A method and device for testing axial load performance of deep groove ball bearings

Abstract

The present application provides a method and device for testing the axial load performance of deep groove ball bearings, which relates to the field of rolling bearing manufacturing technology. The method includes: constructing an axial load change library and a failure discrimination feature library for deep groove ball bearings; constructing a test fixture, constructing load test parameters based on the axial load change library and running the test, continuously monitoring the test process, and generating a continuous test monitoring data set; calling the failure discrimination feature library to identify the continuous test monitoring data set, determining the constant failure load, variable load failure load, impact failure load, and fatigue failure load when failure occurs, and generating load performance test results. The present application solves the technical problem that the existing technology cannot comprehensively and accurately evaluate the operating performance of deep groove ball bearings under complex load conditions due to the disconnection between the test working condition setting and the actual application environment, and achieves the technical effect of improving the accuracy of axial load performance testing and the reliability of test results.

Description

Technical Field

[0001] The present application relates to the technical field of rolling bearing manufacturing, and in particular to a method and device for testing the axial load performance of a deep groove ball bearing. Background Art

[0002] In all aspects of rolling bearing manufacturing, performance testing is a key link to ensure bearing quality and safety in use. Deep groove ball bearings are the most widely used type of rolling bearings. In actual applications, they are often subjected to various types of axial loads. The axial load performance is directly related to the stability and service life of deep groove ball bearings. The current test methods for the axial load performance of deep groove ball bearings mainly use constant axial load loading or fixed-frequency periodic variable load testing to evaluate their stress characteristics through operating tests under a single working condition. However, this type of test method has significant limitations. On the one hand, there is a disconnect between the test conditions and the actual service environment of the bearings, making it difficult to cover complex load modes such as variable loads, impacts, and fatigue. On the other hand, failure identification during the test process often relies on manual observation or simplified indicator judgment, lacking complete and continuous data support, resulting in untimely failure identification and difficulty in accurately quantifying the failure thresholds under different load types. Summary of the Invention

[0003] The present application provides a method and device for testing the axial load performance of deep groove ball bearings, which solves the technical problem in the prior art that the test working condition setting is out of touch with the actual application environment, resulting in the inability to fully cover complex load conditions and thus making it difficult to accurately evaluate the actual operating performance of deep groove ball bearings. The application achieves the technical effect of improving the accuracy of the axial load performance test of deep groove ball bearings and the reliability of the test results.

[0004] In view of the above problems, on the one hand, the present application provides an axial load performance testing method for deep groove ball bearings, the method comprising: determining the target application scenario of the deep groove ball bearing, constructing an axial load change library, including a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library and a fatigue load feature library; constructing a failure discrimination feature library for the deep groove ball bearing; constructing a test fixture for the deep groove ball bearing, and performing an operation test by constructing load test parameters using the constant axial load feature library, the variable load axial load feature library, the impact axial load feature library and the fatigue load feature library, and continuously monitoring using an intelligent sensing system to generate a continuous test monitoring data set; calling the failure discrimination feature library to identify the continuous test monitoring data set, and determining the constant failure load, variable load failure load, impact failure load and fatigue failure load when failure occurs; and generating the load performance test results of the deep groove ball bearing using the constant failure load, variable load failure load, impact failure load and fatigue failure load.

[0005] On the other hand, the present application also provides an axial load performance testing device for deep groove ball bearings, the device comprising: an axial load change library construction module for determining the target application scenario of the deep groove ball bearing and constructing an axial load change library, including a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library and a fatigue load feature library; a failure discrimination feature library construction module for constructing a failure discrimination feature library of the deep groove ball bearing; a testing module for constructing a test fixture for the deep groove ball bearing, and performing a running test by constructing load test parameters using the constant axial load feature library, the variable load axial load feature library, the impact axial load feature library and the fatigue load feature library, and continuously monitoring using an intelligent sensing system to generate a continuous test monitoring data set; a failure discrimination module for calling the failure discrimination feature library to identify the continuous test monitoring data set and determine the constant failure load, variable load failure load, impact failure load and fatigue failure load when failure occurs; a test result generation module for generating the load performance test results of the deep groove ball bearing using a constant failure load, a variable load failure load, an impact failure load and a fatigue failure load.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects:

[0007] By identifying the target application scenarios for deep groove ball bearings, an axial load variation library was constructed, including constant axial load signatures, variable load axial load signatures, impact axial load signatures, and fatigue load signatures. This generated a variety of typical axial load models, providing standardized, scenario-based load inputs for subsequent testing. By constructing a failure discrimination signature library for deep groove ball bearings, a systematic set of failure signature identification criteria was established, providing a basis for accurately determining failure conditions during deep groove ball bearing testing. The axial load variation library was used to set test parameters. A customized test fixture and intelligent sensing system were used to run the test in real time and continuously collect test data, ensuring controllable testing and data integrity, providing comprehensive data support for subsequent analysis. By invoking the failure discrimination signature library to identify test data and extract failure load data, the team achieved precise identification of failure events under different load modes, improving the accuracy of test results and providing a key basis for performance analysis. A comprehensive analysis of constant failure load, variable failure load, impact failure load, and fatigue failure load was performed to generate load performance test results for deep groove ball bearings. This data was then converted into practical performance indicators, providing an intuitive understanding of deep groove ball bearing performance under different axial load conditions.

[0008] In summary, this application constructs a comprehensive axial load change library and failure discrimination feature library, combines the operation test of the test tooling and the continuous monitoring of the intelligent sensing system, comprehensively simulates the complex load state of deep groove ball bearings in the actual use environment, accurately identifies the failure behavior of bearings under various loads, and finally obtains highly representative load performance test results, which significantly improves the accuracy of axial load performance testing and the reliability of test results in the manufacturing process of deep groove ball bearings, provides more guiding information for bearing design optimization and performance improvement, and thus promotes the advancement of rolling bearing manufacturing technology.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic flow chart of a method for testing the axial load performance of a deep groove ball bearing provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of a process for performing an operating test and generating a continuous test monitoring data set in a method for testing the axial load performance of a deep groove ball bearing provided in an embodiment of the present application.

[0012] Figure 3 A schematic structural diagram of a deep groove ball bearing axial load performance testing device provided in an embodiment of the present application.

[0013] Description of the accompanying drawings: axial load variation library construction module 10 , failure discrimination feature library construction module 20 , testing module 30 , failure discrimination module 40 , test result generation module 50 . DETAILED DESCRIPTION

[0014] The embodiments of the present application provide a method and device for testing the axial load performance of deep groove ball bearings, thereby solving the technical problem in the prior art that complex load conditions cannot be fully covered due to the disconnection between the test working condition setting and the actual application environment, and thus it is difficult to accurately evaluate the actual operating performance of deep groove ball bearings. The technical effect of improving the accuracy of the axial load performance test of deep groove ball bearings and the reliability of the test results is achieved.

[0015] Example 1, as Figure 1 As shown, an embodiment of the present application provides a method for testing the axial load performance of a deep groove ball bearing, the method comprising:

[0016] Step S1: Determine the target application scenario of the deep groove ball bearing and construct an axial load variation library, including a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library, and a fatigue load feature library.

[0017] Specifically, by communicating with users and consulting equipment manuals or industry standards, the specific operating environment and requirements of deep groove ball bearings are clarified. For example, for automotive engine bearings, the target application scenarios include high-speed rotation, frequent starts and stops, and impact loads. Using industrial data acquisition systems (such as NI acquisition cards and PLC acquisition modules), record and collect axial load curves for the same type of deep groove ball bearings under the target application scenario. These axial load curves are classified according to specific scenarios and key parameters such as the peak value, frequency, and duration of the curves are extracted. An axial load variation library is constructed, including a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library, and a fatigue load feature library, to facilitate subsequent testing and call-up. Among them, the constant axial load feature library contains the characteristic parameters corresponding to the stable axial force of the deep groove ball bearing, such as the axial force size, action time and other parameters; the variable load axial load feature library contains the load characteristic parameters corresponding to the axial force whose size or direction changes periodically or non-periodically within a certain range, such as the axial force size, change frequency and other parameters; the impact axial load feature library contains the characteristic parameters corresponding to the axial impact force with short-term and drastic changes, such as the impact force size, impact frequency and other parameters; the fatigue load feature library contains the load characteristic parameters corresponding to the long-term repeated axial force, such as the axial force size, number of cycles and other parameters.

[0018] By clarifying the target application scenarios and building a comprehensive axial load variation library, standardized modeling and classification archiving of actual working condition axial loads are achieved, providing reproducible and callable load templates for testing, thereby enhancing the scenario adaptability and representativeness of the test.

[0019] Step S2: constructing a failure discrimination feature library of the deep groove ball bearing.

[0020] Specifically, the failure identification feature library is a database containing bearing failure characteristics, used to identify bearing failure modes under different loads. Failure records of similar deep groove ball bearings in the target application scenario are collected, and the corresponding various bearing operating parameters (including but not limited to vibration frequency, temperature fluctuations, and noise) are organized and recorded. Signal processing tools (such as MATLAB and Python) are used to extract failure characteristics (such as maximum temperature, vibration frequency mutation value, noise mutation value, etc.) to construct the failure identification feature library. This failure feature library achieves a quantitative failure identification mechanism, providing a reliable basis for accurately determining bearing failures.

[0021] Step S3: construct a test fixture for the deep groove ball bearing, construct load test parameters using the constant axial load feature library, variable load axial load feature library, impact axial load feature library, and fatigue load feature library to perform a running test, and use an intelligent sensing system for continuous monitoring to generate a continuous test monitoring data set.

[0022] Specifically, the test fixture is a mechanical platform designed to carry deep groove ball bearings and simulate real-world operating conditions. It includes a fixture for securing the deep groove ball bearings, a loading mechanism for applying axial loads, and measuring equipment. The test fixture is designed and assembled based on the deep groove ball bearing's dimensions, structural characteristics, and test requirements. An intelligent sensing system, including temperature sensors, vibration sensors, displacement sensors, and acoustic signal sensors, is installed on the test fixture to monitor the bearing's condition in real time. Test load parameters, including constant load, variable load, impact load, and fatigue load, are set based on signature libraries for constant axial load, variable load, impact load, and fatigue load. The test fixture is controlled to apply loads to the deep groove ball bearings according to the established load test parameters. During the test, the intelligent sensing system continuously monitors the deep groove ball bearing's condition under load, including temperature changes, vibration amplitude and frequency, displacement of internal bearing components, and noise levels. The sensors transmit the collected data to a data acquisition system (such as a data acquisition card), which processes the data to generate a chronologically ordered continuous test monitoring dataset.

[0023] By constructing test fixtures and intelligent sensing systems, real-time monitoring of bearings under various load conditions is achieved, ensuring the integrity and continuity of test data collection and providing detailed data support for subsequent failure analysis.

[0024] Step S4: calling the failure discrimination feature library to identify the continuous test monitoring data set, and determining the constant failure load, variable load failure load, impact failure load and fatigue failure load when failure occurs.

[0025] Specifically, the parameters in the failure discrimination feature library are called up to compare and analyze the data in the continuous test monitoring data set to determine the specific load value at the time of failure, including constant failure load, variable load failure load, impact failure load, and fatigue failure load. For example, the vibration frequency at a certain moment in the continuous test monitoring data set is compared with the vibration frequency critical value recorded in the failure discrimination feature library. If this critical value is reached or exceeded, it can be determined that the deep groove ball bearing is in a failed state at that time, and the deep groove ball bearing load value at that time is recorded. By calling the failure discrimination feature library to identify the test data, the failure load under different load types can be accurately determined, providing key data support for evaluating bearing performance.

[0026] Step S5: generating a load performance test result of the deep groove ball bearing using a constant failure load, a variable failure load, an impact failure load, and a fatigue failure load.

[0027] Specifically, the data for constant failure load, variable load failure load, impact failure load, and fatigue failure load are integrated to determine the critical load values for deep groove ball bearings under different load types while maintaining a certain safety factor. The output is the load performance test results, providing a basis for bearing design and selection. Taking constant load as an example, after multiple tests, the failure load value under different working conditions was determined to be 1000N. Based on the design requirements and operating environment of the deep groove ball bearing, a safety factor of 1.2 was determined. The final critical value is the failure load value divided by the safety factor, which is 833N.

[0028] Furthermore, step S2 includes:

[0029] Step S21: using the target application scenario and the bearing model of the deep groove ball bearing as index elements, collecting a historical bearing failure data set, wherein any data in the historical bearing failure data set includes temperature, vibration, displacement, and acoustic emission signals when the bearing fails during operation.

[0030] Step S22: performing feature correlation analysis of bearing temperature, vibration, displacement, and acoustic emission signals at the time of failure based on the historical bearing failure data set to construct the failure discrimination feature library.

[0031] Specifically, using the target application scenario and bearing model of deep groove ball bearings as indexes, deep groove ball bearings with the same application scenario and model are selected as target samples. The experimental database or third-party bearing monitoring platform is accessed to extract the original operation monitoring data of the target sample at the time of failure, including the temperature, vibration, displacement and acoustic emission signals at the time of bearing operation failure. These multi-dimensional monitoring data are combined by timestamp to form a historical bearing failure data set for the next step of feature analysis.

[0032] Statistical analysis of historical bearing failure datasets was performed using data processing software (such as Python pandas, SciPy, sklearn, and MATLAB). Key indicators such as average temperature rise rate, spectral high-frequency energy, displacement mutation value, and acoustic emission envelope peak were extracted. Feature correlation analysis was then performed based on these key indicators to explore the relationship between different feature combinations and bearing failure. Feature combinations with a high correlation with bearing failure were integrated to form a structured discriminant template library, namely the failure discrimination feature library, which was used in subsequent testing processes to effectively improve the accuracy of failure detection.

[0033] Furthermore, step S22 includes:

[0034] Step S221: Collect a historical data set of normal bearing operation, and compare and count similar characteristic parameters with the historical data set of failure bearings to construct a temperature abnormality frequent threshold, a vibration abnormality frequent threshold, a displacement abnormality frequent threshold, and an acoustic emission signal abnormality frequent threshold.

[0035] Step S222: Based on the temperature abnormality frequent threshold, the vibration abnormality frequent threshold, the displacement abnormality frequent threshold and the acoustic emission signal abnormality frequent threshold, the abnormal combination correlation analysis of temperature, vibration, displacement and acoustic emission signals of the historical bearing failure data set is performed to construct multiple correlation feature combinations.

[0036] Step S223: mapping the temperature abnormality frequent threshold, the vibration abnormality frequent threshold, the displacement abnormality frequent threshold, and the acoustic emission signal abnormality frequent threshold with the plurality of associated feature combinations to construct the failure discrimination feature library.

[0037] Specifically, the historical normal operation dataset is a collection of data recorded from deep groove ball bearings under normal operating conditions. It includes data such as temperature, vibration, displacement, and acoustic emission signals during normal operation. Multiple batches of normal operating data for deep groove ball bearings of the same application scenario and model are collected from an experimental database or a third-party bearing monitoring platform. This historical normal operation dataset serves as a benchmark for determining bearing failure. Statistical analysis methods such as box plot analysis and standard deviation comparison are used to compare similar characteristic parameters between the normal operation dataset and the historical bearing failure dataset. The frequency of characteristic parameters exceeding the normal range within different characteristic parameter intervals is statistically analyzed. When the frequency of these parameters exceeding the threshold reaches a certain level, the corresponding parameter is considered abnormally frequent, and the corresponding numerical range forms the judgment threshold. Based on the statistical results, abnormal frequency thresholds for temperature, vibration, displacement, and acoustic emission signals are determined. These abnormal frequency thresholds are determined by comparing the normal data to the failure threshold, where the difference from the normal parameter exceeds a preset threshold and the proportion of the failure threshold in the failure data exceeds a preset threshold. Taking temperature characteristics as an example, the normal temperature fluctuation range is 50~70℃, and temperatures greater than 85℃ frequently appear in failure data; statistical results show that temperatures above 85℃ appear in more than 70% of failure samples, so the temperature anomaly frequency threshold is 85℃.

[0038] When a deep groove ball bearing fails, its temperature, vibration, displacement, and acoustic emission (AE) signals are not necessarily all abnormal. Two or three characteristic parameter combinations may be abnormal simultaneously, while the remaining characteristic parameters remain within the normal range. Therefore, it is necessary to mine the correlations between characteristic parameters to improve the accuracy of failure identification. Based on established thresholds for frequent temperature, vibration, displacement, and AE anomalies, the Apriori algorithm and association rule learning algorithms are used to model the multi-feature joint relationships in historical bearing failure datasets. This method mines the associations between abnormal combinations of temperature, vibration, displacement, and AE signals in bearing failure scenarios. For example, based on the temperature anomaly threshold, failure data with abnormal temperatures are screened. Further statistical analysis is performed on the anomalies in the vibration, displacement, and AE signals within these data to identify the most frequently occurring characteristic parameter combinations, thereby constructing multiple correlation feature combinations. For example, statistical analysis results show that in 70% of failure samples, crack-type failures occur when the temperature is greater than 85°C and the AE signal frequency is greater than 30kHz. Thus, a discriminant combination is constructed: temperature greater than 85°C and AE signal frequency greater than 30kHz.

[0039] Based on the logical relationships among various associated feature combinations, the thresholds for frequent temperature anomalies, frequent vibration anomalies, frequent displacement anomalies, and frequent acoustic emission signal anomalies are integrated, a mapping relationship is established, and these are systematically encoded and stored to create a failure discrimination feature library. For example, rule number: R101, bearing model: 6204, operating conditions: high speed, high load; conditions: temperature greater than 85°C and acoustic emission signal frequency greater than 30kHz; failure type: fatigue failure.

[0040] Furthermore, the test fixture includes an upper loading seat and a lower bearing seat; wherein, the upper loading seat is connected to the electro-hydraulic servo cylinder or the electric loader, and has an inner ring pressure ring at the lower end for applying force to the inner ring of the bearing; the lower bearing seat is fixedly installed on the base, and has an outer ring seat at the upper end to fix the outer ring of the bearing.

[0041] Specifically, the test fixture features a split-structure design, consisting of an upper loading seat and a lower bearing seat. The upper loading seat connects to a loading device, such as an electro-hydraulic servo cylinder or electric loader, and applies force to the bearing's inner ring via the inner ring pressure ring at the lower end, thereby transmitting the loading force. If the test requires a high-frequency response or more complex control, an electro-hydraulic servo cylinder is used. For simplicity and ease of control, an electric loader is an option. The lower bearing seat is fixedly mounted on the base, and its upper outer ring seat is used to secure the bearing's outer ring, providing stable support during testing.

[0042] When assembling the test fixture, use bolts, couplings, and other fasteners to mechanically connect the upper loading seat to the electro-hydraulic servo cylinder or electric loader, ensuring a secure connection and effective force transmission. Secure the lower bearing seat to the base with anchor bolts, ensuring the lower bearing seat and base form a stable, integrated unit. When installing the upper and lower loading seats, ensure that the inner ring pressure ring at the lower end of the upper loading seat is accurately aligned with the inner ring of the deep groove ball bearing and that the outer ring seat at the upper end of the lower bearing seat tightly secures the bearing outer ring.

[0043] During the test, the inner ring of the deep groove ball bearing to be tested is placed in the inner ring pressure ring position, and the outer ring is fixed to the outer ring seat. The controller is used to control and adjust the loading force and loading speed and other parameters of the electro-hydraulic servo cylinder or electric loader to load the upper loading seat downward to simulate different axial load conditions. Sensors such as temperature, vibration, displacement, and acoustic emission are used to achieve synchronous monitoring of the test process.

[0044] The test fixture uses a split structural design to achieve independent loading and fixation of the inner and outer rings of the bearing. It is compatible with a variety of loading control devices and can simulate various complex axial loads in real applications, providing an efficient and accurate experimental platform for performance verification in the rolling bearing manufacturing process.

[0045] Further, such as Figure 2 As shown, step S3 includes:

[0046] Step S31: constructing load test parameters respectively using the constant axial load feature library and the fatigue load feature library, performing a reciprocating test on the test fixture, and collecting and generating constant continuous test monitoring data and fatigue continuous test monitoring data through the intelligent sensing system.

[0047] Step S32: constructing load test parameters based on the variable load axial load feature library, performing a periodic test of load fluctuation over time on the test fixture, and collecting and generating variable load continuous test monitoring data through the intelligent sensing system.

[0048] Step S33: constructing load test parameters based on the impact axial load feature library, performing multiple load single mutation tests on the test fixture, and collecting and generating impact test monitoring data through the intelligent sensing system.

[0049] Step S34: generating the continuous test monitoring data set by using the constant continuous test monitoring data, the fatigue continuous test monitoring data, the variable load continuous test monitoring data and the impact test monitoring data.

[0050] Specifically, a constant load value (e.g., 800N) is set based on the constant load signature library, and an electro-hydraulic servo cylinder or electric loader is controlled to apply a constant axial force for a long period of time. The loading frequency and amplitude range (e.g., ±400N, 10Hz sine wave) are set based on the fatigue signature library to simulate fatigue failure conditions. The test fixture is then activated to perform a reciprocating test according to these parameters. During the test, the temperature sensors, vibration sensors, displacement sensors, and acoustic signal sensors in the intelligent sensing system collect real-time data on bearing temperature, vibration acceleration, relative displacement between bearing components, and acoustic signals emitted by the bearing, forming constant continuous test monitoring data and fatigue continuous test monitoring data.

[0051] Based on the variable-load axial load signature library, multiple sets of variable-load parameters are set, including load fluctuation patterns, fluctuation amplitudes, and fluctuation periods. For example, the axial force fluctuates in a sinusoidal pattern within the range of 500N to 1500N, with a period of 2 seconds. These parameters are input into the control system of the test fixture, which then uses the electro-hydraulic servo cylinder or electric loader to perform periodic tests on the test fixture, simulating complex operating conditions such as variable-speed motor startup and bumpy transportation. During the test, each sensor in the intelligent sensing system collects data at its own sampling frequency, generating continuous variable-load test monitoring data.

[0052] Based on the impact axial load signature library, parameters such as the impact load mutation value (e.g., from 500N to 1500N) and impact frequency (e.g., one impact every 10 seconds) were set. These parameters were then configured in the test fixture's control system. The test fixture was then activated to conduct multiple single-load mutation tests to simulate extreme impact scenarios. During the test, the intelligent sensing system's various sensors generated real-time temperature, vibration, displacement, and acoustic emission signals to generate impact test monitoring data.

[0053] Data processing software is used to combine the constant continuous test monitoring data, fatigue continuous test monitoring data, variable load continuous test monitoring data, and impact test monitoring data according to load type to form a complete continuous test monitoring data set. This data set contains information on the operating status of deep groove ball bearings under different types of axial loads (constant load, fatigue load, variable load, and impact load), forming the core data support for the entire load performance test.

[0054] Furthermore, the intelligent sensing system includes a temperature sensor, a vibration sensor, a displacement sensor and an acoustic signal sensor arranged on the test fixture for monitoring the deep groove ball bearing.

[0055] Specifically, temperature sensors are typically attached to the bearing outer ring or the housing near the bearing rolling elements to monitor load-related heating trends. Vibration sensors are mounted on the lower bearing support or test platform bracket, coaxially with the bearing mounting point, to measure the bearing's vibration amplitude and frequency during operation. Displacement sensors, such as laser displacement sensors or eddy current displacement meters, are placed on both sides of the bearing in the loading direction to monitor axial displacement and determine whether there is misalignment, looseness, or changes in internal clearance. Acoustic emission sensors are fixed to a bracket at a certain distance (e.g., 10 to 20 cm) from the bearing to collect high-frequency elastic waves generated during failure. Furthermore, all sensors are connected to a signal acquisition module, which converts analog signals (such as voltage signals from temperature sensors and charge signals from vibration sensors) into digital signals. These signals are then transmitted to an edge computing unit or industrial gateway for processing, including filtering (to remove noise), amplification (to enhance weak signals), and analysis (such as spectrum analysis of vibration signals) to obtain useful bearing status information.

[0056] Furthermore, step S4 includes:

[0057] Step S41: input the constant continuous test monitoring data, fatigue continuous test monitoring data, variable load continuous test monitoring data and impact test monitoring data into the failure discrimination feature library for failure feature matching. If the match is successful, determine the constant failure load-time feature, fatigue failure load-time feature, variable load failure load-time feature and impact failure load.

[0058] Step S42: extracting the constant failure load, fatigue failure load, and variable load failure load that do not meet the preset life span using the constant failure load-time feature, fatigue failure load-time feature, and variable load failure load-time feature.

[0059] Specifically, constant continuous test monitoring data, fatigue continuous test monitoring data, variable load continuous test monitoring data, and impact test monitoring data are input into a failure discrimination feature library. A feature matching algorithm (such as sliding window-based anomaly detection or multi-dimensional signal fusion matching) is used to analyze and extract the temperature, vibration, displacement, and acoustic emission signal features from these data. These features are then compared against the failure discrimination rules in the failure discrimination feature library. If the conditions for a specific failure scenario in the failure discrimination feature library are met, the match is considered successful. At this point, the load magnitude and failure onset time of the bearing failure are extracted to establish failure load-time features, including constant failure load-time features, fatigue failure load-time features, and variable load failure load-time features. For example, if a bearing fails after 50 hours under a constant load of 1000N, the relationship between 1000N and 50 hours is a constant failure load-time feature. Since the failure caused by impact load is an instantaneous failure phenomenon and has little to do with time factors, after matching the failure characteristics of the impact test monitoring data, the load size when the match is successful is extracted to determine the impact failure load.

[0060] From the obtained constant failure load-time signature, fatigue failure load-time signature, and variable load failure load-time signature, failure times corresponding to different types and load values are extracted. These failure times are compared with the preset lifespan durations to identify the constant failure loads, fatigue failure loads, and variable load failure loads that do not meet the preset lifespan durations. These constant failure loads, fatigue failure loads, variable load failure loads, and the impact failure loads determined above are considered incompatible loads for the corresponding working conditions.

[0061] By performing multi-dimensional signal matching on continuous test monitoring data and a pre-built failure discrimination feature library, it is possible to accurately identify the failure moment and corresponding load level of the bearing under different axial load conditions. Further combined with the test life expectancy, the failure load value that cannot be tolerated within the expected life can be extracted, thereby achieving accurate measurement of the load adaptability boundary of deep groove ball bearings under constant, fatigue, variable load and impact conditions.

[0062] Furthermore, step S1 includes:

[0063] Step S11: using the target application scenario as an index element, collecting historical operation data sets of bearings of the same model.

[0064] Step S12: performing axial load analysis and classification on the historical operation data set, and constructing a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library, and a fatigue load feature library.

[0065] Specifically, using the target application scenario as an index, the operation data accumulated during the actual working process of the same model deep groove ball bearings in the target application scenario in the past are collected, including but not limited to the bearing temperature, vibration, displacement, load size, working time and other data, to construct a historical operation data set.

[0066] Signal analysis is performed on the collected load data, and different types of axial load characteristics are extracted using methods such as sliding window and frequency spectrum analysis. These axial load characteristics are clustered according to indicators such as load time series, peak distribution, and impact frequency. The obtained constant (long-term stable loading), variable load (periodic fluctuation), impact (short-term high-amplitude mutation), and fatigue (high-frequency repeated changes) axial load characteristics are classified and library constructed to obtain a constant axial load characteristic library, a variable load axial load characteristic library, an impact axial load characteristic library, and a fatigue load characteristic library.

[0067] By introducing an analysis and compensation mechanism for radial load interference in the load data processing process, the analysis accuracy of the axial load is effectively improved. Combined with the multi-dimensional feature recognition algorithm, the cleaned load data is scientifically classified, and a library of axial load characteristics of various types such as constant, variable load, impact and fatigue is constructed. The actual working condition fitting and classification integrity of the test parameters are significantly enhanced, providing a high-quality and high-reduction load input foundation for the subsequent axial load performance test of deep groove ball bearings, and further serving the structural matching and bearing performance testing in the rolling bearing manufacturing process.

[0068] Furthermore, step S12 includes:

[0069] Step S121: extracting the axial load and radial load from each piece of data in the historical operation data set, analyzing the influence of the radial load on the axial load and performing radial influence compensation to generate an axial load analytical data set.

[0070] Step S122: classifying the axial load analytical data set into constant load, variable load, impact load and fatigue load, and constructing a constant axial load feature library, a variable axial load feature library, an impact axial load feature library and a fatigue load feature library.

[0071] Specifically, the axial load curve and radial load curve corresponding to each record are extracted from the historical operation data. Based on the mechanical principles and the theoretical model of the bearing, the influence of the radial load on the axial load is analyzed. For example, according to the force analysis model of the deep groove ball bearing, the radial load will generate a certain additional axial force inside the bearing. This additional axial force is related to the magnitude of the radial load, the geometric parameters of the bearing, and other factors. This influence can be calculated using formulas such as Fa 附加 =k×Fr(where Fa 附加is the additional axial force generated by the radial load, k is a coefficient related to the bearing structure, and Fr is the radial load. Based on the analyzed formula for the influence of radial load on axial load, radial influence compensation is performed and corresponding corrections are made based on the original axial load value. For example, Fa=Fa 原始 -Fa 附加 The corrected axial load data, including load magnitude, corresponding time, operating parameters, etc., are combined into an axial load analytical data set. This axial load analytical data set is a data set that removes radial load interference and only reflects the actual axial load fluctuation.

[0072] The purified axial load analysis data set is subjected to pattern recognition, and thresholds and change rules are set to identify four types of typical loads. For example, the thresholds and change rules are set as follows: constant load: load change within ±5%; variable load: periodic fluctuation, frequency <10Hz; impact load: short-term mutation, change rate >100N / ms; fatigue load: frequent switching direction, high frequency, and stable peak value. The axial load data is read one by one from the axial load analysis data set, and matched with the above-mentioned thresholds and change rules. According to the matching results, the axial load analysis data set is decomposed into constant axial load data, variable load axial load data, impact axial load data, and fatigue load data. Based on these data, a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library, and a fatigue load feature library are constructed as the basis for setting subsequent test parameters.

[0073] In summary, the axial load performance testing method of a deep groove ball bearing provided in the embodiments of the present application has the following beneficial effects:

[0074] By identifying the target application scenarios for deep groove ball bearings, an axial load variation library was constructed, including constant axial load signatures, variable load axial load signatures, impact axial load signatures, and fatigue load signatures. This generated a variety of typical axial load models, providing standardized, scenario-based load inputs for subsequent testing. By constructing a failure discrimination signature library for deep groove ball bearings, a systematic set of failure signature identification criteria was established, providing a basis for accurately determining failure conditions during deep groove ball bearing testing. The axial load variation library was used to set test parameters. A customized test fixture and intelligent sensing system were used to run the test in real time and continuously collect test data, ensuring controllable testing and data integrity, providing comprehensive data support for subsequent analysis. By invoking the failure discrimination signature library to identify test data and extract failure load data, the team achieved precise identification of failure events under different load modes, improving the accuracy of test results and providing a key basis for performance analysis. A comprehensive analysis of constant failure load, variable failure load, impact failure load, and fatigue failure load was performed to generate load performance test results for deep groove ball bearings. This data was then converted into practical performance indicators, providing an intuitive understanding of deep groove ball bearing performance under different axial load conditions.

[0075] Overall, the embodiments of the present application construct a comprehensive axial load change library and failure discrimination feature library, combine the operation test of the test tooling and the continuous monitoring of the intelligent sensing system, comprehensively simulate the complex load state of deep groove ball bearings in the actual use environment, accurately identify the failure behavior of bearings under various loads, and ultimately obtain highly representative load performance test results, which significantly improves the accuracy of axial load performance testing and the reliability of test results in the manufacturing process of deep groove ball bearings, provides more guiding information for bearing design optimization and performance improvement, and thus promotes the advancement of rolling bearing manufacturing technology.

[0076] Example 2, as Figure 3 As shown, based on the same inventive concept as the aforementioned embodiment 1, the embodiment of the present application provides an axial load performance testing device for a deep groove ball bearing, the device comprising:

[0077] The axial load variation library construction module 10 is used to determine the target application scenario of the deep groove ball bearing and construct an axial load variation library, including a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library and a fatigue load feature library.

[0078] The failure discrimination feature library construction module 20 is used to construct the failure discrimination feature library of the deep groove ball bearing.

[0079] The test module 30 is used to construct a test fixture for the deep groove ball bearing, construct load test parameters using the constant axial load feature library, variable load axial load feature library, impact axial load feature library and fatigue load feature library to perform operating tests, and use an intelligent sensing system for continuous monitoring to generate a continuous test monitoring data set.

[0080] The failure identification module 40 is used to call the failure identification feature library to identify the continuous test monitoring data set and determine the constant failure load, variable load failure load, impact failure load and fatigue failure load when failure occurs.

[0081] The test result generating module 50 is used to generate the load performance test result of the deep groove ball bearing using a constant failure load, a variable failure load, an impact failure load and a fatigue failure load.

[0082] Furthermore, the failure identification feature library construction module 20 of the embodiment of the present application is further configured to perform the following steps:

[0083] A historical bearing failure dataset is collected using the target application scenario and the bearing model of the deep groove ball bearing as index elements, wherein any data in the historical bearing failure dataset includes the temperature, vibration, displacement, and acoustic emission signals at the time of bearing operation failure; based on the historical bearing failure dataset, a feature correlation analysis of the bearing temperature, vibration, displacement, and acoustic emission signals at the time of failure is performed to construct the failure discrimination feature library.

[0084] Furthermore, the failure identification feature library construction module 20 of the embodiment of the present application is further configured to perform the following steps:

[0085] A historical data set of normal operation of bearings is collected, and similar characteristic parameters are compared and counted with the historical data set of bearing failures to construct temperature anomaly frequency thresholds, vibration anomaly frequency thresholds, displacement anomaly frequency thresholds, and acoustic emission signal anomaly frequency thresholds; based on the temperature anomaly frequency thresholds, vibration anomaly frequency thresholds, displacement anomaly frequency thresholds, and acoustic emission signal anomaly frequency thresholds, an abnormal combination correlation analysis of temperature, vibration, displacement, and acoustic emission signals is performed on the historical bearing failure data set to construct a plurality of correlation feature combinations; a relationship mapping is performed between the temperature anomaly frequency thresholds, vibration anomaly frequency thresholds, displacement anomaly frequency thresholds, and acoustic emission signal anomaly frequency thresholds and the plurality of correlation feature combinations to construct the failure discrimination feature library.

[0086] Furthermore, the test fixture includes an upper loading seat and a lower bearing seat; wherein, the upper loading seat is connected to the electro-hydraulic servo cylinder or the electric loader, and has an inner ring pressure ring at the lower end for applying force to the inner ring of the bearing; the lower bearing seat is fixedly installed on the base, and has an outer ring seat at the upper end to fix the outer ring of the bearing.

[0087] Furthermore, the test module 30 in the embodiment of the present application is further configured to perform the following steps:

[0088] Load test parameters are constructed respectively using the constant axial load feature library and the fatigue load feature library, and a reciprocating test is performed on the test fixture, and constant continuous test monitoring data and fatigue continuous test monitoring data are collected and generated through the intelligent sensing system; load test parameters are constructed using the variable load axial load feature library, and a periodic test of load fluctuation over time is performed on the test fixture, and variable load continuous test monitoring data is collected and generated through the intelligent sensing system; load test parameters are constructed using the impact axial load feature library, and multiple load single mutation tests are performed on the test fixture, and impact test monitoring data are collected and generated through the intelligent sensing system; the continuous test monitoring data set is generated using the constant continuous test monitoring data, fatigue continuous test monitoring data, variable load continuous test monitoring data, and the impact test monitoring data.

[0089] Furthermore, the intelligent sensing system includes a temperature sensor, a vibration sensor, a displacement sensor and an acoustic signal sensor arranged on the test fixture for monitoring the deep groove ball bearing.

[0090] Furthermore, the failure determination module 40 in the embodiment of the present application is further configured to perform the following steps:

[0091] The constant continuous test monitoring data, fatigue continuous test monitoring data, variable load continuous test monitoring data and impact test monitoring data are input into the failure discrimination feature library for failure feature matching. If the match is successful, the constant failure load-time feature, fatigue failure load-time feature, variable load failure load-time feature and impact failure load are determined; the constant failure load-time feature, fatigue failure load-time feature and variable load failure load-time feature are used to extract the constant failure load, fatigue failure load and variable load failure load that do not meet the preset life span.

[0092] Furthermore, the axial load variation library construction module 10 of the embodiment of the present application is further configured to perform the following steps:

[0093] Using the target application scenario as an index element, historical operation data sets of bearings of the same model are collected; axial loads of the historical operation data sets are analyzed and classified to construct a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library, and a fatigue load feature library.

[0094] Furthermore, the axial load variation library construction module 10 of the embodiment of the present application is further configured to perform the following steps:

[0095] Axial load and radial load are extracted from each data in the historical operation data set, the influence of radial load on the axial load is analyzed and radial influence compensation is performed to generate an axial load analytical data set; the axial load analytical data set is classified into constant load, variable load, impact load and fatigue load, and a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library and a fatigue load feature library are constructed.

[0096] Through the detailed description of the axial load performance testing method of a deep groove ball bearing in the above specification, those skilled in the art can clearly understand the axial load performance testing device of a deep groove ball bearing in this embodiment. For the device disclosed in Example 2, since it corresponds to the method disclosed in Example 1 and has corresponding functional modules and beneficial effects, the relevant details can be referred to the method section.

[0097] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for testing the axial load performance of a deep groove ball bearing, characterized in that: include: Determine the target application scenarios of deep groove ball bearings and build an axial load variation library, including a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library, and a fatigue load feature library; Constructing a failure discrimination feature library for the deep groove ball bearing; Constructing a test fixture for the deep groove ball bearing, performing an operation test by constructing load test parameters using the constant axial load feature library, the variable load axial load feature library, the impact axial load feature library, and the fatigue load feature library, and continuously monitoring using an intelligent sensing system to generate a continuous test monitoring data set; Calling the failure discrimination feature library to identify the continuous test monitoring data set to determine the constant failure load, variable load failure load, impact failure load and fatigue failure load when failure occurs; generating load performance test results of the deep groove ball bearing using a constant failure load, a variable failure load, an impact failure load, and a fatigue failure load; The test fixture includes an upper loading seat and a lower bearing seat; wherein the upper loading seat is connected to an electro-hydraulic servo cylinder or an electric loader, and has an inner ring pressure ring at the lower end for applying force to the inner ring of the bearing; the lower bearing seat is fixedly installed on the base, and has an outer ring seat at the upper end for fixing the outer ring of the bearing; The load test parameters are constructed using the constant axial load feature library, the variable load axial load feature library, the impact axial load feature library, and the fatigue load feature library to perform the operation test, and the intelligent sensing system is used for continuous monitoring to generate a continuous test monitoring data set, including: constructing load test parameters using the constant axial load feature library and the fatigue load feature library, respectively, performing a reciprocating test on the test fixture, and collecting and generating constant continuous test monitoring data and fatigue continuous test monitoring data through the intelligent sensing system; Constructing load test parameters using the variable load axial load feature library, performing a periodic test of load fluctuation over time on the test fixture, and collecting and generating variable load continuous test monitoring data through the intelligent sensing system; Constructing load test parameters using the impact axial load feature library, performing multiple load single mutation tests on the test fixture, and collecting and generating impact test monitoring data through the intelligent sensing system; The continuous test monitoring data set is generated by the constant continuous test monitoring data, the fatigue continuous test monitoring data, the variable load continuous test monitoring data and the impact test monitoring data.

2. A method for testing the axial load performance of a deep groove ball bearing according to claim 1, characterized in that: Constructing a failure discrimination feature library for the deep groove ball bearing, including: Using the target application scenario and the bearing model of the deep groove ball bearing as index elements, collecting a historical bearing failure dataset, wherein any data in the historical bearing failure dataset includes temperature, vibration, displacement, and acoustic emission signals when the bearing fails during operation; The failure discrimination feature library is constructed by performing feature correlation analysis of bearing temperature, vibration, displacement and acoustic emission signals at the time of failure based on the historical bearing failure data set.

3. A method for testing the axial load performance of a deep groove ball bearing according to claim 2, characterized in that: Based on the historical bearing failure data set, feature correlation analysis of bearing temperature, vibration, displacement and acoustic emission signals at the time of failure is performed to construct the failure discrimination feature library, including: Collect historical data sets of normal bearing operation and compare and count similar characteristic parameters with the historical data sets of bearing failure to establish thresholds for frequent temperature anomalies, frequent vibration anomalies, frequent displacement anomalies, and frequent acoustic emission signal anomalies. Based on the temperature abnormality frequency threshold, vibration abnormality frequency threshold, displacement abnormality frequency threshold and acoustic emission signal abnormality frequency threshold, the abnormal combination correlation analysis of temperature, vibration, displacement and acoustic emission signals of the historical bearing failure data set is performed to construct multiple correlation feature combinations; The temperature abnormality frequent threshold, vibration abnormality frequent threshold, displacement abnormality frequent threshold and acoustic emission signal abnormality frequent threshold are mapped to the multiple associated feature combinations to construct the failure discrimination feature library.

4. A method for testing the axial load performance of a deep groove ball bearing according to claim 1, characterized in that: The intelligent sensing system includes a temperature sensor, a vibration sensor, a displacement sensor and an acoustic signal sensor which are arranged on the test fixture and used for monitoring the deep groove ball bearing.

5. The method for testing the axial load performance of a deep groove ball bearing according to claim 1, wherein: The failure discrimination feature library is called to identify the continuous test monitoring data set to determine the constant failure load, variable load failure load, impact failure load and fatigue failure load when failure occurs, including: Inputting the constant continuous test monitoring data, the fatigue continuous test monitoring data, the variable load continuous test monitoring data and the impact test monitoring data into the failure discrimination feature library for failure feature matching, and if the matching is successful, determining the constant failure load-time feature, the fatigue failure load-time feature, the variable load failure load-time feature and the impact failure load; The constant failure load-time feature, fatigue failure load-time feature and variable load failure load-time feature are used to extract the constant failure load, fatigue failure load and variable load failure load that do not meet the preset life span.

6. A method for testing the axial load performance of a deep groove ball bearing according to claim 1, characterized in that: Determine the target application scenarios of deep groove ball bearings and build an axial load variation library, including a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library, and a fatigue load feature library, including: Using the target application scenario as an index element, collect historical operation data sets of bearings of the same model; Axial load analysis and classification are performed on the historical operation data set to construct a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library, and a fatigue load feature library.

7. A method for testing the axial load performance of a deep groove ball bearing according to claim 6, characterized in that: Performing axial load analysis and classification on the historical operation data set further includes: Extracting axial load and radial load from each piece of data in the historical operation data set, analyzing the influence of radial load on the axial load and performing radial influence compensation to generate an axial load analytical data set; The axial load analytical data set is classified into constant load, variable load, impact load and fatigue load, and a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library and a fatigue load feature library are constructed.

8. A deep groove ball bearing axial load performance testing device, characterized in that: The device is used to perform the axial load performance testing method of a deep groove ball bearing according to any one of claims 1 to 7, comprising: The axial load variation library construction module is used to determine the target application scenario of deep groove ball bearings and construct an axial load variation library, including a constant axial load feature library, a variable load axial load feature library, an impact axial load feature library, and a fatigue load feature library; A failure discrimination feature library construction module is used to construct a failure discrimination feature library of the deep groove ball bearing; a test module, configured to construct a test fixture for the deep groove ball bearing, perform a running test by constructing load test parameters using the constant axial load feature library, the variable load axial load feature library, the impact axial load feature library, and the fatigue load feature library, and continuously monitor using an intelligent sensing system to generate a continuous test monitoring data set; a failure discrimination module, configured to call the failure discrimination feature library to identify the continuous test monitoring data set, and determine a constant failure load, a variable failure load, an impact failure load, and a fatigue failure load when a failure occurs; The test result generating module is used to generate the load performance test result of the deep groove ball bearing using a constant failure load, a variable failure load, an impact failure load and a fatigue failure load.

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