis. The dynamic time load history load spectrum is loaded into the analysis module of the LabView software as an external load input, and the translation of the assembly structure on the three axes of x, y and z must be limited during the analysis process; step S2 includes the following specific steps:

[0047] S201. Perform modal analysis on the PTO assembly model to obtain modal analysis results.

[0048] Input the PTO assembly model in step S1 into the modal analysis, set the elastic modulus EX and density DENS of 45 steel, and completely ignore the nonlinear characteristics of the material. Add constraints for rotation in three axes and limit translation in three axes to solve.

[0049] S202. According to the modal analysis results, the fixed frequency and vibration mode of the PTO assembly model are determined for estimating the control parameters. According to the fixed frequency, under transient dynamic analysis, the time step of the PTO assembly structure meets the accuracy requirements and is within a relatively small range. The elastic modulus, Poisson's ratio and material density of the PTO assembly structure need to be defined in the modal analysis, and the application of boundary conditions needs to limit the translation in the x, y and z directions.

[0050] Combined with the influence of the PTO assembly's own vibration on the input load response under actual working conditions, the natural frequency wi of the assembly during the overall transmission process is determined by modal analysis. The natural frequency is used to determine the reasonable time step of transient dynamic analysis. Assuming that the vibration is free vibration and damping is not considered, the equation is:

[0051]

[0052]

[0053] Where [M] represents the mass matrix, [K] represents the stiffness matrix, [C] represents the damping matrix, and {μ} represents the vibration mode at the natural frequency. is the acceleration under vibration deformation, ω is the natural frequency of the structure, and f is the natural frequency. The vibration frequency f of the mechanism can be calculated from the natural frequency ω.

[0054] S203, the load applied in transient dynamic analysis is the time-load history, which is measured by the time function load test of the input gear shaft of the PTO assembly structure. The first load step is the simple harmonic load input, and the second load step is the measured dynamic load. The vibration excitation of the PTO structural part by the vibration of the rice direct seeding machine during actual operation can be analyzed by comparison. The torque sensor is installed on the gearbox output shaft and the PTO input shaft of the PTO assembly structure near the PTO end. The signal collected by the torque sensor is transmitted to the data processing module after noise reduction filtering and low-pass filtering signal processing to obtain the torque-time history load spectrum for the load input of transient dynamic analysis. The transient dynamic analysis load input will be recorded in two sections. The first load is a simple harmonic load with known frequency and amplitude, and the second load is a dynamic load measured in the experiment.

[0055] S204, inputting the torque-time history load spectrum into the transient dynamics analysis to obtain the transient dynamics analysis result;

[0056] The overall structure of the PTO assembly is subjected to alternating stress under actual working conditions and suffers fatigue damage. At the same time, the impact of the initial excitation of the whole machine on the PTO structure cannot be ignored. The input load is a dynamic stress-time history. During the analysis process, the translation of the assembly on the x, y, and z axes must be limited. The time domain analysis equation is:

[0057]

[0058] Where [M] is the mass matrix, [C] is the damping matrix, [K] is the stiffness matrix, {s} is the displacement vector, is the velocity vector, is the acceleration vector, {F(t)} is the input force vector, where the first loading history F(t) = Fsin(ωt), and the second loading history is the measured dynamic load.

[0059] S3. According to the results of transient dynamics analysis, for multiple dangerous node positions with the largest stress on the PTO assembly model, 7 dangerous node positions with larger stress are selected as dangerous node load spectrum acquisition experiments based on the DAQ strain test system, such as Figure 4 The 7 dangerous node positions are shown. First, the simulated stress spectra of the 7 dangerous node positions are analyzed, and the stress gradients of the dangerous node positions are determined and solved and analyzed to obtain the stress gradients. Then the positions of the PTO assembly model with high stress and the stress distribution cloud composition of the high stress area are determined to obtain the stress curve state. The magnitude of the stress gradient can be calculated by the following formula:

[0060]

[0061] This equation calculates that, where σxy is the stress tensor, χ z is the length in the z direction, and x, y, z represent the directions of the coordinate system.

[0062] The method for determining the magnitude and direction of the stress gradient is as follows: a reasonable position can be selected to paste the strain sensor based on the modal analysis results in step 2 (i.e., the stress distribution cloud diagram) and the local stress gradient distribution.

[0063] S4, DAQ strain test system includes wireless strain sensor system, data acquisition module, data acquisition card and data analysis system, and wireless strain sensor system includes micro power supply, strain sensor and signal transmitter. Based on DAQ strain test system, load collection experiment is carried out on 7 dangerous parts of PTO assembly to obtain load-time history spectrum of 7 dangerous parts. Step S4 includes the following specific steps:

[0064] S401. Determine the local area of ​​the PTO assembly model that is subject to greater stress and the stress distribution cloud map at the local area through the multiple dangerous node positions and stress curve states in step S3. Select at least 4 dangerous parts (7 dangerous parts are selected in this embodiment) in the PTO assembly and calibrate the serial numbers in sequence. The strain sensors of the wireless strain sensor system are installed at the 7 dangerous node positions on the contact surface of the PTO assembly; the pasting at the gear meshing part of the PTO assembly structure requires the gear side wall to be flattened, and the micro power supply and signal transmitter are pasted together on the gear side wall of the PTO assembly.

[0065] S402, connect the data acquisition module, the data acquisition card and the data analysis system in sequence. The LabView data acquisition software is installed in the computer to form the data analysis system. The strain sensor of the wireless strain sensor system transmits the strain signal data through the signal transmitter. The signal receiver receives the strain signal data, the strain signal data is corrected by the data acquisition module, the corrected strain signal data is input into the data acquisition card through the signal amplifier, and the data acquisition card transmits the strain signal data to the computer through the I / O interface.

[0066] S403, the LabView data acquisition software in the computer collects and analyzes the strain signal data, filters the high-frequency noise through low-pass filtering, and processes to obtain the load-time history spectrum.

[0067] S5. Based on linear elastic deformation, the load-time history of 7 dangerous nodes is selected, and the load-time history spectrum is translated to obtain the measured stress spectrum data. According to Hu Ke's law, the stress-strain relationship under linear elastic deformation is:

[0068] σ=Eε

[0069] Among them, E is the elastic modulus, σ is the stress value after considering the stress gradient correction coefficient of the dangerous part, and ε represents the positive strain, which refers to the relative deformation (relative elongation DL / L) under the action of external force. It reflects the size of the deformation of the object. The dynamic course of stress over time is obtained. By comparing and analyzing the finite element simulation and measured load results, the dangerous node position with the largest stress (that is, corresponding to the corresponding dangerous part on the PTO assembly) is selected from multiple dangerous node positions when the data are highly consistent. The fatigue life analysis calculation of the PTO assembly is performed. In this embodiment, the dangerous node position is selected as the dangerous node with the largest stress for fatigue reliability analysis.

[0070] S6. Modify the PSN curve relationship of the material of the PTO assembly (i.e., 45# steel material) to obtain the material fatigue life curve, as shown in the attached figure. Figure 5 As shown;

[0071] The formula of the PSN curve in step S6 is:

[0072] lgN p =α p -b p lg((1+K)σ)

[0073] Where Np is the material fatigue life when the survival rate is P, α p and b p is the material constant of the PTO assembly related to the survival rate. When P = 50%, α p is 45.4561, b p When α is -15.6866 and P=95%, p =41.5280, b p =-13.8957, K is the adjustment coefficient, which comprehensively considers the influence of stress concentration, surface state of smooth specimens, etc. on material PSN, and σ is the stress value after considering the stress gradient correction coefficient of dangerous parts. Here, the stress mean cannot be used to simply calculate the cycle N. Introducing the adjustment coefficient K can eliminate the influence of some curve errors on life.

[0074] S7. According to the transient dynamic analysis results in step S2 (i.e., the stress distribution cloud diagram), the stress concentration factor is obtained by analyzing the position of the maximum stress change of the PTO assembly structure. The formula is:

[0075]

[0076] Where Kt is the stress concentration factor under torsion and shear, τ max is the maximum shear stress, τ is the nominal shear stress, which can be found in the design manual;

[0077] At the same time, when translating the PSN curve of the material of the PTO assembly into the SN curve of the parts of the PTO assembly, the surface state coefficient β is introduced, and the formula is obtained as follows:

[0078]

[0079] Among them, β is the surface state coefficient, δ β is the fatigue limit of 45# steel material of the actual PTO assembly structure, and δ is the fatigue limit of 45# steel material of the standard smooth specimen.

[0080] S8. Translate the material PSN curve in step S6 into the part SN curve, and establish the part SN curve of the PTO assembly based on the PSN curve in step S6 and the calculation of the stress concentration factor and the surface state factor in step S7; the part is affected by local stress concentration, and the surface state of the part will change under actual working conditions. At this time, the SN curve of the part is not completely the material SN curve under the smooth specimen experiment. It should be emphasized here that the turning point N1 value of the SN curve of the high-strength gear of the PTO assembly is much smaller than the N1 value of its material SN curve, and the N1 value of the material SN curve cannot be used to replace the N1 value of the part SN curve. By considering the uneven surface roughness and local stress of the material PSN curve, as well as the influence of the dimensional state on the PSN curve, a comprehensive adjustment coefficient K is introduced;

[0081] The SN curve in step S8 conforms to the formula:

[0082]

[0083] Wherein, Kt is the stress concentration coefficient calculated in step S7, p is the error coefficient, β is the surface state coefficient in step S7, and a is the introduced surface state adjustment coefficient;

[0084] At this time, the SN curve of the PTO assembly structure satisfies:

[0085] C=(SK) a N0

[0086] Among them, a and C are constant parameters related to the material of the PTO assembly, S is stress, and N0 is the life of the part. Fatigue damage is often related to materials and stress concentration. This formula introduces a comprehensive adjustment coefficient correction for stress concentration and surface state, which can obtain more accurate fatigue analysis results and more accurate prediction of the life of the PTO assembly.

[0087] S9. According to the measured stress spectrum data in step S5 and the component life in step S8, the PTO assembly is subjected to stress fatigue analysis, and the fatigue life N of the PTO assembly is obtained based on Miner linear damage accumulation calculation. The damage increases linearly with the increase of the number of cycles at each stress level, and the damage rate D is

[0088]

[0089] Where n is the actual number of load actions, and N is the number of cyclic failures of the part material.

[0090] When the damage accumulation of the PTO assembly reaches the limit under various loads, the damage rate D = 1, which can be regarded as the failure of the structure of the part. The damage caused by different loads is linearly superimposed, and the damage rate is

[0091]

[0092] The estimation of the fatigue life N of the PTO assembly can be obtained through actual load test and Miner linear damage theory. Miner linear damage theory points out that the damage rate D = 1 when fatigue failure occurs,

[0093]

[0094] Among them, n j is the number of cycles under the jth load, N j is the number of cycles at which the part fails under the measured stress; S4 and S5 are based on the DAQ strain test system to collect the load on the dangerous part of the experimental strain spectrum and translate it into the stress spectrum to obtain the measured stress load spectrum, which can be determined The value of N j It can be obtained through the SN curve of the component materials of the PTO assembly, and finally the fatigue life of the PTO assembly can be analyzed, predicted and estimated.

[0095] The above specific implementation modes are preferred embodiments of the present invention and cannot be used to limit the present invention. Any other changes or other equivalent replacement methods that do not deviate from the technical solution of the present invention are included in the protection scope of the present invention.

Claims

1. A method for predicting fatigue life of a PTO assembly of a paddy field power machinery, characterized in that: The following steps are involved: S1. Simplify the structure of the PTO assembly and form a three-dimensional model, wherein the three-dimensional model is used to establish a PTO assembly model using finite element analysis software; S2. Performing a time function load test and a transient dynamics analysis on the PTO assembly model to obtain a transient dynamics analysis result; S3. According to the transient dynamics analysis results, a stress gradient analysis is performed on the PTO assembly model to obtain multiple dangerous node positions and stress curve states thereof; S4. According to the positions of multiple dangerous nodes and their stress curve states, multiple dangerous parts are selected in the PTO assembly based on the DAQ strain test system, and load collection experiments are performed on the multiple dangerous parts to obtain a load-time history spectrum; S5. Based on linear elastic deformation, select the load-time history of multiple dangerous node positions, translate the load-time history spectrum, and obtain measured stress spectrum data; S6. Perform stress gradient correction on the PSN curve of the material of the PTO assembly to obtain a material fatigue life curve; S7, calculating according to the plurality of dangerous parts in step S4, obtaining a stress concentration factor and a surface state factor; S8, establishing a part SN curve of the PTO assembly according to the PSN curve in step S6, the stress concentration factor and the surface condition coefficient in step S7; S9, performing stress fatigue analysis on the PTO assembly according to the measured stress spectrum data in step S5 and the SN curve in step S8, and predicting the fatigue life of the PTO assembly based on Miner linear damage accumulation; Step S2 includes the following steps: S201, performing modal analysis on the PTO assembly model to obtain modal analysis results; S202, determining a fixed frequency of the PTO assembly model according to the modal analysis result, and limiting a translation range of the PTO assembly model by a time step under transient dynamics analysis according to the fixed frequency; S203, the PTO assembly model is equipped with a torque sensor, and the signal collected by the torque sensor is processed by a data processing module to obtain a torque-time history load spectrum; S204, inputting the torque-time history load spectrum into transient dynamics analysis to obtain the transient dynamics analysis result; Step S4 includes the following steps: S401, the DAQ strain test system includes a wireless strain sensor system, a data acquisition module, a data acquisition card and a data analysis system, and the wireless strain sensor system is installed at a plurality of dangerous locations; S402, the wireless strain sensor system transmits the signal data to the data acquisition module, and the data acquisition module corrects and amplifies the signal data and then transmits it to the data acquisition card; S403: The data acquisition card transmits the signal data to the data analysis system, and the data analysis system processes the signal data to obtain the load-time history spectrum.

2. According to claim 1, a method for predicting fatigue life of a paddy field power machinery PTO assembly is characterized in that: The wireless strain sensor system in step S401 includes a micro power supply, a strain sensor and a signal transmitter, the strain sensor and the signal transmitter are both connected to the micro power supply, the strain sensor is installed at multiple dangerous locations, and the strain sensor is connected to the data acquisition module via the signal transmitter.

3. According to claim 1, a method for predicting fatigue life of a paddy field power machinery PTO assembly is characterized in that: The formula of the PSN curve in step S6 is: lgN p =α p -b p lg((1+K)σ) Among them, the N p is the material fatigue life of the PTO assembly when the survival rate is P, and the α p and the b p is a material constant of the PTO assembly related to the survival rate, K is an adjustment coefficient, and σ is a stress value after considering the stress gradient correction coefficient of the dangerous part.

4. According to claim 1, a method for predicting fatigue life of a paddy field power machinery PTO assembly is characterized in that: The number of the dangerous parts in step S4 is 4 or more.

5. According to claim 1, a method for predicting fatigue life of a paddy field power machinery PTO assembly is characterized in that: The SN curve in step S8 conforms to the formula: Wherein, Kt is the stress concentration coefficient in step S7, p is the error coefficient, β is the surface state coefficient in step S7, and a is the introduced surface state adjustment coefficient.

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