Method and apparatus for detecting fatigue life of rear axle bearing

Abstract

This invention discloses a method and equipment for fatigue life testing of rear axle bearings, relating to the field of fatigue life testing of components. The method includes: collecting the service time of the rear axle bearing; performing fatigue state testing on the rear axle bearing to generate fatigue state information; performing fatigue condition back-calculation based on the service time and fatigue state information, including separate back-calculation of stable conditions and combined back-calculation with transient conditions, generating condition back-calculation results; extracting the distribution characteristics of stable and transient conditions based on the condition back-calculation results, performing fatigue life analysis based on the condition distribution characteristics, and generating fatigue life testing results for the rear axle bearing. This invention solves the technical problem of inaccurate testing results in existing rear axle bearing fatigue life testing methods, achieving the technical effect of improving the accuracy of fatigue life testing.

Description

Technical Field

[0001] This application relates to the field of fatigue life testing of components, and in particular to methods and equipment for fatigue life testing of rear axle bearings. Background Technology

[0002] As a critical component of the vehicle's transmission system, the fatigue life of the rear axle bearing directly affects the vehicle's operational safety and reliability. Accurately detecting the fatigue life of rear axle bearings is crucial for ensuring normal vehicle operation, reducing maintenance costs, and preventing potential safety accidents. Currently, fatigue life testing primarily relies on two methods. First, theoretical model-based calculations predict life by inputting ideal load, speed, and other operating condition data, combined with material properties. Second, experimental testing methods involve accelerated fatigue experiments on bearing samples to simulate actual operating conditions and assess life. Theoretical model calculations suffer from poor prediction accuracy due to the complexity and variability of actual operating conditions and significant differences from ideal assumptions. While experimental testing is accurate, it is costly, time-consuming, and has limited sample representativeness, making it difficult to comprehensively reflect all actual operating conditions, especially complex coupled operating conditions.

[0003] Currently, the fatigue life testing of rear axle bearings suffers from inaccurate test results. Summary of the Invention

[0004] This application provides a method and equipment for fatigue life testing of rear axle bearings. It employs techniques such as collecting the service life of the rear axle bearing, simultaneously conducting fatigue condition testing to generate fatigue condition information, and firstly, inferring stable operating conditions based on the service life and fatigue condition information, then combining this with transient operating conditions for joint inference to obtain operating condition inference results. From these results, the distribution characteristics of stable and transient operating conditions are extracted, and fatigue life analysis is performed to generate fatigue life testing results for the rear axle bearings. These techniques solve the technical problem of inaccurate testing results in existing rear axle bearing fatigue life testing methods, thus improving the accuracy of fatigue life testing.

[0005] This application provides a method for fatigue life detection of rear axle bearings, comprising: collecting the service duration of the rear axle bearing; performing fatigue state detection on the rear axle bearing to generate fatigue state information; performing fatigue condition back-calculation based on the service duration and the fatigue state information, including separate back-calculation of stable conditions and joint back-calculation combining transient conditions, to generate condition back-calculation results; extracting the distribution characteristics of stable and transient conditions based on the condition back-calculation results, performing fatigue life analysis based on the condition distribution characteristics, and generating fatigue life detection results for the rear axle bearings.

[0006] In a possible implementation, fatigue condition detection is performed on the rear axle bearing to generate fatigue condition information, and the following processing is performed: The application equipment information of the rear axle bearing is collected, and factory batch test data is read; a test environment is built based on the factory test data, and multi-mode test conditions are configured; the rear axle bearing is tested in the test environment using the multi-mode test conditions, and a sensor network is connected to record multi-mode fatigue data, wherein the sensor network includes at least vibration sensors, temperature sensors, strain sensors, and oil sensors; the multi-mode fatigue data is compared with the factory test data to identify the current fatigue damage level of the rear axle bearing and generate the fatigue condition information.

[0007] In a possible implementation, the multi-mode fatigue data is compared with the factory test data to identify the current fatigue damage level of the rear axle bearing, generate the fatigue state information, and perform the following processing: compare the multi-mode fatigue data with the factory test data to determine the multi-mode data deviation; collect historical fatigue test samples, analyze the relationship between any attribute test deviation and the fatigue damage level, and construct a damage level analysis channel; analyze and fuse the multi-mode data deviation using the damage level analysis channel to generate the fatigue state information.

[0008] In a possible implementation, fatigue condition back-calculation is performed based on the service duration and fatigue state information, including separate back-calculation of stable conditions and joint back-calculation combining transient conditions, generating condition back-calculation results, and performing the following processing: Based on the application equipment information of the rear axle bearing, an integrated model is established by combining historical fatigue test samples to create a bearing twin application model; the road surface features of the actual service area of ​​the application equipment information are collected; the bearing twin application model is configured for environmental simulation using the road surface features, and then the service duration and fatigue state information are loaded to perform separate back-calculation of stable conditions, generating separate back-calculation defects; for the separate back-calculation defects, joint back-calculation of transient conditions is performed to generate the condition back-calculation results.

[0009] In a possible implementation, the following processing is performed: the stable operating condition is an operating condition in which the amplitude of the operating condition change meets a preset amplitude threshold and the rate of change of the operating condition meets a preset rate of change; the transient operating condition is an operating condition in which the amplitude of the operating condition change exceeds the preset amplitude threshold and the rate of change of the operating condition also exceeds the preset rate of change.

[0010] In a possible implementation, the bearing twin application model is configured for environmental simulation based on the road surface features. Then, the service duration and fatigue state information are loaded, and a separate back-calculation of stable operating conditions is performed to generate a separate back-calculation defect. The following processing is performed: the original fatigue state is read based on the factory test data; after the environmental simulation configuration is completed, the fatigue state information is used as the target, the original fatigue state is used as the starting point, and the combination of stable operating conditions and service duration is used as the loading action to perform fatigue operation simulation. The target stable simulation result is determined to be closest to the fatigue state information and does not exceed the fatigue state information; the stable operating condition distribution features in the target stable simulation result are extracted, and the deviation between the simulated fatigue state corresponding to the target stable simulation result and the fatigue state information is calculated to generate the separate back-calculation defect.

[0011] In a possible implementation, for the individual back-calculation defect, a joint back-calculation of transient operating conditions is performed to generate the operating condition back-calculation result, and the following processing is performed: the stable operating condition distribution characteristics are loaded into the bearing twin application model configured in the environmental simulation to establish a first-stage back-calculation model; based on the first-stage back-calculation model, with the individual back-calculation defect as the target and the transient operating condition as the loading action, fatigue operation simulation is performed to generate transient operating condition distribution characteristics that satisfy the individual back-calculation defect; the operating condition back-calculation result is established using the stable operating condition distribution characteristics and the transient operating condition distribution characteristics.

[0012] In a possible implementation, based on the first-stage back-reasoning model, with the individual back-reasoning defect as the target and the transient working condition as the loading action, fatigue operation simulation is performed, and the following processing is also performed: if the number of fatigue operation simulation iterations reaches the preset constraint and the simulation result does not meet the individual back-reasoning defect, the stable working condition distribution characteristics are adjusted to determine the updated back-reasoning defect, and the transient working condition is back-reasoned based on the updated back-reasoning defect.

[0013] In a possible implementation, the distribution characteristics of stable and transient operating conditions are extracted based on the back-calculation results of the operating conditions. Fatigue life analysis is performed based on the operating condition distribution characteristics to generate the fatigue life detection results of the rear axle bearing. The following processing is performed: using the fatigue state information and the distribution characteristics of stable and transient operating conditions as search elements, the remaining life information is traversed and matched in the fatigue life database; the fatigue life detection results are generated using the remaining life information.

[0014] This application also provides an electronic device, including: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement a method for detecting the fatigue life of a rear axle bearing.

[0015] The proposed method and equipment for fatigue life testing of rear axle bearings, as described in this application, first collects the service duration of the rear axle bearing, then performs fatigue state testing on the bearing to generate fatigue state information. Next, based on the service duration and fatigue state information, fatigue condition back-calculation is performed, including separate back-calculation of stable conditions and combined back-calculation with transient conditions, generating condition back-calculation results. Then, based on the condition back-calculation results, the distribution characteristics of stable and transient conditions are extracted, and fatigue life analysis is performed based on these characteristics to generate the fatigue life test results for the rear axle bearing. This achieves the technical effect of improving the accuracy of fatigue life testing. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the method according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a flowchart illustrating the fatigue life testing method for rear axle bearings provided in an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0019] Explanation of reference numerals in the attached drawings: Input device 201, processor 202, memory 203, output device 204. Detailed Implementation

[0020] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0023] This application provides a method for detecting the fatigue life of rear axle bearings, such as... Figure 1 As shown, the method includes:

[0024] Step S100: Collect the service life of the rear axle bearing.

[0025] Specifically, service life refers to the cumulative working time of a rear axle bearing from the time it is first put into use to the current inspection time, which can be measured in hours. It is a time parameter for measuring the degree of wear and fatigue damage experienced by the bearing. Over time, continuous operation under various conditions leads to the gradual accumulation of damage to the internal materials of the bearing.

[0026] One approach is to integrate a time-recording module into the vehicle's onboard computer system. This module is linked to the vehicle's ignition system, and it begins timing whenever the rear axle bearing starts working, continuously recording the bearing's operating time until the vehicle is turned off. Alternatively, a separate sensor unit with timing functionality can be installed near the rear axle bearing. This unit triggers timing by sensing signals such as the bearing's rotation, similarly recording the bearing's service life. This time data can be periodically transmitted via a wireless communication module (such as Bluetooth, Wi-Fi, or a mobile network communication module) to a backend monitoring system for storage and subsequent processing.

[0027] Step S200: Perform fatigue condition detection on the rear axle bearing and generate fatigue condition information.

[0028] Specifically, fatigue condition information refers to the collection of various characteristic information that reflects the degree of fatigue of a rear axle bearing, obtained through methods such as vibration detection, oil monitoring, and temperature detection. For example, characteristic frequency and amplitude changes in vibration signals, characteristic data of abrasive particles in the oil, and abnormal temperature changes, etc., are processed and analyzed and presented in different fatigue condition levels (such as normal, slight fatigue, moderate fatigue, and severe fatigue) to characterize the current fatigue condition of the bearing.

[0029] Specifically, for vibration detection methods, high-precision vibration sensors (such as piezoelectric accelerometers) need to be installed around the rear axle bearing (e.g., on the outer wall of the bearing housing). These sensors can detect vibration signals during bearing operation in real time, convert the vibration signals into electrical signals, and transmit them to the signal processing system via a data acquisition card. The signal processing system can use algorithms such as Fast Fourier Transform (FFT) to perform frequency domain analysis on the vibration signals, extracting parameters such as characteristic frequencies and amplitudes. Based on the changes in these parameters, the fatigue state of the bearing can be determined. For example, when a bearing experiences fatigue spalling or other faults, its vibration signal spectrum will show specific fault characteristic frequencies and their harmonic components, and the vibration amplitude will increase significantly.

[0030] For oil monitoring, an oil sampling device needs to be installed in the vehicle's lubrication system to periodically collect samples of abrasive particles from the rear axle bearing grease or lubricating oil (e.g., after a certain mileage or at fixed intervals). The oil samples are then analyzed using a spectrometer. By detecting the content of different metal elements (such as iron and copper) and the size and shape of the abrasive particles, the wear condition of the bearing can be assessed, and its fatigue state can be inferred. For example, if the iron content in the oil suddenly increases and a large number of larger abrasive particles are detected, it is very likely that the bearing has experienced severe fatigue wear.

[0031] For temperature detection methods, a temperature sensor (such as a resistance temperature detector or thermocouple sensor) needs to be installed near the rear axle bearing housing to monitor temperature changes during bearing operation in real time. After the temperature data is transmitted to the data processing system, the trend of temperature changes and the degree of deviation from the normal operating temperature are analyzed to determine whether the bearing has signs of fatigue such as abnormal heating. For example, when fatigue causes increased contact stress between the rolling elements and raceways, generating more frictional heat and causing the bearing temperature to rise continuously and exceed a set threshold, the bearing can be considered to be in a fatigue state.

[0032] In one possible implementation, fatigue condition detection is performed on the rear axle bearing to generate fatigue condition information. Step S200 further includes step S210, which involves collecting the application equipment information of the rear axle bearing and reading the factory batch's factory operating condition test data. Specifically, detailed application equipment information of the vehicle on which the rear axle bearing is installed is obtained through the vehicle's on-board diagnostic system (OBD) or the database interface provided by the manufacturer. This information includes vehicle type (e.g., tricycle, truck, bus, construction machinery vehicle, etc.), vehicle load range, power system type (fuel, electric, hybrid, etc.), and driving condition type (road transport, mining operations, urban public transport, etc.). Simultaneously, data collected during the factory operating condition test at the quality inspection stage for the batch to which the rear axle bearing belongs is read from the manufacturer's database. This data includes the bearing's vibration characteristic parameters, temperature change curves, strain distribution, and oil sample analysis results under standard test conditions (e.g., simulating different loads, speeds, road conditions, and other common conditions).

[0033] Step S220: Based on the factory condition test data, a test environment is built, and multi-mode test conditions are configured. Specifically, based on the load, speed, temperature, and other parameter ranges in the factory condition test data obtained in step S210, a test scenario similar to the factory test environment is built using a bearing test bench or vehicle simulation test system in the laboratory. By adjusting the loading device, speed control device, temperature control device, etc., of the test equipment, various test modes simulating actual working conditions are configured, i.e., multi-mode test conditions. For example, a stable working condition mode with constant load and constant speed can be configured to simulate the stress on the bearing when the vehicle is traveling at a fixed speed on a flat road; or a transient working condition mode with variable load and variable speed can be configured to simulate the working conditions of the vehicle under complex road conditions such as starting, acceleration, climbing, and braking.

[0034] Step S230: The rear axle bearing is tested in the test environment under the multi-mode test conditions. A sensor network is connected to record fatigue data under the multi-mode conditions. The sensor network includes at least vibration sensors, temperature sensors, strain sensors, and oil sensors. Specifically, the rear axle bearing is installed in the test environment set up in step S220 and connected to a comprehensive sensor network. This sensor network includes at least vibration sensors (for real-time monitoring of vibration acceleration, frequency, and other characteristics during bearing operation), temperature sensors (for measuring temperature changes in the bearing housing and surrounding environment), strain sensors (for detecting the strain of bearing components under load, reflecting their stress state), and oil sensors (for analyzing information such as the contamination level and abrasive content of lubricating grease or lubricating oil). The test equipment is started, and the rear axle bearing is tested sequentially according to the preset multi-mode test conditions. The test is run for a certain period under each condition, and the data recorded by these sensors is synchronously collected through a data acquisition system to obtain the fatigue data of the rear axle bearing under the multi-mode test conditions.

[0035] Step S240 involves comparing the multi-mode fatigue data with the factory test data to identify the current fatigue damage level of the rear axle bearing and generate fatigue state information. Specifically, the multi-mode fatigue data collected in step S230 is compared and analyzed with the factory test data obtained in step S210. This includes comparing changes in characteristic parameters such as the root mean square (RMS) value, peak factor, and kurtosis value of the vibration signal; the magnitude and trend of temperature increase; the magnitude and distribution of strain; and differences in indicators such as abrasive particle concentration and particle size distribution in the oil. This process identifies changes in the fatigue damage level of the rear axle bearing after actual use. Based on these characteristics, quantitative fatigue state information is generated, including the degree of wear (e.g., the proportion of wear to original size), the degree of deformation (e.g., raceway deformation, cage deformation), and the initiation and propagation of fatigue cracks.

[0036] In one possible implementation, the multi-mode fatigue data is compared with the factory test data to identify the current fatigue damage level of the rear axle bearing and generate fatigue state information. Step S240 further includes step S241, comparing the multi-mode fatigue data with the factory test data to determine the multi-mode data deviation. Specifically, the multi-mode fatigue data collected in step S230, including characteristic parameters such as vibration acceleration and frequency recorded by vibration sensors, temperature change data measured by temperature sensors, strain conditions detected by strain sensors, and oil sample information analyzed by oil sensors, are compared one by one with the factory test data obtained in step S210, such as the bearing vibration characteristic parameters, temperature change curves, strain distribution, and oil sample analysis results under standard test conditions. The multi-mode data deviation is determined by calculating the difference between the two at each corresponding parameter. For example, the differences in the root mean square (RMS) values ​​of vibration signals, the differences in temperature rise, the differences in strain magnitude, and the differences in abrasive particle concentration in the oil are calculated to determine the degree of difference in various performance indicators of the rear axle bearing under actual test conditions and factory test conditions.

[0037] Step S242 involves collecting historical fatigue test samples and analyzing the relationship between any attribute test deviation and the degree of fatigue damage to construct a damage degree analysis channel. Specifically, a large amount of sample data from previous fatigue tests on rear axle bearings is collected, including test results from different usage stages and operating conditions. For each sample, the test deviation of any attribute (such as the aforementioned deviation of the root mean square value of vibration signals, temperature rise deviation, etc.) is analyzed in relation to the corresponding degree of fatigue damage (such as wear, deformation, fatigue cracking, etc.). Through statistical analysis and mathematical modeling, the inherent relationship and patterns between the two are identified, thereby constructing a damage degree analysis channel capable of assessing the degree of fatigue damage based on attribute test deviations. This channel can be an algorithmic model or a set of evaluation standards based on empirical summaries.

[0038] Step S243 involves analyzing and fusing the multi-mode data deviations using the damage degree analysis channel to generate the fatigue state information. Specifically, the multi-mode data deviations determined in step S241 are input into the damage degree analysis channel constructed in step S242. The damage degree analysis channel analyzes each attribute test deviation according to pre-set analysis rules and models, evaluating the corresponding fatigue damage degree. Then, the fatigue damage degrees evaluated by each attribute test deviation are fused, and the influence weights of different attributes on the fatigue state of the rear axle bearing are combined. Through weighted averaging, fuzzy comprehensive evaluation, and other methods, a comprehensive and accurate fatigue state information is generated. This fatigue state information includes the degree of wear (e.g., the proportion of wear to the original size), the degree of deformation (e.g., raceway deformation, cage deformation, etc.), and the initiation and propagation of fatigue cracks, which can intuitively reflect the current fatigue damage status of the rear axle bearing.

[0039] Step S300: Based on the service duration and the fatigue state information, perform fatigue condition back-calculation, including separate back-calculation of stable conditions and joint back-calculation of transient conditions, and generate condition back-calculation results.

[0040] Specifically, fatigue condition inference refers to the process of inferring the characteristic parameters and distribution of stable and transient operating conditions experienced by a rear axle bearing during its service life, based on its service duration and fatigue state information, using techniques such as data fusion analysis, model calculation, or database matching. This is used to reconstruct the actual operating history of the bearing for more accurate fatigue life assessment, as different operating conditions have significantly different impacts on bearing fatigue life. Stable operating conditions refer to the working state of the rear axle bearing under relatively stable load and speed conditions. Under these conditions, the magnitude and direction of the load on the bearing remain essentially constant or exhibit relatively simple variation patterns, and the speed fluctuates minimally within a relatively fixed range. For example, when a vehicle travels at a relatively constant speed on a flat road, the operating condition of the rear axle bearing can be approximated as a stable condition. Under this condition, the accumulation of fatigue damage in the bearing is relatively regular, and life prediction can be made relatively accurately based on stable load and speed factors. Transient operating conditions, in contrast to stable operating conditions, refer to the working conditions of the rear axle bearing when parameters such as load and speed change rapidly or fluctuate irregularly. When a vehicle is starting, accelerating, braking, or driving over bumpy roads, the rear axle bearing is subjected to load impacts that change drastically in magnitude and direction, and its speed also fluctuates significantly. Transient conditions generate additional dynamic stress on the bearing, significantly impacting its fatigue life. Furthermore, the damage accumulation pattern is relatively complex, requiring comprehensive analysis in conjunction with stable conditions for a more complete assessment of bearing life. The fatigue condition back-calculation result is a data set of detailed characteristic parameters and distributions of the stable and transient conditions experienced by the rear axle bearing, obtained after the fatigue condition back-calculation step. This includes load levels, speed ranges, and durations under stable conditions, and load change rates, speed fluctuation amplitudes, and the frequency and duration of transient processes under transient conditions. These results provide fundamental data support for fatigue life analysis.

[0041] Specifically, a bearing fatigue damage model can be established. This model integrates the cumulative fatigue damage patterns of bearings under different operating conditions (stable and transient). Using service duration and fatigue state information as input parameters, mathematical modeling and numerical calculation methods are employed to deduce the characteristic parameters of the stable and transient operating conditions experienced by the bearing. These parameters include load magnitude, speed range, and duration under stable conditions, and load change rate, speed fluctuation amplitude, and duration of the transient process under transient conditions, thus obtaining the operating condition inference result. Alternatively, a database containing a large amount of known bearing fatigue state characteristic data under different combinations of operating conditions can be established. The currently collected service duration and fatigue state information are matched and compared with the data in the database. By finding similar data records and using interpolation algorithms, the distribution characteristics of the stable and transient operating conditions currently experienced by the bearing can be estimated, thereby obtaining the operating condition inference result. For example, if the database contains data records showing specific fatigue state characteristics after a certain period of time under a certain stable load and speed, when the currently detected fatigue state information is similar to it, the corresponding stable operating conditions and possible transient operating conditions can be deduced by interpolation and other methods, combined with the current service time.

[0042] In one possible implementation, fatigue condition back-calculation is performed based on the service duration and fatigue state information, including separate back-calculation of stable conditions and joint back-calculation combining transient conditions, generating condition back-calculation results. Step S300 further includes step S310, which involves integrated modeling based on the application equipment information of the rear axle bearing and historical fatigue test samples to establish a bearing twin application model. Specifically, application equipment information of the rear axle bearing is collected, including vehicle type, load, power system, and driving conditions; historical fatigue test sample data is also collected, including fatigue life and damage characteristics under different conditions. This data is cleaned and normalized to eliminate data noise and discrepancies. Digital twin technology is used, and parametric modeling software (such as CAD / CAE software) is used to construct the geometric model of the bearing. Combined with finite element analysis (FEA), the application equipment information and historical sample data are input into the simulation model. By adjusting model parameters (such as material properties, contact stress, load spectrum, etc.), the simulation results of the model are matched with the historical test data, thereby establishing a bearing twin application model. The model was validated using historical data to assess its accuracy. Error analysis methods (such as mean squared error and relative error) were employed to quantify the model's prediction error, and the model parameters were further optimized based on the validation results.

[0043] Step S320: Collect the concentrated road surface features of the actual service area of ​​the application equipment information. Specifically, high-precision road surface scanning sensors (such as LiDAR, 3D cameras, etc.) are installed on the vehicle. These sensors can collect three-dimensional morphological data of the road surface in real time, including the smoothness, undulation, and pothole extent of the road surface. The collected road surface data is preprocessed using signal processing algorithms (such as filtering algorithms to remove noise), and then concentrated road surface features are extracted using feature extraction algorithms (such as wavelet transform-based feature extraction algorithms), such as the root mean square undulation, maximum pothole depth, and road surface spectrum. The processed road surface feature data is stored in the vehicle's local storage device and transmitted in real time to the backend server via a wireless communication module (such as a 4G / 5G module).

[0044] Step S330: Configure the bearing twin application model for environmental simulation based on the road surface concentrated features, then load the service duration and fatigue state information, perform separate back-calculation of stable operating conditions, and generate separate back-calculation defects. The stable operating condition is defined as an operating condition where the amplitude of the operating condition change meets a preset amplitude threshold and the rate of change meets a preset rate of change. Specifically, in the bearing twin application model, the simulation environment settings are adjusted according to the collected road surface concentrated feature parameters. For example, the road surface roughness parameter in the model is set according to the root mean square undulation of the road surface, and the impact load parameter in the model is set according to the maximum pothole depth. The service duration and fatigue state information are loaded into the model as input conditions, and the operating state of the bearing under stable operating conditions is simulated by running simulation software. The stable operating condition is defined as an operating state where the amplitude and rate of change of the operating condition are within the preset threshold. During the simulation, the bearing response is monitored in real time using sensor data (such as virtual sensors) in the model. By analyzing the simulation results of the model, feature parameters related to the fatigue state are extracted. By comparing these parameters with the actual fatigue state information, the differences between the model prediction and the actual detection are identified, thereby determining the deficiencies in the independent back-inference process under stable conditions, generating independent back-inference defects, and using them to conduct a more comprehensive and accurate joint back-inference in conjunction with transient conditions.

[0045] Step S340: For the individual back-calculation defects, perform joint back-calculation of transient operating conditions to generate the operating condition back-calculation results. The transient operating conditions are operating conditions where the amplitude of the operating condition change exceeds a preset amplitude threshold, and the rate of change also exceeds a preset rate of change. Specifically, combining the characteristics of individual back-calculation defects under stable operating conditions and transient operating conditions, a multi-physics coupling analysis method (such as coupling mechanical load, thermal load, contact stress, and other multi-physics factors together) is adopted. The parameters of the transient operating conditions (such as load change amplitude, rate of change of velocity, etc.) are continuously adjusted through iterative algorithms (such as Newton's iteration method) to match the simulation results of the model with the actual fatigue state information. Clustering algorithms (such as K-means clustering) are used to classify the transient operating conditions, identifying different types of transient operating conditions (such as impact load, frequent start-stop, etc.). Based on the characteristics of each transient operating condition, the back-calculation results are further refined to obtain detailed transient operating condition characteristic parameters. The back-calculation results of stable and transient operating conditions are fused to obtain the final operating condition back-calculation results.

[0046] In one possible implementation, the bearing twin application model is configured for environmental simulation based on the road surface features, and then the service duration and fatigue state information are loaded to perform a separate back-calculation of stable operating conditions, generating a separate back-calculation defect. Step S330 further includes step S331, reading the original fatigue state based on the factory operating condition test data. Specifically, the factory operating condition test data of the rear axle bearing batch is retrieved from the manufacturer's database, which includes the original fatigue state information of the bearing at the time of manufacture. This information is obtained under standard test conditions through a series of detection methods (such as vibration detection, oil analysis, etc.), and can reflect the fatigue characteristics of the bearing in the initial state, including the original vibration characteristic parameters (such as vibration amplitude, frequency distribution, etc.), the background content of abrasive particles in the oil, etc., to provide a benchmark for fatigue state back-calculation. That is, the original fatigue state is a starting reference point, which allows the subsequent fatigue back-calculation process to have a clear starting point, making it easier to more accurately measure the degree of change of the bearing's fatigue state during actual service.

[0047] Step S332: After the environment simulation configuration is completed, using the fatigue state information as the target, the original fatigue state as the starting point, and the combination of the stable operating condition and the service duration as the loading action, fatigue operation simulation is performed to determine the target stable simulation result that does not exceed the fatigue state information and is closest to the fatigue state information. Specifically, in the built bearing twin application model environment, the actual detected fatigue state information is first set as the target to be achieved in the simulation. Then, starting from the original fatigue state, the stable operating condition (including parameters such as load and speed) and service duration are applied to the model as a comprehensive loading condition, and the fatigue simulation program is run. During the simulation process, the parameter details of the stable operating condition (such as load size, speed range, etc.) are continuously adjusted. Through a large number of iterative calculations and simulations, a simulation result after loading is found that neither exceeds the fatigue level represented by the actual detected fatigue state information, nor does it approach this actual state as closely as possible.

[0048] Step S333: Extract the stable operating condition distribution features from the target stability simulation results, and calculate the deviation between the simulated fatigue state corresponding to the target stability simulation results and the fatigue state information, generating the individual back-calculation defect. Specifically, from the target stability simulation results obtained in step S332, extract the stable operating condition distribution features, such as the distribution ratio of load in different ranges and the fluctuation frequency of rotational speed. Simultaneously, compare the simulated fatigue state corresponding to the simulation results (such as simulated vibration characteristics and oil wear particle content) with the actually detected fatigue state information, and calculate the degree of deviation between them (which can be quantified by indicators such as relative error and absolute error). These deviations and the extracted operating condition distribution features together constitute the individual back-calculation defect, used to describe the differences between the individual back-calculation process of stable operating conditions and the actual situation.

[0049] In one possible implementation, to address the individual back-calculation defects, a joint back-calculation of transient operating conditions is performed to generate the operating condition back-calculation result. Step S340 further includes step S341, loading the stable operating condition distribution characteristics into the bearing twin application model configured with environmental simulation, and establishing a first-stage back-calculation model. Specifically, in the bearing twin application model that has completed environmental simulation configuration (including information such as road surface characteristics in the actual service area), the stable operating condition distribution characteristics obtained from individual back-calculation of stable operating conditions are input. These distribution characteristics include load range and proportion, speed fluctuation frequency, etc., enabling the model to simulate the bearing's operation under stable operating conditions, thereby establishing a first-stage back-calculation model and laying the foundation for the joint back-calculation of transient operating conditions.

[0050] Step S342: Based on the first-stage back-reasoning model, targeting the individual back-reasoning defect, and using the transient operating conditions as the loading action, fatigue operation simulation is performed to generate transient operating condition distribution characteristics that satisfy the individual back-reasoning defect. Specifically, based on the first-stage back-reasoning model, the previously obtained individual back-reasoning defect is used as the simulation target. Transient operating conditions (such as rapid load changes, rapid speed fluctuations, etc.) are applied to the model as loading conditions, and the fatigue simulation program is run. By continuously adjusting the parameters of the transient operating conditions (such as the load change amplitude, speed change rate, etc.), transient operating condition distribution characteristics that can match the simulation results with the individual back-reasoning defect are found, such as the frequency of transient operating conditions and the duration of each transient process. By simulating different transient operating conditions, transient factors that can explain the individual back-reasoning defect are found. These transient operating condition distribution characteristics can reflect the complex and variable operating conditions experienced by the bearing in actual operation, thereby more completely restoring the source of bearing fatigue damage.

[0051] Step S343: Establish the back-calculation result of the operating conditions based on the stable operating condition distribution characteristics and the transient operating condition distribution characteristics. Specifically, the stable operating condition distribution characteristics and the transient operating condition distribution characteristics obtained in step S342 are integrated to form a complete back-calculation result of the operating conditions. This includes details of various operating conditions experienced by the bearing under stable operation and transient changes, such as the load and speed distribution under stable operating conditions, and the load change law and speed fluctuation characteristics under transient operating conditions, comprehensively describing the actual working environment and fatigue damage causes of the bearing.

[0052] In one possible implementation, fatigue operation simulation is performed based on the first-stage back-reasoning model, with the individual back-reasoning defect as the target and the transient operating condition as the loading action. Step S340 further includes step S344: if the fatigue operation simulation iteration number reaches the preset constraint and the simulation result does not meet the individual back-reasoning defect, the stable operating condition distribution characteristics are adjusted to determine the updated back-reasoning defect, and the transient operating condition is back-reasoned based on the updated back-reasoning defect.

[0053] Specifically, when performing fatigue simulation based on the first-stage back-reasoning model, if the simulation results still fail to meet the requirements of a single back-reasoning defect after a preset number of iterations (e.g., 100), meaning a transient operating condition distribution characteristic matching the single back-reasoning defect cannot be found, then the stable operating condition distribution characteristics need to be adjusted. Adjustments can include changing the load distribution ratio, adjusting the speed fluctuation range, etc. Based on the adjusted stable operating condition distribution characteristics, the updated back-reasoning defect is redefined, and then, using the updated back-reasoning defect as the new target, transient operating condition back-reasoning is performed again. In other words, when it is found that the current stable operating condition distribution characteristics cannot adequately explain a single back-reasoning defect, the back-reasoning defect is redefined by adjusting the stable operating condition distribution characteristics, allowing subsequent transient operating condition back-reasoning to be based on a more realistic starting point. This helps improve the adaptability and accuracy of the entire joint back-reasoning process, avoids falling into ineffective iterative loops, and ensures that the back-reasoning work can continue to advance and ultimately obtain reasonable results.

[0054] Step S400: Based on the back-calculation results of the operating conditions, extract the distribution characteristics of stable and transient operating conditions, perform fatigue life analysis according to the operating condition distribution characteristics, and generate the fatigue life test results of the rear axle bearing.

[0055] Specifically, statistical analysis is performed on the stable and transient operating condition data obtained from the back-calculation results. Statistical characteristics such as the frequency and cumulative duration of different load levels and speed ranges under stable operating conditions are calculated. For transient operating conditions, characteristic parameters such as the severity of load changes (e.g., the distribution of load change rate), the amplitude of speed fluctuations, and the frequency of transient processes are extracted. These statistical characteristics and characteristic parameters together constitute the operating condition distribution characteristics.

[0056] Fatigue life analysis can utilize fatigue life prediction models based on linear cumulative damage theory or Miner's rule, using extracted operating condition distribution characteristic parameters as input variables. Based on pre-determined bearing material SN curves (stress-life curves) and contact fatigue life calculation formulas (such as those specified in relevant ISO standards), the model calculates the remaining fatigue life of the bearing under the experienced operating conditions (i.e., determining how long the bearing can continue to operate normally under the current operating and fatigue state, providing a basis for developing maintenance, repair, and replacement plans) through mathematical operations and numerical simulations. The fatigue life test results are output in specific numerical form (such as remaining working hours, remaining mileage, etc.). Furthermore, probabilistic statistical methods can be combined to assess the reliability of the fatigue life prediction results, providing information such as the confidence interval of the life prediction, thereby improving the credibility and practicality of the test results.

[0057] In one possible implementation, the distribution features of stable and transient operating conditions are extracted based on the back-calculation results of the operating conditions. Fatigue life analysis is performed based on the operating condition distribution features to generate the fatigue life detection results of the rear axle bearing. Step S400 further includes step S410, using the fatigue state information and the distribution features of stable and transient operating conditions as search elements to traverse and match the remaining life information in the fatigue life database. Specifically, the acquired fatigue state information (such as wear degree, deformation degree, etc.) and the distribution features of stable and transient operating conditions (such as load distribution, speed fluctuation frequency, etc.) are used as key search elements. In the constructed fatigue life database, a traversal search is performed according to a certain matching algorithm (such as Euclidean distance, cosine similarity, etc.). The database stores a large number of mapping relationships between different operating condition combinations and corresponding remaining life information. By calculating the similarity between the search element and the records in the database, the remaining life information that best matches the current situation is found.

[0058] Step S420: Generate the fatigue life test result using the remaining life information. Specifically, based on the remaining life information found in step S410, and combined with necessary formatting and visualization processing (such as generating charts, text descriptions, etc.), a complete fatigue life test result is formed. The test result includes a numerical representation of the remaining life (such as remaining working hours, remaining mileage, etc.), an assessment of the severity of fatigue damage, and maintenance recommendations based on the current condition.

[0059] This application embodiment employs techniques such as collecting the service life of the rear axle bearing, simultaneously conducting fatigue condition detection on the rear axle bearing to generate fatigue condition information, and firstly, independently inferring stable operating conditions based on the service life and fatigue condition information, and then combining them with transient operating conditions for joint inference to obtain operating condition inference results. The distribution characteristics of stable and transient operating conditions are extracted from the operating condition inference results, and fatigue life analysis is performed accordingly to generate fatigue life detection results for the rear axle bearing. These techniques solve the technical problem of inaccurate detection results in existing rear axle bearing fatigue life detection methods, achieving the technical effect of improving the accuracy of fatigue life detection.

[0060] Based on the foregoing embodiments, this application also provides an electronic device. Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 2The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention. This electronic device is in the form of a general-purpose computing device, and its components may include, but are not limited to, an input device 201, a processor 202, a memory 203, and an output device 204. The processor 202 may be one or more; the memory 203 may include a computer-readable medium and at least one program product having a set (at least one) of program modules configured to perform the functions of the embodiments of this application.

[0061] The memory 203 shown in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable storage medium can be, but is not limited to, infrared, semiconductor systems, devices or components, or any combination thereof, used to store software programs, computer-executable programs and modules, such as the program instructions / modules corresponding to the fatigue life detection method for rear axle bearings in this embodiment of the invention. The processor 202 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 203, thereby realizing the above-mentioned fatigue life detection method for rear axle bearings.

[0062] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for testing the fatigue life of rear axle bearings, characterized in that, include: Collect the service life of the rear axle bearings; The rear axle bearing is subjected to fatigue condition detection to generate fatigue condition information; Based on the service duration and fatigue state information, fatigue condition back-calculation is performed, including separate back-calculation of stable conditions and joint back-calculation combining transient conditions, to generate condition back-calculation results. Based on the back-calculation results of the operating conditions, the distribution characteristics of stable and transient operating conditions are extracted. Fatigue life analysis is performed according to the operating condition distribution characteristics to generate the fatigue life test results of the rear axle bearing. Based on the service duration and fatigue state information, fatigue condition inversion is performed, including separate inversion of stable conditions and joint inversion combining transient conditions, generating condition inversion results, including: Based on the application equipment information of the rear axle bearing, and combined with historical fatigue test samples, an integrated model is created to establish a bearing twin application model. The road surface concentration characteristics of the actual service area where the application equipment information is collected; The bearing twin application model is configured for environmental simulation based on the road surface features, and then the service duration and fatigue state information are loaded to perform a separate back-calculation of stable working conditions, generating a separate back-calculation defect. To address the individual back-calculation defects, a joint back-calculation of transient operating conditions is performed to generate the operating condition back-calculation results; The stable operating condition is an operating condition in which the amplitude of the operating condition change meets a preset amplitude threshold and the rate of change meets a preset rate of change; the transient operating condition is an operating condition in which the amplitude of the operating condition change exceeds the preset amplitude threshold and the rate of change also exceeds the preset rate of change. The bearing twin application model is configured for environmental simulation based on the road surface features, and then the service duration and fatigue state information are loaded to perform individual back-calculation under stable operating conditions, generating individual back-calculation defects, including: Read the original fatigue state based on the factory condition test data; After the environmental simulation configuration is completed, fatigue operation simulation is performed with the fatigue state information as the target, the original fatigue state as the starting point, and the combination of the stable working condition and the service duration as the loading action. The loading result is determined to be the target stable simulation result that does not exceed the fatigue state information and is closest to the fatigue state information. Extract the stable operating condition distribution characteristics from the target stability simulation results, and calculate the deviation between the simulated fatigue state corresponding to the target stability simulation results and the fatigue state information to generate the individual back-inference defect.

2. The fatigue life testing method for rear axle bearings as described in claim 1, characterized in that, The rear axle bearing is subjected to fatigue condition detection to generate fatigue condition information, including: Collect the application equipment information of the rear axle bearing and read the factory batch-collected factory operating condition test data; A test environment was built based on the factory test data, and multi-mode test conditions were configured. The rear axle bearing is tested in the test environment under the multi-mode test conditions, and fatigue data under the multi-mode conditions is recorded by connecting a sensor network. The sensor network includes at least a vibration sensor, a temperature sensor, a strain sensor, and an oil sensor. The fatigue data under the multi-mode operating conditions is compared with the factory operating condition test data to identify the current fatigue damage level of the rear axle bearing and generate the fatigue state information.

3. The fatigue life testing method for rear axle bearings as described in claim 2, characterized in that, The fatigue data under multi-mode operating conditions is compared with the factory operating condition test data to identify the current fatigue damage level of the rear axle bearing and generate the fatigue state information, including: Compare the multi-mode fatigue data with the factory test data to determine the multi-mode data deviation; Collect historical fatigue test samples, analyze the relationship between test deviation of any attribute and fatigue damage degree, and construct a damage degree analysis channel; The fatigue state information is generated by analyzing and fusing the multi-mode data deviation using the damage degree analysis channel.

4. The fatigue life testing method for rear axle bearings as described in claim 1, characterized in that, To address the individual back-calculation defects, a joint back-calculation of transient operating conditions is performed to generate the operating condition back-calculation results, including: The stable operating condition distribution characteristics are loaded into the bearing twin application model configured for environmental simulation to establish the first-stage back-inference model; Based on the first-stage back-reasoning model, with the individual back-reasoning defect as the target and the transient working condition as the loading action, fatigue operation simulation is performed to generate transient working condition distribution characteristics that satisfy the individual back-reasoning defect. The working condition back-inference result is established based on the stable working condition distribution characteristics and the transient working condition distribution characteristics.

5. The fatigue life testing method for rear axle bearings as described in claim 4, characterized in that, Based on the first-stage back-reasoning model, targeting the individual back-reasoning defect, and using the transient operating condition as the loading action, fatigue operation simulation is performed, which also includes: If the number of fatigue simulation iterations reaches the preset constraint and the simulation result does not meet the individual back-inference defect, the stable operating condition distribution characteristics are adjusted to determine the updated back-inference defect, and the transient operating condition is back-inferred based on the updated back-inference defect.

6. The fatigue life testing method for rear axle bearings as described in claim 1, characterized in that, Based on the back-calculation results of the aforementioned operating conditions, the distribution characteristics of steady-state and transient operating conditions are extracted. Fatigue life analysis is then performed based on these distribution characteristics to generate the fatigue life test results for the rear axle bearing, including: Using the fatigue state information and the distribution characteristics of stable and transient conditions as search elements, the remaining life information is matched by traversing the fatigue life database. The fatigue life test result is generated using the remaining life information.

7. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the fatigue life detection method for rear axle bearings as described in any one of claims 1 to 6.

Images