Method and device for determining power system parameter tolerance, storage medium and equipment

Abstract

The application provides a method and device for determining power system parameter tolerance, a storage medium and equipment, and relates to the field of power system engineering. The method comprises the following steps: obtaining an initial preset range of power parameter tolerance; constructing a simulation model of the power system according to the initial preset range of power parameter tolerance, which represents the relationship between the power parameter tolerance and the crankshaft performance parameter, and the crankshaft performance parameter comprises the maximum bearing bush contact pressure; and optimizing the power parameter tolerance according to the initial preset range of power parameter tolerance and the simulation model to obtain a target preset range of power parameter tolerance, so that the maximum bearing bush contact pressure is less than the preset pressure. The method integrates parameterized modeling, multi-physical field coupling simulation, tolerance contribution quantitative analysis, dynamic discrimination and closed-loop optimization and other methods, accurately determines the parameter tolerance of the power system, reduces the local contact pressure of the bearing bush, and improves the reliability and service life of the power system.

Description

Technical Field

[0001] This application relates to the field of power system engineering, and more specifically, to a method for determining power system parameter tolerances, a device for determining power system parameter tolerances, a computer-readable storage medium, and an electronic device. Background Technology

[0002] As a core component of large mining trucks, the reliability of the powertrain directly impacts the overall service life of the truck. Under high-speed, heavy-load conditions, the powertrain of large mining trucks inevitably places higher demands on tolerances such as shaft alignment, engine block and crankshaft cylindricity, and runout. Traditional evaluation methods are often based on idealized assumptions and do not fully consider the influence of manufacturing and assembly tolerances and their coupling effects. Inadequate tolerance design can easily lead to uneven bearing load distribution, excessively high local contact pressure, accelerated wear, and even thermal failure, severely restricting the reliability and lifespan of large mining trucks. Summary of the Invention

[0003] The main objective of this application is to provide a method, apparatus, computer-readable storage medium, and electronic device for determining power system parameter tolerances, so as to at least solve the problem that the parameter tolerances of existing power systems lead to uneven bearing load distribution, excessive local contact pressure, accelerated wear, and low reliability and lifespan of large mining trucks.

[0004] To achieve the above objectives, according to one aspect of this application, a method for determining the tolerance of power system parameters is provided, comprising: obtaining an initial preset range of power parameter tolerances, wherein the power parameter tolerances are parameter tolerances affecting the reliability of crankshaft bearings in the power system; constructing a simulation model of the power system based on the initial preset range of power parameter tolerances, wherein the simulation model characterizes the relationship between the power parameter tolerances and crankshaft performance parameters, wherein the crankshaft performance parameters include the maximum contact pressure of the bearings; and optimizing the power parameter tolerances based on the initial preset range of power parameter tolerances and the simulation model to obtain a target preset range of power parameter tolerances, such that the maximum contact pressure of the bearings is less than a preset pressure.

[0005] Optionally, the crankshaft performance parameters also include crankshaft friction work and minimum crankshaft oil film thickness. The dynamic parameter tolerances are optimized based on the initial preset range and the simulation model to obtain a target preset range. This includes: determining an updated preset range for the dynamic parameter tolerances based on the simulation model and the initial preset range, where the updated preset range is related to the degree of influence of the dynamic parameter tolerances on the reliability of the bearing bush; constructing an optimization model, and optimizing the optimization model based on the simulation model, optimization variables, constraints, and optimization objectives. The optimization results include the target preset range of the power parameter tolerance, the optimization variable is the power parameter tolerance, and the constraints are the maximum contact pressure of the bearing bush within a first preset range, the crankshaft friction work within a second preset range, the minimum oil film thickness of the crankshaft being greater than a preset oil film thickness, and the power parameter tolerance being within the updated preset range. The optimization objective is to minimize the cost of the power system and maximize the reliability of the bearing bush. The cost of the power system is related to the material and material value of the bearing bush, and the reliability of the bearing bush is inversely correlated with the maximum contact pressure of the bearing bush.

[0006] Optionally, determining an updated preset range for the power parameter tolerance based on the simulation model and the initial preset range of the power parameter tolerance includes: determining the change value of the power parameter tolerance, and determining the change value of the crankshaft performance parameter based on the change value of the power parameter tolerance and the simulation model; determining the degree of influence of the power parameter tolerance on the crankshaft performance parameter based on the change value of the crankshaft performance parameter, wherein the degree of influence is positively correlated with the change value of the crankshaft performance parameter; determining a range adjustment parameter for the power parameter tolerance based on the degree of influence of the power parameter tolerance on the crankshaft performance parameter, wherein the range adjustment parameter is inversely correlated with the degree of influence, and the range adjustment parameter is less than 1 when the degree of influence is greater than a preset value; and determining the maximum value of the updated preset range of the power parameter tolerance as the product of the range adjustment parameter and the maximum value of the initial preset range.

[0007] Optionally, the optimization model is optimized according to the simulation model, optimization variables, constraints, and optimization objectives to obtain optimization results, including: optimizing the optimization model according to the simulation model, optimization variables, constraints, and optimization objectives to obtain an initial optimization range for the dynamic parameter tolerance; if the initial optimization range for the dynamic parameter tolerance satisfies the constraints and the optimization objectives, and the maximum contact pressure of the bearing bush is less than or equal to a first preset pressure, the initial optimization range for the dynamic parameter tolerance is determined as the target preset range for the dynamic parameter tolerance; if the initial optimization range for the dynamic parameter tolerance satisfies the constraints and the optimization objectives, and the maximum contact pressure of the bearing bush is greater than the first preset pressure and less than or equal to a second preset pressure, the maximum value of the initial optimization range for the dynamic parameter tolerance is reduced until the maximum contact pressure of the bearing bush is less than or equal to the first preset pressure; if the initial optimization range for the dynamic parameter tolerance satisfies the constraints and the optimization objectives, and the maximum contact pressure of the bearing bush is greater than the second preset pressure, the initial preset range for the dynamic parameter tolerance is redefined.

[0008] Optionally, the objective function of the optimization model is a weighted function of the cost of the power system and the reliability of the bearing bush. After optimizing the optimization model according to the simulation model, optimization variables, constraints, and optimization objective to obtain the initial optimization range of the power parameter tolerance, the method further includes: when the initial optimization range of the power parameter tolerance satisfies the constraints and the optimization objective, and the maximum contact pressure of the bearing bush is greater than the first preset pressure and less than or equal to the second preset pressure, reducing the weight of the cost of the power system and increasing the weight of the weighted function of the reliability of the bearing bush, until the maximum contact pressure of the bearing bush is less than or equal to the first preset pressure.

[0009] Optionally, there are multiple power parameter tolerances. Based on the initial preset range of the power parameter tolerances, a simulation model of the power system is constructed, including: obtaining an initial oil film thickness and determining the sum of the initial oil film thickness and the tolerance disturbance term as an oil film thickness expression, wherein the minimum value of the oil film thickness expression within one operating cycle of the power system is the minimum oil film thickness of the crankshaft; determining multiple parameter tolerance samples based on the initial preset range of the power parameter tolerances, where each parameter tolerance sample includes all the power parameter tolerances, and all the power parameter tolerances in the parameter tolerance sample are within the initial preset range; obtaining the oil film thickness expression and boundary conditions, and analyzing each parameter tolerance sample according to the average Reynolds equation to obtain analysis results, wherein the boundary conditions are the cylinder pressure, inertial load, and rotational speed at the rated point of the bearing bush, and the rated point is the point on the bearing bush with the highest power and highest rotational speed; and using a second-order polynomial to fit the response surface model based on the analysis results to obtain the simulation model of the power system.

[0010] Optionally, the method further includes: constructing a sensitivity map, wherein the first coordinate axis of the sensitivity map represents a first parameter tolerance, the second coordinate axis of the sensitivity map represents a second parameter tolerance, and the third coordinate axis of the sensitivity map represents the maximum contact pressure of the bearing bush, wherein the first parameter tolerance is the parameter tolerance with the highest influence on the crankshaft performance parameters, and the second parameter tolerance has the second highest influence on the crankshaft performance parameters; and visualizing the relationship between the power parameter tolerance and the maximum contact pressure of the bearing bush based on the sensitivity map.

[0011] According to another aspect of this application, a device for determining the tolerance of power system parameters is provided, comprising: an acquisition unit for acquiring an initial preset range of power parameter tolerance, wherein the power parameter tolerance is a parameter tolerance affecting the reliability of the crankshaft bearing of the power system; a construction unit for constructing a simulation model of the power system based on the initial preset range of the power parameter tolerance, wherein the simulation model characterizes the relationship between the power parameter tolerance and crankshaft performance parameters, wherein the crankshaft performance parameters include the maximum contact pressure of the bearing; and an optimization unit for optimizing the power parameter tolerance based on the initial preset range of the power parameter tolerance and the simulation model to obtain a target preset range of the power parameter tolerance, such that the maximum contact pressure of the bearing is less than a preset pressure.

[0012] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform any of the aforementioned methods for determining the tolerances of power system parameters.

[0013] According to another aspect of this application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a method for performing any of the methods for determining the tolerances of the power system parameters.

[0014] Applying the technical solution of this application, the method for determining the tolerance of power system parameters first obtains an initial preset range for the power parameter tolerance, which refers to the parameter tolerance affecting the reliability of the crankshaft bearings in the power system. Based on the initial preset range, a simulation model of the power system is constructed. This simulation model characterizes the relationship between the power parameter tolerance and the crankshaft performance parameters, including the maximum contact pressure of the bearings. The power parameter tolerance is then optimized based on the initial preset range and the simulation model to obtain a target preset range, ensuring that the maximum contact pressure of the bearings is less than the preset pressure. This method integrates parametric modeling, multiphysics coupling simulation, tolerance contribution quantification analysis, dynamic discrimination, and closed-loop optimization. It accurately determines the parameter tolerance of the power system, reduces the local contact pressure of the bearings, and improves the reliability and lifespan of the power system. This solves the problem that existing power system parameter tolerances lead to uneven bearing load distribution, excessively high local contact pressure, accelerated wear, and consequently, low reliability and lifespan for large mining trucks. Attached Figure Description

[0015] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for performing a method for determining power system parameter tolerances according to an embodiment of this application is shown.

[0017] Figure 2 A flowchart illustrating a method for determining the tolerance of power system parameters according to an embodiment of this application is shown.

[0018] Figure 3 A flowchart illustrating another method for determining the tolerance of power system parameters according to an embodiment of this application is shown.

[0019] Figure 4 A flowchart illustrating another method for determining the tolerance of power system parameters according to an embodiment of this application is shown.

[0020] Figure 5A flowchart illustrating another method for determining the tolerance of power system parameters according to an embodiment of this application is shown.

[0021] Figure 6 A structural block diagram of a device for determining the parameter tolerances of a power system according to an embodiment of this application is shown.

[0022] The above figures include the following reference numerals:

[0023] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0024] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] As described in the background section, the power system, as a core component of large mining trucks, directly impacts the overall service life of the truck. Under high-speed, heavy-load conditions, the power system of large mining trucks inevitably places higher demands on tolerances such as shaft alignment, engine block and crankshaft cylindricity, and runout. Traditional evaluation methods are often based on idealized assumptions and do not fully consider the influence of manufacturing and assembly tolerances and their coupling on the power system. Inadequate tolerance design can easily lead to uneven bearing load distribution, excessively high local contact pressure, accelerated wear, and even thermal failure, severely restricting the reliability and lifespan of large mining trucks.

[0028] To address the problem that existing power system parameter tolerances can lead to uneven bearing load distribution, excessively high local contact pressure, accelerated wear, and consequently, low reliability and lifespan of large mining trucks, embodiments of this application provide a method for determining power system parameter tolerances, a device for determining power system parameter tolerances, a computer-readable storage medium, and an electronic device.

[0029] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0030] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a method of determining the tolerance of power system parameters according to an embodiment of the present invention. For example... Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0031] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to method Z in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0032] This embodiment provides a method for determining the tolerance of power system parameters running on a mobile terminal, computer terminal or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] Figure 2 This is a flowchart illustrating a method for determining the tolerances of power system parameters according to an embodiment of this application. For example... Figure 2 As shown, the method includes the following steps:

[0034] Step S201: Obtain the initial preset range of the power parameter tolerance, where the power parameter tolerance is the parameter tolerance that affects the reliability of the crankshaft bearing of the power system;

[0035] Specifically, the key tolerance parameters (i.e., dynamic parameter tolerances) affecting the reliability of crankshaft bearings are first identified and defined. These dynamic parameter tolerances include crankshaft / engine block runout tolerance (T1), crankshaft-motor shaft alignment tolerance (T2), and main bearing housing bore cylindricity tolerance (T3), etc. Simultaneously, a reasonable initial preset range is set for each parameter [T]. min T maxThese tolerances are set to fully reflect the uncertainties in the manufacturing and assembly process, ensuring that the model can comprehensively evaluate the performance of the power system in actual production environments. By reasonably defining the initial preset range of tolerance parameters, it is possible to ensure that the evaluation method covers all possible manufacturing and assembly tolerances, thereby making the evaluation results closer to reality and improving the comprehensiveness and accuracy of the evaluation.

[0036] Step S202: Based on the initial preset range of the above-mentioned power parameter tolerance, construct the above-mentioned power system simulation model. The above-mentioned simulation model characterizes the relationship between the above-mentioned power parameter tolerance and the crankshaft performance parameters, including the maximum contact pressure of the bearing bush.

[0037] Specifically, parametric modeling techniques were used to establish a dynamic system model that incorporates the coupling of elastohydrodynamic lubrication and multibody dynamics. The model includes not only the geometry of the crankshaft and bearings but also their dynamic characteristics and lubrication effects, particularly the dynamic characteristics and contact pressure distribution under the influence of tolerances. The accuracy of the model depends on the rationality of the input tolerance parameters to ensure the reliability of the simulation results. By establishing a high-precision coupled multibody dynamics and elastohydrodynamic lubrication model, the agreement between the simulated bearing contact pressure distribution and the actual wear morphology was significantly improved, increasing from 35% to 80%, effectively reducing evaluation errors and improving prediction accuracy.

[0038] Step S203: Optimize the dynamic parameter tolerance based on the initial preset range of the dynamic parameter tolerance and the simulation model to obtain the target preset range of the dynamic parameter tolerance, so that the maximum contact pressure of the bearing bush is less than the preset pressure.

[0039] Specifically, response surface methodology and global sensitivity analysis are employed to quantify the impact of each tolerance parameter and its interactions on bearing performance indicators. Then, an iterative optimization algorithm continuously adjusts the tolerance parameters until the maximum contact pressure of the bearing meets a preset limit. This preset pressure is a safety threshold, ensuring that the bearing does not fail prematurely due to excessive contact pressure under extreme operating conditions. By dynamically optimizing tolerance parameters, manufacturing costs can be minimized while ensuring the contact pressure of the powertrain crankshaft bearing remains within a safe range. The optimized tolerance design significantly improves the load distribution of the bearing, avoids excessively high local contact pressure, reduces the risk of wear and thermal failure, and thus extends the service life of the powertrain.

[0040] The method for determining the power system parameter tolerances described in this application first obtains an initial preset range for the power parameter tolerances, which are the parameter tolerances affecting the reliability of the crankshaft bearings in the power system. Based on the initial preset range, a simulation model of the power system is constructed. This simulation model characterizes the relationship between the power parameter tolerances and the crankshaft performance parameters, including the maximum contact pressure of the bearings. The power parameter tolerances are then optimized based on the initial preset range and the simulation model to obtain a target preset range, ensuring that the maximum contact pressure of the bearings is less than the preset pressure. This method integrates parametric modeling, multiphysics coupling simulation, tolerance contribution quantification analysis, dynamic discrimination, and closed-loop optimization. It accurately determines the power system parameter tolerances, reduces the local contact pressure of the bearings, and improves the reliability and lifespan of the power system. This solves the problem that existing power system parameter tolerances lead to uneven bearing load distribution, excessively high local contact pressure, accelerated wear, and consequently, low reliability and lifespan for large mining trucks.

[0041] This embodiment provides a method for determining the tolerances of power system parameters to address the problems of insufficient accuracy and limited bearing life caused by the inadequate consideration of multi-tolerance coupling effects in traditional evaluation methods. This technical solution obtains an initial preset range for the power parameter tolerances and constructs a simulation model that characterizes the relationship between power parameter tolerances and crankshaft performance parameters, with the maximum bearing contact pressure being the key performance indicator. In the simulation model, changes in the power parameter tolerances directly affect the crankshaft performance parameters, particularly the maximum bearing contact pressure. Subsequently, based on the initial preset range of the power parameter tolerances and the constructed simulation model, tolerance optimization is performed to obtain a target preset range that keeps the maximum bearing contact pressure below a preset pressure. This effectively avoids problems such as excessively high local contact pressure and uneven load distribution, preventing accelerated bearing wear and thermal failure, and significantly improving the reliability and lifespan of bearings in large power systems. Through this parameter tolerance optimization method, accurate prediction and control of power system bearing reliability are achieved, providing strong technical support for the high-performance, long-life design of power systems.

[0042] In some embodiments, such as Figure 3 As shown, there are multiple tolerances for the aforementioned dynamic parameters. Based on the initial preset range of these dynamic parameter tolerances, a simulation model of the aforementioned dynamic system is constructed, including the following steps:

[0043] Step S2021: Obtain the initial oil film thickness, and determine the sum of the initial oil film thickness and the tolerance disturbance term as the oil film thickness expression, wherein the minimum value of the oil film thickness expression in one operating cycle of the power system is the minimum oil film thickness of the crankshaft.

[0044] Step S2022: Based on the initial preset range of the above-mentioned dynamic parameter tolerance, determine multiple parameter tolerance samples. Each parameter tolerance sample includes all the above-mentioned dynamic parameter tolerances, and all the above-mentioned dynamic parameter tolerances in the parameter tolerance sample are within the above-mentioned initial preset range.

[0045] Step S2023: Obtain the above oil film thickness expression and boundary conditions, and analyze the tolerance samples of each of the above parameters according to the average Reynolds equation to obtain the analysis results. The above boundary conditions are the cylinder pressure, inertial force load and rotational speed at the rated point of the above bearing. The rated point is the point with the highest power and the highest rotational speed on the above bearing.

[0046] Step S2024: Based on the above analysis results, a second-order polynomial is used to fit the response surface model to obtain the simulation model of the above dynamic system.

[0047] First, a tolerance parameter library is established to identify and define tolerance parameters that affect the reliability of crankshaft bearings, including crankshaft / engine block runout tolerance (T1). This runout tolerance only includes radial runout tolerance, as radial runout tolerance has a greater impact on bearing reliability; axial runout tolerance does not affect bearing reliability. Several power parameter tolerances are also included, such as crankshaft-motor shaft alignment tolerance (T2), crankshaft-motor shaft alignment tolerance, and main bearing housing bore cylindricity tolerance (T3), etc. An initial preset range is set for each parameter [T]. min T max The crankshaft-motor shaft alignment tolerance and the crankshaft-motor shaft alignment tolerance both only include radial offset.

[0048] Secondly, a coupled multibody dynamics and elastohydrodynamic lubrication model is constructed, a detailed crankshaft system multibody dynamics model is established, and an elastohydrodynamic lubrication model is integrated at the main bearing location, coupled with the average Reynolds equation:

[0049] ;

[0050] Where t is the current operating time of the engine where the bearing bush is located, x and y are the local coordinates of the lubrication zone of the main bearing friction pair; h and p are the lubricating oil film thickness and oil film pressure, respectively; u is the relative sliding speed between the main journal and the main bearing bush; σ c It is the combined surface roughness of the main journal and the main bearing; μ o It is the dynamic viscosity of the lubricating oil; x , y , S and c These are the pressure-flow factor in the x-direction, the pressure-flow factor in the y-direction, the shear flow factor, and the contact factor, respectively.

[0051] The mean Reynolds equation is the core of elastohydrodynamic lubrication and is now widely used. Subsequent calculations will focus on the maximum contact pressure P. max Minimum oil film thickness h min and friction work W f All of these results were calculated based on the average Reynolds equation.

[0052] The local coordinates of the lubrication zone are the local coordinates of the inner surface of the main bearing, as illustrated in the diagram below. Figure 3 As shown, the inner surface of the bearing bush is unfolded, where x is the circumferential coordinate of the bearing bush and y is the axial coordinate of the bearing bush. The lubricating oil film is located on the inner surface of the bearing bush.

[0053] For the oil film thickness expression h, a tolerance disturbance term h is introduced. j :

[0054] h = h0 + h j ;

[0055] Wherein, the oil film thickness h changes with time, that is, the minimum value within one cycle, and h0 is the initial oil film thickness, that is, the initial oil film thickness without considering tolerance.

[0056] The boundary conditions are then redefined, namely the constant values ​​of the input, load, speed, and lubricating oil properties. Tolerance parameters are defined parametrically in the model. Among the load and speed effects on the bearing bush, the rated point is the most demanding. Optimization only optimizes the rated point. The load is the cylinder pressure and inertial force load corresponding to the rated point, and the speed is the speed corresponding to the rated point. Parameters are variable inputs in the model, directly adjusting and controlling them. Parametrically defining tolerances means modifying the dynamic model by defining tolerances through parameters. Subsequent optimization, under given conditions, adjusts these parameters to achieve the optimal bearing bush evaluation indicators. Parametrically defining tolerances sets the tolerances as adjustable variables, and subsequent optimizations all optimize these variables.

[0057] Design of Experiments (DOE) and automated simulation: Using methods such as Latin hypercube sampling, 200 sets of tolerance parameter combinations are generated within the set tolerance zone, covering the initial preset range [T]. min T max ] Scripts were written to automatically drive simulation software, performing dynamic and lubrication analyses on each parameter combination, i.e., based on the average Reynolds equation analysis, to batch calculate the maximum contact pressure P of the output bearing. max Minimum oil film thickness h min and friction work W f Key performance indicators, etc. Each set of tolerance parameters includes three tolerances: crankshaft / body runout tolerance (T1), crankshaft-motor shaft alignment tolerance (T2), and main bearing housing bore cylindricity tolerance (T3).

[0058] Analysis of Response Surface Model Establishment and Coupling Effects: Based on DOE simulation results, a response surface model (such as a Kriging response surface) is constructed to connect tolerance parameters and performance indicators. The strength of the coupling effect between different tolerances is quantified by analyzing the interaction terms in the approximate model. Essentially, the response surface model is a mathematical approximation used to establish the relationship between design variables, such as tolerance parameters, and system responses, such as the maximum contact pressure of the bearing bush, thus replacing the computationally expensive ergonomic simulation model. Through dynamic and lubrication analysis, the response corresponding to the sample point, i.e., the maximum contact pressure P, is obtained. max Minimum oil film thickness h min and friction work W f Then, a second-order polynomial is used to fit the response surface model. After fitting, the fitting accuracy is evaluated, and the correlation coefficient R² is commonly used for evaluation. The closer it is to 1, the more accurate the simulation model fit.

[0059] The response surface model includes interaction terms. The strength of the coupling effect is confirmed by analysis of variance, which is the proportion of the variance obtained through the interaction terms to the total variance. The larger the proportion, the more significant the interaction.

[0060] In this embodiment, for the reliability assessment of the engine powertrain bearing bush, a power system simulation model containing various tolerance parameters was first constructed. These tolerance parameters cover key factors such as crankshaft / engine block runout tolerance T1, crankshaft-motor shaft alignment tolerance T2, and main bearing housing bore cylindricity tolerance T3. In the model, the dynamic expression of the oil film thickness was determined by adding the initial oil film thickness to the tolerance disturbance term. This expression ensures that the minimum oil film thickness is accurately reflected during the power system's operating cycle. To comprehensively explore the influence of various tolerance combinations, multiple sets of parameter samples were generated within a preset tolerance range. Each set of samples included all power parameter tolerances, allowing coverage of the entire tolerance space without overlooking any potentially significant influences. Subsequently, using the average Reynolds equation, combined with the oil film thickness expression and boundary conditions such as cylinder pressure at the rated point, inertial load, and rotational speed, each set of parameter samples was analyzed in depth, yielding simulation results for key performance indicators such as bearing bush contact pressure distribution, oil film thickness, and friction work. Based on this data, a response surface model was established using second-order polynomial fitting. This model not only reveals the specific impact of different tolerance parameters on bearing performance but also quantifies the coupling effect between them, providing a solid foundation for subsequent tolerance optimization. Next, based on the simulation results and the response surface model, global sensitivity analysis clarified the contribution of each tolerance parameter to bearing performance, and tolerances were classified into critical, important, and general tolerances, effectively guiding tolerance control strategies. Furthermore, by setting dynamic discrimination criteria and a closed-loop iteration mechanism, the tolerance zone can be intelligently adjusted until an ideal balance point is reached, minimizing manufacturing costs while ensuring bearing reliability. These measures significantly improve the accuracy and efficiency of bearing reliability assessment, providing strong support for the design and manufacturing of engine powertrains. Finally, bench tests validated the simulation results, and the model was continuously optimized based on test feedback, ensuring the practicality and continuous improvement capability of the evaluation method. min and T max The initial range for each tolerance parameter is defined, while the tolerance disturbance term h... j This reflects the variation in oil film thickness caused by manufacturing and assembly fluctuations in the actual system, which is crucial for understanding the behavior of the bearing under dynamic conditions. Through the above steps, we can not only predict the performance of the bearing under specific tolerance combinations, but also clearly identify which tolerance parameters need to be controlled more strictly and which can be appropriately relaxed, thereby achieving effective cost control while ensuring the reliability of the bearing.

[0061] In some embodiments, the crankshaft performance parameters also include crankshaft friction work and minimum crankshaft oil film thickness. The power parameter tolerances are optimized based on the initial preset range of the power parameter tolerances and the simulation model to obtain the target preset range of the power parameter tolerances, including the following steps:

[0062] Step S301: Based on the above simulation model and the initial preset range of the above dynamic parameter tolerance, determine the updated preset range of the above dynamic parameter tolerance. The updated preset range is related to the degree of influence of the above dynamic parameter tolerance on the reliability of the above bearing.

[0063] Step S302: Construct an optimization model, and optimize the model according to the simulation model, optimization variables, constraints, and optimization objectives to obtain optimization results. The optimization results include the target preset range of the power parameter tolerance, the optimization variables are the power parameter tolerance, the constraints are the maximum contact pressure of the bearing bush within a first preset range, the crankshaft friction work within a second preset range, the minimum oil film thickness of the crankshaft is greater than the preset oil film thickness, and the power parameter tolerance is within the updated preset range. The optimization objective is to minimize the cost of the power system and maximize the reliability of the bearing bush. The cost of the power system is related to the material and material value of the bearing bush, and the reliability of the bearing bush is inversely correlated with the maximum contact pressure of the bearing bush.

[0064] First, tolerance optimization design is carried out, an optimization model is established, and the tolerance design is optimized. An objective function is established using a weighted average of manufacturing cost and bearing reliability to minimize crankshaft manufacturing cost and maximize bearing reliability. Contact pressure P is used as the basis for this optimization. max Evaluation indicators assess bearing reliability, and bearing constraints include bearing contact pressure P. max and frictional work W f Not exceeding the limit, oil film thickness h min Greater than the limit and the initial preset range [T] min T max ].

[0065] In this embodiment, the powertrain bearing reliability assessment method further considers crankshaft friction work and minimum crankshaft oil film thickness as crankshaft performance parameters. By optimizing the power parameter tolerances, the method ensures that the powertrain cost is minimized while maximizing bearing reliability. Based on the initial preset range of power parameter tolerances, a simulation model is used to determine an updated preset range of power parameter tolerances, which is closely related to the degree of influence on bearing reliability. Subsequently, an optimization model is constructed, based on the simulation model, with power parameter tolerances as optimization variables. The constraints involve that the maximum contact pressure of the bearing must be kept within a first preset range, the crankshaft friction work should be within a second preset range, the minimum crankshaft oil film thickness must be greater than the preset oil film thickness, and the power parameter tolerances must be within the updated preset range. The optimization objective focuses on reducing the cost of the powertrain, where cost is related to bearing materials and material value, and enhancing bearing reliability, which is inversely proportional to the maximum contact pressure of the bearing. The target preset range of power parameter tolerances is determined through an intelligent closed-loop iterative process, which includes simulation, discrimination, adjustment, and further iteration until the established constraints and optimization objectives are met. Specifically, if the maximum contact pressure of the bearing exceeds the specified threshold, a tolerance adjustment mechanism is triggered, particularly for the refined management of key tolerances until they reach the acceptable range. This achieves an effective balance between bearing reliability and economic cost. This not only improves the accuracy of simulation predictions, increasing the agreement between the bearing contact pressure distribution and actual wear morphology from 35% to 80%, but also promotes continuous optimization of bearing design and real-time updates to the tolerance zone database, ensuring the high-performance of the bearings in the powertrain of large mining trucks throughout their entire life cycle.

[0066] In some embodiments, such as Figure 4 As shown, based on the above simulation model and the initial preset range of the above dynamic parameter tolerances, the updated preset range of the above dynamic parameter tolerances is determined, including the following steps:

[0067] Step S401: Determine the change value of the above-mentioned dynamic parameter tolerance, and determine the change value of the above-mentioned crankshaft performance parameters based on the change value of the above-mentioned dynamic parameter tolerance and the above-mentioned simulation model;

[0068] Step S402: Based on the changes in the crankshaft performance parameters, determine the degree of influence of the power parameter tolerance on the crankshaft performance parameters. The degree of influence is positively correlated with the changes in the crankshaft performance parameters.

[0069] Step S403: Based on the degree of influence of the above-mentioned power parameter tolerance on the above-mentioned crankshaft performance parameters, determine the range adjustment parameter of the above-mentioned power parameter tolerance. The range adjustment parameter is inversely correlated with the degree of influence. When the degree of influence is greater than a preset value, the range adjustment parameter is less than 1.

[0070] Step S404: The product of the above-mentioned range adjustment parameter and the maximum value of the above-mentioned initial preset range is determined as the maximum value of the updated preset range of the above-mentioned dynamic parameter tolerance.

[0071] The degree of influence refers to the extent to which the tolerance affects the bearing performance, i.e., its contribution to the bearing performance. A global sensitivity analysis method is used to quantify the influence of each tolerance parameter and its interaction on the bearing performance indicators. Based on the magnitude of the influence, the tolerance is divided into the following three aspects:

[0072] Tolerances with an impact greater than 20% are considered "critical tolerances," such as T1 (crankshaft / engine block runout tolerance) and T2 (crankshaft-motor shaft alignment tolerance). The range of "critical tolerances" must be strictly controlled within 60% of the initial preset upper limit. This is because crankshaft / engine block runout tolerance and crankshaft-motor shaft alignment tolerance have the greatest impact on bearing performance; the larger the tolerance, the worse the bearing performance. Therefore, the range of "critical tolerances" needs to be strictly controlled. Furthermore, generally only the upper limit is given, and optimization only optimizes the upper limit.

[0073] Specifically, tolerances with an impact of 5% to 20% are considered "significant tolerances," such as T3 (cylindricity tolerance of the main bearing housing bore), and are optimized based on cost-performance balance. Tolerances with an impact of ≤5% are considered "general tolerances," and should be appropriately relaxed to the initial preset range [T]. min T max The upper limit is 90%, but since "general tolerance" has little impact on bearing performance, it can be relaxed to 120% of the upper limit of the initial preset range.

[0074] In this embodiment, determining the updated preset range for power parameter tolerances includes: identifying changes in power parameter tolerance values ​​and determining their impact on crankshaft performance parameters based on a simulation model; quantifying the correlation between power parameter tolerances and changes in crankshaft performance parameters, where the degree of influence is proportional to the change in crankshaft performance parameters; defining a range adjustment parameter based on the degree of influence of power parameter tolerances on crankshaft performance parameters, where this parameter takes a value less than 1 when the influence of power parameter tolerances on crankshaft performance parameters exceeds a preset threshold, to achieve fine-tuning of the tolerance range; finally, by multiplying the range adjustment parameter by the maximum value of the initial preset range, the maximum value of the updated power parameter tolerance range is obtained, ensuring that the tolerance design meets both performance requirements and economic costs. This method improves the accuracy of bearing reliability assessment, optimizes tolerance design, and thus enhances the stability and lifespan of the powertrain.

[0075] In some embodiments, the optimization model is optimized based on the simulation model, optimization variables, constraints, and optimization objectives to obtain optimization results, including the following steps:

[0076] Step S501: Based on the above simulation model, optimization variables, constraints and optimization objectives, optimize the above optimization model to obtain the initial optimization range of the above dynamic parameter tolerances;

[0077] Step S502: If the initial optimization range of the above-mentioned dynamic parameter tolerance satisfies the above-mentioned constraint conditions and the above-mentioned optimization objectives, and the maximum contact pressure of the above-mentioned bearing bush is less than or equal to the first preset pressure, the initial optimization range of the above-mentioned dynamic parameter tolerance is determined as the target preset range of the above-mentioned dynamic parameter tolerance.

[0078] Step S503: If the initial optimization range of the above-mentioned dynamic parameter tolerance satisfies the above-mentioned constraint conditions and the above-mentioned optimization objectives, and the maximum contact pressure of the above-mentioned bearing is greater than the above-mentioned first preset pressure and less than or equal to the second preset pressure, the maximum value of the initial optimization range of the above-mentioned dynamic parameter tolerance is reduced until the maximum contact pressure of the above-mentioned bearing is less than or equal to the above-mentioned first preset pressure.

[0079] Step S504: If the initial optimization range of the above-mentioned dynamic parameter tolerance satisfies the above-mentioned constraints and optimization objectives, and the maximum contact pressure of the above-mentioned bearing bush is greater than the above-mentioned second preset pressure, the initial preset range of the above-mentioned dynamic parameter tolerance is redefined.

[0080] The first preset pressure can be set to 45 MPa, and the second preset pressure can be set to 50 MPa. Under the above parameter settings, a dynamic discrimination and closed-loop iteration are adopted: a dynamic discrimination mechanism is used, setting three levels of discrimination criteria: qualified zone, critical zone, and unqualified zone.

[0081] In P max If the dynamic parameter tolerance is ≤45MPa, and the tolerance scheme is determined to be within the acceptable range, the tolerance scheme can be directly output.

[0082] First preset pressure 45MPa <P max When the second preset pressure is ≤50MPa, if the power parameter tolerance is determined to be in the critical zone, tolerance zone adjustment is initiated, prioritizing the adjustment of critical tolerances, such as compressing critical tolerances, i.e., reducing the crankshaft / engine block runout tolerance T1 to 80% of its current value;

[0083] In P max Under the second preset pressure of 50MPa, it was determined that the power parameter tolerance was in the unacceptable range, and the design was returned for redesign.

[0084] In this process, a closed-loop iterative method is adopted, that is, by using the maximum number of iterations (N) max =10) and convergence condition (adjacent iterations P) max (Change rate <2%) control optimization process.

[0085] In this embodiment, for the reliability assessment of powertrain bearing bushes, this solution provides an assessment method considering multiple tolerance couplings. This method improves the operational stability of the bearing bushes and extends their service life through refined management of tolerance parameters. First, a tolerance parameter library is established, covering multiple key factors affecting bearing bush reliability, such as crankshaft / engine block runout tolerance T1, crankshaft-motor shaft alignment tolerance T2, and main bearing housing bore cylindricity tolerance T3. The initial preset ranges [T] for each of these factors are clearly defined. min T max Subsequently, using a coupled multibody dynamics and elastohydrodynamic lubrication model, the influence of tolerance parameters on bearing performance was analyzed in depth, especially by quantifying the coupling effect strength between different tolerances through interaction terms in the approximate model. Through designed experiments and automated simulation processes, a large amount of data was collected, and a response surface model was constructed to demonstrate the nonlinear relationship between tolerance parameters and bearing performance indicators, particularly for the maximum contact pressure P. max and minimum oil film thickness h min The impact of this was analyzed, and tolerances were categorized into critical tolerances, significant tolerances, and general tolerances, guiding subsequent tolerance optimization design. During the optimization process, a weighted objective function of manufacturing cost and bearing reliability was adopted, with contact pressure P as the criterion. max As a key evaluation indicator, the tolerance design is ensured to be both economical and reliable. Dynamic discrimination and closed-loop iteration mechanisms are implemented throughout the entire optimization process. By setting acceptable, critical, and unacceptable zones, effective screening and adjustment of tolerance design schemes are achieved until the ideal tolerance range is reached. Specifically, when the maximum contact pressure P of the bearing bush... max When P is less than or equal to 45 MPa, this tolerance combination is considered acceptable and can be used directly; if P max If the pressure is between 45MPa and 50MPa, it falls into the critical zone, at which point tolerance band adjustment will be initiated, prioritizing the adjustment of the critical tolerance to 80% of its current value; once P... max If the pressure exceeds 50 MPa, it is considered unqualified, and the tolerance scheme needs to be redesigned and passed through the maximum number of iterations N. max Controlling the convergence conditions ensures the efficiency of the optimization process.

[0086] In some embodiments, the objective function of the optimization model is a weighted function of the cost of the power system and the reliability of the bearing bush. After optimizing the optimization model according to the simulation model, optimization variables, constraints, and optimization objectives to obtain the initial optimization range of the power parameter tolerance, the method further includes: when the initial optimization range of the power parameter tolerance satisfies the constraints and the optimization objectives, and the maximum contact pressure of the bearing bush is greater than the first preset pressure and less than or equal to the second preset pressure, reducing the weight of the cost of the power system and increasing the weight of the weighted function of the bearing bush reliability until the maximum contact pressure of the bearing bush is less than or equal to the first preset pressure.

[0087] In this embodiment, the optimization model for powertrain bearing reliability assessment uses a cost-reliability weighted function as the objective function. After initially determining the initial optimization range of the powertrain parameter tolerances, if the maximum contact pressure of the bearing within this range is higher than a first preset pressure but not exceeding a second preset pressure threshold, and other constraints are met simultaneously, the cost weight of the powertrain system will be gradually reduced, while the bearing reliability weight will be increased accordingly, until the maximum contact pressure of the bearing drops below the first preset pressure. This dynamic adjustment strategy effectively balances manufacturing costs and bearing performance, prompting the optimization process to iterate towards a better tolerance design, achieving the best match between economy and reliability. Through closed-loop iteration, each optimized tolerance scheme is re-evaluated and adjusted until the convergence condition is met, i.e., the rate of change of the maximum contact pressure of the bearing between two adjacent iterations is less than 2%, and the number of iterations does not exceed the set upper limit N. max =10 times. This iterative optimization ensures that the final tolerance scheme, while controlling costs, allows the bearing bush to withstand high-intensity working conditions, avoiding thermal failure and excessive wear, and extending the service life of the powertrain. In addition, the feedback correction mechanism based on experimental data continuously optimizes the multiphysics coupled simulation model, further improving the prediction accuracy and applicability of the evaluation method, making the design process more scientific and rigorous, and providing a solid data foundation for subsequent bearing bush performance improvements.

[0088] In some embodiments, the above method further includes the following steps:

[0089] Step S601: Construct a sensitivity map. The first axis of the sensitivity map represents the first parameter tolerance, the second axis of the sensitivity map represents the second parameter tolerance, and the third axis of the sensitivity map represents the maximum contact pressure of the bearing bush. The first parameter tolerance is the parameter tolerance with the highest influence on the crankshaft performance parameters, and the influence of the second parameter tolerance on the crankshaft performance parameters is second only to that of the first parameter tolerance.

[0090] Step S602: Based on the above sensitivity spectrum, visualize the relationship between the above dynamic parameter tolerance and the above maximum contact pressure of the bearing bush.

[0091] The first step is to generate a sensitivity map: plot the sensitivity map to show the effect of the tolerance parameter on P. max and h min The direction and extent of the influence are intuitively shown, demonstrating the impact of different tolerance parameter combinations on the maximum contact pressure (P) of the bearing bush. max The degree of influence of ( ). Specifically, it includes three indicators: X-axis, Y-axis, and Z-axis:

[0092] X-axis (tolerance parameter 1): indicates crankshaft / body runout tolerance (T1);

[0093] Y-axis (tolerance parameter 2): indicates the crankshaft-motor shaft alignment tolerance (T2);

[0094] Z-axis (performance indicator): Represents the maximum contact pressure of the bearing bush (P). max ), the unit is MPa.

[0095] For the above three indicators, due to the limitations of coordinate representation, only two types of tolerances can be subjected to sensitivity analysis. It can be changed to perform sensitivity analysis on the two tolerances with the highest importance, such as T1 being the crankshaft / body runout tolerance and T2 being the crankshaft-motor shaft alignment tolerance.

[0096] In the sensitivity map, the surface will exhibit a clear color gradient or contour line change: High-sensitivity area (red area): located in the upper right corner of the sensitivity map (i.e., the area where both T1 and T2 tolerance values ​​are large). The corresponding Z-axis value (P) for this area... max The pressure will rise sharply, for example, exceeding 45 MPa, indicating that the bearing reliability risk is extremely high under this tolerance combination; Medium sensitive area (yellow area): located in the transition zone from the red area to the center of the sensitivity spectrum. P max Values ​​at a moderate level, such as 35-45 MPa, indicate a certain degree of reliability risk and require attention; the low-sensitivity region (green area) is located in the lower left corner of the sensitivity spectrum (i.e., the area where both T1 and T2 tolerance values ​​are small). This region P max A lower value, such as below 35 MPa, indicates that the bearing bush is in a relatively safe operating condition under this tolerance combination.

[0097] Next, experimental verification is conducted: Through bench tests, such as durability tests under single operating conditions, the simulation results are verified to clarify the test boundaries and measure the wear amount of the bearing coating. Data feedback: Based on the experimental data, the multibody dynamics and elastohydrodynamic lubrication coupling model is revised to achieve continuous optimization. Solution solidification and database updates: Based on the simulation and experimental verification results, the tolerance scheme is solidified, and the tolerance zone database is updated; the entire product lifecycle is monitored to continuously optimize tolerances.

[0098] In this embodiment, the process of constructing the sensitivity map further improves the bearing reliability assessment method. The X-axis of the sensitivity map represents the crankshaft / engine block runout tolerance T1, the Y-axis represents the crankshaft-motor shaft alignment tolerance T2, and the Z-axis displays the maximum contact pressure P of the bearing. max T1 and T2 are ranked according to their influence on crankshaft performance parameters. Through visualization of the relationships, this graph intuitively reveals the impact of different tolerance combinations on P. max The influence of this helps to quickly identify risk areas. In the highly sensitive area, i.e., the red area where both T1 and T2 tolerance values ​​are large, P max A sharp increase to over 45 MPa indicates a high risk to the bearing reliability; the medium-sensitive area is the yellow region transitioning from the red area to the center of the sensitivity spectrum, P max Between 35-45 MPa, a certain reliability risk is indicated; the low-sensitivity area, i.e., the green area with smaller T1 and T2 tolerance values, P max Below 35 MPa, the bearing's operating condition is confirmed to be relatively safe. This visualization analysis supports tolerance classification and optimization strategies, ensuring the optimal balance between economy and reliability in the design. In subsequent iterative optimization, the sensitivity map served as a key tool, dynamically guiding tolerance adjustments until the optimal design was achieved. This significantly improved the agreement between the bearing's contact pressure distribution and the actual wear morphology from 35% to 80%, enhancing the accuracy of the assessment and the robustness of the design.

[0099] The method for determining the tolerances of the aforementioned dynamic system parameters proposed in this application has the following advantages compared to traditional methods:

[0100] (i) From single-factor to multivariate analysis, this study is the first to incorporate multiple tolerance factors and their coupling effects into the evaluation of bearing bushes, breaking through the limitations of traditional idealized assumptions and revealing the comprehensive influence mechanism of complex tolerances;

[0101] (ii) Tolerance classification and control strategy based on sensitivity analysis: The concept of tolerance contribution analysis and sensitivity analysis is introduced to realize the classification of tolerance quantification, which can be directly used to guide design optimization and effectively balance reliability and economy.

[0102] (III) Dynamic discrimination and closed-loop optimization: A three-level discrimination mechanism replaces the linear process, forming an intelligent loop of "simulation-discrimination-adjustment-iteration" to achieve optimal tolerance design;

[0103] (iv) Improved simulation accuracy: Through high-precision coupled modeling, the consistency between the simulated bearing contact pressure distribution and the actual wear morphology has increased from 35% to 80%, significantly improving the accuracy of simulation prediction.

[0104] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the method for determining the tolerance of power system parameters of this application will be described in detail below with reference to specific embodiments.

[0105] This embodiment relates to a specific method for determining the tolerances of power system parameters, such as... Figure 5 As shown, it includes the following steps:

[0106] Step S1: First, establish the initial parameter tolerances, then perform a multibody dynamics and elastohydrodynamic lubrication coupling model, and parameterize the key tolerances;

[0107] Step S2: Perform DOE experimental design and simulation automation, followed by corresponding surface model and coupling effect analysis, contribution analysis and tolerance classification;

[0108] Step S3: Make a judgment in the tolerance zone performance discrimination module. If it is a qualified zone, output the optimal tolerance zone; if it is a critical zone, adjust the common tolerance zone and optimize the weight, and then return to the multibody dynamics and elastohydrodynamic lubrication coupling model to repeat the above steps in parameterizing the key tolerance; if it is an unqualified zone, reconstruct the structure and then return to the initial parameter tolerance establishment to repeat the above steps.

[0109] Step S4: After outputting the optimal tolerance zone, conduct manufacturing implementation, quality monitoring, experimental verification, and data collection. That is, use the optimal tolerance zone to manufacture the bearing bush, and determine whether the tolerance of the manufactured bearing bush falls within the designed optimal tolerance zone. If the tolerance of the manufactured bearing bush falls within the designed optimal tolerance zone, the contact pressure of the bearing bush is collected by the sensor. If the tolerance of the manufactured bearing bush does not fall within the designed optimal tolerance zone, the manufacturing process of the bearing bush is adjusted.

[0110] Step S5: Perform performance compliance judgment. If the tolerance of the manufactured bearing bush falls within the optimal tolerance zone of the design, determine whether the contact pressure of the manufactured bearing bush is less than the preset pressure (i.e., whether it meets the standard). If the contact pressure of the manufactured bearing bush is less than the preset pressure, proceed with mass production, and perform scheme solidification, database update, life cycle monitoring and continuous optimization. If it does not meet the standard, provide feedback to the parameter library and model correction, adjust the simulation model, and then return to the initial parameter tolerance establishment to repeat the above steps.

[0111] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0112] This application also provides a device for determining power system parameter tolerances. It should be noted that this device can be used to execute the method for determining power system parameter tolerances provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0113] The following describes the device for determining the tolerance of power system parameters provided in the embodiments of this application.

[0114] Figure 6 This is a schematic diagram of a device for determining the tolerance of power system parameters according to an embodiment of this application. Figure 6 As shown, the device includes: an acquisition unit 10, a construction unit 20, and an optimization unit 30. The acquisition unit 10 is used to acquire an initial preset range of power parameter tolerance, wherein the power parameter tolerance is the parameter tolerance that affects the reliability of the crankshaft bearing of the power system. The construction unit 20 is used to construct a simulation model of the power system based on the initial preset range of the power parameter tolerance, wherein the simulation model characterizes the relationship between the power parameter tolerance and the crankshaft performance parameters, including the maximum contact pressure of the bearing. The optimization unit 30 is used to optimize the power parameter tolerance based on the initial preset range of the power parameter tolerance and the simulation model to obtain a target preset range of the power parameter tolerance, so that the maximum contact pressure of the bearing is less than the preset pressure.

[0115] The aforementioned device for determining power system parameter tolerances includes an acquisition unit, a construction unit, and an optimization unit. The acquisition unit acquires an initial preset range for the power parameter tolerances, which are parameter tolerances affecting the reliability of the crankshaft bearings in the power system. The construction unit constructs a simulation model of the power system based on the initial preset range of the power parameter tolerances. The simulation model characterizes the relationship between the power parameter tolerances and the crankshaft performance parameters, including the maximum contact pressure of the bearings. The optimization unit optimizes the power parameter tolerances based on the initial preset range and the simulation model to obtain a target preset range for the power parameter tolerances, ensuring that the maximum contact pressure of the bearings is less than the preset pressure. This device integrates multiple methods, including parametric modeling, multiphysics coupling simulation, tolerance contribution quantification analysis, dynamic discrimination, and closed-loop optimization. It significantly improves the accuracy of bearing reliability simulation prediction and guides tolerance design. It solves the problem that existing power system parameter tolerances lead to uneven bearing load distribution, excessively high local contact pressure, accelerated wear, and consequently, low reliability and lifespan of large mining trucks.

[0116] In some embodiments, the crankshaft performance parameters further include crankshaft friction work and minimum crankshaft oil film thickness. The optimization unit includes a first determining module and a first optimizing module. The first determining module is used to determine an updated preset range of the power parameter tolerance based on the simulation model and the initial preset range of the power parameter tolerance. The updated preset range is related to the degree of influence of the power parameter tolerance on the reliability of the bearing. The first optimizing module is used to construct an optimization model and optimize the optimization model based on the simulation model, optimization variables, constraints, and optimization objectives to obtain optimization results. The optimization results include the target preset range of the power parameter tolerance. The optimization variables are the power parameter tolerance. The constraints are the maximum contact pressure of the bearing within a first preset range, the crankshaft friction work within a second preset range, the minimum crankshaft oil film thickness greater than a preset oil film thickness, and the power parameter tolerance within the updated preset range. The optimization objective is to minimize the cost of the power system and maximize the reliability of the bearing. The cost of the power system is related to the material and material value of the bearing, and the reliability of the bearing is inversely correlated with the maximum contact pressure of the bearing. This facilitates continuous optimization of bearing design and real-time updates to the tolerance database, ensuring high-performance bearings of large mining truck powertrains throughout their entire life cycle.

[0117] In some embodiments, the first determining module includes a second determining module, a third determining module, a fourth determining module, and a fifth determining module. The second determining module is used to determine the change value of the aforementioned power parameter tolerance, and based on the change value of the aforementioned power parameter tolerance and the aforementioned simulation model, determine the change value of the aforementioned crankshaft performance parameters. The third determining module is used to determine the degree of influence of the aforementioned power parameter tolerance on the aforementioned crankshaft performance parameters based on the change value of the aforementioned crankshaft performance parameters, wherein the degree of influence is positively correlated with the change value of the aforementioned crankshaft performance parameters. The fourth determining module is used to determine the range adjustment parameter of the aforementioned power parameter tolerance based on the degree of influence of the aforementioned power parameter tolerance on the aforementioned crankshaft performance parameters, wherein the range adjustment parameter is inversely correlated with the degree of influence, and wherein the range adjustment parameter is less than 1 when the degree of influence is greater than a preset value. The fifth determining module is used to determine the product of the aforementioned range adjustment parameter and the maximum value of the aforementioned initial preset range as the maximum value of the updated preset range of the aforementioned power parameter tolerance, thereby improving the accuracy of bearing reliability assessment, optimizing tolerance design, and thus improving the stability and lifespan of the powertrain.

[0118] In some embodiments, the first optimization module includes a second optimization module, a sixth determining module, a third optimization module, and a seventh determining module. The second optimization module is used to optimize the optimization model according to the simulation model, optimization variables, constraints, and optimization objectives to obtain an initial optimization range for the dynamic parameter tolerance. The sixth determining module is used to determine the initial optimization range of the dynamic parameter tolerance as a target preset range for the dynamic parameter tolerance when the initial optimization range of the dynamic parameter tolerance satisfies the constraints and optimization objectives, and the maximum contact pressure of the bearing bush is less than or equal to a first preset pressure. The third optimization module is used to determine the target preset range of the dynamic parameter tolerance when the initial optimization range of the dynamic parameter tolerance satisfies the constraints and optimization objectives, and the maximum contact pressure of the bearing bush is less than or equal to a first preset pressure. If the initial optimization range satisfies the above constraints and optimization objectives, and the maximum contact pressure of the bearing bush is greater than the first preset pressure and less than or equal to the second preset pressure, the maximum value of the initial optimization range of the dynamic parameter tolerance is reduced until the maximum contact pressure of the bearing bush is less than or equal to the first preset pressure. The seventh determining module is used to redetermine the initial preset range of the dynamic parameter tolerance when the initial optimization range of the dynamic parameter tolerance satisfies the above constraints and optimization objectives, and the maximum contact pressure of the bearing bush is greater than the second preset pressure. This achieves effective screening and adjustment of the tolerance design scheme until the ideal tolerance range is reached.

[0119] In some embodiments, the objective function of the optimization model is a weighted function of the cost of the power system and the reliability of the bearing. The device further includes a fourth optimization module, which is used to optimize the optimization model according to the simulation model, optimization variables, constraints, and optimization objectives to obtain an initial optimization range for the power parameter tolerances. Then, if the initial optimization range of the power parameter tolerances satisfies the constraints and the optimization objective, and the maximum contact pressure of the bearing is greater than the first preset pressure and less than or equal to the second preset pressure, the module reduces the weight of the power system cost and increases the weight of the weighted function for the bearing reliability until the maximum contact pressure of the bearing is less than or equal to the first preset pressure. This method ensures that, under the premise of cost control, the bearing can withstand high-intensity working conditions, avoid thermal failure and excessive wear, and extend the service life of the powertrain.

[0120] In some embodiments, there are multiple power parameter tolerances. The construction unit further includes a first acquisition module, an eighth determination module, a second acquisition module, and a first construction module. The first acquisition module is used to acquire the initial oil film thickness and determine the sum of the initial oil film thickness and the tolerance disturbance term as the oil film thickness expression. The minimum value of the oil film thickness expression in one operating cycle of the power system is the minimum oil film thickness of the crankshaft. The eighth determination module is used to determine multiple parameter tolerance samples according to the initial preset range of the power parameter tolerances. One parameter tolerance sample includes all the power parameter tolerances. All the power parameter tolerances in the parameter tolerance sample are within the initial preset range. The second acquisition module is used to acquire the oil film thickness expression and boundary conditions, and analyze each parameter tolerance sample according to the average Reynolds equation to obtain the analysis results. The boundary conditions are the cylinder pressure, inertial load, and rotational speed at the rated point of the bearing. The rated point is the point with the highest power and the highest rotational speed on the bearing. The first construction module is used to fit the response surface model with a second-order polynomial according to the analysis results to obtain the simulation model of the power system. This method significantly improves the accuracy and efficiency of bearing reliability assessment, providing strong support for the design and manufacturing of engine powertrains.

[0121] In some embodiments, the above-described apparatus further includes a second building module and a third building module. The second building module is used to construct a sensitivity map, wherein the first coordinate axis of the sensitivity map represents a first parameter tolerance, the second coordinate axis of the sensitivity map represents a second parameter tolerance, and the third coordinate axis of the sensitivity map represents the maximum contact pressure of the bearing bush. The first parameter tolerance is the parameter tolerance with the highest influence on the crankshaft performance parameters, and the second parameter tolerance has the second highest influence on the crankshaft performance parameters. The third building module is used to visualize the relationship between the power parameter tolerance and the maximum contact pressure of the bearing bush based on the sensitivity map, thereby enhancing the accuracy of the evaluation and the robustness of the design.

[0122] The determination of the aforementioned power system parameter tolerances includes a processor and a memory. The aforementioned optimization units are all stored as program units in the memory, and the processor executes these program units to achieve the corresponding functions. All of the aforementioned modules reside in the same processor; alternatively, the aforementioned modules may be located in different processors in any combination.

[0123] The processor contains a core, which retrieves the corresponding program unit from memory. One or more cores can be configured. Adjusting the core parameters can address the problem that existing power system parameter tolerances can lead to uneven bearing load distribution, excessively high local contact pressure, accelerated wear, and consequently, lower reliability and lifespan for large mining trucks.

[0124] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0125] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the method for determining the tolerance of power system parameters.

[0126] This invention provides a processor for running a program, wherein the program executes the method for determining the tolerance of power system parameters.

[0127] This invention provides a device comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for implementing the aforementioned drone inspection procedures for a data center. The device described herein may be a server, PC, PAD, mobile phone, etc.

[0128] This application also provides a computer program product that, when executed on a data processing device, is suitable for performing the steps of a method for determining the tolerances of parameters of a dynamic system.

[0129] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0130] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0135] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0136] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0137] 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.

[0138] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0139] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0140] 1) The method for determining the power system parameter tolerances in this application first obtains an initial preset range for the power parameter tolerances, which are the parameter tolerances affecting the reliability of the crankshaft bearings in the power system. Based on the initial preset range of the power parameter tolerances, a simulation model of the power system is constructed. The simulation model characterizes the relationship between the power parameter tolerances and the crankshaft performance parameters, including the maximum contact pressure of the bearings. Based on the initial preset range of the power parameter tolerances and the simulation model, the power parameter tolerances are optimized to obtain a target preset range for the power parameter tolerances, so that the maximum contact pressure of the bearings is less than the preset pressure. This method integrates multiple methods such as parametric modeling, multiphysics coupling simulation, tolerance contribution quantification analysis, dynamic discrimination, and closed-loop optimization. It accurately determines the parameter tolerances of the power system, reduces the local contact pressure of the bearings, improves the reliability and lifespan of the power system, and solves the problem that existing power system parameter tolerances lead to uneven bearing load distribution, excessively high local contact pressure, accelerated wear, and low reliability and lifespan of large mining trucks.

[0141] 2) The apparatus for determining the tolerance of power system parameters of this application includes an acquisition unit, a construction unit, and an optimization unit. The acquisition unit is used to acquire an initial preset range of the power parameter tolerance, wherein the power parameter tolerance is the parameter tolerance that affects the reliability of the crankshaft bearing of the power system. The construction unit is used to construct a simulation model of the power system based on the initial preset range of the power parameter tolerance. The simulation model characterizes the relationship between the power parameter tolerance and the crankshaft performance parameters, wherein the crankshaft performance parameters include the maximum contact pressure of the bearing. The optimization unit is used to optimize the power parameter tolerance based on the initial preset range of the power parameter tolerance and the simulation model to obtain a target preset range of the power parameter tolerance, so that the maximum contact pressure of the bearing is less than the preset pressure. This device integrates multiple methods such as parametric modeling, multiphysics coupling simulation, tolerance contribution quantification analysis, dynamic discrimination and closed-loop optimization to accurately determine the parameter tolerances of the power system, reduce the local contact pressure of the bearing bush, improve the reliability and life of the power system, and solve the problem that the parameter tolerances of the existing power system will lead to uneven load distribution of the bearing bush, excessive local contact pressure, accelerated wear, and low reliability and life of large mining trucks.

[0142] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining the parameter tolerances of a dynamic system, characterized in that, include: Obtain the initial preset range of the power parameter tolerance, wherein the power parameter tolerance is the parameter tolerance that affects the reliability of the crankshaft bearing of the power system; Based on the initial preset range of the power parameter tolerance, a simulation model of the power system is constructed. The simulation model characterizes the relationship between the power parameter tolerance and the crankshaft performance parameters, including the maximum contact pressure of the bearing bush. The dynamic parameter tolerance is optimized based on the initial preset range of the dynamic parameter tolerance and the simulation model to obtain the target preset range of the dynamic parameter tolerance, so that the maximum contact pressure of the bearing bush is less than the preset pressure. The crankshaft performance parameters also include crankshaft friction work and minimum crankshaft oil film thickness. Based on the initial preset range of the power parameter tolerances and the simulation model, the power parameter tolerances are optimized to obtain the target preset range of the power parameter tolerances, including: Based on the simulation model and the initial preset range of the dynamic parameter tolerance, an updated preset range of the dynamic parameter tolerance is determined, wherein the updated preset range is related to the degree of influence of the dynamic parameter tolerance on the reliability of the bearing bush. An optimization model is constructed, and the model is optimized based on the simulation model, optimization variables, constraints, and optimization objectives to obtain optimization results. The optimization results include the target preset range of the power parameter tolerance, the optimization variable being the power parameter tolerance, and the constraints being the maximum contact pressure of the bearing bush within a first preset range, the crankshaft friction work within a second preset range, the minimum oil film thickness of the crankshaft being greater than a preset oil film thickness, and the power parameter tolerance being within the updated preset range. The optimization objective is to minimize the cost of the power system and maximize the reliability of the bearing bush. The cost of the power system is related to the material and material value of the bearing bush, and the reliability of the bearing bush is inversely correlated with the maximum contact pressure of the bearing bush. Based on the simulation model and the initial preset range of the dynamic parameter tolerance, an updated preset range of the dynamic parameter tolerance is determined, including: Determine the variation value of the dynamic parameter tolerance, and based on the variation value of the dynamic parameter tolerance and the simulation model, determine the variation value of the crankshaft performance parameters; Based on the changes in the crankshaft performance parameters, the degree of influence of the power parameter tolerance on the crankshaft performance parameters is determined, and the degree of influence is positively correlated with the changes in the crankshaft performance parameters. Based on the degree of influence of the power parameter tolerance on the crankshaft performance parameters, a range adjustment parameter for the power parameter tolerance is determined. The range adjustment parameter is inversely correlated with the degree of influence. When the degree of influence is greater than a preset value, the range adjustment parameter is less than 1. The product of the range adjustment parameter and the maximum value of the initial preset range is determined as the maximum value of the updated preset range of the dynamic parameter tolerance; There are multiple dynamic parameter tolerances. Based on the initial preset range of the dynamic parameter tolerances, a simulation model of the dynamic system is constructed, including: The initial oil film thickness is obtained, and the sum of the initial oil film thickness and the tolerance disturbance term is determined as the oil film thickness expression, wherein the minimum value of the oil film thickness expression in one operating cycle of the power system is the minimum oil film thickness of the crankshaft. Based on the initial preset range of the dynamic parameter tolerance, multiple parameter tolerance samples are determined. Each parameter tolerance sample includes all the dynamic parameter tolerances, and all the dynamic parameter tolerances in the parameter tolerance sample are within the initial preset range. The oil film thickness expression and boundary conditions are obtained, and the tolerance samples of each parameter are analyzed according to the average Reynolds equation to obtain the analysis results. The boundary conditions are the cylinder pressure, inertial load and rotational speed at the rated point of the bearing. The rated point is the point on the bearing with the highest power and the highest rotational speed. Based on the analysis results, a second-order polynomial is used to fit the response surface model to obtain the simulation model of the dynamic system.

2. The method according to claim 1, characterized in that, Based on the simulation model, optimization variables, constraints, and optimization objective, the optimization model is optimized to obtain optimization results, including: Based on the simulation model, optimization variables, constraints, and optimization objectives, the optimization model is optimized to obtain the initial optimization range of the dynamic parameter tolerance. If the initial optimization range of the dynamic parameter tolerance satisfies the constraint conditions and the optimization objective, and the maximum contact pressure of the bearing bush is less than or equal to the first preset pressure, the initial optimization range of the dynamic parameter tolerance is determined as the target preset range of the dynamic parameter tolerance. If the initial optimization range of the dynamic parameter tolerance satisfies the constraint conditions and the optimization objective, and the maximum contact pressure of the bearing bush is greater than the first preset pressure and less than or equal to the second preset pressure, the maximum value of the initial optimization range of the dynamic parameter tolerance is reduced until the maximum contact pressure of the bearing bush is less than or equal to the first preset pressure. If the initial optimization range of the dynamic parameter tolerance satisfies the constraints and the optimization objective, and the maximum contact pressure of the bearing bush is greater than the second preset pressure, the initial preset range of the dynamic parameter tolerance is redefined.

3. The method according to claim 2, characterized in that, The objective function of the optimization model is a weighted function of the cost of the power system and the reliability of the bearing. After optimizing the optimization model according to the simulation model, optimization variables, constraints, and optimization objective to obtain the initial optimization range of the power parameter tolerance, the method further includes: If the initial optimization range of the power parameter tolerance satisfies the constraints and the optimization objective, and the maximum contact pressure of the bearing bush is greater than the first preset pressure and less than or equal to the second preset pressure, the weight of the power system cost reduction and the weight of the bearing bush reliability weighting function are increased until the maximum contact pressure of the bearing bush is less than or equal to the first preset pressure.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: A sensitivity map is constructed, wherein the first axis of the sensitivity map represents the first parameter tolerance, the second axis of the sensitivity map represents the second parameter tolerance, and the third axis of the sensitivity map represents the maximum contact pressure of the bearing bush. The first parameter tolerance is the parameter tolerance with the highest influence on the crankshaft performance parameters, and the second parameter tolerance has the second highest influence on the crankshaft performance parameters, which is second only to the influence of the first parameter tolerance on the crankshaft performance parameters. Based on the sensitivity spectrum, the relationship between the dynamic parameter tolerance and the maximum contact pressure of the bearing bush is visualized.

5. A device for determining the parameter tolerances of a power system, characterized in that, The apparatus determines the power system parameter tolerance using the method for determining power system parameter tolerances as described in any one of claims 1 to 4, and the apparatus comprises: The acquisition unit is used to acquire the initial preset range of the power parameter tolerance, wherein the power parameter tolerance is the parameter tolerance that affects the reliability of the crankshaft bearing of the power system; A construction unit is used to construct a simulation model of the power system based on the initial preset range of the power parameter tolerance. The simulation model characterizes the relationship between the power parameter tolerance and the crankshaft performance parameters, including the maximum contact pressure of the bearing bush. The optimization unit is used to optimize the dynamic parameter tolerance based on the initial preset range of the dynamic parameter tolerance and the simulation model to obtain the target preset range of the dynamic parameter tolerance, so that the maximum contact pressure of the bearing bush is less than the preset pressure.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the method for determining the tolerance of power system parameters as described in any one of claims 1 to 4.

7. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a method for performing the determination of power system parameter tolerances as described in any one of claims 1 to 4.

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