Method, device and equipment for determining reliability of drive axle bearing system and medium

Abstract

The invention discloses a method, device and equipment for determining reliability of a drive axle bearing system and a medium, and belongs to the technical field of vehicle tests.The method comprises the steps that according to a key structure of a target drive axle bearing system, a main failure mode and stress influence parameters corresponding to the key structure are determined; wherein the stress influence parameters comprise size parameters, material parameters and assembly gaps; constructing a plurality of drive axle finite element models according to the stress influence parameters and the parameter design tolerance and demand confidence of the target drive axle bearing system; according to the main failure mode and a preset load history, determining a stress level set and a load cycle index corresponding to the preset load history under the drive axle finite element model; and determining the target reliability of the target drive axle bearing system according to the stress level set and the load cycle index corresponding to the preset load history under each drive axle finite element model and the life shape parameter corresponding to the damaged part in the target drive axle bearing system.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle testing, and particularly to a method, device, equipment and medium for determining the reliability of a drive axle bearing system. Background Art

[0002] As a key and typical functional assembly system of the whole vehicle, the reliability of the drive axle bearing system directly affects the performance of the whole vehicle. Therefore, reliable durability verification of the drive axle bearing system has become an indispensable step in the design and production process of the drive axle.

[0003] However, due to the insufficient consideration of factors in the existing reliable durability verification methods for drive axle bearing systems, the fatigue life discreteness of drive axle bearing systems is large, resulting in high verification costs and ineffective reliable durability verification of drive axle bearing systems, that is, the reliability of drive axle bearing systems obtained by verification is inaccurate. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for determining the reliability of a drive axle bearing system, so as to reduce the fatigue life discreteness of the drive axle bearing system, reduce verification costs, and improve the accuracy of the reliability of the drive axle bearing system.

[0005] According to one aspect of the present invention, a method for determining the reliability of a drive axle bearing system is provided. The method includes:

[0006] Determine the main failure modes and stress influence parameters corresponding to the key structures according to the key structures of the target drive axle bearing system; wherein, the stress influence parameters include dimension parameters, material parameters and assembly clearances;

[0007] Construct a plurality of drive axle finite element models according to the stress influence parameters, the parameter design tolerances and the required confidence levels of the target drive axle bearing system;

[0008] Determine the stress level set and the number of load cycles corresponding to the preset load history under the drive axle finite element model according to the main failure mode and the preset load history;

[0009] Determine the target reliability of the target drive axle bearing system according to the stress level set and the number of load cycles corresponding to the preset load history under each drive axle finite element model, and the life shape parameters corresponding to the damaged parts in the target drive axle bearing system.

[0010] According to another aspect of the present invention, a device for determining the reliability of a drive axle bearing system is provided. The device includes:

[0011] A key data determination module, configured to determine the main failure modes and stress influence parameters corresponding to the key structures according to the key structures of the target drive axle bearing system; wherein, the stress influence parameters include dimensional parameters, material parameters, and assembly clearances;

[0012] A finite element model construction module, configured to construct multiple drive axle finite element models according to the stress influence parameters, the parameter design tolerances of the target drive axle bearing system, and the required confidence level;

[0013] A stress data determination module, configured to determine the stress level set and the number of load cycles corresponding to the preset load history under the drive axle finite element model according to the main failure modes and the preset load history;

[0014] A target reliability determination module, configured to determine the target reliability of the target drive axle bearing system according to the stress level set and the number of load cycles corresponding to the preset load history under each drive axle finite element model, and the life shape parameters corresponding to the damaged parts in the target drive axle bearing system.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device includes:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the reliability determination method of the drive axle bearing system according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, there is provided a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the reliability determination method of the drive axle bearing system according to any embodiment of the present invention when executed by a processor.

[0020] According to another aspect of the present invention, there is provided a computer program product, including a computer program, and the computer program implements the reliability determination method of the drive axle bearing system according to any embodiment of the present invention when executed by a processor.

[0021] In the technical solution of the embodiment of the present invention, according to the key structure of the target drive axle bearing system, the main failure modes and stress influence parameters corresponding to the key structure are determined; wherein, the stress influence parameters include dimension parameters, material parameters, and assembly clearances; according to the stress influence parameters, as well as the parameter design tolerances and required confidence levels of the target drive axle bearing system, a plurality of drive axle finite element models are constructed; according to the main failure modes and the preset load history, the stress level sets and load cycle numbers corresponding to the preset load history under the drive axle finite element models are determined; according to the stress level sets and load cycle numbers corresponding to the preset load history under each drive axle finite element model, and the life shape parameters corresponding to the damaged parts in the target drive axle bearing system, the target reliability of the target drive axle bearing system is determined. The above technical solution, aiming at the key structure of the target drive axle bearing system, considers its main failure modes and the influence of dimension parameters, material parameters, and assembly clearances that affect the stress it bears on the reliable durability of the target drive axle bearing system, and combines the actual design requirements of the target drive axle bearing system to construct a plurality of drive axle finite element models for verifying the reliable durability of the target drive axle bearing system; according to the data for determining the reliable durability of the target drive axle bearing system under a plurality of drive axle finite element models, the target reliability of the target drive axle bearing system is determined, reducing the discreteness of the fatigue life of the target drive axle bearing system, reducing the number of samples when verifying the reliable durability of the target drive axle bearing system, thereby reducing the verification cost, effectively realizing the verification of the reliable durability of the target drive axle bearing system, improving the accuracy of the target reliability, providing more accurate data support for subsequent structural optimization of the target drive axle bearing system, and further improving the service life of the target drive axle bearing system and the market competitiveness of the target drive axle bearing system.

[0022] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0024] Figure 1 is a flowchart of a method for determining the reliability of a drive axle bearing system according to Embodiment 1 of the present invention;

[0025] Figure 2It is a flowchart of a method for determining the reliability of a drive axle load-bearing system according to Embodiment 2 of the present invention;

[0026] Figure 3 It is a schematic structural diagram of a device for determining the reliability of a drive axle load-bearing system according to Embodiment 3 of the present invention;

[0027] Figure 4 It is a schematic structural diagram of an electronic device for implementing the method for determining the reliability of a drive axle load-bearing system according to the embodiment of the present invention. Detailed implementation manners

[0028] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0029] It should be noted that the terms "target", "candidate", "first", and "second" in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0030] In addition, it should also be noted that in the technical solution of the present invention, the collection, storage, use, processing, transmission, provision, and disclosure of parameters design tolerances, requirement confidence levels, requirement reliabilities, etc. of the target drive axle load-bearing system involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0031] Embodiment 1

[0032] Figure 1 It is a flowchart of a method for determining the reliability of a drive axle load-bearing system provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of reliable durability verification of the drive axle load-bearing system. This method can be executed by a device for determining the reliability of the drive axle load-bearing system. The device can be implemented in the form of hardware and / or software and can be configured in an electronic device, which can be a vehicle. AsFigure 1 As shown in the figure, the method includes:

[0033] S101. Determine the main failure modes and stress influence parameters corresponding to the key structures according to the key structures of the target drive axle bearing system.

[0034] Among them, the target drive axle bearing system refers to the drive axle bearing system that needs to be verified for reliable durability. The key structure refers to the structure in the target drive axle bearing system that affects the service life of the target drive axle bearing system; optionally, the key structures include the axle housing and the main reducer housing. The main failure mode refers to the mode that causes the failure of the key structure; optionally, the main failure modes include but are not limited to housing fracture, weld oil leakage, and deformation.

[0035] Among them, the stress influence parameter refers to the parameter that affects the stress borne by the key structure; optionally, the stress influence parameters include dimension parameters, material parameters, and assembly clearances. Among them, the dimension parameters include the dimension parameters of the axle housing and the dimension parameters of the main reducer housing; the dimension parameters of the axle housing include but are not limited to the width, height, and thickness of the axle housing cross-section at the leaf spring seat, and the diameter of the half axle sleeve at the end connected to the differential; the dimension parameters of the main reducer housing include but are not limited to the thickness of the main reducer housing, and the fillet dimension at the connection between the bearing seat and the main reducer housing. The material parameters include but are not limited to the elastic modulus and Poisson's ratio. The assembly clearances include the assembly clearance between the axle housing and the main reducer housing, and the assembly clearance between the main reducer housing and the bearing.

[0036] Specifically, the main failure modes and stress influence parameters corresponding to the key structures can be determined according to the key structures of the target drive axle bearing system in combination with the expert experience of those skilled in the art.

[0037] S102. Construct multiple drive axle finite element models according to the stress influence parameters, the parameter design tolerances of the target drive axle bearing system, and the required confidence level.

[0038] Among them, the parameter design tolerance refers to the tolerance corresponding to the stress influence parameter formulated in advance when designing the target drive axle bearing system; optionally, the parameter design tolerances include dimension design tolerances, material design tolerances, and assembly design tolerances. The tolerance refers to the allowable variation of the stress influence parameter. The required confidence level refers to the confidence level expected to be achieved by the target drive axle bearing system; optionally, the required confidence level can be determined according to the actual business requirements, and the embodiments of the present invention do not make specific limitations on it. The drive axle finite element model refers to a mathematical model that can be solved by a computer by converting the geometric structure, material properties, boundary conditions, and load conditions of the target drive axle bearing system through finite element analysis (FEA, Finite Element Analysis) technology.

[0039] Specifically, according to the required confidence level of the target drive axle load-bearing system, based on the corresponding relationship between the confidence level and the sample sampling quantity in the preset relationship table, the target sampling quantity corresponding to the required confidence level can be determined; for each stress influence parameter, according to the stress influence parameter and the parameter design tolerance of the target drive axle load-bearing system, the parameter samples that satisfy the 6σ normal distribution of this stress influence parameter are determined, and the total number of samples in the parameter samples is counted; according to the total number of samples and the target sampling quantity, based on the Monte Carlo method, random sampling is performed on the parameter samples to obtain the random samples corresponding to this stress influence parameter; according to the random samples corresponding to each stress influence parameter, multiple drive axle finite element models are constructed.

[0040] Among them, the preset relationship table refers to a data table used to store the corresponding relationship between the confidence level and the sample sampling quantity; optionally, the preset relationship table can be preset according to experimental experience or the expert experience of those skilled in the art, and the embodiments of the present invention do not make specific limitations thereto. The sample sampling quantity refers to the number of samples drawn. The target sampling quantity refers to the sample sampling quantity corresponding to the required confidence level, that is, the number of samples that can be drawn for each stress influence parameter under the required confidence level.

[0041] More specifically, the required confidence level of the target drive axle load-bearing system can be matched with the confidence level in the preset relationship table to obtain the matching confidence level; among them, the matching confidence level refers to the confidence level that successfully matches the required confidence level in the preset relationship table; based on the corresponding relationship between the confidence level and the sample sampling quantity in the preset relationship table, the sample sampling quantity corresponding to the matching confidence level is extracted from the preset relationship table as the target sampling quantity corresponding to the required confidence level. For example, if the required confidence level of the target drive axle load-bearing system is 70%, and the sample sampling quantity corresponding to the confidence level of 70% in the preset relationship table is 4, then the target sampling quantity corresponding to the required confidence level is 4.

[0042] After that, for each stress influence parameter, the target parameter design tolerance corresponding to this stress influence parameter is obtained from the parameter design tolerance of the target drive axle load-bearing system; according to this stress influence parameter and the target parameter design tolerance, through the following mean determination formula and standard deviation determination formula, the mean and standard deviation of this stress influence parameter are determined:

[0043]

[0044] Among them, μ represents the mean of this stress influence parameter; σ represents the standard deviation of this stress influence parameter; p represents the parameter value of this stress influence parameter; a represents the lower limit value of the target parameter design tolerance; b represents the upper limit value of the target parameter design tolerance.

[0045] In other words, according to the dimensional parameters and dimensional design tolerances (i.e., the target parameter design tolerances corresponding to the dimensional parameters), the mean and standard deviation of the dimensional parameters can be determined through the above mean determination formula and standard deviation determination formula; according to the material parameters and material design tolerances (i.e., the target parameter design tolerances corresponding to the material parameters), the mean and standard deviation of the material parameters can be determined through the above mean determination formula and standard deviation determination formula; according to the assembly clearance and assembly design tolerances (i.e., the target parameter design tolerances corresponding to the assembly clearance), the mean and standard deviation of the assembly clearance can be determined.

[0046] After that, for each stress influence parameter, based on the mean and standard deviation of the stress influence parameter and the random number generation algorithm, a normal distribution sample of the stress influence parameter that conforms to N(μ,σ 2 ) is generated; and based on the mean and standard deviation of the stress influence parameter, through the following specification limit determination formula, the upper specification limit and upper specification limit of the stress influence parameter are determined:

[0047]

[0048] where USL represents the upper specification limit of the stress influence parameter; LSL represents the lower specification limit of the stress influence parameter, μ represents the mean of the stress influence parameter; σ represents the standard deviation of the stress influence parameter. After that, based on the upper specification limit and upper specification limit of the stress influence parameter, the normal distribution sample of the stress influence parameter that conforms to N(μ,σ 2 ) is filtered to obtain a parameter sample of the stress influence parameter that satisfies the 6σ normal distribution.

[0049] Based on the principle of determining the parameter sample of the stress influence parameter that satisfies the 6σ normal distribution as described above, a first parameter sample of the dimensional parameters that satisfies the 6σ normal distribution, a second parameter sample of the material parameters that satisfies the 6σ normal distribution, and a third parameter sample of the assembly clearance that satisfies the 6σ normal distribution can be obtained.

[0050] After that, for each stress influence parameter, the total number of samples of the parameter sample of the stress influence parameter that satisfies the 6σ normal distribution is counted, that is, the total number of samples of the first parameter sample is counted and denoted as the first sample quantity; the total number of samples of the second parameter sample is counted and denoted as the second sample quantity; the total number of samples of the third parameter sample is counted and denoted as the third sample quantity.

[0051] After that, according to the first sample quantity and the target sampling quantity, based on the Monte Carlo method, random sampling is performed on the first parameter sample to obtain the first random sample corresponding to the dimensional parameter; according to the second sample quantity and the target sampling quantity, based on the Monte Carlo method, random sampling is performed on the second parameter sample to obtain the second random sample corresponding to the material parameter; according to the third sample quantity and the target sampling quantity, based on the Monte Carlo method, random sampling is performed on the third parameter sample to obtain the third random sample corresponding to the assembly gap.

[0052] After that, according to the first random sample corresponding to the dimensional parameter, the second random sample corresponding to the material parameter, and the third random sample corresponding to the assembly gap, determine the experimental combination methods of the dimensional parameter, the material parameter, and the assembly gap, and construct a corresponding drive axle finite element model for each experimental combination method.

[0053] It can be understood that for each stress influence parameter, determining the parameter sample for which the stress influence parameter satisfies the 6σ normal distribution can make the parameter sample coverage of the stress influence parameter more comprehensive; after that, the Monte Carlo method is used to perform random sampling on the parameter sample for which the stress influence parameter satisfies the 6σ normal distribution to obtain the random sample corresponding to the stress influence parameter, improving the reliability of the random sample.

[0054] S103. According to the main failure mode and the preset load history, determine the stress level set and the load cycle number corresponding to the preset load history under the drive axle finite element model.

[0055] Among them, the preset load history refers to the load history determined in advance according to the actual service conditions, which describes the load magnitude and frequency received by the target drive axle bearing system at different time points or time periods under the actual service conditions. The stress level set refers to a set composed of multiple stress levels discretized from the preset load history. The load cycle number refers to the number of times the material experiences the complete process of loading and unloading under the action of the load history.

[0056] Specifically, the simulation analysis scheme of the target drive axle bearing system can be determined according to the main failure mode; according to the simulation analysis scheme and the preset load history, determine the stress level set and the load cycle number corresponding to each load in the preset load history under the drive axle finite element model.

[0057] More specifically, with the help of the expert experience of those skilled in the art, according to the main failure modes, the simulation analysis scheme of the target drive axle bearing system can be determined through CAE (Computer Aided Engineering) simulation software; for each drive axle finite element model, replace the load spectrum in the simulation analysis scheme with the load spectrum transformed by the preset load history, and replace the finite element model in the simulation analysis scheme with this drive axle finite element model to obtain a new simulation analysis scheme; conduct a simulation experiment on the target drive axle bearing system according to the new simulation analysis scheme to obtain the stress level set and load cycle times corresponding to the preset load history under this drive axle finite element model. Similarly, the stress level set and load cycle times corresponding to the preset load history under each drive axle finite element model can be obtained.

[0058] S104. Determine the target reliability of the target drive axle bearing system according to the stress level set and load cycle times corresponding to the preset load history under each drive axle finite element model, and the life shape parameters corresponding to the damaged parts in the target drive axle bearing system.

[0059] Among them, the target reliability refers to the reliability of the target drive axle bearing system finally determined through simulation experiments. It should be noted that there is more than one damaged part in the drive axle bearing system, and the damaged parts in the drive axle bearing system can be specified in advance. The life shape parameter refers to the shape parameter of the Weibull Distribution.

[0060] Specifically, for each drive axle finite element model, input the stress level set and load cycle times corresponding to the preset load history under this drive axle finite element model, and the life shape parameters corresponding to the damaged parts in the target drive axle bearing system into the preset reliability calculation model, and the target reliability of the target drive axle bearing system can be obtained after being processed by the reliability calculation model.

[0061] The technical solution of the embodiment of the present invention determines the main failure modes and stress influence parameters corresponding to the key structures according to the key structures of the target drive axle bearing system; wherein, the stress influence parameters include dimension parameters, material parameters and assembly clearances; constructs a plurality of drive axle finite element models according to the stress influence parameters, the tolerance designed according to the parameters of the target drive axle bearing system and the required confidence level; determines the stress level set and the number of load cycles corresponding to the preset load history under the drive axle finite element model according to the main failure modes and the preset load history; determines the target reliability of the target drive axle bearing system according to the stress level set and the number of load cycles corresponding to the preset load history under each drive axle finite element model and the life shape parameters corresponding to the damaged parts in the target drive axle bearing system. The above technical solution, aiming at the key structures of the target drive axle bearing system, considers its main failure modes and the influence of the dimension parameters, material parameters and assembly clearances affecting the stress it bears on the reliable durability of the target drive axle bearing system, and constructs a plurality of drive axle finite element models for verifying the reliable durability of the target drive axle bearing system in combination with the actual design requirements of the target drive axle bearing system; determines the target reliability of the target drive axle bearing system according to the data for determining the reliability of the target drive axle bearing system under a plurality of drive axle finite element models, reduces the discreteness of the fatigue life of the target drive axle bearing system, reduces the number of samples when verifying the reliable durability of the target drive axle bearing system, thereby reducing the verification cost, effectively realizes the verification of the reliable durability of the target drive axle bearing system, improves the accuracy of the target reliability, provides more accurate data support for the subsequent structural optimization of the target drive axle bearing system, and further improves the service life of the target drive axle bearing system and the market competitiveness of the target drive axle bearing system.

[0062] Embodiment 2

[0063] Figure 2 The flowchart of a method for determining the reliability of a drive axle bearing system provided in Embodiment 2 of the present invention. On the basis of the above embodiment, this embodiment further optimizes "determining the target reliability of the target drive axle bearing system according to the stress level set and the number of load cycles corresponding to the preset load history under each drive axle finite element model and the life shape parameters corresponding to the damaged parts in the target drive axle bearing system", and provides an optional implementation solution. It should be noted that for the parts not described in detail in the embodiments of the present invention, reference may be made to the relevant descriptions of other embodiments. As Figure 2 shown, the method includes:

[0064] S201. Determine the main failure modes and stress influence parameters corresponding to the key structures according to the key structures of the target drive axle bearing system.

[0065] Wherein, the stress influence parameters include dimension parameters, material parameters and assembly clearances.

[0066] S202. Design tolerances and required confidence levels based on stress influence parameters and parameters of the target drive axle bearing system, and construct multiple drive axle finite element models.

[0067] S203. Determine the stress level set and number of load cycles corresponding to the preset load history under the drive axle finite element model according to the main failure modes and preset load history.

[0068] S204. Determine the median life corresponding to each stress level in the stress level set corresponding to the preset load history under the drive axle finite element model.

[0069] Among them, the median life refers to the number of cycles when 50% of the samples fail at a specific stress level. Specifically, under each drive axle finite element model, for each stress level in the stress level set corresponding to the preset load history, obtain the median life corresponding to this stress level by referring to a specific S-N curve.

[0070] S205. For each damaged part in the target drive axle bearing system, determine the cumulative damage of this damaged part according to the number of load cycles corresponding to the preset load history under the drive axle finite element model and the median life corresponding to each stress level in the stress level set corresponding to the preset load history.

[0071] Among them, the cumulative damage is the damage accumulated by the damaged part under cyclic loading. Specifically, for the jth (j = 1, 2,..., M) damaged part in the target drive axle bearing system, under each drive axle finite element model, according to the number of load cycles corresponding to the preset load history under this drive axle finite element model and the median life corresponding to each stress level in the stress level set corresponding to the preset load history, determine the cumulative damage of the jth damaged part in the target drive axle bearing system through the following cumulative damage determination formula:

[0072]

[0073] Among them, M is a positive integer representing the total number of damaged parts in the target drive axle bearing system; D j represents the cumulative damage of the jth damaged part in the target drive axle bearing system; k represents the total number of stress levels in the stress level set corresponding to the preset load history; n i represents the number of load cycles corresponding to the preset load history; N i represents the median life corresponding to the ith stress level in the stress level set corresponding to the preset load history.

[0074] S206. Determine the reliability of this damaged part according to the cumulative damage of this damaged part and the life shape parameter corresponding to this damaged part.

[0075] Specifically, for the j-th damaged part in the target drive axle bearing system, under each drive axle finite element model, according to the cumulative damage of the j-th damaged part and the life shape parameter corresponding to the j-th damaged part, the reliability of the j-th damaged part in the target drive axle bearing system is determined through the following reliability determination formula for the damaged part:

[0076]

[0077] where D j represents the cumulative damage of the j-th damaged part; β j represents the life shape parameter corresponding to the j-th damaged part; R j represents the reliability of the j-th damaged part.

[0078] S207. Determine the candidate reliability of the target drive axle bearing system under the drive axle finite element model according to the number of damaged parts in the target drive axle bearing system and the reliability of each damaged part in the target drive axle bearing system.

[0079] Among them, the candidate reliability refers to the reliability of the target drive axle bearing system under a certain drive axle finite element model. It should be noted that one drive axle finite element model corresponds to one candidate reliability.

[0080] Specifically, for each drive axle finite element model, according to the number of damaged parts in the target drive axle bearing system and the reliability of each damaged part in the target drive axle bearing system, the candidate reliability of the target drive axle bearing system under this drive axle finite element model is determined through the following reliability determination formula for the drive axle bearing system:

[0081]

[0082] where R represents the candidate reliability of the target drive axle bearing system under this drive axle finite element model; L represents the total number of preset load histories; i represents the i-th preset load history; P i represents the probability of occurrence of the i-th preset load history; M represents the number of damaged parts in the target drive axle bearing system; R j represents the reliability of the j-th damaged part in the target drive axle bearing system.

[0083] S208. Determine the target reliability of the target drive axle bearing system based on the median rank estimation method according to the candidate reliability of the target drive axle bearing system under each drive axle finite element model.

[0084] Specifically, count the number of models of the statistical drive axle finite element model, and sort the candidate reliability of the target drive axle bearing system under each drive axle finite element model from smallest to largest to obtain a sorting result; for each candidate reliability, according to the number of models and the position number of this candidate reliability in the sorting result, determine the median rank corresponding to this candidate reliability through the following median rank determination formula:

[0085]

[0086] Among them, I represents the position number of this candidate reliability; n represents the number of models; γ represents the median rank corresponding to this candidate reliability. It should be noted that since one drive axle finite element model corresponds to one candidate reliability, the number of models is equal to the total number of candidate reliabilities.

[0087] After that, calculate the absolute difference between the median rank corresponding to each candidate reliability and the preset rank respectively, and screen out the candidate reliability with the smallest absolute difference from all the obtained absolute differences as the target reliability of the target drive axle bearing system. Among them, the preset rank is generally 0.5.

[0088] Optionally, if there are multiple candidate reliabilities with the smallest absolute difference, the mean value of multiple candidate reliabilities with the smallest absolute difference can be calculated to obtain the target reliability of the target drive axle bearing system.

[0089] Exemplarily, if there are 5 drive axle finite element models, namely drive axle finite element model 1, drive axle finite element model 2, drive axle finite element model 3, drive axle finite element model 4, and drive axle finite element model 5, and the candidate reliability of the target drive axle bearing system under drive axle finite element model 1 is candidate reliability A, the candidate reliability of the target drive axle bearing system under drive axle finite element model 2 is candidate reliability B, the candidate reliability of the target drive axle bearing system under drive axle finite element model 3 is candidate reliability C, the candidate reliability of the target drive axle bearing system under drive axle finite element model 4 is candidate reliability D, and the candidate reliability of the target drive axle bearing system under drive axle finite element model 5 is candidate reliability E. It can be seen from this that the number of models of the drive axle finite element model is 5. If the candidate reliabilities A, B, C, D, and E are sorted from smallest to largest, and the obtained sorting result is: candidate reliability C, candidate reliability B, candidate reliability A, candidate reliability D, candidate reliability E, then it can be known from this that the position number of candidate reliability A is 3, the position number of candidate reliability B is 2, the position number of candidate reliability C is 1, the position number of candidate reliability D is 4, and the position number of candidate reliability E is 5; the median rank corresponding to candidate reliability A can be obtained through the above median rank determination formula: The median rank corresponding to candidate reliability B: Median rank corresponding to candidate reliability C: Median rank corresponding to candidate reliability D: Median rank corresponding to candidate reliability E: If the preset rank is 0.5, the absolute difference between the calculated γ1 and the preset rank is 0, the absolute difference between the calculated γ2 and the preset rank is 0.185, the absolute difference between the γ3 and the preset rank is 0.37, the absolute difference between the γ4 and the preset rank is 0.185, and the absolute difference between the γ5 and the preset rank is 0.37. From this, it can be seen that the absolute difference between the median rank γ1 corresponding to candidate reliability A and the preset rank is the smallest. Therefore, candidate reliability A is used as the target reliability of the target drive axle bearing system.

[0090] It can be understood that the target reliability of the target drive axle bearing system is screened from multiple candidate reliabilities by the median rank estimation method, which improves the accuracy of the target reliability.

[0091] In the technical solution of the embodiment of the present invention, according to the key structure of the target drive axle bearing system, the main failure modes and stress influence parameters corresponding to the key structure are determined; wherein, the stress influence parameters include dimension parameters, material parameters, and assembly clearances; according to the stress influence parameters, the parameter design tolerances and the required confidence level of the target drive axle bearing system, a plurality of drive axle finite element models are constructed; according to the main failure modes and the preset load history, the stress level sets and the number of load cycles corresponding to the preset load history under the drive axle finite element models are determined; the median life corresponding to each stress level in the stress level set corresponding to the preset load history under the drive axle finite element model is determined; for each damaged part in the target drive axle bearing system, according to the number of load cycles corresponding to the preset load history under the drive axle finite element model and the median life corresponding to each stress level in the stress level set corresponding to the preset load history, the cumulative damage of the damaged part is determined; according to the cumulative damage of the damaged part and the life shape parameter corresponding to the damaged part, the reliability of the damaged part is determined; according to the number of damaged parts in the target drive axle bearing system and the reliability of each damaged part in the target drive axle bearing system, the candidate reliability of the target drive axle bearing system under the drive axle finite element model is determined; according to the candidate reliability of the target drive axle bearing system under each drive axle finite element model, based on the median rank estimation method, the target reliability of the target drive axle bearing system is determined. The above technical solution, aiming at the key structure of the target drive axle bearing system, considers its main failure modes and the influence of the dimension parameters, material parameters, and assembly clearances that affect the stress it bears on the reliable durability of the target drive axle bearing system, and combines the actual design requirements of the target drive axle bearing system to construct a plurality of drive axle finite element models for verifying the reliable durability of the target drive axle bearing system; according to the candidate reliability of the target drive axle bearing system determined under each drive axle finite element model, based on the median rank estimation method, the target reliability of the target drive axle bearing system is determined, reducing the discreteness of the fatigue life of the target drive axle bearing system, reducing the number of samples when verifying the reliable durability of the target drive axle bearing system, thereby reducing the verification cost, effectively realizing the verification of the reliable durability of the target drive axle bearing system, improving the accuracy of the target reliability, providing more accurate data support for the subsequent structural optimization of the target drive axle bearing system, and further improving the service life of the target drive axle bearing system and the market competitiveness of the target drive axle bearing system.

[0092] On the basis of the above embodiment, as an optional way of the embodiment of the present invention, after determining the target reliability of the target drive axle bearing system, it is also possible to detect whether the target reliability is greater than or equal to the required reliability of the target drive axle bearing system; if not, the structure of the target drive axle bearing system is optimized. Wherein, the required reliability refers to the reliability that is expected to be achieved by the target drive axle bearing system.

[0093] Specifically, after determining the target reliability of the target drive axle bearing system, it is detected whether the target reliability is greater than or equal to the required reliability of the target drive axle bearing system; if so, it indicates that the target drive axle bearing system meets the actual business requirements and can be put into production; if not, it indicates that the target drive axle bearing system does not meet the actual business requirements, and the structure of the target drive axle bearing system needs to be optimized to improve the reliability of the target drive axle bearing system, extend the service life of the target drive axle bearing system, and further enhance the market competitiveness of the target drive axle bearing system.

[0094] Embodiment III

[0095] Figure 3 FIG. is a schematic structural diagram of a device for determining the reliability of a drive axle bearing system provided in Embodiment III of the present invention. This embodiment is applicable to the situation of verifying the reliable durability of the drive axle bearing system. The device can be implemented in the form of hardware and / or software and can be configured in an electronic device, which can be a vehicle. As Figure 3 shown, the device includes:

[0096] A key data determination module 301, configured to determine the main failure modes and stress influence parameters corresponding to the key structures according to the key structures of the target drive axle bearing system; wherein, the stress influence parameters include dimension parameters, material parameters, and assembly clearances;

[0097] A finite element model construction module 302, configured to construct a plurality of drive axle finite element models according to the stress influence parameters, the parameter design tolerances of the target drive axle bearing system, and the required confidence level;

[0098] A stress data determination module 303, configured to determine the stress level set and the number of load cycles corresponding to the preset load history under the drive axle finite element model according to the main failure mode and the preset load history;

[0099] A target reliability determination module 304, configured to determine the target reliability of the target drive axle bearing system according to the stress level set and the number of load cycles corresponding to the preset load history under each drive axle finite element model, and the life shape parameters corresponding to the damaged parts in the target drive axle bearing system.

[0100] In the technical solution of the embodiment of the present invention, according to the key structure of the target drive axle bearing system, the main failure modes and stress influence parameters corresponding to the key structure are determined; wherein, the stress influence parameters include dimension parameters, material parameters and assembly clearances; according to the stress influence parameters, as well as the parameter design tolerances and required confidence levels of the target drive axle bearing system, multiple drive axle finite element models are constructed; according to the main failure modes and the preset load history, the stress level sets and load cycle numbers corresponding to the preset load history under the drive axle finite element models are determined; according to the stress level sets and load cycle numbers corresponding to the preset load history under each drive axle finite element model, as well as the life shape parameters corresponding to the damaged parts in the target drive axle bearing system, the target reliability of the target drive axle bearing system is determined. The above technical solution, aiming at the key structure of the target drive axle bearing system, considers its main failure modes and the influence of dimension parameters, material parameters and assembly clearances that affect the stress it bears on the reliable durability of the target drive axle bearing system, and combines the actual design requirements of the target drive axle bearing system to construct multiple drive axle finite element models for verifying the reliable durability of the target drive axle bearing system; according to the data for determining the reliable durability of the target drive axle bearing system under multiple drive axle finite element models, the target reliability of the target drive axle bearing system is determined, reducing the fatigue life discreteness of the target drive axle bearing system, reducing the number of samples when verifying the reliable durability of the target drive axle bearing system, thereby reducing the verification cost, effectively realizing the verification of the reliable durability of the target drive axle bearing system, improving the accuracy of the target reliability, providing more accurate data support for subsequent structural optimization of the target drive axle bearing system, further increasing the service life of the target drive axle bearing system, and enhancing the market competitiveness of the target drive axle bearing system.

[0101] Optionally, the finite element model construction module 302 is specifically configured to:

[0102] According to the required confidence level of the target drive axle bearing system, based on the corresponding relationship between the confidence level and the sample sampling quantity in the preset relationship table, determine the target sampling quantity corresponding to the required confidence level;

[0103] For each stress influence parameter, according to the stress influence parameter and the parameter design tolerance of the target drive axle bearing system, determine the parameter samples that satisfy the 6σ normal distribution of the stress influence parameter, and count the total number of samples of the parameter samples;

[0104] According to the total number of samples and the target sampling quantity, based on the Monte Carlo method, randomly sample the parameter samples to obtain the random samples corresponding to the stress influence parameter;

[0105] According to the random samples corresponding to each stress influence parameter, construct multiple drive axle finite element models.

[0106] Optionally, the stress data determination module 303 is specifically configured to:

[0107] Determine a simulation analysis plan for the target drive axle bearing system according to the main failure mode;

[0108] Determine the stress level set and the number of load cycles corresponding to the preset load history in the drive axle finite element model according to the simulation analysis plan and the preset load history.

[0109] Optionally, the target reliability determination module 304 is specifically configured to:

[0110] Determine the median life corresponding to each stress level in the stress level set corresponding to the preset load history in the drive axle finite element model;

[0111] For each damaged part in the target drive axle bearing system, determine the cumulative damage of the damaged part according to the number of load cycles corresponding to the preset load history in the drive axle finite element model and the median life corresponding to each stress level in the stress level set corresponding to the preset load history;

[0112] Determine the reliability of the damaged part according to the cumulative damage of the damaged part and the life shape parameter corresponding to the damaged part;

[0113] Determine the candidate reliability of the target drive axle bearing system in the drive axle finite element model according to the number of damaged parts in the target drive axle bearing system and the reliability of each damaged part in the target drive axle bearing system;

[0114] Determine the target reliability of the target drive axle bearing system based on the median rank estimation method according to the candidate reliability of the target drive axle bearing system in each drive axle finite element model.

[0115] Optionally, the device further includes:

[0116] A structure optimization module, configured to detect whether the target reliability is greater than or equal to the required reliability of the target drive axle bearing system after determining the target reliability of the target drive axle bearing system; if not, perform structure optimization on the target drive axle bearing system.

[0117] Optionally, the parameter design tolerance includes dimension design tolerance, material design tolerance, and assembly design tolerance.

[0118] The device for determining the reliability of a drive axle bearing system provided by an embodiment of the present invention can execute the method for determining the reliability of a drive axle bearing system provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to executing each method for determining the reliability of a drive axle bearing system.

[0119] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0120] Embodiment 4

[0121] Figure 4 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0122] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0123] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0124] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for determining the reliability of the drive axle bearing system.

[0125] In some embodiments, the method for determining the reliability of the drive axle bearing system can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the reliability of the drive axle bearing system described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the method for determining the reliability of the drive axle bearing system by any other suitable means (e.g., by means of firmware).

[0126] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0127] The computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0128] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0129] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0130] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0131] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0132] It should be understood that various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitations are imposed herein.

[0133] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining the reliability of a drive axle bearing system, characterized in that, Including: Based on the key structure of the target drive axle bearing system, determine the main failure modes and stress influence parameters corresponding to the key structure; wherein, the stress influence parameters include dimensional parameters, material parameters, and assembly clearances; According to the stress influence parameters, the parameter design tolerances of the target drive axle bearing system, and the required confidence level, construct multiple drive axle finite element models; According to the main failure modes and the preset load history, determine the stress level sets and load cycle numbers corresponding to the preset load history under the drive axle finite element models; According to the stress level sets and load cycle numbers corresponding to the preset load history under each drive axle finite element model, and the life shape parameters corresponding to the damaged parts in the target drive axle bearing system, determine the target reliability of the target drive axle bearing system.

2. The method according to claim 1, characterized in that, The constructing multiple drive axle finite element models according to the stress influence parameters, the parameter design tolerances of the target drive axle bearing system, and the required confidence level includes: According to the required confidence level of the target drive axle bearing system, based on the corresponding relationship between the confidence level and the sample sampling quantity in the preset relationship table, determine the target sampling quantity corresponding to the required confidence level; For each stress influence parameter, according to the stress influence parameter and the parameter design tolerances of the target drive axle bearing system, determine the parameter samples that satisfy the 6σ normal distribution for the stress influence parameter, and count the total number of the parameter samples; Based on the Monte Carlo method, randomly sample the parameter samples according to the total number of the samples and the target sampling quantity to obtain the random samples corresponding to the stress influence parameter; Construct multiple drive axle finite element models according to the random samples corresponding to each stress influence parameter.

3. The method according to claim 1, wherein The determining the stress level sets and load cycle numbers corresponding to the preset load history under the drive axle finite element models according to the main failure modes and the preset load history includes: According to the main failure modes, determine the simulation analysis scheme of the target drive axle bearing system; According to the simulation analysis scheme and the preset load history, determine the stress level sets and load cycle numbers corresponding to the preset load history under the drive axle finite element models.

4. The method according to claim 1, wherein The determining the target reliability of the target drive axle bearing system according to the stress level sets and load cycle numbers corresponding to the preset load history under each drive axle finite element model, and the life shape parameters corresponding to the damaged parts in the target drive axle bearing system includes: Determine the median life corresponding to each stress level in the stress level set corresponding to the preset load history under the drive axle finite element models; For each damaged part in the target drive axle bearing system, according to the load cycle number corresponding to the preset load history under the drive axle finite element models, and the median life corresponding to each stress level in the stress level set corresponding to the preset load history, determine the cumulative damage of the damaged part; According to the cumulative damage of the damaged part and the life shape parameter corresponding to the damaged part, determine the reliability of the damaged part. Determine the candidate reliability of the target drive axle bearing system in the drive axle finite element model according to the number of damaged parts in the damaged parts of the target drive axle bearing system and the reliability of each damaged part in the target drive axle bearing system; Based on the candidate reliability of the target drive axle bearing system under each drive axle finite element model, determine the target reliability of the target drive axle bearing system based on the median rank estimation method.

5. The method according to claim 1 or 4, characterized in that, After determining the target reliability of the target drive axle bearing system, it further includes: Detect whether the target reliability is greater than or equal to the required reliability of the target drive axle bearing system; If not, perform structural optimization on the target drive axle bearing system.

6. The method according to claim 1, characterized in that, The parameter design tolerance includes dimensional design tolerance, material design tolerance, and assembly design tolerance.

7. A reliability determination device for a drive axle bearing system, characterized in that, It includes: A key data determination module, configured to determine the main failure mode and stress influence parameters corresponding to the key structure according to the key structure of the target drive axle bearing system; wherein, the stress influence parameters include dimensional parameters, material parameters, and assembly clearances; A finite element model construction module, configured to construct multiple drive axle finite element models according to the stress influence parameters, the parameter design tolerance of the target drive axle bearing system, and the required confidence level; A stress data determination module, configured to determine the stress level set and the number of load cycles corresponding to the preset load history in the drive axle finite element model according to the main failure mode and the preset load history; A target reliability determination module, configured to determine the target reliability of the target drive axle bearing system according to the stress level set and the number of load cycles corresponding to the preset load history under each drive axle finite element model, and the life shape parameters corresponding to the damaged parts in the target drive axle bearing system.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the reliability of the drive axle bearing system according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to execute the method for determining the reliability of the drive axle bearing system according to any one of claims 1-6 when executed.

10. A computer program product, including a computer program, where the computer program realizes the method for determining the reliability of the drive axle bearing system according to any one of claims 1-6 when executed by a processor.

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