Method and system for testing fatigue life of bearing

Abstract

The invention provides a bearing fatigue life test method and system, and relates to the technical field of bearing testing, and the method comprises the steps: obtaining the application scene characteristics of a target electric tricycle, and obtaining K test conditions; historical bearing fault record mining is carried out in combination with the model of the target electro-tricycle and the K test conditions; the working condition parameters are used as indexes, and the K historical bearing fault record sets are retrieved; traversing K historical bearing fault working condition parameter sets for parameter screening; and performing fatigue life test on the target bearing based on the K test working conditions and the K test working condition parameters, and weighting the obtained fatigue life test results of the K test working conditions to obtain a target fatigue test result. Through the bearing fatigue life testing method and device, the technical problem that the fatigue life testing accuracy is low due to the fact that the performance of the bearing under complex and changeable actual working conditions is difficult to adapt in the prior art can be solved, and the fatigue life testing accuracy of the bearing is improved by testing the fatigue life under different scenes.

Description

Technical Field

[0001] This application relates to the technical field of bearing testing, and particularly to a method and system for testing the fatigue life of bearings. Background Art

[0002] The bearings of electric tricycles are mainly responsible for supporting the rotating components of the wheels, drive systems, and steering systems, ensuring the stability of the mechanical structure and the efficiency of operation. Since electric tricycles may face frequent load changes, severe road surface impacts, and harsh external environments during long-term operation, the bearings are prone to fatigue damage and even failure. The current bearing fatigue life testing methods are mainly based on standardized laboratory conditions, simulating the operating state of bearings under fixed load, speed, and temperature conditions to test their durability performance. However, existing methods are difficult to comprehensively simulate the variable operating environments of electric tricycles in actual use and do not fully consider the actual operating characteristics of electric tricycles under complex and variable working conditions, resulting in low accuracy and reliability of test results.

[0003] In summary, there is a technical problem in the prior art that the accuracy of fatigue life test results is low because it is difficult to adapt to the performance of bearings under complex and variable actual working conditions. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for testing the fatigue life of bearings, so as to solve the technical problem in the prior art that the accuracy of fatigue life test results is low because it is difficult to adapt to the performance of bearings under complex and variable actual working conditions.

[0005] In view of the above problems, this application provides a method and system for testing the fatigue life of bearings.

[0006] In a first aspect, this application provides a method for testing the fatigue life of bearings. The method for testing the fatigue life of bearings is implemented through a system for testing the fatigue life of bearings. Among them, the method for testing the fatigue life of bearings includes: obtaining the application scenario characteristics of a target electric tricycle, performing test working conditions on a target bearing based on the application scenario characteristics to obtain K test working conditions, where K is an integer greater than or equal to 1, and the target bearing is applied to the target electric tricycle; combining the model of the target electric tricycle and the K test working conditions to mine historical bearing failure records to obtain K sets of historical bearing failure records; indexing with working condition parameters, retrieving the K sets of historical bearing failure records to obtain K sets of historical bearing failure working condition parameters; traversing the K sets of historical bearing failure working condition parameters for parameter screening to obtain K sets of test working condition parameters; performing K fatigue life tests on the target bearing based on the K test working conditions and the K sets of test working condition parameters, and weighting the K obtained test working condition fatigue life test results to obtain a target fatigue test result.

[0007] In a second aspect, the present application also provides a bearing fatigue life test system for implementing the bearing fatigue life test method as described in the first aspect. The bearing fatigue life test system includes: a scenario acquisition module configured to acquire the application scenario characteristics of a target electric tricycle, perform test working conditions of a target bearing based on the application scenario characteristics, and obtain K test working conditions, where K is an integer greater than or equal to 1, and the target bearing is applied to the target electric tricycle; a fault record mining module configured to combine the model of the target electric tricycle and the K test working conditions to mine historical bearing fault records and obtain K sets of historical bearing fault records; a fault retrieval module configured to retrieve the K sets of historical bearing fault records with working condition parameters as an index and obtain K sets of historical bearing fault working condition parameters; a parameter screening module configured to traverse the K sets of historical bearing fault working condition parameters for parameter screening and obtain K test working condition parameters; a fatigue life test module configured to perform K fatigue life tests on the target bearing based on the K test working conditions and the K test working condition parameters, and weight the K obtained fatigue life test results of the test working conditions to obtain a target fatigue test result.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages: By acquiring the application scenario characteristics of the target electric tricycle, performing test working conditions of the target bearing based on the application scenario characteristics, and obtaining K test working conditions, where K is an integer greater than or equal to 1, and the target bearing is applied to the target electric tricycle; combining the model of the target electric tricycle and the K test working conditions to mine historical bearing fault records and obtaining K sets of historical bearing fault records; retrieving the K sets of historical bearing fault records with working condition parameters as an index and obtaining K sets of historical bearing fault working condition parameters; traversing the K sets of historical bearing fault working condition parameters for parameter screening and obtaining K test working condition parameters; performing K fatigue life tests on the target bearing based on the K test working conditions and the K test working condition parameters, and weighting the K obtained fatigue life test results of the test working conditions to obtain a target fatigue test result. That is, through the usage scenarios of electric tricycles driven by new energy, different test working conditions are selected to perform fatigue life tests on the target bearing, and the fatigue life test results of each test working condition are weighted and calculated to obtain a comprehensive target fatigue test result, improving the accuracy of the fatigue life test of the bearings of electric tricycles driven by new energy.

[0009] The above description is only an overview of the technical solution of the present application. In order to better understand the technical means of the present application, it can be implemented according to the content of the specification. In order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the specific embodiments of the present application are given below. 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 application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description of the specification. Brief Description of the Drawings

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0011] Figure 1 It is a schematic flowchart of the method for testing the fatigue life of bearings in the present application; Figure 2 It is a schematic structural diagram of the system for testing the fatigue life of bearings in the present application.

[0012] Description of the reference numerals: Scenario acquisition module 11, Fault record mining module 12, Fault retrieval module 13, Parameter screening module 14, Fatigue life testing module 15. Detailed Embodiments

[0013] By providing a method and system for testing the fatigue life of bearings, the present application solves the technical problem in the prior art that the accuracy of the fatigue life test results is relatively low because it is difficult to adapt to the performance of bearings under complex and changeable actual working conditions. Through the use scenario of an electric tricycle driven by new energy, different test working conditions are selected to conduct fatigue life tests on the target bearing, and the fatigue life test results of each test working condition are weighted and calculated to obtain a comprehensive target fatigue test result, improving the accuracy of the fatigue life test of the bearings of the electric tricycle driven by new energy.

[0014] Next, the technical solutions in the present application will be clearly and completely described with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application. Additionally, it should be noted that for the sake of convenience of description, only the parts related to the present application are shown in the drawings rather than all of them.

[0015] Example 1, please refer to the appendix Figure 1 , this application provides a method for testing the fatigue life of bearings. Among them, the method for testing the fatigue life of bearings is executed by a system for testing the fatigue life of bearings. The method for testing the fatigue life of bearings specifically includes the following steps: S100: Obtain the application scenario characteristics of the target electric tricycle, and based on the application scenario characteristics, perform the test working conditions of the target bearing to obtain K test working conditions, where K is an integer greater than or equal to 1, and the target bearing is applied to the target electric tricycle.

[0016] Specifically, obtain the application scenario characteristics of the target electric tricycle, that is, various conditions faced by the electric tricycle during actual use, including but not limited to load conditions (low load, high load), road conditions (flat, bumpy), environmental factors (temperature, humidity), etc. Install sensor devices (such as load sensors, acceleration sensors, temperature and humidity sensors) at key parts of the electric tricycle to collect operation data in real time, including vehicle speed, load changes, vibration intensity, temperature and humidity environment, and the type of driving road conditions (such as flat roads, slopes, gravel roads).

[0017] Divide the operating environment into multiple typical working condition scenarios through a data clustering method (such as K-means clustering), such as low load - flat road, high load - rough road, low temperature - humid environment, etc. K-means clustering is an iterative optimization clustering algorithm. By dividing data points into a preset number of K clusters and minimizing the sum of the squared distances from data points to the cluster centers, the best grouping is found. Since the dimensions and numerical ranges of load, vibration, temperature, and humidity are different, in order to avoid a certain characteristic having too much influence on the clustering result, it is necessary to standardize the data to unify the dimensions. Determine the number of clusters K, randomly initialize the K cluster centers, calculate the distances between each data point and the K cluster centers, assign each data point to the nearest cluster, and recalculate the center of each cluster as the mean of the data points within the cluster. Continuously repeat this process until the cluster center positions no longer change or reach the iteration count limit. Through the clustering results, divide the operating environment into K typical working condition scenarios, that is, K test working conditions.

[0018] The test conditions of the target bearing are determined according to the application scenario characteristics, which are a series of simulated operating environments designed to evaluate the performance of the target bearing and used to simulate the performance of the bearing in actual operation. The K test conditions refer to multiple working condition scenarios designed according to the application scenario characteristics of the target electric tricycle. K is an integer greater than or equal to 1, representing the number of working conditions. For example, the low-load condition simulates the load situation during normal driving; the high-load condition simulates the working state under heavy load, such as the overloading situation when transporting goods; the different road condition conditions simulate the influence of different road surface types, such as flat roads, rough roads, slopes, etc. on the bearing; the temperature and humidity change conditions are used to simulate the influence of different climate conditions on the bearing performance. By obtaining the operating characteristics of the target electric tricycle, the designed test conditions can accurately simulate the actual performance of the bearing under complex and changeable conditions and accurately simulate the working conditions of the bearing in actual use, thereby improving the reliability and relevance of the test results.

[0019] S200: Combine the model of the target electric tricycle and the K test conditions to mine the historical bearing fault records and obtain K sets of historical bearing fault records.

[0020] Specifically, according to the model of the target electric tricycle and the K test conditions, extract the bearing operation and fault records related to the target electric tricycle from the electric tricycle fault database, including working condition conditions such as load, speed, environmental temperature and humidity, as well as the time points and causes of fault occurrence. The model of the target electric tricycle is the specific model and category of the electric tricycle for testing (such as logistics tricycle, express tricycle, etc.), and its structure and use determine the load and operating conditions of the bearing. Use data mining tools, such as SQL queries, to screen all historical bearing fault records in the electric tricycle fault database. Use a matching algorithm to screen out the records closest to each test condition from the historical bearing fault records according to keywords. For example, if a certain test condition is set as a high-load and high-temperature scenario, then screen out the fault records with a load greater than 150 kg and a temperature higher than 30 °C in the historical data. For each test condition, organize the historical fault records that match it to form K sets of historical bearing fault records, and each set of historical bearing fault records corresponds to each test condition one by one. Combining the model of the target electric tricycle and the working condition characteristics, screening out the historical fault records related to the actual application and adjusting the test method according to the common fault types and life rules helps to deeply understand the performance of the bearing under different working conditions.

[0021] S300: Index by the working condition parameters to retrieve the K sets of historical bearing fault records and obtain K sets of historical bearing fault working condition parameters.

[0022] Specifically, the parameters of K test conditions are defined, and the condition parameters are used as indices to retrieve in the set of K historical bearing fault records. The condition parameters refer to the specific parameters of the electric tricycle under test conditions, such as load, speed, temperature, humidity, etc., which directly affect the working conditions of the bearing. For each set of historical bearing fault records, retrieve according to its condition characteristics (such as load, temperature, humidity, etc.), and use conditional matching methods (such as the select statement in SQL) or similarity calculation methods (such as Euclidean distance, cosine similarity, etc.) to compare the similarity between the conditions in the historical data and the target condition. By traversing the set of K historical bearing fault records, K sets of historical bearing fault condition parameters are obtained, which contain the fault parameters related to specific condition parameters. By using the condition parameters as the retrieval condition, the fault records related to the current test condition are extracted from the historical data, avoiding the interference of irrelevant data.

[0023] S400: Traverse the set of K historical bearing fault condition parameters for parameter screening to obtain K test condition parameters.

[0024] Specifically, traverse each condition parameter in the historical fault records and perform screening one by one to identify the key factors from the existing historical data to ensure the accuracy of subsequent test conditions. Parameter screening refers to selecting, through a certain screening method, those key condition parameters from the set of historical bearing fault condition parameters that can represent or affect the fatigue life of the bearing. Randomly select a set of historical bearing fault condition parameters from the K sets of historical bearing fault condition parameters as the first set of historical bearing fault condition parameters, and take the first set of historical bearing fault condition parameters as an example.

[0025] Randomly extract the first historical bearing fault condition parameter and the second historical bearing fault condition parameter from the first set of historical bearing fault condition parameters. The first historical bearing fault condition parameter is used as the initial center for the first-round screening, and the second historical bearing fault condition parameter is used as the initial center for the second-round screening. Preset a preset parameter screening bandwidth to determine the range of the parameter screening neighborhood, that is, the set of parameters that meet the conditions around the center point. Taking the first historical bearing fault condition parameter as the center, screen out all bearing fault condition parameters that meet the preset parameter screening bandwidth in the first set of historical bearing fault condition parameters as the first parameter screening neighborhood, that is, all bearing fault condition parameters whose distance from the first historical bearing fault condition parameter does not exceed the preset parameter screening bandwidth.

[0026] Similarly, centered on the second historical bearing fault condition parameters, all bearing fault condition parameters that meet the preset parameter screening bandwidth are screened out from the first historical bearing fault condition parameter set according to the preset parameters to form a second parameter screening neighborhood. Calculate the neighborhood density of the first parameter screening neighborhood and the neighborhood density of the second parameter screening neighborhood, judge the magnitudes of the neighborhood densities, and use the parameter screening center corresponding to the screening neighborhood with the larger neighborhood density as the iterative parameter screening center. That is to say, if the neighborhood density of the first parameter screening neighborhood is greater than or equal to the neighborhood density of the second parameter screening neighborhood, use the first parameter screening center as the iterative parameter screening center, and use the direction from the iterative parameter screening center to the second parameter screening center as the first iterative direction.

[0027] Extract the historical bearing fault condition parameter closest to the first iterative direction in the first parameter screening neighborhood as the third parameter screening center. Screen out all bearing fault condition parameters that meet the preset parameter screening bandwidth from the first historical bearing fault condition parameter set according to the preset parameter screening bandwidth to form a third parameter screening neighborhood. Judge whether the neighborhood density of the third parameter screening neighborhood is greater than or equal to the neighborhood density of the iterative parameter screening neighborhood corresponding to the iterative parameter screening center. If so, update the third parameter screening center corresponding to the third parameter screening neighborhood to the iterative parameter screening center; otherwise, keep the screening center unchanged. Continuously repeat the above process until the preset iteration stop condition is met, that is, the updated iteration count is greater than or equal to the maximum updated iteration count, or the difference between the neighborhood densities of two iterative parameter screening neighborhoods obtained in two adjacent iterations is less than or equal to the preset neighborhood density difference, and then stop the iterative update.

[0028] Use the historical bearing fault condition parameter corresponding to the currently obtained iterative parameter screening center as the test condition parameter. Perform the above steps for all other historical bearing fault condition parameter sets in the K historical bearing fault condition parameter sets, so as to obtain K test condition parameters. By parameter screening, selecting test condition parameters closely related to bearing faults can reduce unnecessary parameter interference and improve the accuracy and reliability of fatigue life tests.

[0029] S500: Conduct K fatigue life tests on the target bearing based on the K test conditions and the K test condition parameters, and weight the K test condition fatigue life test results obtained to obtain the target fatigue test result.

[0030] Specifically, the K test conditions are determined according to the application scenario characteristics of the target electric tricycle, simulating various conditions that the electric tricycle may encounter during actual use, and are used to test specific environmental parameters and operating conditions of the bearing performance. The K test condition parameters are a specific set of parameters associated with the K test conditions, and are parameters selected from the historical bearing failure condition parameter set, including actual values of conditions such as load, temperature, and rotational speed. These parameters are used to conduct fatigue life tests on the bearing. For each test condition, the test equipment is set according to the corresponding test condition parameters, and the fatigue life test of the target bearing is carried out under specific test conditions. Each test condition simulates the working state of the bearing in a specific use environment, thereby measuring the fatigue life of the bearing under this condition.

[0031] Monitor the process of the K fatigue life tests in real time, track the changes in the test condition parameters in real time, and analyze whether they are stable during the test process. If the condition parameters change greatly, it may affect the reliability of the test results. The standard deviation or coefficient of variation is used to measure the stability. The coefficient of variation is the ratio of the standard deviation to the mean, and is used to measure the relative dispersion degree of the data. Through the monitoring of the stability of the test condition parameters, the stability factors of the K test condition parameters are obtained, indicating whether the test condition parameters remain stable during the test process. Calculate the sum of the stability factors of the K test condition parameters, and normalize each stability factor of the test condition parameters, that is, divide each stability factor of the test condition parameters by the sum to obtain the corresponding weight value, that is, the K weight values.

[0032] Weight the obtained fatigue life test results of the K test conditions, multiply the fatigue life test results of the K test conditions by the corresponding K weight values, and then sum all the weighted results to obtain the target fatigue test result. The target fatigue test result refers to the comprehensive evaluation result after weighting the fatigue life test results of the K test conditions, reflecting the fatigue life performance of the bearing under multiple conditions, and more accurately reflecting the true performance of the bearing under various complex conditions. By weighting the results of different test conditions, the fatigue life of the bearing under various conditions that may be encountered during actual use can be evaluated more comprehensively. The weighted result can eliminate the errors that may occur under a single condition, making the test result more in line with the actual service life of the bearing.

[0033] Furthermore, the S400 of the present application includes: Randomly extract K first historical bearing fault condition parameters and K second historical bearing fault condition parameters from the K sets of historical bearing fault condition parameters respectively; use the K first historical bearing fault condition parameters as K first parameter screening centers, and perform matching in the K sets of historical bearing fault condition parameters according to the preset parameter screening bandwidth to obtain K first parameter screening neighborhoods; use the K second historical bearing fault condition parameters as K second parameter screening centers, and perform matching in the K sets of historical bearing fault condition parameters according to the preset parameter screening bandwidth to obtain K second parameter screening neighborhoods; based on the K first parameter screening centers, K first parameter screening neighborhoods, K second parameter screening centers and K second parameter screening centers, perform parameter screening on the K sets of historical bearing fault condition parameters to obtain the K test condition parameters.

[0034] Specifically, randomly extract K first historical bearing fault condition parameters and K second historical bearing fault condition parameters from the K sets of historical bearing fault condition parameters respectively. That is to say, for each set of historical bearing fault condition parameters, randomly extract the first historical bearing fault condition parameter and the second historical bearing fault condition parameter from it. The preset parameter screening bandwidth is a threshold set in advance, which is used to limit the acceptable parameter range in the screening process to ensure that the screening results are within a reasonable fluctuation range. Take the first historical bearing fault condition parameter in each set of historical bearing fault condition parameters as the starting point of the first screening, perform matching in the set of historical bearing fault condition parameters, and select the condition parameters that meet the preset parameter screening bandwidth to obtain the first parameter screening neighborhood in each set of historical bearing fault condition parameters. All condition parameters in the first parameter screening neighborhood meet the preset parameter screening bandwidth.

[0035] Similarly, take the second historical bearing fault condition parameter in each set of historical bearing fault condition parameters as the starting point of the second screening, perform matching in the set of historical bearing fault condition parameters, and select the condition parameters that meet the preset parameter screening bandwidth to obtain the second parameter screening neighborhood in each set of historical bearing fault condition parameters. All condition parameters in the second parameter screening neighborhood meet the preset parameter screening bandwidth.

[0036] For each set of historical bearing fault condition parameters, calculate the neighborhood density of its first parameter screening neighborhood and the neighborhood density of its second parameter screening neighborhood, and judge the magnitudes of the neighborhood density of the first parameter screening neighborhood and the neighborhood density of the second parameter screening neighborhood. If the neighborhood density of the first parameter screening neighborhood is greater than the neighborhood density of the second parameter screening neighborhood, then take the first parameter screening center as the iterative parameter screening center, and take the direction from the iterative parameter screening center to the second parameter screening center as the first iterative direction. Then, extract the historical bearing fault condition parameter closest to the first iterative direction in the first parameter screening neighborhood as the third parameter screening center, and again match the third parameter screening neighborhood of the third parameter screening center in the set of historical bearing fault condition parameters according to the preset parameter screening bandwidth.

[0037] Judge the neighborhood density of the third parameter screening neighborhood and the neighborhood density of the iterative parameter screening neighborhood corresponding to the iterative parameter screening center, and update the parameter screening center corresponding to the neighborhood with the greater neighborhood density to be the iterative parameter screening center. Continuously repeat this process until, when the preset iteration stop condition is met, take the historical bearing fault condition parameter corresponding to the iterative parameter screening center at this time as the test condition parameter. Perform the above steps for the K sets of historical bearing fault condition parameters to obtain the corresponding K test condition parameters, that is, the finally screened parameters, which are used to simulate the actual conditions of the target bearing. Through the dual constraints of the parameter screening center and the screening bandwidth, the search range of the test condition parameters is effectively reduced. The screened test parameters make full use of the distribution characteristics of historical data, can truly reflect the actual conditions, and avoid selecting meaningless condition parameters through the screening based on neighborhood density, improving the test reliability.

[0038] Furthermore, the present application further includes the following steps: Judge whether the neighborhood density of the K first parameter screening neighborhoods is greater than or equal to the neighborhood density of the K second parameter screening neighborhoods. If so, then take the K first parameter screening centers as the K iterative parameter screening centers, and take the directions from the K iterative parameter screening centers to the K second parameter screening centers as the K first iterative directions; based on the K first iterative directions and the K first parameter screening neighborhoods, update and iterate the K iterative parameter screening centers in the K sets of historical bearing fault condition parameters until the preset iteration stop condition is met, and take the K historical bearing fault condition parameters corresponding to the K iterative parameter screening centers obtained after the last update and iteration as the K test condition parameters.

[0039] Furthermore, the present application further includes the following steps: Count the total number of historical bearing fault condition parameters in the K first parameter screening neighborhoods respectively, and obtain the neighborhood density of the K first parameter screening neighborhoods by dividing the statistical result by twice the preset parameter screening bandwidth.

[0040] Specifically, for each of the K first parameter screening neighborhoods, the following calculations are performed. Count the total number of historical bearing fault condition parameters in the first parameter screening neighborhood, calculate the ratio of the total number to twice the preset parameter screening bandwidth, and obtain the neighborhood density of the first parameter screening neighborhood. The neighborhood density is an index to measure the density of the distribution of condition data within the parameter screening neighborhood, indicating the density of data points within this neighborhood. According to this calculation process, the neighborhood densities of the K first parameter screening neighborhoods and the neighborhood densities of the K second parameter screening neighborhoods are obtained.

[0041] Judge whether each first neighborhood density is greater than or equal to the corresponding second neighborhood density. If the first neighborhood density is greater than or equal to the second neighborhood density, select the first parameter screening center as the iterative parameter screening center for the next screening, and determine the direction from the first parameter screening center to the second parameter screening center as the first iteration direction.

[0042] Extract the historical bearing fault condition parameter closest to the corresponding first iteration direction from each of the K first parameter screening neighborhoods as the third parameter screening center. Then, repeat the above steps, that is, perform neighborhood matching on the third parameter screening center in each set of historical bearing fault condition parameters according to the preset parameter screening bandwidth to obtain the third parameter screening neighborhood. Then judge the neighborhood density of the third parameter screening neighborhood and the neighborhood density of the iterative parameter screening neighborhood corresponding to the iterative parameter screening center, and update the parameter screening center corresponding to the parameter screening neighborhood with a larger neighborhood density to the current iterative parameter screening center. Calculate the neighborhood density of the current iteration, compare the difference in neighborhood density between the current iteration and the previous iteration, and judge whether the iteration stop condition is met. If the number of iterations exceeds the maximum update iteration number, stop the iteration; or, if the difference in neighborhood density is less than or equal to the preset threshold, it means that the screening result has stabilized, and stop the iteration. When the stop condition is met, save the screening center and neighborhood parameters of the last iteration, and use the historical bearing fault condition parameters corresponding to the current iterative parameter screening center as the test condition parameters.

[0043] The above steps are performed for each of the K sets of historical bearing fault condition parameters, thereby obtaining K sets of test condition parameters. Through neighborhood density comparison and iterative update, the parameter screening center is gradually optimized to be closer to the actual test conditions. During the dynamic adjustment process, the distribution characteristics of historical fault data can be fully considered, and test parameters that better conform to the actual conditions are screened out. Through the dual constraints of neighborhood density and iterative direction, repeated calculations of invalid parameters are avoided, and the test efficiency is significantly improved.

[0044] Furthermore, the present application further includes the following steps: Respectively extract the historical bearing fault condition parameters in the K first parameter screening neighborhoods that are closest to the K first iterative directions as the K third parameter screening centers; perform neighborhood matching on the K third parameter screening centers in the K sets of historical bearing fault condition parameters according to a preset parameter screening bandwidth to obtain K third parameter screening neighborhoods; determine whether the neighborhood density of the K third parameter screening neighborhoods is greater than or equal to the neighborhood density of the K iterative parameter screening neighborhoods corresponding to the K iterative parameter screening centers. If so, update the K third parameter screening centers to the K iterative parameter screening centers; based on the K first iterative directions and the K third parameter screening neighborhoods, perform update iteration on the updated K iterative parameter screening centers in the K sets of historical bearing fault condition parameters until a preset iteration stop condition is met, and use the K historical bearing fault condition parameters corresponding to the K iterative parameter screening centers obtained after the last update iteration as the K test condition parameters.

[0045] Furthermore, the present application further includes the following steps: The preset iteration stop condition is that the number of update iterations is greater than or equal to the maximum number of update iterations and / or the difference between the neighborhood densities of two iterative parameter screening neighborhoods obtained in two adjacent iterations is less than or equal to a preset neighborhood density difference.

[0046] Specifically, for the K first parameter screening neighborhoods, respectively extract the historical bearing fault condition parameters in each first parameter screening neighborhood that are closest to the first iterative direction as the third parameter screening centers. That is to say, for the historical bearing fault condition parameters in each first parameter screening neighborhood, calculate their distances to the first iterative direction (which can use Euclidean distance or other distance measurement methods). In each neighborhood, select the condition parameter that is closest to the first iterative direction as the third parameter screening center. According to the preset bandwidth, perform neighborhood matching on the corresponding sets of historical bearing fault condition parameters in the K sets of historical bearing fault condition parameters, and form K third parameter screening neighborhoods with all the condition parameters that meet the preset parameter screening bandwidth.

[0047] Calculate the neighborhood density of the K third-parameter screening neighborhoods, respectively count the total number of historical bearing fault condition parameters in the third-parameter screening neighborhoods, and obtain the neighborhood density of the K third-parameter screening neighborhoods by dividing the statistical result by twice the preset parameter screening bandwidth. The neighborhood density of the K iterative screening parameter centers is also the neighborhood density of the K first-parameter screening centers. If the neighborhood density of the K third-parameter screening neighborhoods is greater than the neighborhood density of the K iterative parameter screening neighborhoods corresponding to the K iterative parameter screening centers, update the K third-parameter screening centers to the K iterative parameter screening centers. Otherwise, keep the current iterative parameter screening centers unchanged.

[0048] Continue to execute the above steps. According to the K first iterative directions and the K third-parameter screening neighborhoods, screen in the K sets of historical bearing fault condition parameters, judge the neighborhood density, and continuously update the K iterative parameter screening centers until the preset iteration stop condition is met. At this time, the K historical bearing fault condition parameters corresponding to the K iterative parameter screening centers obtained are used as the K test condition parameters.

[0049] The preset iteration stop conditions include that the updated iteration times are greater than or equal to the maximum updated iteration times (such as 10 times), and the difference between the neighborhood densities of two iterative parameter screening neighborhoods obtained in two adjacent iterations is less than or equal to the preset neighborhood density difference (such as less than 1 data / unit bandwidth). When any one of them is met, stop the iteration. The maximum updated iteration times is the upper limit value of the iteration set according to the actual situation to prevent falling into an infinite loop. The preset neighborhood density difference is a threshold set according to the data stability requirement. Calculate the neighborhood density of the current iteration, compare the neighborhood density difference between the current iteration and the previous iteration; judge whether the stop condition is met: if the iteration times exceed the maximum updated iteration times, stop the iteration; or, if the neighborhood density difference is less than or equal to the preset threshold, it means that the screening result has been stable, and stop the iteration.

[0050] By dynamically adjusting the screening centers, the centers of each iteration gradually approach the parameters with high representativeness and high density distribution, significantly improving the accuracy of the screening results. Combining multiple iterations with neighborhood density judgment can adapt to complex working conditions and ensure a higher matching degree between the test condition parameters and the actual application scenarios. By presetting the iteration stop conditions (neighborhood density difference or maximum iteration times), unnecessary repeated iterations are avoided, and the convergence speed of the optimization is accelerated.

[0051] Further, the present application S500 includes: Monitor the stability of the test condition parameters for the K fatigue life tests to obtain K test condition parameter stability factors; divide each of the K test condition parameter stability factors by the sum of the K test condition parameter stability factors to obtain K weight values; weight the K fatigue life test results of the test conditions based on the K weight values to obtain the target fatigue test result.

[0052] Specifically, monitor the stability of the test condition parameters for the K fatigue life tests, record the changes in the condition parameters (such as load, temperature, speed, etc.) during the test, and determine the parameter stability factor for each test condition. This can be done using the standard deviation or coefficient of variation (CV) by calculating the fluctuation range or standard deviation of the condition over a period of time. The smaller the standard deviation, the more stable the condition. For example, the coefficient of variation is the ratio of the standard deviation to the mean and is used to measure the relative dispersion of the data.

[0053] By measuring the stability factor of the condition, the stability score for each test condition can be obtained, which is the K test condition parameter stability factors. Calculate the sum of the K test condition parameter stability factors, and divide the value of each test condition parameter stability factor by the sum to obtain the K weight values for the K test conditions. The weight value is the relative importance or contribution calculated based on the stability factor. The test condition parameter with a larger stability factor will be assigned a greater weight.

[0054] Weight the K fatigue life test results of the test conditions according to the K weight values to obtain the target fatigue test result. That is, multiply the fatigue life test result of each test condition by the corresponding weight value, and then sum all the weighted results to obtain the target fatigue test result. Through stability monitoring and weighted calculation, more stable test condition parameters can be given higher weights, reducing the impact of unstable conditions on the test results, improving the credibility of the results, and automatically adjusting the weights according to the stability of different condition parameters, so that the test results are more in line with the actual operating environment.

[0055] In summary, the bearing fatigue life test method provided by this application has the following technical effects: By obtaining the application scenario features of the target electric tricycle, based on the application scenario features, the test conditions of the target bearing are carried out to obtain K test conditions, where K is an integer greater than or equal to 1, and the target bearing is applied to the target electric tricycle; combining the model of the target electric tricycle and the K test conditions to mine the historical bearing fault records, and obtaining K historical bearing fault record sets; indexing with the working condition parameters, retrieving the K historical bearing fault record sets, and obtaining K historical bearing fault working condition parameter sets; traversing the K historical bearing fault working condition parameter sets to perform parameter screening, and obtaining K test condition parameters; based on the K test conditions and the K test condition parameters, performing K fatigue life tests on the target bearing, and weighting the obtained K test condition fatigue life test results to obtain the target fatigue test result. That is to say, through the use scenario of the electric tricycle driven by new energy, different test conditions are selected to perform fatigue life tests on the target bearing, and the fatigue life test results of each test condition are weighted and calculated to obtain a comprehensive target fatigue test result, which improves the accuracy of the fatigue life test of the bearings of the electric tricycle driven by new energy.

[0056] Embodiment 2. Based on the same inventive concept as the method for testing the fatigue life of bearings in the foregoing Embodiment 1, the present application also provides a system for testing the fatigue life of bearings. Please refer to the attached Figure 2 , the system for testing the fatigue life of bearings includes: A scenario acquisition module 11, which is used to obtain the application scenario features of the target electric tricycle, and based on the application scenario features, carry out the test conditions of the target bearing to obtain K test conditions, where K is an integer greater than or equal to 1, and the target bearing is applied to the target electric tricycle; a fault record mining module 12, which is used to combine the model of the target electric tricycle and the K test conditions to mine the historical bearing fault records and obtain K historical bearing fault record sets; a fault retrieval module 13, which is used to index with the working condition parameters, retrieve the K historical bearing fault record sets, and obtain K historical bearing fault working condition parameter sets; a parameter screening module 14, which is used to traverse the K historical bearing fault working condition parameter sets to perform parameter screening and obtain K test condition parameters; a fatigue life test module 15, which is used to perform K fatigue life tests on the target bearing based on the K test conditions and the K test condition parameters, and weight the obtained K test condition fatigue life test results to obtain the target fatigue test result.

[0057] Furthermore, the parameter screening module 14 in the system for testing the fatigue life of bearings is further used for: Randomly extract K first historical bearing fault condition parameters and K second historical bearing fault condition parameters from the K sets of historical bearing fault condition parameters respectively; use the K first historical bearing fault condition parameters as K first parameter screening centers, and perform matching in the K sets of historical bearing fault condition parameters according to the preset parameter screening bandwidth to obtain K first parameter screening neighborhoods; use the K second historical bearing fault condition parameters as K second parameter screening centers, and perform matching in the K sets of historical bearing fault condition parameters according to the preset parameter screening bandwidth to obtain K second parameter screening neighborhoods; based on the K first parameter screening centers, K first parameter screening neighborhoods, K second parameter screening centers and K second parameter screening centers, perform parameter screening on the K sets of historical bearing fault condition parameters to obtain the K test condition parameters.

[0058] Further, the parameter screening module 14 in the bearing fatigue life test system is further configured to: Judge whether the neighborhood density of the K first parameter screening neighborhoods is greater than or equal to the neighborhood density of the K second parameter screening neighborhoods. If so, use the K first parameter screening centers as K iterative parameter screening centers, and use the directions from the K iterative parameter screening centers to the K second parameter screening centers as K first iterative directions; based on the K first iterative directions and K first parameter screening neighborhoods, update and iterate the K iterative parameter screening centers in the K sets of historical bearing fault condition parameters until the preset iteration stop condition is met, and use the K historical bearing fault condition parameters corresponding to the K iterative parameter screening centers obtained after the last update and iteration as the K test condition parameters.

[0059] Further, the parameter screening module 14 in the bearing fatigue life test system is further configured to: Extract the historical bearing fault condition parameters in the K first parameter screening neighborhoods that are closest to the K first iteration directions respectively as the K third parameter screening centers; perform neighborhood matching on the K third parameter screening centers respectively in the set of the K historical bearing fault condition parameters according to a preset parameter screening bandwidth to obtain K third parameter screening neighborhoods; determine whether the neighborhood density of the K third parameter screening neighborhoods is greater than or equal to the neighborhood density of the K iteration parameter screening neighborhoods corresponding to the K iteration parameter screening centers. If so, update the K third parameter screening centers to the K iteration parameter screening centers; based on the K first iteration directions and the K third parameter screening neighborhoods, perform update iteration on the updated K iteration parameter screening centers in the set of the K historical bearing fault condition parameters until a preset iteration stop condition is met, and use the K historical bearing fault condition parameters corresponding to the K iteration parameter screening centers obtained after the last update iteration as the K test condition parameters.

[0060] Further, the parameter screening module 14 in the bearing fatigue life test system is further configured to: The preset iteration stop condition is that the number of update iterations is greater than or equal to the maximum number of update iterations and / or the difference between the neighborhood densities of two iteration parameter screening neighborhoods obtained in two adjacent iterations is less than or equal to a preset neighborhood density difference.

[0061] Further, the parameter screening module 14 in the bearing fatigue life test system is further configured to: Count the total number of historical bearing fault condition parameters in the K first parameter screening neighborhoods respectively, and obtain the neighborhood density of the K first parameter screening neighborhoods by dividing the statistical result by the ratio of twice the preset parameter screening bandwidth.

[0062] Further, the fatigue life test module 15 in the bearing fatigue life test system is further configured to: Monitor the stability of the test condition parameters for the K fatigue life tests to obtain K test condition parameter stability factors; divide the K test condition parameter stability factors by the sum of the K test condition parameter stability factors respectively to obtain K weight values; weight the K test condition fatigue life test results based on the K weight values to obtain the target fatigue test result.

[0063] The embodiments in this specification are described in a progressive manner, and the key points of each embodiment are the differences from other embodiments. The foregoing Figure 1The method and specific examples for bearing fatigue life testing in Embodiment 1 are equally applicable to the bearing fatigue life testing system in this embodiment. Through the detailed description of the method for bearing fatigue life testing above, those skilled in the art can clearly understand the bearing fatigue life testing system in this embodiment. Therefore, for the sake of brevity of the specification, it will not be elaborated here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0064] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0065] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A method for testing the fatigue life of a bearing, characterized in that, Including: Obtain the application scenario characteristics of the target electric tricycle, and based on the application scenario characteristics, conduct test working conditions for the target bearing to obtain K test working conditions, where K is an integer greater than or equal to 1, and the target bearing is applied to the target electric tricycle; Combine the model of the target electric tricycle and the K test working conditions to mine historical bearing fault records, and obtain K sets of historical bearing fault records; Index by working condition parameters, and retrieve the K sets of historical bearing fault records to obtain K sets of historical bearing fault working condition parameters; Traverse the K sets of historical bearing fault working condition parameters for parameter screening to obtain K test working condition parameters; Based on the K test working conditions and the K test working condition parameters, conduct K fatigue life tests on the target bearing, and weight the K test working condition fatigue life test results obtained to obtain the target fatigue test result.

2. The method for testing the fatigue life of a bearing according to claim 1, characterized in that, Traverse the K sets of historical bearing fault working condition parameters for parameter screening to obtain K test working condition parameters, including: Randomly extract K first historical bearing fault working condition parameters and K second historical bearing fault working condition parameters from the K sets of historical bearing fault working condition parameters respectively; Use the K first historical bearing fault working condition parameters as K first parameter screening centers, and perform matching in the K sets of historical bearing fault working condition parameters according to the preset parameter screening bandwidth to obtain K first parameter screening neighborhoods; Use the K second historical bearing fault working condition parameters as K second parameter screening centers, and perform matching in the K sets of historical bearing fault working condition parameters according to the preset parameter screening bandwidth to obtain K second parameter screening neighborhoods; Based on the K first parameter screening centers, K first parameter screening neighborhoods, K second parameter screening centers, and K second parameter screening centers, conduct parameter screening on the K sets of historical bearing fault working condition parameters to obtain the K test working condition parameters.

3. The method for testing the fatigue life of a bearing according to claim 2, wherein, Based on the K first parameter screening centers, K first parameter screening neighborhoods, K second parameter screening centers, and K second parameter screening centers, conduct parameter screening on the K sets of historical bearing fault working condition parameters to obtain the K test working condition parameters, including: Judge whether the neighborhood density of the K first parameter screening neighborhoods is greater than or equal to the neighborhood density of the K second parameter screening neighborhoods. If so, use the K first parameter screening centers as K iterative parameter screening centers, and use the direction from the K iterative parameter screening centers to the K second parameter screening centers as K first iterative directions; Based on the K first iterative directions and K first parameter screening neighborhoods, update and iterate the K iterative parameter screening centers in the K sets of historical bearing fault working condition parameters until the preset iteration stop condition is met, and use the K historical bearing fault working condition parameters corresponding to the K iterative parameter screening centers obtained after the last update iteration as the K test working condition parameters.

4. The method for testing the fatigue life of a bearing according to claim 3, wherein Screen the neighborhood based on the K first iterative directions and K first parameters, and update and iterate the K iterative parameter screening centers in the K historical bearing fault condition parameter sets until a preset iteration stop condition is met. Take the K historical bearing fault condition parameters corresponding to the K iterative parameter screening centers obtained after the last update iteration as the K test condition parameters, including: Extract the historical bearing fault condition parameters closest to the K first iterative directions in the K first parameter screening neighborhoods respectively as the K third parameter screening centers; In the K historical bearing fault condition parameter sets, perform neighborhood matching on the K third parameter screening centers respectively according to a preset parameter screening bandwidth to obtain K third parameter screening neighborhoods; Judge whether the neighborhood density of the K third parameter screening neighborhoods is greater than or equal to the neighborhood density of the K iterative parameter screening neighborhoods corresponding to the K iterative parameter screening centers. If so, update the K third parameter screening centers to the K iterative parameter screening centers; Based on the K first iterative directions and the K third parameter screening neighborhoods, update and iterate the updated K iterative parameter screening centers in the K historical bearing fault condition parameter sets until a preset iteration stop condition is met. Take the K historical bearing fault condition parameters corresponding to the K iterative parameter screening centers obtained after the last update iteration as the K test condition parameters.

5. The method for testing the fatigue life of a bearing according to claim 4, characterized in that The preset iteration stop condition is that the number of update iterations is greater than or equal to the maximum number of update iterations and / or the difference in the neighborhood density between two iterative parameter screening neighborhoods obtained in two adjacent iterations is less than or equal to a preset neighborhood density difference.

6. The method for testing the fatigue life of a bearing according to claim 4, wherein, Judge whether the neighborhood density of the K first parameter screening neighborhoods is greater than or equal to the neighborhood density of the K second parameter screening neighborhoods, and further include: Count the total number of historical bearing fault condition parameters in the K first parameter screening neighborhoods respectively, and obtain the neighborhood density of the K first parameter screening neighborhoods by dividing the statistical result by twice the preset parameter screening bandwidth.

7. The method for testing the fatigue life of a bearing according to claim 1, wherein Perform K fatigue life tests on the target bearing based on the K test conditions and the K test condition parameters, and weight the K test condition fatigue life test results obtained to obtain the target fatigue test result, and further include: Monitor the stability of the test condition parameters for the K fatigue life tests to obtain K test condition parameter stability factors; Divide the K test condition parameter stability factors by the sum of the K test condition parameter stability factors respectively to obtain K weight values; Weight the K test condition fatigue life test results based on the K weight values to obtain the target fatigue test result.

8. For a bearing fatigue life test system, characterized in that, For implementing the steps of the method for bearing fatigue life test according to any one of claims 1 to 7, the system for bearing fatigue life test includes: A scenario acquisition module, which is used to acquire the application scenario characteristics of a target electric tricycle, perform test working conditions of a target bearing based on the application scenario characteristics, and obtain K test working conditions, where K is an integer greater than or equal to 1, and the target bearing is applied to the target electric tricycle; A fault record mining module, which is used to mine historical bearing fault records by combining the model of the target electric tricycle and K test working conditions, and obtain K historical bearing fault record sets; A fault retrieval module, which is used to retrieve the K historical bearing fault record sets with the working condition parameters as the index, and obtain K historical bearing fault working condition parameter sets; A parameter screening module, which is used to traverse the K historical bearing fault working condition parameter sets for parameter screening, and obtain K test working condition parameters; A fatigue life test module, which is used to perform K fatigue life tests on the target bearing based on the K test working conditions and the K test working condition parameters, and weight the obtained K test working condition fatigue life test results to obtain a target fatigue test result.

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