Bearing gap optimization method, device, electronic equipment and storage medium

Abstract

The application provides a bearing gap optimization method and device, electronic equipment and storage medium, and relates to the technical field of mechanical parameter processing. The method comprises the following steps: constructing a bearing bush wear prediction model based on a bearing sample database. The bearing sample data comprises different bearing gaps and corresponding maximum bearing bush wear. The bearing comprises a main bearing and a connecting rod bearing. The bearing bush wear prediction model is used to predict the maximum bearing bush wear under different bearing gaps. Based on the bearing bush wear prediction model, each bearing gap is taken as an optimization variable, and the minimum maximum wear of each bearing bush is taken as a target function to obtain a first bearing gap optimization result. The embodiment can improve the bearing gap optimization effect.

Description

Technical Field

[0001] This application relates to the field of mechanical parameter processing technology, and in particular to a method, apparatus, electronic device, and storage medium for optimizing bearing clearance. Background Technology

[0002] Bearings include shaft bearings, a subset of bearings used in the specific application of internal combustion engine shaft systems. Shaft bearings are key components supporting rotating parts such as crankshafts and connecting rods, primarily consisting of main bearings and connecting rod bearings. The main bearings support the crankshaft and withstand the pressure and inertial forces of combustion gas explosions, while the connecting rod bearings convert the reciprocating motion of the piston into the rotational motion of the crankshaft. Bearing clearance refers to the radial fit clearance between the bearing shell and the journal. The lubrication performance of the bearing and the rationality of the fit clearance directly determine the vibration, noise, durability, and reliability of the internal combustion engine. Therefore, setting a reasonable bearing clearance is crucial to the performance of the internal combustion engine.

[0003] The bearing clearance value must simultaneously meet multiple requirements for friction pair wear reduction and vibration reduction. However, traditional bearing clearance design often uses a single performance target for verification, and vibration suppression often relies on post-construction damping structures for remedies. This single performance target often leads to mismatch between bearing clearance and shaft stiffness and inertia parameters, resulting in the serious consequence of optimizing one target while degrading other performance aspects.

[0004] Therefore, a more reasonable method for optimizing shaft bearing clearance is urgently needed.

[0005] It should be noted that the above statements are only used to provide background information related to this application and do not necessarily constitute prior art. Summary of the Invention

[0006] In view of this, the purpose of this application is to propose a bearing clearance optimization method, device, electronic device, and storage medium, which can specifically solve the problem of low performance in existing bearing clearance calculations.

[0007] Based on the above objectives, in a first aspect, this application proposes a bearing clearance optimization method, comprising: constructing a bearing wear prediction model based on a bearing sample database, wherein the bearing sample data includes different bearing clearances and corresponding maximum bearing wear amounts, and the bearings include main bearings and connecting rod bearings; the bearing wear prediction model is used to predict the maximum bearing wear amount under different bearing clearances; and based on the bearing wear prediction model, using each bearing clearance as an optimization variable and minimizing the maximum wear amount of each bearing as the objective function, a first bearing clearance optimization result is obtained.

[0008] Secondly, a bearing clearance optimization device is also provided. The device includes: a model building module for building a bearing wear prediction model based on a bearing sample database, wherein the bearing sample data includes different bearing clearances and corresponding maximum bearing wear, and the bearings include main bearings and connecting rod bearings; the bearing wear prediction model is used to predict the maximum bearing wear under different bearing clearances; and a first optimization module for obtaining a first bearing clearance optimization result based on the bearing wear prediction model, using each bearing clearance as an optimization variable and minimizing the maximum wear of each bearing as the objective function.

[0009] Thirdly, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor running the computer program to implement the method of the first aspect.

[0010] Fourthly, a computer-readable storage medium is also provided, on which a computer program is stored, the program being executed by a processor to implement the method described in any one of the first aspects.

[0011] Fifthly, a computer program product is also provided, including a computer program that is executed by a processor to implement the method described in the first aspect.

[0012] In summary, this application has at least the following beneficial effects:

[0013] By constructing a bearing sample database containing different bearing clearances and corresponding maximum bearing wear, a bearing wear prediction model is built based on this database to quickly predict the maximum bearing wear under different bearing clearances. Then, based on this bearing wear prediction model, a multi-objective optimization algorithm is used to automatically optimize between conflicting objectives of main bearing wear and connecting rod bearing wear. Finally, an optimal clearance scheme that balances the wear of both bearings, based on model and algorithm decisions, is output, enabling component-level optimization of bearings—that is, preliminary optimization of the clearances of each bearing component. Compared to related technologies that use a single performance objective, the optimized bearing clearance in this embodiment satisfies both the minimization of main bearing wear and the minimization of connecting rod bearing wear, coordinating the conflicting wear performance of the main bearing and connecting rod bearing, improving the bearing clearance optimization effect, and increasing the life and performance of the internal combustion engine.

[0014] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0015] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in this application and should not be construed as limiting the scope of this application. Furthermore, the same reference numerals denote the same parts throughout all the drawings.

[0016] Figure 1 This paper presents a flowchart illustrating the steps of the bearing clearance optimization method of this application.

[0017] Figure 2 This paper shows another step of the bearing clearance optimization method of this application;

[0018] Figure 3 This application shows a flowchart illustrating the steps involved in creating a bearing sample database.

[0019] Figure 4 This diagram illustrates another step of the bearing clearance optimization method of this application.

[0020] Figure 5 This invention provides a flowchart illustrating the overall steps of a bearing clearance optimization method according to an embodiment of the present application.

[0021] Figure 6(a) shows the test verification results of the main bearing after the bearing clearance optimization according to an embodiment of this application;

[0022] Figure 6(b) shows the test verification results of the connecting rod bearing with optimized bearing clearance according to an embodiment of this application;

[0023] Figure 6(c) shows the test verification results of the system vibration amplitude after the bearing clearance optimization according to an embodiment of this application;

[0024] Figure 7 This invention provides a schematic diagram of the structure of a bearing clearance optimization device according to an embodiment of the present application.

[0025] Figure 8 A schematic diagram of the structure of an electronic device provided in one embodiment of this application is shown. Detailed Implementation

[0026] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0028] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0031] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0032] Bearings include shaft bearings, a subset of bearings used in the specific application of internal combustion engine shaft systems. Shaft bearings are key components supporting rotating parts such as crankshafts and connecting rods, primarily consisting of main bearings and connecting rod bearings. The main bearings support the crankshaft and withstand the pressure and inertial forces of combustion gas explosions, while the connecting rod bearings convert the reciprocating motion of the piston into the rotational motion of the crankshaft. Bearing clearance refers to the radial fit clearance between the bearing shell and the journal. The lubrication performance of the bearing and the rationality of the fit clearance directly determine the vibration, noise, durability, and reliability of the internal combustion engine. Therefore, setting a reasonable bearing clearance is crucial to the performance of the internal combustion engine.

[0033] However, the lubrication states of the main bearing and connecting rod bearing are not independent. The oil film pressure distribution of the main bearing affects the supply of lubricating oil, which in turn affects the oil film formation of the connecting rod bearing. Dynamic load changes in the connecting rod bearing are also transmitted to the main bearing through the crankshaft, altering its stress state. Furthermore, the appropriateness of the clearances of both the main bearing and the connecting rod bearing directly affects the vibration characteristics of the system. Therefore, the bearing clearance values ​​must simultaneously meet multiple requirements for friction pair wear reduction and vibration damping. However, traditional bearing clearance design often uses a single performance objective for verification, and vibration suppression often relies on subsequent damping structures for remedies. This single performance objective often leads to mismatches between bearing clearance and shaft stiffness and inertia parameters, resulting in the serious consequence of optimizing a single objective while degrading other performance aspects.

[0034] Based on this, some embodiments of this application provide a bearing clearance optimization method, apparatus, electronic device, and storage medium. By constructing a bearing sample database including different bearing clearances and corresponding maximum bearing wear amounts, a bearing wear prediction model is built based on the bearing sample database to quickly predict the maximum bearing wear amount under different bearing clearances. Then, based on the bearing wear prediction model, each bearing clearance is used as an optimization variable, and minimizing the maximum wear amount of each bearing is used as the objective function to find the clearance combination that best balances the wear performance of the two bearings (the Pareto optimal solution of the objective function), thereby obtaining the first bearing clearance optimization result. Therefore, compared with the use of a single performance objective in related technologies, the optimized bearing clearance in this embodiment can satisfy both the minimization of main bearing wear and the minimization of connecting rod bearing wear, coordinating the conflict between the main bearing and connecting rod bearing wear performance, improving the bearing clearance optimization effect, and improving the life and performance of the internal combustion engine.

[0035] The present application will now be described in detail with reference to the accompanying drawings and embodiments.

[0036] Figure 1 The flowchart illustrating the bearing clearance optimization method of this application is shown below. Figure 1 As shown in the embodiments of this application, the bearing clearance optimization method includes the following steps S101~S102:

[0037] S101. Based on the bearing sample database, construct a bearing wear prediction model.

[0038] S102. Based on the bearing wear prediction model, the clearance of each bearing is used as the optimization variable, and the maximum wear of each bearing is minimized as the objective function to obtain the optimization result of the first bearing clearance.

[0039] In this embodiment, the bearing sample database is a structured dataset where each record contains a specific set of main bearing clearances and connecting rod bearing clearances, along with the corresponding maximum wear of the main bearing shell and connecting rod bearing shell. In other words, the bearing sample data includes different bearing clearances and their corresponding maximum bearing shell wear. In this embodiment, bearing clearance refers to the diameter fit clearance of the bearing; for simplicity, it will be referred to as bearing clearance below.

[0040] The bearing wear prediction model is a mathematical model trained using machine learning algorithms (such as neural networks and Kriging models). It can simulate complex physical processes and is used to predict the maximum wear of bearings under different bearing clearances. The main bearing clearance and connecting rod bearing clearance are used as inputs to the bearing wear prediction model, and the maximum wear of the main bearing and the maximum wear of the connecting rod bearing are used as outputs.

[0041] In some examples, bearings include main bearings and connecting rod bearings; main bearings refer to the bearings that support the crankshaft main journals, while connecting rod bearings refer to the bearings that connect the crankshaft connecting rod journals and the piston connecting rods. Bearing clearance refers to the radial clearance between the inner surface of the bearing bush and the outer surface of the rotating journal (crankshaft main journal or connecting rod journal). This clearance is a key parameter for lubrication, load bearing, and vibration transmission, directly affecting bearing wear, lubrication performance, and the vibration characteristics of the entire shaft system.

[0042] Using the clearance of each bearing as the optimization variable and the maximum wear of each bearing as the objective function, that is, using the main bearing clearance and connecting rod bearing clearance as optimization variables and minimizing the maximum wear of the main bearing shell and the maximum wear of the connecting rod bearing shell as objective functions, a multi-objective optimization algorithm is used to find the optimal clearance scheme that balances the wear of both the main bearing shell and the connecting rod bearing shell based on model and algorithm decisions, which is taken as the first bearing clearance optimization result.

[0043] In some cases, the optimized result of the first bearing clearance may be one or multiple, and relevant personnel can choose one of the solutions based on the actual working conditions or the weight of the spindle and connecting rod shaft.

[0044] The above embodiments construct a bearing sample database including different bearing clearances and corresponding maximum bearing wear amounts. Based on this database, a bearing wear prediction model is built to quickly predict the maximum bearing wear amount under different bearing clearances. Then, based on this model, a multi-objective optimization algorithm is used to automatically optimize among conflicting objectives (main bearing wear and connecting rod bearing wear). Finally, an optimal clearance scheme balancing the wear of both is output, based on model and algorithm decisions. This enables component-level optimization of bearings, allowing for preliminary optimization of the clearances of each bearing component. Compared to related technologies that use a single performance objective, the optimized bearing clearance in this embodiment satisfies both the minimization of main bearing wear and the minimization of connecting rod bearing wear, coordinating the conflict in wear performance between the main bearing and connecting rod bearing, improving the bearing clearance optimization effect, and enhancing the life and performance of the internal combustion engine.

[0045] Figure 2 Another step of the bearing clearance optimization method of this application is shown in the flowchart, as follows: Figure 2 As shown in the embodiments of this application, before constructing the bearing wear prediction model, the method includes the following steps S201~S202:

[0046] S201. Construct an integrated bearing model, which is used to characterize the influence of bearing clearance on bearing lubrication characteristics.

[0047] S202. Based on the integrated bearing model, the preset physical model for calculating the wear of the main bearing bush and the physical model for calculating the wear of the connecting rod bearing bush, a bearing sample database is created.

[0048] In some cases, the bearing clearance and the corresponding maximum wear of the bearing bush can be obtained from experiments, but the amount of data may be insufficient. Therefore, in order to improve the accuracy and reliability of the bearing bush wear prediction model, this embodiment generates samples by constructing an integrated bearing model to increase the amount of data and ensure the high accuracy and physical authenticity of the sample data.

[0049] Specifically, the integrated bearing model is used to characterize the influence of bearing clearance on bearing lubrication characteristics. For example, the input of the integrated bearing model is the main bearing clearance, and the output of the integrated bearing model is the friction parameters of the main bearing. The friction parameters can characterize lubrication characteristics. For example, the friction parameters include the bearing's rough contact pressure and relative slip. The bearing's lubrication capability under the current bearing clearance is reflected by the bearing's rough contact pressure and relative slip.

[0050] In this embodiment, the physical model for calculating bearing wear refers to a model that calculates bearing wear through load and relative slip. In one example, the physical model for calculating bearing wear can be expressed as:

[0051]

[0052] in, h wi Characterizing the bearing i Wear depth of a node under a single operating cycle. t 0 represents the cycle duration. p ci ( t )express i Rough contact pressure at nodes v i ( t )express i Node relative slip velocity, k c This indicates wear parameters.

[0053] In this embodiment, the preset physical models for calculating the wear of the main bearing and the connecting rod bearing are obtained based on the above-mentioned physical models for calculating the wear of the bearing. The difference lies in the different parameters for different bearings.

[0054] Therefore, the integrated bearing model can be obtained. i Rough contact pressure at nodes p ci ( t )and i Node relative slip velocity v i ( t The physical model for calculating the wear of the main bearing bush can obtain the wear depth (i.e., wear amount) of the main bearing bush, and the physical model for calculating the wear of the connecting rod bearing bush can obtain the wear depth of the connecting rod bearing bush. In this way, a bearing sample database can be constructed.

[0055] The above embodiments construct an integrated bearing model by leveraging the physical influence characteristics between bearings. Combined with the preset physical models for calculating the wear of the main bearing bush and the connecting rod bearing bush, this provides a reliable source of sample data for the entire optimization process. This enables the subsequent construction of bearing bush wear prediction models and the prediction of multi-objective optimization results to provide high-precision data, greatly enhancing the scientific validity and credibility of the solution.

[0056] In this embodiment of the application, an integrated bearing model is constructed, including coupling a bearing dynamics model and a lubrication model to obtain an integrated bearing model. The bearing dynamics model is used to characterize the dynamic load transfer relationship between the main bearing and the connecting rod bearing, and the lubrication model is used to characterize the relationship between the dynamic parameters and lubrication state of the bearing.

[0057] In this embodiment, the bearing dynamics model can simulate the power transmission between the crankshaft, main bearings, and connecting rod bearings. It can simulate how, during engine operation, the combustion pressure of the piston's combustion gases and the reciprocating inertial force are transmitted through the connecting rods to the crankshaft and ultimately act on the main bearings and connecting rod bearings. This model can calculate the dynamic load spectrum (forces whose magnitude and direction change over time) borne by the main bearings and connecting rod bearings, and demonstrate the dynamic load transmission relationship between the main bearings and connecting rod bearings via the crankshaft.

[0058] The lubrication model can be established based on fluid dynamic lubrication theories such as the Reynolds equation. It is used to calculate key tribological parameters such as bearing oil film pressure distribution, thickness distribution, flow rate, rough contact pressure, and relative slip velocity under given clearance, load, and motion parameters (speed, eccentricity).

[0059] This embodiment couples the bearing dynamics model and the lubrication model to obtain an integrated bearing model. The dynamic load of the bearing calculated by the bearing dynamics model can be passed as input to the lubrication model. The oil film pressure distribution calculated by the lubrication model generates a reverse oil film force, which significantly affects the transient motion attitude of the crankshaft (such as the shaft center trajectory), thereby changing the bearing load. This embodiment can feed this oil film force back to the bearing dynamics model to recalculate the corrected dynamic load. Through coupled modeling, interdisciplinary joint simulation of mechanical dynamics and tribology is achieved, enabling the model to reveal the complete closed-loop influence mechanism of "clearance change, load redistribution, lubrication state change, and reaction to load". It can more accurately predict wear-related parameters (such as rough contact pressure) under different clearance schemes, thus resulting in higher quality sample data.

[0060] Figure 3 This application illustrates a flowchart of the steps involved in creating a bearing sample database, as shown below. Figure 3 As shown in the embodiments of this application, creating a bearing sample database includes the following steps S301~S304:

[0061] S301. Based on the integrated bearing model, the friction parameters corresponding to different bearing clearances are obtained. The friction parameters include the rough contact pressure and relative slip of the bearing.

[0062] S302. Based on the friction parameters of the main bearing and the physical model for calculating the wear of the main bearing bush, the maximum wear amount of the main bearing bush is obtained;

[0063] S303. Based on the friction parameters of the connecting rod bearing and the physical model for calculating the wear of the connecting rod bearing bush, the maximum wear amount of the connecting rod bearing bush is obtained;

[0064] S304. Based on different main bearing clearances and corresponding maximum wear of the main bearing bush, as well as different connecting rod bearing clearances and corresponding maximum wear of the connecting rod bush, create a bearing sample database.

[0065] In some examples, at least one pre-designed numerical combination of main bearing clearance and connecting rod bearing clearance is used as input parameters to drive an established integrated bearing model. This model simulates the operation of an internal combustion engine within a complete working cycle under this specific clearance combination. Through iterative solving, the dynamic trajectory of the journal center and the oil film thickness variation of the main and connecting rod bearings are calculated, ultimately outputting friction parameters including rough contact pressure and relative slippage. These friction parameters are time-series data that accurately characterize the tribological state of the bearing under the corresponding bearing clearance scheme.

[0066] The time-series data of the main bearing's friction parameters are used as input to the physical model for calculating the main bearing wear. This model integrates the instantaneous wear rate at each calculation node i on the bearing surface within one working cycle to obtain the wear depth at that node. After traversing all nodes, the maximum value among all wear depths is found; this maximum wear depth (i.e., wear amount) is the main bearing wear under the given clearance conditions. The maximum wear depth represents the "worst-case" wear of the main bearing and is a key indicator for assessing its lifespan.

[0067] Similarly, by using the time series data of the friction parameters of the connecting rod bearing as input to the physical model for calculating the wear of the connecting rod bearing bush, the maximum wear amount of the connecting rod bearing bush can be obtained.

[0068] Each time a different combination of main and connecting rod bearing clearances is input, a bearing sample database is obtained based on the different main bearing clearances and the corresponding maximum wear of the main bearing bush, as well as the different connecting rod bearing clearances and the corresponding maximum wear of the connecting rod bush.

[0069] The above embodiments construct a data generation process with high simulation accuracy, providing a high-quality database for bearing clearance optimization.

[0070] It should be noted that when the amount of data in the bearing sample database is greater than a certain value, making the accuracy of the bearing wear prediction model high enough, before using the bearing wear prediction model of this application, there is no need to go through steps S201~S202 and steps S301~S304. The bearing wear prediction model can be directly used to output the bearing wear amount, which greatly improves the optimization efficiency.

[0071] Figure 4 The flowchart illustrates another step of the bearing clearance optimization method of this application, as shown below. Figure 4 As shown in the embodiments of this application, after obtaining the first bearing clearance optimization result, the method further includes:

[0072] S401. Obtain the vibration feedback signal after using the first bearing clearance optimization result as the execution parameter;

[0073] S402. Based on the vibration feedback signal, the optimization result of the first bearing clearance is corrected to obtain the optimization result of the second bearing clearance.

[0074] In this embodiment, the vibration feedback signal can be obtained through a vibration sensor. In the internal combustion engine bench test, the vibration data (time domain signal or frequency domain spectrum) of the system can be collected in real time by a vibration sensor (such as an accelerometer) installed at the bottom of the bearing housing.

[0075] The vibration feedback signal is obtained by using the first bearing clearance optimization result as the execution parameter. That is, the internal combustion engine prototype is assembled according to the main bearing clearance and connecting rod bearing clearance in the first bearing clearance optimization result, and a vibration sensor is installed at the bottom of the bearing housing of the internal combustion engine prototype to obtain the vibration feedback signal.

[0076] The optimization results of the first bearing clearance are corrected based on vibration feedback signals. For example, if the overall vibration acceleration value does not meet the set value when the internal combustion engine is working with the main bearing clearance and connecting rod bearing clearance in the optimization results of the first bearing clearance, it means that the current clearance is not optimal. Therefore, the optimization results of the first bearing clearance are corrected. Based on the initial wear reduction optimization, the bearing clearance scheme that can simultaneously take into account low wear and low vibration is finally determined after combining the actual vibration feedback with the correction.

[0077] Therefore, the optimization result of the first bearing clearance ensures optimal wear performance at the component level, but it may not fully consider the impact of clearance on the vibration characteristics of the entire system. This is because vibration involves more complex factors (overall stiffness, mass distribution, etc.) and is difficult to predict completely and accurately in the model. Therefore, this embodiment expands the optimization objective from wear reduction between components to the dual objectives of "wear reduction and vibration reduction" by introducing vibration feedback signals.

[0078] In this embodiment of the application, the first bearing clearance optimization result is corrected based on the vibration feedback signal to obtain the second bearing clearance optimization result. This includes: adjusting the fitting clearance of the connecting rod bearing and the fitting clearance of the main bearing in the first bearing clearance optimization result in sequence according to a preset step size, until the vibration feedback signal meets the vibration target parameters, which include vibration acceleration or vibration amplitude.

[0079] In some cases, the preset step size refers to the adjustment amount used to fine-tune the bearing clearance, thereby improving the bearing clearance optimization effect.

[0080] In one example, in the internal combustion engine shaft system, the connecting rod bearing directly bears the combustion pressure and reciprocating inertial force from the piston, and is one of the core excitation sources. The operating conditions of the connecting rod bearing may have a greater impact on vibration than the main bearing. Therefore, in this embodiment, the connecting rod bearing clearance is adjusted first, which can more directly try to change the force transmission characteristics from the source of vibration (e.g., by changing the oil film damping).

[0081] First, fix the main bearing clearance, then gradually increase the connecting rod bearing clearance by a preset step size, test the vibration signal until the vibration feedback signal meets the vibration target parameters, obtain a better value, then fix the connecting rod bearing clearance, and then adjust the main bearing clearance by the same step size. This not only improves the optimization efficiency but also makes the whole correction process more stable.

[0082] In some cases, directly adjusting the bearing clearance (i.e., the diameter fit clearance of the bearing) may result in excessive clearance variation and low precision. In this embodiment, the preset step size is, for example, 0.01‰ of the relative clearance. The relative clearance refers to the ratio between the bearing diameter fit clearance and the journal diameter. This ensures the precision and safety of the adjustment, avoiding over-adjustment that could lead to lubrication deterioration. The resulting second bearing clearance optimization can find the optimal point with minimal system vibration within the allowable limits of wear performance.

[0083] In some examples, assuming that the recommended relative clearance value for the main bearing in the first bearing clearance optimization result is 0.81‰ and the recommended relative clearance value for the connecting rod bearing is 0.93‰, after correction, the recommended relative clearance value for the main bearing in the second bearing clearance optimization result is 0.85‰ and the recommended relative clearance value for the connecting rod bearing is 0.98‰.

[0084] In this embodiment, the vibration target parameter is a quantitative indicator used to determine whether the vibration meets the standard. For example, if the effective value of the vibration acceleration or the vibration amplitude is reduced to below a certain threshold, it indicates that the vibration feedback signal meets the vibration target parameter, and the adjustment of the bearing clearance is stopped, so that the dual effects of wear reduction and vibration reduction can be achieved.

[0085] Figure 5 This application shows a flowchart illustrating the overall steps of a bearing clearance optimization method according to an embodiment of the present application. Figure 5As shown in this embodiment, the shaft bearing clearances, including the main bearing clearance and the connecting rod bearing clearance, are input into a pre-constructed integrated bearing model. This integrated bearing model is obtained by coupling the bearing dynamics model and the lubrication model. The integrated bearing model outputs the rough contact pressure and relative slip of the main bearing, as well as the rough contact pressure and relative slip of the connecting rod bearing. The rough contact pressure and relative slip of the main bearing are used as input to the physical model for calculating the main bearing wear, which outputs the maximum wear of the main bearing. Similarly, the rough contact pressure and relative slip of the connecting rod bearing are used as input to the physical model for calculating the connecting rod bearing wear, which outputs the maximum wear of the connecting rod bearing. Based on the main bearing clearance and the corresponding maximum wear of the main bearing, and the connecting rod bearing clearance and the corresponding maximum wear of the connecting rod bearing, a bearing sample database is created.

[0086] Building a bearing wear prediction model based on a bearing sample database can improve the accuracy of the bearing wear prediction model and eliminate the need for calculations using a physical model for bearing wear calculation. The maximum wear amount of the bearing can be obtained directly and quickly through the bearing wear prediction model.

[0087] When the accuracy of the bearing wear prediction model meets the set value, a multi-objective optimization algorithm is used, with each bearing clearance as the optimization variable and minimizing the maximum wear of each bearing as the objective function, to obtain the first bearing clearance optimization result. In this way, component-level wear reduction between bearings can be achieved.

[0088] Furthermore, using the optimization result of the first bearing clearance as the execution parameter, an internal combustion engine prototype was designed, and a bearing system vibration monitoring test was conducted. Vibration feedback signals were collected, and the optimization result of the first bearing clearance was corrected based on the vibration feedback signals. At the same time, a durability test was conducted to verify the optimization result of the second bearing clearance, and the optimization result of the second bearing clearance was determined to be the optimal optimization result.

[0089] Figure 6(a) shows the test verification results of the main bearing after the bearing clearance optimization according to an embodiment of the present application; Figure 6(b) shows the test verification results of the connecting rod bearing after the bearing clearance optimization according to an embodiment of the present application; Figure 6(c) shows the test verification results of the system vibration amplitude after the bearing clearance optimization according to an embodiment of the present application.

[0090] As shown in Figures 6(a) to 6(c), the bearing clearance optimized by the embodiments of this application significantly reduces the wear depth of the main bearing, the wear depth of the connecting rod bearing, and the vibration amplitude of the system, demonstrating a significant wear reduction and vibration reduction effect.

[0091] Figure 7 This invention provides a schematic diagram of the structure of a bearing clearance optimization device according to an embodiment of the present application. Figure 7As shown, the bearing clearance optimization device 700 includes:

[0092] The model building module 701 is used to build a bearing wear prediction model based on a bearing sample database. The bearing sample data includes different bearing clearances and corresponding maximum bearing wear, and the bearings include main bearings and connecting rod bearings. The bearing wear prediction model is used to predict the maximum bearing wear under different bearing clearances.

[0093] The first optimization module 702 is used to obtain the first bearing clearance optimization result based on the bearing wear prediction model, with each bearing clearance as the optimization variable and minimizing the maximum wear of each bearing as the objective function.

[0094] The device also includes: a database creation module 703, used to construct an integrated bearing model, which characterizes the influence of the fit clearance between the main bearing and the connecting rod bearing on the lubrication characteristics of the bearing system; and to create the bearing sample database based on the integrated bearing model, a preset physical model for calculating the wear of the main bearing bush, and a physical model for calculating the wear of the connecting rod bearing bush; and

[0095] The second optimization module 704 is used to acquire the vibration feedback signal after using the first bearing clearance optimization result as the execution parameter; and to correct the first bearing clearance optimization result based on the vibration feedback signal to obtain the second bearing clearance optimization result.

[0096] The bearing clearance optimization device and the bearing clearance optimization method provided in the above embodiments of this application are based on the same application concept and have the same beneficial effects as the methods adopted, run or implemented by the application stored therein.

[0097] This application also provides an electronic device corresponding to the bearing clearance optimization method provided in the foregoing embodiments, for executing the aforementioned bearing clearance optimization method. Please refer to... Figure 8 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 8 As shown, the electronic device 20 includes: a processor 200, a memory 201, a bus 202, and a communication interface 203. The processor 200, the communication interface 203, and the memory 201 are connected via the bus 202. The memory 201 stores a computer program that can run on the processor 200. When the processor 200 runs the computer program, it executes the method provided in any of the foregoing embodiments of this application.

[0098] The memory 201 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 203 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0099] Bus 202 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used to store programs. After receiving an execution instruction, the processor 200 executes the program. The bearing clearance optimization method disclosed in any of the foregoing embodiments of this application can be applied to the processor 200, or implemented by the processor 200.

[0100] The processor 200 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 200 or by instructions in software form. The processor 200 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 201. The processor 200 reads the information in memory 201 and, in conjunction with its hardware, completes the steps of the above method.

[0101] The electronic device provided in this application embodiment and the bearing clearance optimization method provided in this application embodiment are based on the same application concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0102] This application also provides a computer-readable storage medium corresponding to the bearing clearance optimization method provided in the foregoing embodiments. The computer-readable storage medium can be an optical disc, on which a program product is stored. The program product can be an operating system, application software, game, utility software, etc. The program product includes a computer program, which usually exists in source code or compiled binary form. When the computer program is run by a processor, it will execute the bearing clearance optimization method provided in any of the foregoing embodiments.

[0103] It should be noted that examples of the computer-readable storage medium may also 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 optical and magnetic storage media, which will not be elaborated here.

[0104] The computer-readable storage medium provided in the above embodiments of this application and the bearing clearance optimization method provided in the embodiments of this application are based on the same application concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0105] This application also provides a computer program product corresponding to the method provided in the foregoing embodiments. The computer program product includes a computer program that is executed by a processor to implement the bearing clearance optimization method provided in the foregoing embodiments.

[0106] The computer program products provided in the above embodiments of this application and the methods provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0107] It should be noted that:

[0108] In the foregoing text, 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 a process, method, article, or apparatus. Without further limitation, 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. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0110] The embodiments of this application have been described above with reference to the accompanying drawings. These are merely specific implementations of this application, but this application is not limited to the specific implementations described above. The specific implementations described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for optimizing bearing clearance, characterized in that, include: The bearing dynamics model and the lubrication model are coupled to obtain an integrated bearing model. The bearing dynamics model is used to characterize the dynamic load transfer relationship between the main bearing and the connecting rod bearing, and the integrated bearing model is used to characterize the influence of bearing clearance on bearing lubrication characteristics. Based on the integrated bearing model, the preset physical model for calculating main bearing wear, and the physical model for calculating connecting rod bearing wear, a bearing sample database is created. Based on a bearing sample database, a bearing wear prediction model is constructed. The bearing sample data includes different bearing clearances and corresponding maximum bearing wear. The bearings include main bearings and connecting rod bearings. The bearing wear prediction model is used to predict the maximum bearing wear under different bearing clearances. Based on the bearing wear prediction model, the bearing clearance is used as the optimization variable to automatically find the optimal value in the wear of the main bearing and the connecting rod bearing. The objective function is to minimize the maximum wear of each bearing, and the optimization result of the first bearing clearance is obtained.

2. The method according to claim 1, characterized in that, The lubrication model is used to characterize the relationship between the dynamic parameters and lubrication state of the bearing.

3. The method according to claim 1, characterized in that, The creation of the bearing sample database includes: Based on the integrated bearing model, friction parameters corresponding to different bearing clearances are obtained, including the rough contact pressure and relative slip of the bearing. Based on the friction parameters of the main bearing and the physical model for calculating the wear of the main bearing bush, the maximum wear amount of the main bearing bush is obtained; Based on the friction parameters of the connecting rod bearing and the physical model for calculating the wear of the connecting rod bearing bush, the maximum wear amount of the connecting rod bearing bush is obtained; Based on different main bearing clearances and corresponding maximum wear of the main bearing bush, as well as different connecting rod bearing clearances and corresponding maximum wear of the connecting rod bush, the bearing sample database is created.

4. The method according to any one of claims 1-3, characterized in that, After obtaining the optimized result of the first bearing clearance, the method further includes: Obtain the vibration feedback signal after using the first bearing clearance optimization result as the execution parameter; The first bearing clearance optimization result is corrected based on the vibration feedback signal to obtain the second bearing clearance optimization result.

5. The method according to claim 4, characterized in that, The first bearing clearance optimization result is corrected based on the vibration feedback signal to obtain the second bearing clearance optimization result, including: According to the preset step size, the fitting clearance of the connecting rod bearing and the fitting clearance of the main bearing in the first bearing clearance optimization result are adjusted sequentially until the vibration feedback signal meets the vibration target parameters, which include vibration acceleration or vibration amplitude.

6. A bearing clearance optimization device, characterized in that, The device includes: The database creation module is used to couple the bearing dynamics model and the lubrication model to obtain an integrated bearing model. The bearing dynamics model is used to characterize the dynamic load transfer relationship between the main bearing and the connecting rod bearing, and the integrated bearing model is used to characterize the influence of the fit clearance of the main bearing and the connecting rod bearing on the lubrication characteristics of the bearing system. Based on the integrated bearing model, the preset physical model for calculating the wear of the main bearing bush and the physical model for calculating the wear of the connecting rod bearing bush, a bearing sample database is created. The model building module is used to construct a bearing wear prediction model based on a bearing sample database. The bearing sample data includes different bearing clearances and corresponding maximum bearing wear, and the bearings include main bearings and connecting rod bearings. The bearing wear prediction model is used to predict the maximum bearing wear under different bearing clearances. The first optimization module is used to automatically find the optimal value between the main bearing wear and the connecting rod bearing wear based on the bearing wear prediction model, using the clearance of each bearing as the optimization variable, and taking the minimization of the maximum wear of each bearing as the objective function to obtain the first bearing clearance optimization result.

7. The apparatus according to claim 6, characterized in that, The device further includes: The second optimization module is used to acquire the vibration feedback signal after using the first bearing clearance optimization result as the execution parameter; and to correct the first bearing clearance optimization result based on the vibration feedback signal to obtain the second bearing clearance optimization result.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the method as claimed in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to implement the method as described in any one of claims 1-5.

Images