A method, system, device, medium and product for predicting fatigue life of off-road vehicle suspension components

Abstract

The application discloses an off-road vehicle suspension component fatigue life prediction method, system, device, medium and product, and relates to the field of vehicle engineering.The method comprises the following steps: constructing a whole vehicle multi-body dynamics model according to the parameters of each subsystem of an off-road vehicle; dividing a road surface into multiple unevenness levels based on a road surface power spectral density; generating road spectrum data of each unevenness level road surface based on a filtered white noise road unevenness time domain model, and generating a customized road spectrum according to the proportion of each unevenness level road surface; inputting the customized road spectrum as an excitation into the whole vehicle multi-body dynamics model, performing multi-body dynamics simulation, and extracting a load spectrum of a suspension component; performing finite element analysis based on the load spectrum to obtain a transient stress field; performing fatigue analysis based on the transient stress field by using a Miner linear cumulative damage criterion, and outputting fatigue life. The application can reduce manual intervention, improve the precision and efficiency of suspension component fatigue life prediction, and meet the actual engineering requirements.

Description

Technical Field

[0001] This application relates to the field of vehicle engineering, and in particular to a method, system, device, medium, and product for predicting the fatigue life of suspension components for off-road vehicles. Background Technology

[0002] Off-road vehicles are specifically designed to adapt to harsh road conditions and off-road driving, requiring high reliability in their chassis and body structures. As a crucial component of the chassis system connecting the body and wheels, the suspension components endure complex and variable loads during off-road driving, making them prone to fatigue failure. The breakage or failure of critical suspension components can have severe consequences. Therefore, quickly and accurately predicting the fatigue life of suspension components in off-road vehicles under different road conditions is of great significance for improving the safety and reliability of off-road vehicles and extending their service life.

[0003] There are two main methods for predicting the fatigue life of suspension components in off-road vehicles: one is real-vehicle testing, which involves driving a real vehicle on actual roads for a long time and over long distances to observe the reliability performance of the suspension components under real working conditions, or bench testing. This method yields accurate results, but it is time-consuming, costly, and difficult to cover all working conditions. The other method is virtual testing, which involves building a virtual model of the suspension system or the entire vehicle to simulate fatigue life. This method is short-term and low-cost, but due to the complexity and variability of the actual load spectrum, it is difficult to accurately simulate in the simulation. The simulation process requires manual operation of software such as Adams, ANSYS, and nCode, and data transfer relies on manual operation, which is prone to errors and inefficient. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, device, medium, and product for predicting the fatigue life of suspension components for off-road vehicles, which can reduce manual intervention, improve the accuracy and efficiency of fatigue life prediction for suspension components, and meet practical engineering needs.

[0005] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for predicting the fatigue life of suspension components in off-road vehicles, including: A multibody dynamics model of the entire vehicle is constructed based on the parameters of each subsystem of the off-road vehicle. The road surface is classified into multiple roughness levels based on the road surface power spectral density; Based on the time-domain model of road roughness using filtered white noise, road spectrum data for each roughness level of road surface are generated, and customized road spectrum is generated according to the proportion of each roughness level of road surface. The customized road spectrum is used as an excitation input to the vehicle multibody dynamics model to perform multibody dynamics simulation and extract the load spectrum of the suspension components. Finite element analysis based on load spectrum yields transient stress field; Based on the transient stress field, fatigue analysis is performed using the Miner linear cumulative damage criterion, and fatigue life is output.

[0006] Secondly, this application provides a fatigue life prediction system for off-road vehicle suspension components, comprising: The vehicle multibody dynamics model building module is used to build a vehicle multibody dynamics model based on the parameters of each subsystem of the off-road vehicle. The roughness classification module is used to classify the road surface into multiple roughness levels based on the road surface power spectral density; The road spectrum data generation module is used to generate road spectrum data for each roughness level of the road surface based on the time-domain model of road surface roughness with filtered white noise, and to generate customized road spectra according to the proportion of each roughness level of the road surface. The load spectrum extraction module is used to input the customized road spectrum as an excitation into the vehicle multibody dynamics model to perform multibody dynamics simulation and extract the load spectrum of the suspension components. The transient stress field determination module is used to perform finite element analysis based on the load spectrum to obtain the transient stress field; The fatigue life prediction module is used to perform fatigue analysis based on the transient stress field using the Miner linear cumulative damage criterion and output the fatigue life.

[0007] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for predicting the fatigue life of off-road vehicle suspension components.

[0008] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for predicting the fatigue life of off-road vehicle suspension components.

[0009] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for predicting the fatigue life of off-road vehicle suspension components.

[0010] According to the specific embodiments provided in this application, this application has the following technical effects: This application generates road spectrum data for various road surface roughness levels using a filtered white noise road surface roughness time-domain model. Combined with multibody dynamics simulation of the entire vehicle and finite element transient stress analysis, it achieves efficient, accurate, and automated prediction of the fatigue life of off-road vehicle suspension components. Compared to existing technologies, it avoids the high cost and long cycle of real-vehicle testing, and overcomes the problems of cumbersome manual operation, load distortion, and error-prone data transmission in traditional virtual simulation. It significantly improves the engineering practicality, prediction accuracy, and development efficiency of fatigue analysis, providing strong technical support for the reliability design of off-road vehicle chassis systems. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a method for predicting the fatigue life of suspension components for off-road vehicles, provided in an embodiment of this application; Figure 2 This is a schematic diagram of the road spectrum curve; Figure 3 The diagram shows the load spectrum of the suspension components of an off-road vehicle; (a) is the x-axis load spectrum of the shock absorber on the lower control arm, (b) is the y-axis load spectrum of the shock absorber on the lower control arm, (c) is the z-axis load spectrum of the shock absorber on the lower control arm, (d) is the x-axis load spectrum of the steering knuckle on the lower control arm, (e) is the y-axis load spectrum of the steering knuckle on the lower control arm, and (f) is the z-axis load spectrum of the steering knuckle on the lower control arm. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] In one exemplary embodiment, such as Figure 1As shown, a method for predicting the fatigue life of suspension components in off-road vehicles is provided. This method is executed by a computer device, specifically by a computer device such as a terminal or a server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, and includes the following steps S1 to S6.

[0016] S1: Construct a multibody dynamics model of the entire vehicle based on the parameters of each subsystem of the off-road vehicle.

[0017] S2: Based on the road power spectral density, the road surface is divided into multiple roughness levels.

[0018] S3: Generate road spectrum data for each roughness level of the road surface based on the time-domain model of road surface roughness using filtered white noise, and generate customized road spectrum according to the proportion of each roughness level of the road surface.

[0019] S4: The customized road spectrum is used as an excitation input to the vehicle multibody dynamics model to perform multibody dynamics simulation and extract the load spectrum of the suspension components.

[0020] S5: Based on the load spectrum, finite element analysis is performed to obtain the transient stress field.

[0021] S6: Based on the transient stress field, fatigue analysis is performed using the Miner linear cumulative damage criterion, and the fatigue life is output.

[0022] By implementing steps S1-S6 above, multi-software joint simulation and automatic data transfer are achieved, reducing manual intervention, improving prediction accuracy and efficiency, and meeting actual engineering needs.

[0023] In a specific embodiment, step S1 specifically includes: establishing a subsystem model based on the parameters of each subsystem of the off-road vehicle and the motion relationship between the components of each subsystem; creating a communicator for data transmission between the subsystem models; and assembling each subsystem model according to its actual spatial position and connection relationship to obtain a multibody dynamics model of the whole vehicle.

[0024] Based on the parameters and 3D models of various subsystems such as the off-road vehicle's suspension system, steering system, body system, drive system, and tires, a multibody dynamics model of the entire vehicle is established using Adams / Car.

[0025] The parameters of each subsystem of an off-road vehicle include hard point coordinates, bushing stiffness curves, spring stiffness, shock absorber speed damping force curves, mass and moment of inertia of each component, suspension axle load, tire three-dimensional stiffness, vehicle mass, sprung mass, center of gravity height, wheelbase, track width, tire radius, tire width, engine rated power, and engine maximum torque.

[0026] The steps to establish a multibody dynamics model of a vehicle include: ① Establish a subsystem model in Adams / Car: Create hard points, define hard point coordinates, and create geometric information for each component; create component geometry, import the neutral file of the 3D model into Adams software, update the corresponding attribute file and related parameters of the 3D model; determine the constraint type based on the motion relationship between components, and connect each component with constraints to establish a subsystem model; ② Create a communicator: The communicator is used to realize data transfer between various subsystem models; ③ Assemble the models of each subsystem according to their actual spatial positions and connections to form a multibody dynamics model of the whole vehicle.

[0027] In one specific embodiment, step S2 specifically includes: The road surface roughness is classified into 8 levels, from A to H, based on the road surface power spectral density. Level A is the smoothest road surface, and level H is the roughest road surface.

[0028] In a specific embodiment, step S3 specifically includes: Road spectrum data was generated in Matlab using a time-domain model of road roughness based on filtered white noise. The time-domain model of road roughness based on filtered white noise is as follows: In the formula, The road surface roughness excitation is generated by filtered white noise. Lower cutoff frequency 0.011 , For vehicle speed, For reference spatial frequency, ; The road surface roughness coefficient is the power spectral density value at the reference spatial frequency, with units of... ; It is Gaussian white noise with zero mean.

[0029] By adjusting the road surface roughness coefficient, random road spectrum data corresponding to the roughness level of the road can be generated.

[0030] The proportion of different road roughness levels is customized based on the road conditions that off-road vehicles may encounter in actual driving. For example, Class B roads account for 40%, Class C roads for 30%, Class D roads for 20%, and Class E roads for 10%. This customized design ensures that the generated road spectrum data closely matches the actual driving road environment, thereby improving the accuracy and practicality of the simulation analysis.

[0031] Based on the proportion of road surfaces of each unevenness level, the road spectrum data of each level of road surface are trimmed and spliced ​​to obtain a customized road spectrum, such as... Figure 2As shown, the customized road spectrum is converted into a .rdf format road surface file, which will serve as the input file for the next step of conducting multibody dynamics simulation analysis of the vehicle using Adams software. The save path for the .rdf road surface file is set by the user.

[0032] In a specific embodiment, step S4 specifically includes: calling Adams / Car, loading the vehicle multibody dynamics model, setting the vehicle speed of the off-road vehicle on various road surfaces, using the .rdf format road surface file generated in step S2 as input, realizing the simulation of the vehicle multibody dynamics model on the road surface, and extracting the load spectrum of the suspension components to be analyzed (such as the lower control arm, steering knuckle, thrust rod, etc.).

[0033] Users can set the driving speed of the off-road vehicle on roads with different roughness levels, and perform simulations at the corresponding speeds. Then, the load spectrum of the suspension components to be analyzed is extracted, and the load spectrum is trimmed and spliced ​​according to the proportion of each roughness level road surface to finally obtain the load spectrum of the suspension components. The load spectrum is saved in .txt format, and the save path is set by the user.

[0034] For example, a customized road profile is defined as follows: 40% Class B roads, 30% Class C roads, 20% Class D roads, and 10% Class E roads. An off-road vehicle travels at 70 km / h on Class B roads, 50 km / h on Class C roads, 40 km / h on Class D roads, and 20 km / h on Class E roads. After simulation, the load spectrum of the left lower control arm of the front axle is obtained, such as... Figure 3 As shown in (a)-(f).

[0035] Matlab and Adams achieve co-simulation through the interface function in Adams / Controls. Matlab sends control signals and user-set vehicle speed information to Adams, calling the Adams dynamics solver module. Adams performs multibody dynamics simulation, feeding back the load spectrum of the suspension components to be analyzed to Matlab, and finally outputs a .txt format load spectrum file. Using Matlab and Adams co-simulation can reduce the operation in Adams software, improve simulation efficiency, reduce time costs, and quickly complete multibody dynamics simulation of off-road vehicles under different road spectra and working conditions.

[0036] In a specific embodiment, step S5 specifically includes: constructing a finite element model of the off-road vehicle suspension components; performing transient analysis on the finite element model based on the load spectrum to obtain the transient stress field.

[0037] Creating a finite element model of the suspension components to be analyzed in ANSYS mainly includes: ① Open ANSYS Workbench software and drag the Transient Structural analysis module to the right into the Project Schematic project management area; ② Import the 3D model of the suspension components to be analyzed into ANSYS; ③ Set material properties based on the actual materials of the suspension components, including material density, Young's modulus, Poisson's ratio, SN curve, etc. ④ Mesh the suspension component model; ⑤ Based on the actual connection of the components in the suspension, set the contact and constraints in ANSYS, and apply the load spectrum of the suspension components extracted in step S4 to the corresponding positions of the components. ⑥ Perform transient analysis to solve the transient stress field of the suspension components under the load spectrum; ⑦ Analyze the results to determine whether the maximum stress exceeds the allowable stress of the material. If it does not exceed the allowable stress, continue with fatigue life prediction.

[0038] The SN curve of a material: N is the number of stress or strain cycles a material can withstand before fatigue failure. Therefore, the mechanical properties of the material and the level of applied stress determine the fatigue life of the structure. Generally speaking, a longer fatigue life indicates better mechanical properties of the material and lower stress; conversely, a shorter fatigue life indicates a shorter fatigue life. The SN curve is commonly used to describe the relationship between the stress level of a structure and its fatigue life at that point; it is simply called the SN curve.

[0039] In one specific embodiment, step S6 specifically includes: In ANSYS Workbench, drag the nCode SN Constant (DesignLife) module into the Workbench project management area to establish a connection between transient analysis and the nCode module. Perform fatigue life prediction of suspension components within the nCode SN Constant (DesignLife) module, following these steps: ① Double-click the Solution in the nCode SN Constant module to enter the nCode interface. The finite element analysis results from step S5 have been automatically imported. ② Right-click StressLife_Analysis, select Edit Load Mapping, set the load mapping, and convert the transient analysis calculation results into alternating loads that can be used for fatigue calculation; set Loading Type to Time Step, and the Time Step load mapping can directly use the finite element solution results, and select some or all load steps to participate in fatigue calculation; group all load steps into a set as a loop for fatigue calculation; nCode can obtain the stress change process of the component under all load steps in step S5, and perform fatigue analysis through the Miner linear cumulative damage criterion; ③ Right-click StressLife_Analysis, select Edit Material Mapping, and set the material mapping; ④ Perform fatigue analysis to obtain the fatigue life prediction results of suspension components.

[0040] Miner's linear fatigue cumulative damage theory: This theory assumes that the damage to a structure under each load level is independent, and the total damage accumulates linearly. If the accumulated damage reaches a certain critical value, the structure will fail. Based on this assumption, if the material is subjected to... load Failure occurs after the second cycle, then the load acts horizontally. Second-rate( When ), the damage rate is: When fatigue damage reaches its limit, the material fails, resulting in total damage. .

[0041] Similarly, , ... When the damage to the material caused by various loads reaches a critical point, the total damage... D When the value is 1, fatigue failure occurs in the material. Using the linear cumulative formula (the expression for Miner's linear cumulative damage criterion), then: Total damage, The actual number of cycles for the i-th level load. Let be the limiting cycle number of the material under the i-th level load, i = 1, 2, ..., m, where m is the total number of load levels. When the value is 1, the material is considered to have undergone fatigue failure.

[0042] This application utilizes Matlab, the multibody dynamics simulation software Adams, the finite element simulation software ANSYS, and its embedded fatigue simulation tool nCode to complete road spectrum generation, whole vehicle multibody dynamics simulation, suspension component finite element simulation analysis, and fatigue life simulation analysis. A MATLAB GUI application developed in MATLAB App Designer is used to achieve joint simulation of Matlab with Adams, ANSYS, and nCode, enabling automatic data transfer and visualization, thereby reducing manual intervention, lowering the operational threshold, and improving the repeatability and engineering applicability of simulation results.

[0043] Based on the same inventive concept, this application also provides a system for implementing the fatigue life prediction method for off-road vehicle suspension components as described above. The solution provided by this system is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more system embodiments provided below can be found in the limitations of the fatigue life prediction method for off-road vehicle suspension components described above, and will not be repeated here.

[0044] In one exemplary embodiment, a fatigue life prediction system for off-road vehicle suspension components is provided, comprising the following modules.

[0045] The vehicle multibody dynamics model building module is used to build a vehicle multibody dynamics model based on the parameters of each subsystem of the off-road vehicle. The roughness classification module is used to classify the road surface into multiple roughness levels based on the road surface power spectral density; The road spectrum data generation module is used to generate road spectrum data for each roughness level of the road surface based on the time-domain model of road surface roughness with filtered white noise, and to generate customized road spectra according to the proportion of each roughness level of the road surface. The load spectrum extraction module is used to input the customized road spectrum as an excitation into the vehicle multibody dynamics model to perform multibody dynamics simulation and extract the load spectrum of the suspension components. The transient stress field determination module is used to perform finite element analysis based on the load spectrum to obtain the transient stress field; The fatigue life prediction module is used to perform fatigue analysis based on the transient stress field using the Miner linear cumulative damage criterion and output the fatigue life.

[0046] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores data to be processed. The I / O interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal via a network connection. When the computer program is executed by the processor, it implements the steps in the above-described method embodiments.

[0047] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0048] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0049] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0050] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0051] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0052] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0053] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting the fatigue life of suspension components in off-road vehicles, characterized in that, include: A multibody dynamics model of the entire vehicle is constructed based on the parameters of each subsystem of the off-road vehicle. The road surface is classified into multiple roughness levels based on the road surface power spectral density; Based on the time-domain model of road roughness using filtered white noise, road spectrum data for each roughness level of road surface are generated, and customized road spectrum is generated according to the proportion of each roughness level of road surface. The customized road spectrum is used as an excitation input to the vehicle multibody dynamics model to perform multibody dynamics simulation and extract the load spectrum of the suspension components. Finite element analysis based on load spectrum yields transient stress field; Based on the transient stress field, fatigue analysis is performed using the Miner linear cumulative damage criterion, and fatigue life is output.

2. The method for predicting the fatigue life of off-road vehicle suspension components according to claim 1, characterized in that, A multibody dynamics model of the entire vehicle is constructed based on the parameters of each subsystem of the off-road vehicle, specifically including: A subsystem model is established based on the parameters of each subsystem of the off-road vehicle and the kinematic relationships between the components of each subsystem. A communicator for data transmission between the subsystem models is created, and the subsystem models are assembled according to their actual spatial positions and connection relationships to obtain the vehicle multibody dynamics model.

3. The method for predicting the fatigue life of off-road vehicle suspension components according to claim 1, characterized in that, The expression for the time-domain model of road surface roughness with filtered white noise is: in, For road surface unevenness excitation, The lower cutoff frequency, For vehicle speed, For reference spatial frequency, This is the road surface roughness coefficient. It is Gaussian white noise with zero mean.

4. The method for predicting the fatigue life of off-road vehicle suspension components according to claim 1, characterized in that, Finite element analysis based on load spectrum yields the transient stress field, specifically including: Construct finite element models of off-road vehicle suspension components; The transient stress field is obtained by performing transient analysis on the finite element model based on the load spectrum.

5. The method for predicting the fatigue life of off-road vehicle suspension components according to claim 1, characterized in that, The expression for the Miner linear cumulative damage criterion is as follows: in, Total damage, The actual number of cycles for the i-th level load. Let D be the limit number of cycles for the material under the i-th load level, i = 1, 2, ..., m, where m is the total number of load levels. When D = 1, the material is determined to have failed due to fatigue.

6. A fatigue life prediction system for off-road vehicle suspension components, characterized in that, include: The vehicle multibody dynamics model building module is used to build a vehicle multibody dynamics model based on the parameters of each subsystem of the off-road vehicle. The roughness classification module is used to classify the road surface into multiple roughness levels based on the road surface power spectral density; The road spectrum data generation module is used to generate road spectrum data for each roughness level of the road surface based on the time-domain model of road surface roughness with filtered white noise, and to generate customized road spectra according to the proportion of each roughness level of the road surface. The load spectrum extraction module is used to input the customized road spectrum as an excitation into the vehicle multibody dynamics model to perform multibody dynamics simulation and extract the load spectrum of the suspension components. The transient stress field determination module is used to perform finite element analysis based on the load spectrum to obtain the transient stress field; The fatigue life prediction module is used to perform fatigue analysis based on the transient stress field using the Miner linear cumulative damage criterion and output the fatigue life.

7. The fatigue life prediction system for off-road vehicle suspension components according to claim 6, characterized in that, The expression for the time-domain model of road surface roughness with filtered white noise is: in, For road surface unevenness excitation, The lower cutoff frequency, For vehicle speed, For reference spatial frequency, This is the road surface roughness coefficient. It is Gaussian white noise with zero mean.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the fatigue life prediction method for off-road vehicle suspension components according to any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for predicting the fatigue life of off-road vehicle suspension components as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for predicting the fatigue life of off-road vehicle suspension components as described in any one of claims 1-5.

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