Driving shaft reliability test method and system

Abstract

The invention relates to a driving shaft reliability test method and system, and aims to solve the problems of reduction degree and accuracy of a driving shaft test result. The method comprises the steps of collecting operation big data of a user vehicle, calculating driving shaft damage, and selecting a typical working condition route based on a damage analysis result; on the selected typical working condition route, acquiring original road spectrum data by using the test vehicle; dividing original road spectrum data, and extracting a representative data weight fragment with the maximum damage in each speed section; combining the representative data fragments of each speed section according to the corresponding cycle index, and constructing a synthetic road spectrum representing the total mileage of the target; performing damage equivalent concentration treatment on the synthetic road spectrum, so that the fitting degree between the damage amount of the concentrated data and the damage amount of the synthetic road spectrum before concentration reaches a preset threshold value, and obtaining a driving shaft bench test specification; the specifications are imported into a test bed control system; carrying out a loading test on the driving shaft; and evaluating the reliability of the driving shaft according to a test result.

Description

TECHNICAL FIELD

[0001] The application relates to the field of drive shaft testing, in particular to a drive shaft reliability testing method and system. BACKGROUND

[0002] The drive shaft is an important transmission component of an automobile, and the reliability thereof directly affects the safety of the automobile. The use reliability requirement of the drive shaft is higher for an electric automobile (heavy load, fast torque response, energy recovery, etc.). The current bench testing cannot reflect the real use scene, and it is urgently needed to start from the real use condition, comb a new reliability bench specification, and improve the quality and efficiency. The general deficiencies in the entire industry are as follows:

[0003] 1. The drive shaft testing specification is basically based on the experience of a fuel automobile, and there is no drive shaft testing specification specially for an electric automobile.

[0004] 2. The test specification of a transmission fuel automobile is derived from experience values, and the reduction degree of the test result is low. SUMMARY

[0005] The application provides a drive shaft reliability testing method and system to solve the problem of the reduction degree and accuracy of the drive shaft test result, shorten the development cycle, and reduce the development cost.

[0006] The technical scheme of the application is as follows:

[0007] In a first aspect, the application provides a drive shaft reliability testing method, including the following steps:

[0008] Collecting operation big data of a user vehicle and calculating drive shaft damage, and selecting a typical working condition route based on the damage analysis result;

[0009] Collecting original road spectrum data containing drive shaft torque, speed and rotation angle on the selected typical working condition route by using a test vehicle;

[0010] Dividing the original road spectrum data according to a plurality of preset speed segments, and extracting representative data pieces with the maximum damage in each speed segment;

[0011] Combining the representative data pieces of each speed segment according to the corresponding cycle number to construct a synthetic road spectrum representing a target total mileage;

[0012] Performing damage equivalent condensation processing on the synthetic road spectrum, so that the fitting degree between the damage amount of the condensed data and the damage amount of the synthetic road spectrum before condensation reaches a preset threshold, thereby obtaining a drive shaft bench testing specification;

[0013] Importing the drive shaft bench testing specification into a test bench control system;

[0014] According to the driving shaft bench test, the driving shaft is loaded for testing;

[0015] According to the test results, the reliability of the driving shaft is evaluated.

[0016] By mining the typical working conditions with the largest damage from real user vehicle operation big data, and collecting high-fidelity road spectrum data based on this, the accuracy and representativeness of the test input are ensured. By constructing a synthetic road spectrum representing the target total mileage, the dispersed real user behavior is integrated into a complete and high-damage load spectrum, directly addressing the problem of insufficient restoration of traditional experience specifications. Further damage equivalent condensation processing is adopted, which greatly compresses the test time while keeping the total damage consistent, achieving a leap in test efficiency. Ultimately, this method reproduces real user scenarios with high precision and high efficiency on the bench, making the test results truly reflect the reliability performance of the driving shaft in the hands of actual users, essentially improving test accuracy and shortening the verification period, providing a reliable basis for driving shaft design and quality verification.

[0017] In some possible embodiments, the step of collecting operation big data of user vehicles and calculating driving shaft damage, and selecting typical working condition routes based on damage analysis results includes:

[0018] Collecting operation big data generated by a preset number of user vehicles within a continuous preset time period;

[0019] For the operation big data of each vehicle, based on the vehicle power state and the position stay time, the continuous operation big data is segmented into multiple independent driving trip data;

[0020] The segmented independent driving trip data is cleaned to remove invalid data segments caused by signal abnormalities, obtaining valid independent driving trip data;

[0021] Based on the damage calculation model, the driving shaft damage of all valid independent driving trip data is calculated and equivalent extrapolated to the target total mileage, obtaining the simulated damage value corresponding to each valid independent driving trip data;

[0022] According to the order of the simulated damage value, the actual vehicle operation routes corresponding to the top N independent driving trip data with the highest simulated damage value are selected to form the typical working condition routes.

[0023] By collecting the running data of the vehicles of the preset scale users in the continuous period, a statistically significant data basis is established. A trip segmentation method based on the vehicle power state and the position stay duration is used to identify independent trip units with complete driving characteristics. Through cleaning and processing of abnormal data, the consistency of data quality is ensured. A damage calculation model is used to quantify the damage of each independent trip, and through equivalent extrapolation to the target total mileage, the trip data of different mileages are made comparable. Based on the ranking results of the simulated damage values, the top N actual operating routes with the highest damage degree are selected as the typical working condition routes. This selection method ensures that the selected routes represent the real user usage scenarios and cover the working condition conditions challenging to the drive shaft, providing data support for the development of subsequent test specifications.

[0024] In some possible embodiments, based on the damage calculation model, the step of calculating the drive shaft damage of all valid independent driving trip data and equivalently extrapolating to the target total mileage to obtain the simulated damage value corresponding to each valid independent driving trip data includes:

[0025] Extracting the torque-speed load spectrum in each valid independent driving trip data based on the rainflow counting method;

[0026] According to the torque-speed load spectrum, combining the S-N fatigue curve of the drive shaft material, the first damage value of each valid independent driving trip data under the corresponding real mileage is calculated by the Miner linear cumulative damage theory;

[0027] Each of the first damage values is equivalently extrapolated to the target total mileage to obtain the simulated damage value corresponding to each of the valid independent driving trip data;

[0028] The target total mileage is a pre-set total mileage reference value for representing the whole life cycle of the vehicle and performing damage equivalent extrapolation calculation.

[0029] By processing each valid independent driving trip data by the rainflow counting method, the torque-speed load spectrum reflecting the actual load change is extracted. Based on the load spectrum, combining the S-N fatigue characteristics of the drive shaft material, the Miner linear cumulative damage theory is used for calculation to obtain the first damage value corresponding to each trip in its real mileage. By equivalently extrapolating the first damage value to the pre-set target total mileage, a unified evaluation reference is established, so that driving trips of different lengths and characteristics can be compared in terms of damage degree under the same standard. This calculation process provides quantitative data basis for subsequent selection of typical working condition routes, supporting the development of test specifications.

[0030] In some possible embodiments, on the selected typical working condition route, the step of collecting the original road spectrum data including the drive shaft torque, speed and angle by the test vehicle includes:

[0031] A data acquisition system is arranged on at least one test vehicle, which comprises: torque sensors mounted on left and right drive shafts for acquiring drive shaft torque signals; pull wire sensors for acquiring drive shaft displacement and converting into drive shaft angle signals; CANFD acquisition devices for acquiring motor speed, motor torque and vehicle speed signals from the bus of the test vehicle;

[0032] Under the condition that the test vehicle is fully loaded with weights, the drive system is in the maximum energy recovery mode and the motion mode is set, real vehicle road spectrum acquisition is performed along each of the typical working condition routes to obtain original road spectrum data containing drive shaft torque, speed and angle.

[0033] In some possible embodiments, the step of dividing the original road spectrum data into a plurality of preset speed segments and extracting a representative data piece with the maximum damage in each speed segment comprises:

[0034] The original road spectrum data is divided into a plurality of continuous speed segments in the range of 0-120 km / h;

[0035] Based on a damage calculation model, the drive shaft damage value of all data pieces in each speed segment per unit mileage is calculated;

[0036] From each speed segment, the data piece with the highest unit mileage damage value is selected as the representative data piece of the speed segment.

[0037] In the data acquisition link, through the arrangement of the data acquisition system containing a plurality of sensors, the drive shaft torque, angle and vehicle operation related signals can be synchronously acquired. Under the condition that the test vehicle is fully loaded with weights and the drive system is in a specific working mode, real vehicle road spectrum acquisition is performed to ensure that the obtained original road spectrum data can reflect the true load characteristics of the vehicle under typical working conditions.

[0038] In the data processing link, the original road spectrum data is divided into a plurality of preset speed segments, the unit mileage damage value of the data pieces in each speed segment is calculated based on a damage calculation model, and the data piece with the highest damage value in each segment is selected as the representative data. This method can extract the feature working condition with the most significant impact on the reliability of the drive shaft from the massive road spectrum data, and provide a data basis for the construction of the subsequent synthesized road spectrum.

[0039] In some possible embodiments, the step of combining the representative data pieces of each of the speed segments according to the corresponding cycle number to construct a synthesized road spectrum representing the target total mileage comprises:

[0040] Based on the vehicle operation big data analysis, the cumulative proportion of each speed segment in the target total mileage is obtained;

[0041] Calculate the assessment mileage corresponding to each speed segment in the total target driving mileage based on the cumulative ratio;

[0042] Divide the test mileage of each speed segment by the single mileage of its representative data segment to obtain the number of cycles corresponding to the representative data segment.

[0043] By combining all representative data segments according to the stated number of iterations, a synthetic road spectrum representing the total mileage of the target is constructed.

[0044] By analyzing big data on vehicle operation, the cumulative proportional distribution of each speed segment within the target total mileage is obtained. Based on this proportional distribution, the assessment mileage corresponding to each speed segment is calculated, establishing a mapping relationship from statistical characteristics to specific mileage. By dividing the assessment mileage of each speed segment by the single mileage of its representative data segment, the number of cycles required for each representative data segment in the synthetic road spectrum is determined. Finally, according to the calculated number of cycles, all representative data segments are combined to form a synthetic road spectrum that represents the load characteristics of the target total mileage. This method ensures that the constructed synthetic road spectrum not only includes the typical damage characteristics of each speed segment but also conforms to the speed distribution patterns in actual use.

[0045] In some possible embodiments, the steps of performing damage equivalent condensation on the synthesized road spectrum, such that the degree of fit between the damage amount of the condensed data and the damage amount of the synthesized road spectrum before condensation reaches a preset threshold, thereby obtaining the drive shaft bench test specification, include:

[0046] The time axis is condensed by increasing the torque and speed loads in the synthesized road spectrum;

[0047] Based on the damage calculation model, the condensation parameters are iteratively adjusted so that the fitting degree between the damage amount of the condensed data and the damage amount of the synthetic road spectrum before condensation reaches a preset threshold.

[0048] The condensed data that meets the fitting requirements is determined as the test specification for the drive shaft bench.

[0049] By increasing the torque and speed loads in the synthetic road spectrum, the test time is shortened while maintaining the total damage amount. The condensation parameters are iteratively adjusted based on the damage calculation model to ensure that the damage amount in the condensed data meets the preset goodness of fit requirements with the damage amount in the original synthetic road spectrum. This method, through parameter optimization, establishes a balance between test acceleration and damage fidelity, ultimately forming a drive shaft bench test specification that meets both test efficiency requirements and maintains sufficient accuracy.

[0050] Secondly, this application also provides a drive shaft reliability testing system, comprising:

[0051] The data acquisition subsystem is used to collect big data on user vehicle operation and real-vehicle road spectrum data of test vehicle. It includes: torque sensors installed on the left and right drive shafts of the test vehicle to collect drive shaft torque; cable sensors arranged on the test vehicle to collect drive shaft displacement and convert it into drive shaft rotation angle; and CANFD acquisition equipment installed on the test vehicle to collect motor speed, motor torque and vehicle speed signals from the vehicle bus.

[0052] The data processing and specification generation subsystem is configured to: calculate drive axle damage based on the large operational data of user vehicles collected by the data acquisition subsystem; select a typical operating route based on the damage analysis results; acquire the original road spectrum data of the test vehicle running on the selected typical operating route collected by the data acquisition subsystem; divide the original road spectrum data into multiple preset speed segments and extract the representative data weight segment with the largest damage in each speed segment; combine the representative data segments of each speed segment according to their corresponding number of cycles to construct a synthetic road spectrum representing the target total mileage; perform damage equivalence condensation processing on the synthetic road spectrum so that the degree of fit between the damage amount of the condensed data and the damage amount of the synthetic road spectrum before condensation reaches a preset threshold, thereby obtaining the drive axle bench test specification;

[0053] Test bench system for mounting drive shafts and performing load tests;

[0054] The control and evaluation subsystem is used to import the test specifications of the drive shaft bench, control the operation of the test bench subsystem, and record and evaluate the test results.

[0055] In some specific embodiments, the test bench system includes a drive unit, a fixed support, and a sensing unit.

[0056] The drive unit is connected to the moving joint of the drive shaft via a spline;

[0057] The fixed section of the drive shaft is connected to the hub bearing via a spline;

[0058] The wheel hub bearing is connected to the fixed support via a connecting fixture, wherein the connecting fixture is configured to simulate the vehicle suspension geometry and fix the flange mounting surface of the wheel hub bearing to the fixed support;

[0059] The sensing device is mounted on the fixed support and is used to monitor the torque, speed and angle of the drive shaft in real time during the test.

[0060] In some specific embodiments, the sensing device includes at least one of a torque sensor, an angle sensor, and a speed sensor. Attached Figure Description

[0061] Figure 1 This is a flowchart of the drive shaft reliability test method in the embodiments of this application;

[0062] Figure 2 This is a flowchart of the drive shaft reliability test method in the embodiments of this application;

[0063] Figure 3 This is a schematic diagram illustrating the big data collection process in an embodiment of this application.

[0064] Figure 4 This is a schematic diagram showing the corresponding proportions of speed segments and mileage in the embodiments of this application;

[0065] Figure 5 This is a schematic diagram of the road spectrum acquisition signal device in the embodiments of this application;

[0066] Figure 6 This is a road spectrum data map in the embodiments of this application;

[0067] Figure 7 This is a schematic diagram of the test bench in the embodiments of this application. Detailed Implementation

[0068] Reference Figure 1 This application provides a drive shaft reliability test method, including the following steps:

[0069] S101: Collect big data on the operation of user vehicles and calculate drive shaft damage; select typical working condition routes based on damage analysis results.

[0070] S102, On the selected typical working condition route, raw road spectrum data including drive shaft torque, speed and steering angle are collected using test vehicles;

[0071] S103, the original road spectrum data is divided into multiple preset speed segments, and the representative data weight segment with the greatest damage in each speed segment is extracted.

[0072] S104, Combine the representative data segments of each speed segment according to their corresponding number of cycles to construct a synthetic road spectrum representing the total target driving distance;

[0073] S105, the synthetic road spectrum is subjected to damage equivalent condensation processing so that the fitting degree between the damage amount of the condensed data and the damage amount of the synthetic road spectrum before condensation reaches a preset threshold, thereby obtaining the drive shaft bench test specification.

[0074] S106, Import the drive shaft bench test specification into the test bench control system;

[0075] S107, Perform a loading test on the drive shaft according to the aforementioned drive shaft bench test;

[0076] S108, Evaluate the reliability of the drive shaft based on the test results.

[0077] By leveraging big data analysis to match user driving habits, common road conditions, and corresponding vehicle speeds with user operating conditions, road spectra were collected in real-world scenarios. Speed ​​segments were divided using big data to obtain the proportion (af) of each speed range from 0-120km / h. The segment with the greatest damage in each speed segment was extracted from the road spectra, and its mileage was extracted as the single-cycle mileage (p) for this speed segment and scaled up to 100km. The cycle mileage for each speed segment was calculated as 30*af, and 30*af / 100 yielded the number of cycles for each speed segment, from which the total damage was calculated. Accelerated data condensation tests were conducted by increasing torque and speed to obtain bench specifications. When extended to other drive shaft segment types, a control group was used to correct distortion until the damage fit reached over 95%.

[0078] This method combines big data and real vehicle road profiles, taking into account both the breadth of statistics and the accuracy of individual cases: the obtained bench specifications are close to the actual user working conditions, the final bench test specifications have high credibility, the test results have strong reproducibility, and can save a lot of drive axle road test certification cycle, saving at least one round of road test time.

[0079] This method combines big data analysis and road spectrum collection analysis. The big data analyzed includes motor torque, speed, throttle opening, braking frequency, and latitude and longitude information. The obtained data is equivalently condensed, and a control group is set up to correct the test bench specifications of the promotion section type.

[0080] Reference Figure 2 The method specifically includes:

[0081] S1 data acquisition and analysis yielded the route for obtaining the road spectrum.

[0082] Combination Figure 3 Collect big data on the operation of a preset number (e.g., 500 vehicles) of user vehicles for a preset duration (e.g., 6 months).

[0083] Based on the vehicle's power status and location dwell time, the continuous operation big data of each vehicle is divided into multiple independent driving trip data.

[0084] The segmented independent driving trip data is cleaned to remove invalid data segments caused by signal abnormalities, thus obtaining the number of valid independent driving trips.

[0085] By analyzing data such as throttle opening, braking frequency, motor torque, speed, vehicle speed, and common road conditions from the big data analysis, we can obtain driving habits based on the collected road spectrum and the corresponding speed for the route.

[0086] Reference Figure 4Based on the damage calculation model, drive shaft damage is calculated separately for each effective independent driving trip, and the total damage equivalent to the target total driving mileage is calculated, thus obtaining the simulated damage value corresponding to each effective independent driving trip. The target total driving mileage is a pre-set benchmark value, such as 300,000 kilometers, used to represent the entire life cycle of the vehicle and for damage equivalent extrapolation calculations.

[0087] In this embodiment of the application, the torque-speed load spectrum is extracted from each valid independent driving trip data based on the rainflow counting method;

[0088] Based on the torque-speed load spectrum and combined with the SN fatigue curve of the drive shaft material, the first damage value of each effective independent driving stroke data under the corresponding real driving mileage is calculated by Miner linear cumulative damage theory.

[0089] Each of the first damage values ​​is extrapolated to the target total driving mileage to obtain the simulated damage value corresponding to each of the effective independent driving trip data;

[0090] The target total mileage is a pre-set benchmark value that represents the entire life cycle of the vehicle and is used for damage equivalent extrapolation calculations.

[0091] Based on the damage ranking of all simulated damage values, select the actual vehicle operation routes corresponding to the top N (e.g., 3) independent driving trip data with the highest simulated damage values ​​to form typical working condition routes.

[0092] The selected typical operating condition routes were used as the road spectrum collection routes, covering frequently traveled routes within the data while also covering as many road conditions as possible (such as mountain roads, congested urban roads, highways, and rough roads). Each typical operating condition route was collected twice, with a single route mileage of no less than 40km, and data was collected during the morning and evening rush hours. The collection routes of the two test vehicles were kept consistent.

[0093] S2 collects road spectrum.

[0094] Reference Figure 5 Before data collection, a torque sensor 203 is installed on each of the left drive shaft 201 and right drive shaft 202 of the test vehicle. A cable sensor 204 is installed to collect displacement and convert it into drive shaft rotation angle. A CANFD acquisition device 205 is connected to the vehicle diagnostic port to collect motor voltage, motor current, motor speed, motor torque, accelerator pedal opening, two rear wheel speeds, vehicle speed, and steering wheel angle signals. A GPS acquisition device 206 is connected to the vehicle body to collect position signals. The road spectrum data is transmitted to the data acquisition instrument 207 through the signal line, and the collected signals are viewed through the terminal device 208, and the sampling frequency is set to 1024Hz.

[0095] Two electric vehicles were prepared as test vehicles. During the data collection process, the battery level of the test vehicles was kept above 20%. The test vehicles were set to: maximum energy recovery, sport mode, and reduced other power consumption during the data collection process; the vehicle counterweight was fully loaded.

[0096] Begin debugging by connecting power supply 209, turning on terminal device 208 and data acquisition instrument 207, and debugging the entire acquisition equipment to confirm that the signal input and storage functions are normal. Then begin data acquisition. Acquire data according to pre-selected typical operating route until all typical operating route acquisitions are completed, outputting raw road spectrum data including drive shaft torque, speed, and angle.

[0097] S3 road spectrum data processing.

[0098] The NCode software was used to process the collected raw road spectrum data and remove outliers.

[0099] By obtaining the proportion of different speed segments through big data, the original road spectrum data is segmented according to these speed segments. The damage of all the same speed segments at 100km is calculated, and the data segment with the highest damage in each speed segment is obtained. Similarly, the speed segment proportion 'af' is used as the proportion of each speed segment in 300,000km, and the mileage of each speed segment is obtained as 30*af. Then, based on the ratio of the mileage of each speed segment in 300,000km (30*af) to the mileage of the speed segment with the highest damage in the road spectrum, the number of cycles for that speed segment is obtained as 30*af / 100.

[0100] In other words, the original road spectrum data is divided into multiple continuous speed segments ranging from 0 to 120 km / h; based on the damage calculation model, the drive shaft damage value per unit mileage for all data segments within each speed segment is calculated; the data segment with the highest damage value per unit mileage is selected from each speed segment as the representative data segment for that speed segment. The cumulative proportion of each speed segment in the target total mileage is obtained based on the vehicle operation big data analysis; the assessment mileage corresponding to each speed segment in the target total mileage is calculated based on the cumulative proportion; the assessment mileage of each speed segment is divided by the single mileage of its representative data segment to obtain the number of cycles corresponding to the representative data segment; all representative data segments are combined according to the number of cycles to construct a synthetic road spectrum representing the target total mileage.

[0101] Finally, the time axis is condensed by increasing the torque and speed loads in the synthesized road spectrum; based on the damage calculation model, the condensation parameters are iteratively adjusted so that the degree of fit between the damage amount of the condensed data and the damage amount of the synthesized road spectrum before condensation reaches a preset threshold; the condensed data that meets the degree of fit requirement is determined as the test specification for the drive shaft bench.

[0102] Reference Figure 6By using NCode software to perform equivalent condensation based on the total damage of 300,000 kilometers of original road spectrum data, the damage values ​​of the condensed data are compared with the damage values ​​of the original data until the damage fitting degree reaches more than 95%, and the test specifications for this type of section are obtained.

[0103] To promote the use of segment types, a certain proportion of test bench specifications for different segment types were selected. The test bench specifications were then corrected using a control group until the damage fit reached more than 95%, thus obtaining the test bench specifications for the promoted segment types.

[0104] The time axis is condensed by increasing the torque and speed loads in the synthesized road spectrum; based on the damage calculation model, the condensation parameters are iteratively adjusted so that the degree of fit between the damage amount of the condensed data and the damage amount of the synthesized road spectrum before condensation reaches a preset threshold; the condensed data that meets the degree of fit requirement is determined as the test specification for the drive shaft bench.

[0105] S4 test bench construction.

[0106] Reference Figure 7 The moving section 401 of the drive shaft (composed of an outer fixed section, a shaft, and an inner moving section) is connected to the drive device 402 via a spline. The fixed section 403 is connected to the hub bearing 404 via a spline. The hub bearing 404 is connected to the fixed support 405 via a tooling. The position of the drive shaft device 402 is adjusted according to the length of the shaft 406. The fixed support 405 is equipped with a torque sensor 407, an angle sensor 408, and a speed sensor 409, which are connected to the control terminal and the display device 410 via signal lines.

[0107] S5 device loading.

[0108] The drive unit 2 transmits power to the moving section 1, which then passes through the drive shaft 6 and the fixed section 3 to the fixed support 5. The torque sensor 7, angle sensor 8, and speed sensor 9 on the braking device transmit signals to the terminal and display device 10.

[0109] The drive device 2 is set to load a sinusoidal load of 5% of the static torque on the control terminal and display device 10. The speed is set to 900 rpm, the positive torque is 10 min, and the negative torque is 20 s. After running continuously for 10 h, the aforementioned drive shaft bench specification is used as the input source (see Table (1) below) and imported into the control terminal and display device 10. After running for a certain number of cycles or when the parts are damaged, the torque, speed and angle of the test process are recorded. Finally, the test sample is analyzed to see the internal wear condition. The design is confirmed to meet the requirements according to the wear level. The conditions that the test analysis results should meet are shown in Table 2.

[0110]

[0111] Table 1

[0112]

[0113] Table 2

[0114] S6 data reading and recording.

[0115] Torque, speed, and angle are read on the control terminal and display device 10, and the test process data is recorded.

[0116] S7 Result Evaluation.

[0117] The samples after the test are analyzed, and the evaluation table is used to assess whether they meet the ≥6 level standard. If they do not meet the standard, the design needs to be improved and re-verified.

[0118] By extracting the most damaging typical operating conditions from real-world vehicle operation big data and collecting high-fidelity road spectrum data based on this, the accuracy and representativeness of the test input are ensured. By constructing a synthetic road spectrum representing the target total mileage, dispersed real-world user behavior is integrated into a complete and highly damaged load spectrum, directly addressing the problem of insufficient reproduction accuracy in traditional empirical specifications. Furthermore, damage equivalence condensation processing is employed to significantly reduce test time while maintaining consistency in total damage, achieving a leapfrog improvement in test efficiency. Ultimately, this method reproduces real-world vehicle usage scenarios on the test bench with high precision and efficiency, ensuring that test results truly reflect the reliability performance of the drive shaft in the hands of actual users. This fundamentally improves test accuracy and shortens the verification cycle, providing a reliable basis for drive shaft design and quality verification.

[0119] It should be understood that the application of this application is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Those skilled in the art can understand that implementing all or part of the processes of the above embodiments and making equivalent changes according to the claims of this application still fall within the scope of this application.

Claims

1. A method for testing the reliability of a drive shaft, characterized in that, Includes the following steps: Collect big data on the operation of user vehicles and calculate drive shaft damage, and select typical working condition routes based on the damage analysis results; On the selected typical working condition route, raw road spectrum data including drive shaft torque, speed and steering angle were collected using the test vehicle; The original road spectrum data is divided into multiple preset speed segments, and the representative data weight segment with the greatest damage in each speed segment is extracted. Representative data segments of each speed range are combined according to their corresponding number of cycles to construct a synthetic road spectrum representing the total target driving distance. The synthetic road spectrum is subjected to damage equivalent condensation processing so that the fitting degree between the damage amount of the condensed data and the damage amount of the synthetic road spectrum before condensation reaches a preset threshold, thereby obtaining the drive shaft bench test specification. Import the drive shaft bench test specifications into the test bench control system; The drive shaft was subjected to a loading test according to the aforementioned drive shaft bench test. The reliability of the drive shaft was evaluated based on the test results.

2. The method according to claim 1, characterized in that, The steps for collecting big data on user vehicle operation and calculating drive shaft damage, and selecting typical operating conditions based on the damage analysis results, include: Collect the operational big data generated by a preset number of user vehicles within a continuous preset time period; For each vehicle's operational big data, based on the vehicle's power status and location dwell time, the continuous operational big data is divided into multiple independent driving trip data. The segmented independent driving trip data is cleaned to remove invalid data segments caused by signal abnormalities, thus obtaining the number of valid independent driving trips; Based on the damage calculation model, drive shaft damage is calculated for all effective independent driving trip data and extrapolated to the target total driving mileage to obtain the simulated damage value corresponding to each effective independent driving trip. Based on the ranking of the simulated damage values, the actual vehicle routes corresponding to the top N independent driving trip data with the highest simulated damage values ​​are selected to form typical operating condition routes.

3. The method according to claim 2, characterized in that, Based on the damage calculation model, the steps for calculating drive shaft damage for all valid independent driving trip data and extrapolating it to the target total mileage to obtain the simulated damage value corresponding to each valid independent driving trip include: Torque-speed load spectrum extracted from each valid independent driving trip data using rainflow counting method; Based on the torque-speed load spectrum and combined with the SN fatigue curve of the drive shaft material, the first damage value of each effective independent driving stroke data under the corresponding real driving mileage is calculated by Miner linear cumulative damage theory. Each of the first damage values ​​is extrapolated to the target total driving mileage to obtain the simulated damage value corresponding to each of the effective independent driving trip data; The target total mileage is a pre-set benchmark value that represents the entire life cycle of the vehicle and is used for damage equivalent extrapolation calculations.

4. The method according to claim 1, characterized in that, On a selected typical operating route, the steps for collecting raw road spectrum data, including drive shaft torque, speed, and steering angle, using a test vehicle include: A data acquisition system is deployed on at least one test vehicle. The data acquisition system includes: torque sensors mounted on the left and right drive shafts for acquiring drive shaft torque signals; a cable sensor for acquiring drive shaft displacement and converting it into drive shaft rotation angle signals; and a CANFD acquisition device for acquiring motor speed, motor torque, and vehicle speed signals from the test vehicle's bus. With the test vehicle fully loaded with counterweight and the drive system in maximum energy recovery mode and motion mode, real-vehicle road spectrum data was collected along the typical working condition routes to obtain raw road spectrum data including drive shaft torque, speed and steering angle.

5. The method according to claim 1, characterized in that, The steps of dividing the original road spectrum data into multiple preset speed segments and extracting the representative data weight segment with the greatest damage within each speed segment include: The original road spectrum data is divided into multiple continuous speed segments according to the range of 0-120km / h; Based on the damage calculation model, the drive shaft damage value per unit mileage is calculated for all data segments within each speed range. The data segment with the highest damage value per unit mileage is selected from each speed segment and used as the representative data segment of that speed segment.

6. The method according to claim 1, characterized in that, The steps of combining representative data segments from each speed range according to their corresponding number of loops to construct a synthetic road spectrum representing the total target mileage include: Based on the big data analysis of vehicle operation, the cumulative proportion of each speed segment in the target total mileage is obtained; Calculate the assessment mileage corresponding to each speed segment in the total target driving mileage based on the cumulative ratio; Divide the test mileage of each speed segment by the single mileage of its representative data segment to obtain the number of cycles corresponding to the representative data segment. By combining all representative data segments according to the stated number of iterations, a synthetic road spectrum representing the total mileage of the target is constructed.

7. The method according to claim 1, characterized in that, The steps for performing damage equivalent condensation on the synthesized road spectrum to achieve a good fit between the damage amount of the condensed data and the damage amount of the synthesized road spectrum before condensation, thereby obtaining the drive shaft bench test specification, include: The time axis is condensed by increasing the torque and speed loads in the synthesized road spectrum; Based on the damage calculation model, the condensation parameters are iteratively adjusted so that the fitting degree between the damage amount of the condensed data and the damage amount of the synthetic road spectrum before condensation reaches a preset threshold. The condensed data that meets the fitting requirements is determined as the test specification for the drive shaft bench.

8. A drive shaft reliability testing system, characterized in that, include: The data acquisition subsystem is used to collect big data on user vehicle operation and real-vehicle road spectrum data of test vehicle. It includes: torque sensors installed on the left and right drive shafts of the test vehicle to collect drive shaft torque; cable sensors arranged on the test vehicle to collect drive shaft displacement and convert it into drive shaft rotation angle; and CANFD acquisition equipment installed on the test vehicle to collect motor speed, motor torque and vehicle speed signals from the vehicle bus. The data processing and specification generation subsystem is configured to: calculate drive axle damage based on the large operational data of user vehicles collected by the data acquisition subsystem; select a typical operating route based on the damage analysis results; acquire the original road spectrum data of the test vehicle running on the selected typical operating route collected by the data acquisition subsystem; divide the original road spectrum data into multiple preset speed segments and extract the representative data weight segment with the largest damage in each speed segment; combine the representative data segments of each speed segment according to their corresponding number of cycles to construct a synthetic road spectrum representing the target total mileage; perform damage equivalence condensation processing on the synthetic road spectrum so that the degree of fit between the damage amount of the condensed data and the damage amount of the synthetic road spectrum before condensation reaches a preset threshold, thereby obtaining the drive axle bench test specification; Test bench system for mounting drive shafts and performing load tests; The control and evaluation subsystem is used to import the test specifications of the drive shaft bench, control the operation of the test bench subsystem, and record and evaluate the test results.

9. The drive shaft reliability testing system according to claim 8, characterized in that, The test bench system includes a drive unit, a fixed support, and a sensing device. The drive unit is connected to the moving joint of the drive shaft via a spline; The fixed section of the drive shaft is connected to the hub bearing via a spline; The wheel hub bearing is connected to the fixed support via a connecting fixture, wherein the connecting fixture is configured to simulate the vehicle suspension geometry and fix the flange mounting surface of the wheel hub bearing to the fixed support; The sensing device is mounted on the fixed support and is used to monitor the torque, speed and angle of the drive shaft in real time during the test.

10. The drive shaft reliability testing system according to claim 8, characterized in that, The sensing device includes at least one of a torque sensor, an angle sensor, and a speed sensor.

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