Rolling bearing automatic fitting optimization matching method based on equal probability grouping

Abstract

The invention is suitable for the technical field of bearing processing data processing, and provides a rolling bearing automatic fitting optimization matching method based on equal probability grouping, and the method comprises the steps: firstly obtaining the actual measurement distribution information of a to-be-fitted bearing, and then according to the size deviation distribution information of an inner ring and an outer ring and the preset bearing service optimal clearance information, carrying out the matching of the inner ring and the outer ring; and determining inner and outer ring dynamic size deviation range grouping information. The invention aims to effectively reduce the risk that the space of the waiting area is gradually fully occupied by the deviation assembly, remarkably improve the fitting efficiency, avoid interruption in the production process, greatly improve the overall reliability, optimize the efficiency of fitting process configuration, determine the relationship between the number of bearing inner and outer ring groups and the number of stock bins, and improve the reliability of fitting. And the corresponding relation between the matching success rate and the matching success rate is determined, so that the matching success rate of the bearing is remarkably improved, and the stable and efficient operation of the production process is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of bearing processing data processing, and in particular to a method for automatic fitting and optimization of rolling bearings based on equal probability grouping. Background Art

[0002] With the increasing automation of bearing assembly, some companies have achieved unmanned operation in their modern assembly lines. However, current automated bearing assembly lines generally rely on the traditional optimal matching principle. While this improves assembly accuracy, in practice, it can easily cause components with large deviations to remain in the waiting area for extended periods of time. As the automated assembly line continues to operate, these deviating components can gradually fill the waiting area, affecting assembly efficiency and even causing production interruptions. This leads to low reliability and requires further improvement. Summary of the Invention

[0003] Based on this, an embodiment of the present application provides a method for automatic fitting and optimization of rolling bearings based on equal probability grouping to solve the problem of low reliability in the prior art.

[0004] In a first aspect, an embodiment of the present application provides a method for automatically optimizing and matching rolling bearings based on equal probability grouping, the method comprising: Obtaining measured distribution information of the bearings to be assembled, wherein the measured distribution information is used to describe the distribution information of the inner and outer ring size deviations of the bearings to be assembled within any production batch, which changes dynamically with the number of measurements, and the distribution information of the inner and outer ring size deviations is a probability density function model; Based on the inner and outer ring size deviation distribution information and the preset bearing service optimal clearance information, the inner and outer ring dynamic size deviation range grouping information is determined, wherein the inner and outer ring dynamic size deviation range grouping information is obtained by performing equal probability division on the probability density function model.

[0005] Compared with the prior art, the beneficial effects are: the embodiment of the present application provides an automatic fitting optimization and selection method for rolling bearings based on equal probability grouping, and the terminal equipment can first obtain the measured distribution information of the bearings to be fitted, and then determine the grouping information of the dynamic size deviation range of the inner and outer rings based on the inner and outer ring size deviation distribution information and the preset bearing service optimal clearance information, thereby reducing the possibility of the deviation component gradually filling the space in the waiting area, improving the fitting efficiency, avoiding production interruptions, effectively improving reliability, and solving the current low reliability problem to a certain extent.

[0006] In a second aspect, an embodiment of the present application provides a rolling bearing automatic fitting optimization and selection system based on equal probability grouping, the system comprising: Measured distribution information acquisition module: used to obtain measured distribution information of the bearings to be assembled, wherein the measured distribution information is used to describe the distribution information of the inner and outer ring size deviations of the bearings to be assembled in any production batch, which changes dynamically with the number of measurements. The distribution information of the inner and outer ring size deviations is a probability density function model; Inner and outer ring dynamic size deviation range grouping information determination module: used to determine the inner and outer ring dynamic size deviation range grouping information based on the inner and outer ring size deviation distribution information and the preset bearing service optimal clearance information, wherein the inner and outer ring dynamic size deviation range grouping information is obtained by performing equal probability division on the probability density function model.

[0007] In a third aspect, an embodiment of the present application provides a terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect described above when executing the computer program.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method of the first aspect described above are implemented.

[0009] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art.

[0011] Figure 1 This is a flow chart of a method for optimizing the automatic fitting of rolling bearings provided in one embodiment of the present application; Figure 2 This is a flow chart of step S200 in the rolling bearing automatic fitting optimization and selection method provided in one embodiment of the present application; Figure 3 This is a flow chart before step S210 in the rolling bearing automatic fitting optimization and selection method provided by one embodiment of the present application; Figure 4 This is a flow chart before step S201 in the rolling bearing automatic fitting optimization matching method provided by one embodiment of the present application; Figure 5 This is a flow chart of step S201 in the rolling bearing automatic fitting optimization matching method provided by one embodiment of the present application; Figure 6This is a flow chart of step S240 in the rolling bearing automatic fitting optimization matching method provided in one embodiment of the present application; Figure 7 (a) is a prediction diagram of the outer ring standard deviation provided by an embodiment of the present application. Figure 7 (b) is a prediction diagram of the outer circle mean value provided by an embodiment of the present application; Figure 8 (a) is a prediction diagram of the inner circle standard deviation provided by an embodiment of the present application. Figure 8 (b) is a prediction diagram of the inner circle mean value provided by an embodiment of the present application; Figure 9 (a) is a schematic diagram of the outer ring window change provided by an embodiment of the present application. Figure 9 (b) is a schematic diagram of the change of the inner circle window provided by an embodiment of the present application; Figure 10 This is a flowchart of a traversal and assembly process provided by an embodiment of the present application; Figure 11 (a) is a schematic diagram of outer circle grouping with equal spacing provided by an embodiment of the present application, Figure 11 (b) is a schematic diagram of grouping with equal spacing in the inner circle provided by an embodiment of the present application; Figure 12 (a) is a schematic diagram of outer circle equal probability grouping provided by an embodiment of the present application. Figure 12 (b) is a schematic diagram of inner circle equal probability grouping provided by an embodiment of the present application; Figure 13 (a) is a schematic diagram of the outer raceway size distribution provided in an embodiment of the present application. Figure 13 (b) is a schematic diagram of the inner raceway size distribution provided by an embodiment of the present application; Figure 14 (a) is a schematic diagram of the comparison between the outer circle original data and the Monte Carlo sample provided in an embodiment of the present application. Figure 14 (b) is a schematic diagram of the comparison between the inner circle original data and the Monte Carlo sample provided in an embodiment of the present application; Figure 15 This is a schematic diagram comparing the quantities of three assembly methods provided in one embodiment of the present application; DETAILED DESCRIPTION In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0012] In order to illustrate the technical solution described in this application, specific embodiments are provided below.

[0013] See also Figure 1 , Figure 1 The flowchart of the method for automatically optimizing the matching of rolling bearings based on equal probability grouping provided in an embodiment of the present application is shown. In this embodiment, the method is executed by a terminal device. It is understood that the types of terminal devices include, but are not limited to, tablet computers, laptop computers, ultra-mobile personal computers (UMPCs), netbooks, and personal digital assistants (PDAs). This embodiment of the present application does not impose any restrictions on the specific type of terminal device.

[0014] See also Figure 1 The rolling bearing automatic fitting optimization selection method provided in the embodiment of the present application includes but is not limited to the following steps: In S100, the measured distribution information of the bearing to be assembled is obtained.

[0015] Specifically, measured distribution information describes the distribution of inner and outer ring dimensional deviations within any production batch, as measured over time. This information includes the upper and lower limits of the inner ring dimensional deviations. This distribution information can be fitted into a probability density function model for dimensional deviations.

[0016] In S200 , the grouping information of the dynamic size deviation ranges of the inner and outer rings is determined based on the size deviation distribution information of the inner and outer rings and the preset optimal bearing service clearance information.

[0017] Specifically, the dynamic dimensional deviation range grouping information for the bearing's inner and outer rings can be obtained by equally probabilistically partitioning the probability density function model. In one possible implementation, the dimensional deviations for the bearing's inner and outer rings and rolling elements can be the difference between the designed and measured diameters of the bearing's inner ring raceway's lowest point and the outer ring raceway's highest point.

[0018] In some possible implementations, in order to effectively determine the dynamic size deviation range grouping information of the inner and outer rings and improve the reliability of the bearing automated assembly line, please refer to Figure 2 Step S200 includes but is not limited to the following steps: In S210 , preset optimal bearing service clearance information is obtained.

[0019] In S220 , the lower limit value of the bearing inner ring size deviation is generated by subtracting the lower limit value of the bearing inner ring size deviation from the upper limit value of the bearing outer ring size deviation.

[0020] In S230, on the premise that the upper limit of the bearing inner ring size deviation minus the lower limit of the bearing inner ring size deviation is within the optimal bearing service clearance information, the probability density function model is divided into equal probability to determine the grouping information of the dynamic size deviation range of the inner and outer rings.

[0021] Specifically, the bearing service optimum clearance information is used to describe the bearing optimum clearance range obtained by the designer based on the bearing service conditions.

[0022] In S240, a bearing assembly size distribution grouping optimization scheme is determined based on the optimal bearing clearance range corresponding to any batch of bearings to be assembled, the number of silo lanes of the bearing assembly machine, and the production rhythm of the target enterprise.

[0023] Specifically, the target enterprise is a bearing manufacturing unit; the bearing assembly size distribution grouping optimization plan is used to guide the target enterprise to improve processing accuracy, or to guide the target enterprise to relax the grouping interval range while meeting the minimum clearance range required for bearing service performance.

[0024] Among some possible implementations, in order to help improve the reliability of the bearing automated assembly line, please refer to Figure 3 Before step S210, the method further includes but is not limited to the following steps: In S2101, bearing service clearance information is obtained.

[0025] Specifically, the bearing service clearance information can be: , Where, Indicates the lower limit of the bearing service clearance information, Indicates the highest point diameter of the outer ring raceway of the bearing to be assembled. Indicates the lowest point diameter of the inner ring raceway of the bearing to be assembled. Indicates the bearing rolling element diameter information of the bearing to be assembled. Indicates the upper limit of the bearing service clearance information.

[0026] In S2102, the bearing outer ring raceway size range information and the bearing inner ring raceway size range information are obtained.

[0027] Specifically, the bearing outer ring raceway size range information is , the bearing inner ring raceway size range information is .

[0028] In S2103, the spacing between each group of bearing outer ring raceways is Based on the premise, the bearing outer ring raceway size range information is divided into n groups, and the first Information on the size range of the bearing outer ring raceway for the group.

[0029] Specifically, after the terminal device groups the bearing outer ring raceway size range information according to this premise, , , No. The bearing outer ring raceway size range information of the group is .

[0030] In S2104, the spacing between each group of bearing inner ring raceways is Based on the premise, the bearing inner ring raceway size range information is divided into n groups, and the first Information on the size range of the bearing inner ring raceways for the group.

[0031] Specifically, after the terminal device groups the bearing inner ring raceway size range information according to this premise, , , No. The bearing inner ring raceway size range information of the group is .

[0032] In S2105, based on Bearing outer ring raceway size range information and The inner ring raceway size range information of the bearing group is used to determine the inner and outer ring size difference information that meets the bearing service clearance.

[0033] Specifically, the inner and outer ring size difference information that meets the bearing service clearance is used to ensure that the outer ring raceway and inner ring raceway size difference of the bearing is within range to ensure that any selection of the outer ring raceway and inner ring raceway of the group can be successfully assembled and meet the clearance requirements.

[0034] Specifically, the inner and outer ring size difference information that satisfies the bearing service clearance can be: .

[0035] In S2106, based on the inner and outer ring size difference information, the maximum size information and the minimum size information of each group of outer ring raceways and inner ring raceways are determined.

[0036] Specifically, the maximum size information is , the maximum size information is less than ,Right now ,in, is the maximum diameter value of the bearing rolling element; the minimum size information is , the minimum size information is greater than ,Right now ,in, It is the minimum diameter of the bearing rolling element.

[0037] In some possible implementations, before step S200, the method further includes but is not limited to the following steps: In S201 , a probability density function model is constructed.

[0038] Specifically, the probability density function model includes a mean update formula and a standard deviation update formula.

[0039] In some possible implementations, in order to facilitate the construction of effective probability density function models, see Figure 4 Before step S201, the method further includes but is not limited to the following steps: In S2011, based on a preset sensor, the size deviation information of the inner and outer rings of the bearing is obtained, and based on preset historical data, the size deviation information of the inner and outer rings of the bearing is obtained, and the prior distribution detailed information is obtained.

[0040] Specifically, the terminal device can use sensors and other equipment on the assembly line to monitor the dimensional deviation of the inner and outer rings of the bearing in real time, and extract the dimensional deviation data of the inner and outer rings of the bearing from the historical data of the assembly line. Assuming that these data obey a certain known distribution, such as a normal distribution, and need to determine its mean and standard deviation, that is, to obtain the prior distribution detailed information, the prior distribution detailed information includes prior variance information and prior mean information, where the prior distribution detailed information can be recorded as , the prior variance information can be recorded as , the prior mean information can be recorded as .

[0041] In S2012, the current sample set information is constructed based on the bearing inner and outer ring size deviation information, the bearing inner ring size deviation information, the bearing outer ring size deviation information and the prior distribution detailed information.

[0042] Specifically, the terminal device can construct an observation sample set based on the newly input data and build a mathematical model of the probability of the current sample information. .

[0043] In some possible implementations, to build a probability density function model, see Figure 5 Step S201 includes but is not limited to the following steps: In S202, a dynamic window model is constructed based on the Dynamic-Window model.

[0044] Specifically, in the fields of time series analysis and dynamic system modeling, the Baseline model is widely used as a benchmark method to evaluate the effectiveness of complex model improvements. Its core concept is based on fixed-window sliding average theory, assuming that the data generation process follows a stationary Gaussian distribution. The model estimates the current state by analyzing the statistical characteristics of historical window data. By simplifying the parameter update mechanism and making specific assumptions about window dynamics, this model provides a benchmark for performance comparison for subsequent dynamic window models.

[0045] In S203 , new measurement data information is determined based on a preset sampling period.

[0046] Specifically, assuming the new measurement data is , these data will be continuously updated in each sampling period (for example, every 100 milliseconds).

[0047] In S204 , new data mean information of the new measurement data information is calculated.

[0048] Specifically, the terminal device can calculate the mean and standard deviation of the newly collected data points to provide data for the likelihood function for subsequent Bayesian updates.

[0049] Specifically, the new data mean information can be: , Where, Represents the mean information of new data, Indicates the number of new measurement data information, Indicates the serial number of the new measurement data information, Indicates the New measurement data information.

[0050] In S205 , the standard deviation information of the new measurement data information is calculated.

[0051] Specifically, the standard deviation information is: , Where, Indicates standard deviation information, represents the mean; In S206 , prior mean influence information is determined based on the prior distribution detailed information and standard deviation information.

[0052] Specifically, the terminal device may determine a specific weight allocation formula for different weights of the prior distribution and the new likelihood distribution based on the prior information and the relative uncertainty of the new data (ie, the standard deviation).

[0053] Specifically, the prior mean influence information is: , Where, Represents the prior mean influence information, which is used to describe the influence of the prior mean on the posterior mean. Represents the prior standard deviation information, and the value of the prior standard deviation information represents the square root of the prior variance information.

[0054] In S207 , the new data mean impact information is determined based on the prior distribution detailed information and standard deviation information.

[0055] Specifically, the new data mean impact information is: , Where, Represents the new data mean influence information, which is used to describe the impact of the new data mean information on the posterior mean; In S208 , the fluctuation change rate information is generated according to the preset posterior standard deviation information and the prior standard deviation information.

[0056] Specifically, the volatility change rate information is: , Where, Indicates the fluctuation rate information, represents the posterior standard deviation information, Represents the prior standard deviation information; In S209 , target window length information is determined based on the fluctuation change rate information.

[0057] Specifically, the target window length information is: , Where, The target window length information; It is the maximum window length information; It is the current window length information; is the window threshold information, , A typical value of is 1.5.

[0058] It should be noted that the scaling factors of 0.7 and 1.3 in the formula can balance the adjustment range of the window length through exponential decay, ensuring the stability and adaptability of the model in a dynamic environment.

[0059] In S2091 , estimated mean information and estimated variance information are determined based on the normal-inverse gamma conjugate prior distribution and the target window length information.

[0060] Specifically, the dynamic window model usually uses the normal-inverse gamma conjugate prior distribution to estimate the mean and variance. With the addition of each new data, the prior distribution is transformed into the posterior distribution through Bayesian updating and becomes the new prior at the next time point. Therefore, the likelihood function of the new data is: , Where, represents the likelihood function based on new data; Represents the normal-inverse gamma prior distribution. The terminal device can achieve real-time estimation and dynamic adjustment of parameters through recursive updating.

[0061] In S2092, a posterior distribution mathematical model is determined based on the prior distribution model and the probability model corresponding to the current sample set information.

[0062] Specifically, the posterior distribution mathematical model is: , Where, represents the mathematical model of the posterior distribution, represents the prior distribution model, is the probability model corresponding to the current sample set information, is the marginal likelihood value.

[0063] In S2093, based on the posterior distribution mathematical model, a mean update formula and a standard deviation update formula are determined.

[0064] Specifically, the mean update formula is: , The standard deviation update formula is: , Where, represents the mean information of the prior distribution, Represents the standard deviation information of the prior distribution, represents the mean information of the likelihood of new data, represents the standard deviation information of the likelihood of new data, represents the updated forecast mean, represents the updated forecast standard deviation; In S2094, based on the Monte Carlo method and kernel density estimation method, the probability density function of the large sample is estimated according to the small sample, and based on the kernel function estimation, the distribution of the original data is compared with the distribution of the Monte Carlo sample to verify the consistency of the small sample size in fitting the deviation distribution model of all bearing components.

[0065] Specifically, the terminal equipment can use a small sample to estimate the probability density function of a large sample based on the Monte Carlo method and kernel density estimation method, and randomly sample the overall dimensional deviation data of the inner and outer rings of the bearing in a small sample size at the beginning of the bearing assembly to generate a large amount of simulated data to approximately fit the potential distribution of the dimensional deviation data of all bearing components. Through kernel function estimation, the distribution of the original data and the Monte Carlo sample are compared to verify the consistency of the small sample size fitting the deviation distribution model of all bearing components.

[0066] In some possible implementations, in order to determine the optimal grouping scheme for the size distribution of bearing sets, please refer to Figure 6 Step S240 includes but is not limited to the following steps: In S241 , a probability density function model is fitted based on the new measurement data information to generate a fitted probability density function model.

[0067] Specifically, the fitted probability density function model obeys the normal distribution.

[0068] In S242 , based on the bearing service clearance information, inequality information that the grouping should satisfy is determined.

[0069] Specifically, the terminal device can derive the manufacturing accuracy tolerance and clearance deviation range of the inner and outer rings of the bearing based on the mean and standard deviation of the probability density function, and list the maximum deviation values ​​of the inner and outer rings that satisfy the inequality relationship between their optimal clearance ranges. The inequality relationship is: , Where, is the outer ring raceway diameter, is the inner ring raceway diameter, is the diameter of the steel ball; Since the tolerance of steel ball diameter in actual products is generally smaller than the tolerance of bearing inner and outer ring diameters, this technical solution mainly studies the matching and grouping method for the inner and outer ring diameter tolerances of deep groove ball bearings to ensure that the radial clearance meets the product requirements when the bearings are assembled. Therefore, the inequality information that the grouping should satisfy is: .

[0070] In S243, the deviation interval of the extreme end group of the probability density function of the inner and outer rings of the bearing is calculated to determine the extreme end group width of the inner ring of the bearing and the extreme end group width of the outer ring of the bearing.

[0071] Specifically, the width of the extreme end group of the bearing inner ring is: , Where, is the width of the extreme end group of the bearing inner ring, is the deviation value of the overall diameter of the bearing outer ring, is the first set of deviation values ​​of the bearing inner ring; The width of the extreme end group of the bearing outer ring is: , Where, is the width of the extreme end group of the bearing outer ring, is the deviation value of the overall diameter of the bearing inner ring, This is the first set of deviation values ​​for the bearing outer ring.

[0072] In S244, the function intervals corresponding to the widths of the extreme end groups of the inner and outer rings of the bearing are integrated to obtain the integral area. , , then integrate the probability density functions of the inner and outer rings of the bearing to obtain the integral area .

[0073] In S245, the probability density function of the inner and outer rings of the bearing is calculated and integrated to obtain the integral area The area occupied by the width of the extreme end group of the probability density function of the inner and outer rings of the bearing , The ratio between them generates the number of equal probability groups of the inner and outer rings of the bearing.

[0074] Specifically, the number of equal probability groups of the bearing inner ring is: , The number of equal probability groups of the bearing outer ring is: , In S246, if and / or If is not an integer, and / or Round up to the nearest integer.

[0075] Specifically, after rounding up, It can be recorded as , after rounding up It can be recorded as .

[0076] In S247, if , then determine the number of groups .

[0077] In S248, if , then determine the number of groups .

[0078] It should be noted that the lengths of the probability density function curves of the groups are equal. Since the area of ​​the probability density function curve occupied by each group is the same, the probability of random occurrence of bearings in each group represented by the occupied area is also the same.

[0079] In S249, based on the number of silo lanes of the bearing assembly machine, Position start with inner circle Traverse, if the outer circle With the inner circle If the same group number cannot be found after traversal, the outer circle will start from the next digit and the inner circle will start from the next digit. Traverse.

[0080] Specifically, the next .

[0081] In S2491, if the outer ring The group number and inner circle If the group numbers are the same, the matching and closing actions are performed.

[0082] In S2492, after the matching and closing action is performed, the outer ring Restart with the inner circle Traverse and randomly replenish inner and outer ring accessories to fill the waiting area silo.

[0083] In S2493, if there is no identical group number for matching in the inner and outer ring waiting areas, the traversal stops and the number of paired inner and outer rings of the bearing is recorded.

[0084] Specifically, the information about the number of bearing inner and outer ring pairs is used to describe the number of bearing inner and outer ring pairs that have been completed.

[0085] In S2494, the number of paired inner and outer rings of the bearing is compared with the number of target matching bearings.

[0086] Specifically, the target number of bearings to be assembled is used to describe the number of bearings that a bearing manufacturing unit needs to assemble within one day.

[0087] In S2495, if the number of bearing inner and outer ring pairs is greater than the target number of assembled bearings, it is determined that the assembly process of the bearing inner and outer rings will not be stopped within a single shift of the bearing manufacturing unit.

[0088] In S2496, if the number of paired inner and outer rings of the bearing is less than the target number of matched bearings, the first guidance information or the second guidance information is generated.

[0089] Specifically, the first guidance information is used to describe how to guide bearing manufacturing units to improve manufacturing accuracy, and the second guidance information is used to describe how to relax the grouping interval range of bearing manufacturing units on the premise of meeting the minimum required clearance range of bearing service performance.

[0090] For example, in order to facilitate those skilled in the art to better understand the technical solution of this application, the following is combined with specific cases and the attached Figures 7 to 15 This technical solution is described in detail again.

[0091] Step 1: Before the bearings are assembled and matched, the terminal equipment uses the high-precision contact sensor equipped with the assembly instrument to measure the dimensional deviation of the inner and outer ring grooves of the bearings. Using the inductive displacement detection principle, the probe scans along the groove busbar with a constant contact force, and the inner and outer ring groove diameter dimensions are collected in real time through the analog-to-digital converter (ADC). The measurement system is calibrated with a laser interferometer, with a repeatability accuracy of ±1.5μm and a single sampling period of 20ms, which can meet the high-frequency continuous measurement requirements of 1,000 sets of samples. The dimensional deviation data of the outer raceway and the inner raceway are stored through independent channels to construct a dual-variable quality feature data set. The terminal equipment can obtain the inner and outer ring dimensional deviation of the 16 bearings before measurement and calculate the sample mean using the following formula. and sample variance

[0092] , Step 2: The terminal device calculates the logarithm of the probability density function of step 1, and then simplifies it according to the logarithmic formula to derive the maximum estimation conclusion: , , Step 3: The terminal device calculates the outer ring parameters through step 2, that is, and , and the inner circle parameter, that is, the mean and standard deviation , then the terminal device can pass the Kolmogorov-Smirnov test Confirm data normality and provide a statistical basis for subsequent tolerance matching optimization.

[0093] Step 4: The terminal device can calculate the synthetic posterior distribution of the random variable sample prior distribution and the latest current sample information based on the Bayesian update method. Its basic mathematical model can be expressed as: , in, is the prior distribution, is the probability of the current sample information. The posterior distribution can be determined by the Bayesian principle, and its calculation model is as follows: , Step 5: In the Bayesian update formula, weight allocation is a critical step, which determines the relative importance of prior information and new data, and thus affects the degree of update of the prediction results. Weight allocation is mainly determined based on the uncertainty of the prior distribution and the new data (i.e., standard deviation). The Dynamic Window model is an analysis based on Bayesian inference adaptive time series. The core assumption of the model is that the data follows a normal distribution within any time window. , but the mean and standard deviation By modeling the joint probability distribution of the mean and variance over time, the error accumulation problem that may occur in the step-by-step estimation process can be effectively avoided.

[0094] Step 6: In this model, the adjustment of the window length is driven by the relative rate of change of volatility to adapt to the dynamic characteristics of the data. Based on the adaptive time series analysis method of Bayesian inference, by combining the normal-inverse gamma conjugate prior distribution with the volatility-driven dynamic window adjustment mechanism, robust modeling of non-stationary data is achieved. The specific implementation is as follows: , , in, Describes the impact of the prior mean on the posterior mean; Describes the effect of the new data mean on the posterior mean.

[0095] When the standard deviation of the prior is small, it indicates a high degree of reliability, giving it a greater weight, and the updated result will be closer to the prior mean. Conversely, if the standard deviation of the new data is small, it indicates low uncertainty, giving it a higher weight, and the updated result will be closer to the new data mean. This approach fully integrates prior information with new data, enabling dynamic monitoring and precise grouping of bearing inner and outer ring dimensional deviations, effectively optimizing the assembly line's matching process.

[0096] Step 7: Set the sliding window at time t to include Historical data points Based on the Dynamic Window adaptive time series analysis method and the combination of the normal-inverse gamma conjugate prior distribution and the volatility-driven dynamic window adjustment mechanism, we achieve robustness to non-stationary data and model the response to data mutations, as follows: The volatility change rate is defined as the ratio of the posterior standard deviation to the prior standard deviation, that is: , according to The value of window length The adjustment rules are as follows: .

[0097] Step 8: The terminal device can jointly model the parameters according to the normal-inverse gamma conjugate prior distribution: Assume that there is a random variable , where the mean and variance If both are unknown, then the normal-inverse gamma distribution is used as the joint prior distribution, and its probability density function is: , Where, 、 、 and are all hyperparameters of the formula; represents the location parameter of the prior mean; Indicates the precision of the mean estimate, i.e., the equivalent sample size; represents the shape parameter of the variance inverse gamma distribution; Represents the scale parameter of the variance inverse gamma distribution.

[0098] Assume that n independent data points are observed , based on Bayesian theory, its posterior distribution is also normal-inverse gamma distribution, and the parameter update rules are as follows: , , , , Among them, the likelihood function is a multivariate normal distribution, and its expression is: , Step 9: The terminal device can simplify the posterior distribution to: , Based on this, the update formula of the mean can be: , The standard deviation update formula can be: , in, and denote the mean and standard deviation of the prior distribution respectively; and represents the mean and standard deviation of the likelihood of new data; and is the updated forecast mean.

[0099] Step 10: The terminal device can randomly sample the overall dimensional deviation data of the inner and outer rings of the bearings based on the Monte Carlo method at the beginning of the bearing assembly to generate a large amount of simulation data to approximately fit the potential distribution of the dimensional deviation data of all bearing components.

[0100] Assume the original data set is , n samples are drawn each time, and M simulation experiments are conducted. Its mathematical expression is: , Therefore, the final aggregate distribution is the joint distribution of all samples: , Step 11: The terminal device can apply a smooth kernel function to each data point based on kernel density estimation (KDE), and superimpose all kernel contributions to obtain a density curve: , Where K is the kernel function, is the bandwidth parameter, and commonly used kernel functions include Gaussian kernel, Epanechnikov kernel, etc., so the Gaussian kernel is: , bandwidth By using Silverman's empirical method, we can get: , in is the sample standard deviation.

[0101] Step 12: After the bearing has completed the measurement of its inner and outer rings and constructed its probability density function, the terminal device can calculate the radial clearance of the bearing after assembly. Grouping, so the radial clearance of the bearing after assembly It is calculated by this formula: , in, is the outer ring raceway diameter, is the inner ring raceway diameter, is the diameter of the steel ball, Since the tolerance of steel ball diameter in actual products is generally smaller than the tolerance of bearing inner and outer ring diameters, this paper mainly studies the selection and grouping method for the inner and outer ring diameter tolerances of deep groove ball bearings to ensure that the radial clearance meets the product requirements when the bearings are assembled. The size deviation range of the inner and outer ring raceway diameters of deep groove ball bearings produced in the same batch basically obeys the normal distribution, and the upper and lower deviations are basically symmetrical relative to the zero point. Assuming that the clearance value range of a certain bearing is The overall size deviation range of the bearing outer ring is measured to be , the overall size deviation range of the inner ring is Ignoring the steel ball deviation in the formula, we can get: .

[0102] Step 13: The terminal device can use the equal-interval grouping method to calculate the spacing width of the first group at the extreme end of the probability density function. When pairing the bearing inner rings, in order to ensure that the inner and outer rings fall within the product's required clearance range after pairing, the formula can be obtained: , in, is the overall diameter deviation of the bearing outer ring, is the lower deviation range of the inner circle extreme end. The numerical value is known, and the bearing inner ring size deviation can be obtained The value range of is: , , In actual production, The value may not be equal to , but greater than The first group of bearing inner rings is , theoretical inner circle group width .

[0103] Step 14: The terminal device integrates the width of the first group of inner circle groups to obtain the integrated area of ​​the first group , then perform equal-area integration on the probability density function based on the integral area of ​​the first group to obtain the overall integral area of ​​the probability density function , and then based on the overall integral area of ​​the probability density function curve of the overall size deviation of the bearing inner ring The first group of integral areas with the extreme end of the probability density function , the theoretical value of equal probability grouping is: , However, It is not necessarily an integer. In order to ensure that the clearance accuracy meets the requirements, it is necessary to improve the grouping accuracy and increase the number of groups, that is, the actual number of groups for Round up.

[0104] For the bearing outer ring assembly, the same formula can be obtained: , in The first group of diameter deviation range of the extreme end of the bearing outer ring is is the overall lower deviation of the bearing inner ring. The numerical value is known, and the size deviation of the bearing outer ring can be obtained The value range is: , In actual production, The value may not be equal to , but is smaller than The first group of bearing outer rings is , theoretical outer circle group width .

[0105] Step 15: The terminal device first integrates the width of the first group of outer circle groups to obtain the integrated area of ​​the first group , then perform equal-area integration on the probability density function based on the integral area of ​​the first group to obtain the overall integral area of ​​the probability density function , and then based on the overall integral area of ​​the probability density function curve of the overall size deviation of the bearing inner ring The first group of integral areas with the extreme end of the probability density function , the theoretical value of equal probability grouping is: .

[0106] Similarly, It is not necessarily an integer. In order to ensure that the clearance accuracy meets the requirements, it is necessary to improve the grouping accuracy and increase the number of groups, that is, the actual number of groups for Round up.

[0107] like , then the number of groups .like , then the number of groups The actual grouping width is the probability density function curve of the bearing inner and outer ring size deviation divided according to the number of groups and equal integral areas.

[0108] Step 16: The terminal device can set the number of lanes in the waiting area. The inner and outer ring accessories will randomly fill the empty spaces in the waiting area. Position start with inner circle Start traversing, when the outer circle The group number and inner circle If the group numbers are the same, the matching operation is executed. Restart with the inner circle Start traversal; when the outer circle With the inner circle If the same group number cannot be found after traversal, the outer circle will start from the next one (i.e. ) and the inner ring Traverse.

[0109] Step 17: After the matching and fitting action is executed, the terminal device can randomly replenish the inner and outer ring accessories to fill the waiting area silo. When there is no same group number for matching in the inner and outer ring waiting areas, the traversal stops and the inner and outer rings are recorded.

[0110] Step 18: The terminal device can complete the number of matching pairs m according to the inner and outer rings of the bearing. f , m f The number of bearings that need to be assembled with the bearing manufacturing unit in one day p For comparison. f >m p , then the process of fitting the inner and outer rings of the bearing will not stop within a day's shift of the bearing manufacturing unit; if m f <m p , then the process of fitting the inner and outer rings of the bearings will be stopped within the one-day shift of the bearing manufacturing unit. The manufacturing unit needs to be required to appropriately improve the manufacturing accuracy or relax the grouping range while meeting the minimum clearance range required for the bearing service performance.

[0111] The implementation principle of the method for automatic assembly optimization and selection of rolling bearings based on equal probability grouping in the embodiment of the present application is as follows: the terminal device can first obtain the measured distribution information of the bearings to be assembled, and then determine the grouping information of the dynamic size deviation range of the inner and outer rings based on the inner and outer ring size deviation distribution information and the preset bearing service optimal clearance information, thereby reducing the possibility of the deviation component gradually filling the space in the waiting area, improving the assembly efficiency, avoiding production interruptions, and effectively improving reliability.

[0112] It should be noted that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0113] The embodiment of the present application further provides a rolling bearing automatic fitting optimization and selection system based on equal probability grouping. For ease of description, only the parts related to the present application are shown. The system includes: Measured distribution information acquisition module: used to obtain measured distribution information of the bearings to be assembled. The measured distribution information is used to describe the distribution information of the inner and outer ring size deviations of the bearings to be assembled in any production batch, which changes dynamically with the number of measurements. The distribution information of the inner and outer ring size deviations is a probability density function model. Inner and outer ring dynamic size deviation range grouping information determination module: used to determine the inner and outer ring dynamic size deviation range grouping information based on the inner and outer ring size deviation distribution information and the preset bearing service optimal clearance information, wherein the inner and outer ring dynamic size deviation range grouping information is obtained by equal probability division of the probability density function model.

[0114] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0115] The present application also provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned rolling bearing automatic fitting optimization method are implemented, such as Figure 1 Steps S100 to S200 shown An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the steps of the above-mentioned method embodiments.

[0116] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the methods, principles, and structures of the present application should be included in the scope of protection of the present application.

Claims

1. A rolling bearing automatic fitting optimization matching method based on equal probability grouping, characterized in that: The method comprises: Obtaining measured distribution information of the bearings to be assembled, wherein the measured distribution information is used to describe the distribution information of the inner and outer ring size deviations of the bearings to be assembled within any production batch, which changes dynamically with the number of measurements, and the distribution information of the inner and outer ring size deviations is a probability density function model; Based on the inner and outer ring size deviation distribution information and the preset bearing service optimal clearance information, the inner and outer ring dynamic size deviation range grouping information is determined, wherein the inner and outer ring dynamic size deviation range grouping information is obtained by performing equal probability division on the probability density function model.

2. The method according to claim 1, characterized in that The measured distribution information includes an upper limit value of the bearing inner ring size deviation, an upper limit value of the bearing outer ring size deviation, and a lower limit value of the bearing inner ring size deviation; and determining the grouping information of the dynamic size deviation ranges of the inner and outer rings based on the inner and outer ring size deviation distribution information and preset bearing service optimal clearance information, including: Obtain preset optimal bearing clearance information; Subtracting the lower limit value of the bearing inner ring size deviation from the upper limit value of the bearing outer ring size deviation to generate a lower limit value of the bearing inner ring size deviation; On the premise that the upper limit of the bearing inner ring size deviation minus the lower limit of the bearing inner ring size deviation is within the bearing service optimal clearance information, the probability density function model is divided into equal probability groups to determine the grouping information of the dynamic size deviation ranges of the inner and outer rings, wherein the bearing service optimal clearance information is used to describe the bearing optimal clearance range obtained by the designer based on the bearing service working condition; An optimization scheme for grouping the size distribution of bearings to be assembled is determined based on the optimal bearing clearance range corresponding to any batch of bearings to be assembled, the number of silo channels of the bearing assembly machine, and the production rhythm of the target enterprise. The target enterprise is a bearing manufacturing unit, and the optimization scheme for grouping the size distribution of bearings to be assembled is used to guide the target enterprise to improve machining accuracy, or to relax the grouping range on the premise of meeting the minimum clearance range required for the bearing's service performance.

3. The method according to claim 2, characterized in that Before obtaining the preset optimal bearing service clearance information, the method further includes: Obtain bearing service clearance information, wherein the bearing service clearance information is: ， Where, The lower limit value of the bearing service clearance information, is the highest point diameter information of the outer ring raceway of the bearing to be assembled, is the lowest point diameter information of the inner ring raceway of the bearing to be assembled, is the bearing rolling element diameter information of the bearing to be assembled, The upper limit value of the bearing service clearance information; Obtain the bearing outer ring raceway size range information and the bearing inner ring raceway size range information, wherein the bearing outer ring raceway size range information is , the bearing inner ring raceway size range information is ; According to the spacing between each group of bearing outer ring raceways Based on the premise, the bearing outer ring raceway size range information is divided into n groups, and the first The bearing outer ring raceway size range information of the group, among which The bearing outer ring raceway size range information of the group is ; According to the spacing between each group of bearing inner ring raceways Based on the premise, the bearing inner ring raceway size range information is divided into n groups, and the first The bearing inner ring raceway size range information of the group, among which The bearing inner ring raceway size range information for the group is ; Based on the Bearing outer ring raceway size range information and The inner ring raceway size range information of the bearing group is used to determine the inner and outer ring size difference information that meets the bearing service clearance, wherein the inner and outer ring size difference information that meets the bearing service clearance is used to ensure that the outer ring raceway and inner ring raceway size difference of the bearing is within The range is within which the outer ring raceway and inner ring raceway of the group can be assembled successfully and meet the clearance requirements at any selection. The inner and outer ring size difference information is: ； Based on the inner and outer ring size difference information, the maximum size information and the minimum size information of each group of outer ring raceway and inner ring raceway are determined, wherein the maximum size information is , the maximum size information is less than , is the maximum diameter value of the bearing rolling element, and the minimum size information is , the minimum size information is greater than , It is the minimum diameter of the bearing rolling element.

4. The method according to claim 3, characterized in that Before determining the grouping information of the dynamic size deviation ranges of the inner and outer rings based on the size deviation distribution information of the inner and outer rings and the preset optimal bearing service clearance information, the method further includes: Construct a probability density function model; Before constructing the probability density function model, the method further includes: Based on a preset sensor, obtaining dimensional deviation information of the inner and outer rings of the bearing, and based on preset historical data, obtaining dimensional deviation information of the inner and outer rings of the bearing, and obtaining prior distribution detailed information, wherein the prior distribution detailed information includes prior variance information and prior mean information; Constructing current sample set information based on the bearing inner and outer ring size deviation information, the bearing inner ring size deviation information, the bearing outer ring size deviation information, and the prior distribution detailed information; The probability density function model includes a mean update formula and a standard deviation update formula, and constructing the probability density function model includes: Based on the Dynamic-Window model, a dynamic window model is constructed; Determine new measurement data information based on a preset sampling period; Calculate new data mean information of the new measurement data information, wherein the new data mean information is: ， Where, is the mean information of the new data, is the number of new measurement data information, is the serial number of the new measurement data information, For the New measurement data information; Calculate the standard deviation information of the new measurement data information, wherein the standard deviation information is: ， Where, is the standard deviation information, is the mean; Determine prior mean impact information based on the prior distribution detailed information and standard deviation information, wherein the prior mean impact information is: , Where, is the priori mean influence information, which is used to describe the influence of the priori mean on the posterior mean. is the prior standard deviation information, the value of the prior standard deviation information is the square root of the prior variance information; Determine the new data mean impact information based on the prior distribution detailed information and standard deviation information, wherein the new data mean impact information is: ， Where, is the new data mean impact information, which is used to describe the impact of the new data mean information on the posterior mean; According to the preset posterior standard deviation information and the prior standard deviation information, the fluctuation change rate information is generated, wherein the fluctuation change rate information is: ， Where, is the fluctuation change rate information, is the posterior standard deviation information, is the prior standard deviation information; Based on the fluctuation change rate information, target window length information is determined, wherein the target window length information is: ， Where, is the target window length information, is the maximum window length information, is the current window length information, is the window threshold information, ; Determining estimated mean information and estimated variance information based on a normal-inverse gamma conjugate prior distribution and the target window length information; Based on the prior distribution model and the probability model corresponding to the current sample set information, a posterior distribution mathematical model is determined, wherein the posterior distribution mathematical model is: ， Where, is the mathematical model of the posterior distribution, is the prior distribution model, is the probability model corresponding to the current sample set information, is the marginal likelihood value; Based on the posterior distribution mathematical model, the mean update formula and the standard deviation update formula are determined. The mean update formula is: ， The standard deviation update formula is: ， Where, is the mean information of the prior distribution, is the standard deviation information of the prior distribution, is the mean information of the likelihood of new data, is the standard deviation information of the new data likelihood, is the updated forecast mean, is the updated forecast standard deviation; Based on the Monte Carlo method and kernel density estimation method, the probability density function of the large sample is estimated according to the small sample. Based on the kernel function estimation, the distribution of the original data is compared with the distribution of the Monte Carlo sample to verify the consistency of the small sample size in fitting the deviation distribution model of all bearing components.

5. The method according to claim 4, characterized in that The method of determining the bearing assembly size distribution grouping optimization scheme based on the optimal bearing clearance range corresponding to any batch of bearings to be assembled, the number of silo channels of the bearing assembly machine, and the production rhythm of the target enterprise includes: Based on the new measurement data information, the probability density function model is fitted to generate a fitted probability density function model, wherein the fitted probability density function model obeys a normal distribution; Based on the bearing service clearance information, inequality information that the grouping should satisfy is determined, wherein the inequality information is: ； By calculating the deviation interval of the extreme value end group of the probability density function of the inner and outer rings of the bearing, the extreme value end group width of the inner ring of the bearing and the extreme value end group width of the outer ring of the bearing are determined, wherein the extreme value end group width of the inner ring of the bearing is: ， Where, is the width of the extreme end group of the bearing inner ring, is the deviation value of the overall diameter of the bearing outer ring, is the first set of deviation values ​​of the bearing inner ring; The width of the extreme end group of the bearing outer ring is: ， Where, is the extreme end group width of the bearing outer ring, is the deviation value of the overall diameter of the bearing inner ring, is the first set of deviation values ​​of the bearing outer ring; Integrate the function interval corresponding to the width of the extreme end group of the inner and outer rings of the bearing to obtain the integral area , , then integrate the probability density functions of the inner and outer rings of the bearing to obtain the integral area ; Calculate the probability density function of the inner and outer rings of the bearing and integrate them to obtain the integral area The area occupied by the width of the extreme end group of the probability density function of the inner and outer rings of the bearing , The ratio between them generates the number of equal probability groups of the inner and outer rings of the bearing. Among them, the number of equal probability groups of the inner ring of the bearing is: ， The number of equal probability groups of the bearing outer ring is: ， like and / or If is not an integer, and / or Perform rounding up processing, wherein after rounding up processing Recorded as , after rounding up Recorded as ; like , then determine the number of groups ; like , then determine the number of groups ; Based on the number of silo lanes of the bearing assembly machine, from the outer ring Position start with inner circle Traverse, if the outer circle With the inner circle If the same group number cannot be found after traversal, the outer circle will start from the next digit and the inner circle will start from the next digit. Perform traversal; If the outer circle The group number and inner circle If the group numbers are the same, the matching and closing action will be performed; After the matching action is performed, the outer ring Restart with the inner circle Traverse and randomly replenish inner and outer ring accessories to fill the waiting area silo; If there is no matching group number in the waiting area for the inner and outer rings, the traversal stops and the number of paired inner and outer rings of the bearing is recorded. The number of paired inner and outer rings of the bearing is used to describe the number of completed matched inner and outer rings of the bearing. Comparing the number of paired inner and outer rings of the bearing with information on a target number of assembled bearings, wherein the target number of assembled bearings is used to describe the number of bearings that a bearing manufacturing unit needs to assemble within one day; If the number of bearing inner and outer ring pairs is greater than the target number of bearings to be assembled, it is determined that the assembly process of the bearing inner and outer rings will not be stopped within a single shift of the bearing manufacturing unit. If the number of paired inner and outer rings of the bearing is less than the target number of matched bearings, first guidance information or second guidance information is generated, wherein the first guidance information is used to describe and guide the bearing manufacturing unit to improve manufacturing accuracy, and the second guidance information is used to describe the bearing manufacturing unit to relax the grouping interval range on the premise of meeting the minimum required clearance range of the bearing service performance.

6. A rolling bearing automatic fitting optimization and selection system based on equal probability grouping, characterized in that: The system comprises: Measured distribution information acquisition module: used to obtain measured distribution information of the bearings to be assembled, wherein the measured distribution information is used to describe the distribution information of the inner and outer ring size deviations of the bearings to be assembled in any production batch, which changes dynamically with the number of measurements. The distribution information of the inner and outer ring size deviations is a probability density function model; Inner and outer ring dynamic size deviation range grouping information determination module: used to determine the inner and outer ring dynamic size deviation range grouping information based on the inner and outer ring size deviation distribution information and the preset bearing service optimal clearance information, wherein the inner and outer ring dynamic size deviation range grouping information is obtained by performing equal probability division on the probability density function model.

7. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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