A comprehensive intelligent testing method and system for high load-bearing bearing performance

Abstract

This invention discloses a comprehensive intelligent testing method and system for high-load bearing performance, relating to the field of mechanical engineering technology. The testing method includes the following steps: performing a loading test on the stiffness performance of a heavy-duty wind power bearing based on loading condition script parameters constructed according to the specifications for heavy-duty wind power bearings, and obtaining the original test dataset; analyzing the phase coupling degree between the micro-vibration signal and charge signal of the heavy-duty wind power bearing during the test duration using the Hilbert transform method to determine whether the contact behavior between the rolling elements and the raceway inside the heavy-duty wind power bearing is normal, and issuing a stiffness attenuation analysis command; and analyzing the stiffness attenuation degree of the heavy-duty wind power bearing during the test duration using the loading condition script parameters, obtaining the test stiffness attenuation value Zsj, to determine whether the stiffness performance of the currently tested heavy-duty wind power bearing is qualified, and marking it as a bearing of the corresponding stiffness performance level.

Description

Technical Field

[0001] This invention relates to the field of mechanical engineering technology, specifically to a comprehensive intelligent testing method and system for high load-bearing bearing performance. Background Technology

[0002] With the continuous evolution of mechanical engineering technology towards higher reliability, higher load-bearing capacity, and greater intelligence, high-load-bearing bearings, as core supporting components, play a crucial role in various high-end equipment systems. Especially in the field of large-scale renewable energy equipment, such as wind power generation systems, the main shaft bearings are subjected to variable loads, strong impacts, and complex environmental conditions for a long time, and their operational reliability directly affects the overall operating efficiency and service life of the machine. As a key core component connecting the impeller system and the power generation system, the stiffness performance of wind power heavy-duty industrial bearings is not only related to load-bearing capacity, but also directly determines the mechanical stability and failure threshold during operation. Therefore, the construction of stiffness performance testing methods for wind power heavy-duty industrial bearings has extremely high engineering value and is an urgent technical requirement.

[0003] In current research on the stiffness performance testing of heavy-duty industrial bearings for wind power, although some testing systems can achieve basic load test response analysis, significant deficiencies remain in key parameter modeling, micro-contact behavior identification, and stiffness evolution trend determination. Firstly, the construction of loading condition script parameters generally relies on manual experience or static typical values, lacking comprehensive consideration of dynamic factors such as wind speed, impact, and start-stop, making it difficult to accurately reproduce the bearing's service conditions. Secondly, traditional stiffness determination methods are mostly based on macroscopic load and displacement response curves, making it difficult to reveal the micro-contact stiffness state between the rolling elements and raceways under high-speed changing conditions. Especially in actual operation, the phase coupling characteristics between the micro-vibration signal and the triboelectric charge signal caused by rolling contact are long-term... The neglect or difficulty in effective analysis leads to a lag in the identification of internal contact states and stiffness evolution trends. In addition, the lack of a multi-dimensional evaluation mechanism that integrates coupling and disturbance in judging the degree of stiffness decay makes it difficult to accurately determine whether the test results can meet the requirements of heavy-duty wind power industrial bearings under complex working conditions. Therefore, in order to effectively overcome the problems of rough loading condition modeling, insufficient perception of micro-contact states, and lag in stiffness decay assessment in the existing stiffness performance testing technology of heavy-duty wind power industrial bearings, it is urgent to establish a comprehensive intelligent testing method and system for high-load bearing performance that is oriented towards real working conditions, coordinates multiple sources of signals, and has the ability to identify micro-stiffness changes, so as to achieve accurate evaluation and dynamic classification of bearing stiffness performance.

[0004] The reason for the existence of the above problems is that the existing stiffness testing methods are mostly driven by single variables and based on single signal acquisition, which ignore the multi-source disturbance characteristics of wind power conditions and the micro-nonlinear nature of contact behavior. In reality, when bearings are subjected to variable wind speed, start-stop impact or non-steady-state load, the internal rolling contact surface may experience stiffness decay or structural relaxation. Such changes are often accompanied by subtle phase deviations and charge mismatches. If these micro-abnormal signals cannot be captured in real time, it will lead to the accumulation and amplification of potential fatigue crack propagation, lubrication film failure or local seizing latent faults. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a comprehensive intelligent testing method and system for high-load bearing performance, solving the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a comprehensive intelligent testing method for high load-bearing bearing performance, comprising the following steps:

[0007] S1. Based on the loading condition script parameters constructed according to the requirements of the wind power heavy-duty bearing operating condition specification, load test the stiffness performance of the wind power heavy-duty industrial bearing and obtain the original test dataset.

[0008] S2. Based on the acquired original test dataset, combined with the Hilbert transform method and phase coupling analysis algorithm, analyze the phase coupling degree between the micro-vibration signal and the charge signal of the wind power heavy-duty industrial bearing when the speed and load change during the test period, so as to determine whether the contact behavior between the rolling elements and the raceway inside the wind power heavy-duty industrial bearing is normal, and issue corresponding stiffness attenuation analysis commands.

[0009] S3. After receiving the stiffness attenuation analysis command, combine the loading condition script parameters to analyze the degree of stiffness attenuation of the wind power heavy-duty industrial bearing during the test period and obtain the test stiffness attenuation value Zsj.

[0010] S4. Compare and analyze the test stiffness attenuation value Zsj with the preset attenuation threshold S to determine whether the stiffness performance of the wind power heavy-duty industrial bearing under test is qualified, and mark it as a bearing with the corresponding stiffness performance level.

[0011] Preferably, step S1 specifically includes:

[0012] S11. During the stiffness performance test of wind power heavy-duty industrial bearings, the typical external load conditions of wind power heavy-duty industrial bearings are identified according to the requirements of the wind power heavy-duty bearing operating condition specifications, and the characteristics of the typical external load conditions of wind power heavy-duty industrial bearings are extracted. The characteristics of the typical external load conditions include working time, wind speed change and impact load change.

[0013] S12. Based on the extracted external typical load condition characteristics and through long-term wind speed change data measured on-site, wind speed, rotational speed and load response curves are constructed. At the same time, combined with the measured historical load data and digital simulation results, the bearing load time series data of the load, rotational speed and start-stop impact borne by the heavy-duty industrial bearing of wind power are analyzed. Combined with time series statistical analysis, the bearing load time series data is characterized to construct typical working condition characteristic parameters.

[0014] S13. Combine and analyze the constructed typical working condition characteristic parameters with the duration and probability of occurrence of the load to form a load spectrum characteristic parameter set covering the entire working condition, which can be used as the loading condition script parameters for the performance test of wind power heavy-duty industrial bearings. The loading condition script parameters include load, speed and test duration.

[0015] S14. Based on the loading condition script parameters constructed in step S13, the wind power heavy-duty industrial bearing is subjected to real-time loading test through the servo loading control unit.

[0016] Preferably, step S1 further includes:

[0017] S15. Deploy multiple sets of micro sensors in the non-interference area of ​​the sealing structure of the heavy-duty industrial bearing for wind power. Among them, the multiple sets of micro sensors include micro-vibration acceleration sensors and triboelectric charge collection electrode sheets.

[0018] S16. During the loading test in step S14, based on the deployed micro-vibration acceleration sensor and the loading test duration, the high-frequency micro-vibration signal generated by the rolling elements and raceway inside the wind power heavy-duty industrial bearing when the rotational speed and load change is monitored in real time, so as to obtain the micro-vibration frequency Pzd at each monitoring time point within the test duration.

[0019] S17. During the loading test in step S14, based on the deployed triboelectric charge collection electrode and the test duration, the instantaneous charge density signal generated by the internal rolling elements and raceway of the wind power heavy-duty industrial bearing due to triboelectric charging is monitored in real time by electric field induction when the rotational speed and load change, so as to obtain the triboelectric charge Dmc at each monitoring time point within the test duration.

[0020] S18. The micro-vibration frequency Pzd and triboelectric charge Dmc at each monitoring time point within the obtained test duration are used to construct the original test dataset. The original test dataset is then preprocessed using a wavelet threshold denoising algorithm. The preprocessing process includes denoising, filtering, and time-series alignment.

[0021] Preferably, step S2 specifically includes:

[0022] S21. By performing feature recognition on the original test dataset constructed in step S18, the micro-vibration frequency Pzd at each monitoring time point within the test duration is extracted. Combined with the Hilbert transform method, the phase information structure of the high-frequency micro-vibrations generated by the rolling elements and raceways inside the heavy-duty wind turbine industrial bearing during changes in speed and load within the test duration is analyzed. The instantaneous phase coefficient Xzd of the micro-vibration frequency at each monitoring time point within the test duration is obtained, specifically through the following formula:

[0023] ;

[0024] In the formula, Let represent the micro-vibration frequency at the i-th monitoring time point within the test duration. It is represented as the instantaneous phase coefficient of the micro-vibration frequency at the i-th monitoring time point within the test duration. This is represented as the orthogonal component of the micro-vibration frequency at the i-th monitoring time point within the test duration after Hilbert transformation. Represented as the arctangent function;

[0025] S22. By performing feature recognition on the original test dataset constructed in step S18, the friction charge Dmc at each monitoring time point within the test duration is extracted. Combined with the Hilbert transform method, the phase information structure of the friction charge generated by frictional charging of the rolling elements and raceways inside the heavy-duty wind turbine industrial bearing during changes in speed and load within the test duration is analyzed. The instantaneous phase coefficient Xdh of the friction charge at each monitoring time point within the test duration is obtained using the following formula:

[0026] ;

[0027] In the formula, The triboelectric charge at the i-th monitoring time point within the test duration is represented as . It is represented as the instantaneous phase coefficient of the triboelectric charge at the i-th monitoring time point within the test duration. It is represented as the orthogonal component of the triboelectric charge at the i-th monitoring time point within the test duration after performing a Hilbert transform. It is represented as the arctangent function.

[0028] Preferably, step S2 further includes:

[0029] S23. Subtract the instantaneous phase coefficient Xzd of the micro-vibration frequency at each monitoring time point within the test duration from the corresponding instantaneous phase coefficient Xdh of the friction charge, analyze the phase deviation between the micro-vibration signal and the charge signal when the internal rolling elements and raceways of the wind power heavy-duty industrial bearing change speed and load within the test duration, and obtain the phase deviation coefficient Xxp at each monitoring time point within the test duration.

[0030] S24. Based on the phase deviation coefficient Xxp obtained in step S23 for each monitoring time point within the test duration, and combined with the phase coupling analysis algorithm, analyze the phase coupling degree between the micro-vibration signal and the charge signal when the internal rolling elements and raceways of the wind power heavy-duty industrial bearing change speed and load within the test duration, and obtain the phase coupling degree coefficient Xoh, which is specifically obtained through the following formula:

[0031] ;

[0032] In the formula, Let represent the phase deviation coefficient at the i-th monitoring time point within the test duration, where i = 1, 2, 3, ..., n, and n represents the monitoring period. It is represented as the cosine value of the phase deviation coefficient at the i-th monitoring time point within the test duration.

[0033] Preferably, step S2 further includes:

[0034] S25. Based on the value of the phase coupling coefficient Xoh in step S24, indicate whether the mechanical behavior and triboelectric effect of the rolling elements and raceways inside the heavy-duty wind turbine industrial bearing are highly coupled during the test period when the rotational speed and load change. This is to determine whether the stiffness contact behavior of the rolling elements and raceways inside the heavy-duty wind turbine industrial bearing is normal, and to issue a corresponding stiffness attenuation analysis command. The specific content is as follows:

[0035] If the phase coupling coefficient Xoh is within the range If the mechanical behavior and triboelectric effect of the rolling elements and raceway inside the heavy-duty industrial bearing of the wind power are not highly coupled when the speed and load change during the test period, it can be determined that the stiffness contact between the rolling elements and raceway inside the heavy-duty industrial bearing of the wind power is abnormal. At this time, a stiffness attenuation analysis command is issued.

[0036] If the phase coupling coefficient Xoh=1, it indicates that the mechanical behavior and triboelectric effect of the rolling elements and raceways inside the heavy-duty industrial bearing of the wind power are highly coupled when the speed and load change during the test period. This indicates that the stiffness contact between the rolling elements and raceways inside the heavy-duty industrial bearing of the wind power is normal, and no additional stiffness attenuation analysis command is issued at this time.

[0037] Preferably, step S3 specifically includes:

[0038] S31. Upon receiving the stiffness attenuation analysis command, based on the constructed loading condition script parameters, the load changes during the wind power heavy-duty industrial bearing loading test are monitored in real time to obtain the load value Nzh at each monitoring time point within the test duration. The average load value Nzh within the test duration is then obtained using a statistical averaging algorithm. avg ;

[0039] S32. Based on the average load value Nzh acquired during the test duration. avg The load value Nzh at each monitoring time point was correlated with the load value Nzh. After dimensionless processing, the load disturbance degree during the heavy-duty industrial bearing loading test of wind power was analyzed to obtain the load fluctuation coefficient Xzh.

[0040] Preferably, step S3 further includes:

[0041] S33. After extracting features from bearing sample data during the manufacturing stage of heavy-duty wind power industrial bearings, the standard reference stiffness value K0 of the heavy-duty wind power industrial bearings is obtained. This value is then correlated with the phase coupling coefficient Xoh and the load fluctuation coefficient Xzh. After dimensionless processing, the stiffness attenuation degree of the heavy-duty wind power industrial bearings during the test period is analyzed to obtain the test stiffness attenuation value Zsj, which is specifically obtained using the following formula:

[0042] ;

[0043] In the formula, Represented as an exponential function, and All are represented as weight values.

[0044] Preferably, step S4 specifically includes:

[0045] S41. Based on the test stiffness attenuation value Zsj obtained in step S33, compare and analyze it with the preset attenuation threshold S to determine whether the stiffness performance of the currently tested wind power heavy-duty industrial bearing is qualified, and mark it as a bearing of the corresponding stiffness performance level. The specific content is as follows:

[0046] If the test stiffness attenuation value Zsj < attenuation threshold S, it means that the stiffness performance of the wind power heavy-duty industrial bearing under the current test is qualified, meets the use of the wind power heavy-duty industrial bearing under typical external load conditions, and is marked as a first-class stiffness performance bearing.

[0047] If the test stiffness attenuation value Zsj ≥ attenuation threshold S, it indicates that the stiffness performance of the currently tested wind power heavy-duty industrial bearing is unqualified and cannot meet the use of wind power heavy-duty industrial bearings under typical external load conditions, and it is marked as a secondary stiffness performance bearing.

[0048] A comprehensive intelligent testing system for high load-bearing bearing performance includes a working condition loading module, a phase analysis module, an attenuation analysis module, and a performance determination module;

[0049] The loading condition module is used to perform loading tests on the stiffness performance of heavy-duty wind power industrial bearings based on the loading condition script parameters constructed according to the requirements of the wind power heavy-duty bearing specifications, and to obtain the original test dataset.

[0050] Based on the acquired original test dataset, the phase analysis module combines the Hilbert transform method and phase coupling analysis algorithm to analyze the phase coupling degree between the micro-vibration signal and the charge signal of the wind power heavy-duty industrial bearing when the speed and load change during the test period. This is to determine whether the contact behavior between the rolling elements and the raceway inside the wind power heavy-duty industrial bearing is normal and to issue corresponding stiffness attenuation analysis commands.

[0051] The stiffness attenuation analysis module is used to analyze the degree of stiffness attenuation of the wind power heavy-duty industrial bearing within the test duration and obtain the test stiffness attenuation value Zsj after receiving the stiffness attenuation analysis command and combining the loading condition script parameters.

[0052] The performance determination module is used to compare and analyze the test stiffness attenuation value Zsj with the preset attenuation threshold S to determine whether the stiffness performance of the currently tested wind power heavy-duty industrial bearing is qualified, and to mark it as a bearing with the corresponding stiffness performance level.

[0053] This invention provides a comprehensive intelligent testing method and system for high-load bearing performance, which has the following beneficial effects:

[0054] (1) This invention integrates working condition modeling, multi-source signal acquisition and processing and coupling attenuation evaluation mechanism to realize accurate evaluation and classification of stiffness performance of wind power heavy-duty industrial bearings. This scheme effectively overcomes the problems of insufficient working condition reproduction, insensitive identification of micro-contact behavior and delayed response to stiffness degradation trend of existing testing methods. It has high practicality, robustness and engineering adaptability. It not only improves the authenticity and accuracy of bearing performance testing, but also enhances the early failure identification capability under complex variable load conditions, and significantly improves the operational reliability and safety redundancy guarantee level of wind power main shaft system.

[0055] (2) By combining the wind power heavy load bearing operating condition specifications, wind speed change curves, historical data of impact loading and start-stop dynamic characteristics, a set of loading condition script parameters with coverage and representativeness is constructed, avoiding the defects of single static operating condition input in traditional test methods. The constructed script parameters not only consider the typical load paths that the bearing may face during its service life, but also combine the long-term operation data of the wind farm to carry out multi-dimensional statistical modeling, so that the loading process is highly close to the actual service environment in terms of stress amplitude, rapid change impact and time distribution, thereby improving the real matching degree of the test conditions and the reliability of stiffness response test.

[0056] (3) Innovatively, micro-vibration signals and triboelectric charge signals are used as synchronous observation objects, and Hilbert transform and phase coupling analysis algorithm are introduced to extract instantaneous phase structure, and a dynamic coupling identification model of the contact behavior between rolling elements and raceways is established. Compared with the traditional stiffness judgment method based on single-channel displacement or load response, this scheme can capture the consistency of micro-contact response inside the bearing in real time during the process of speed and load changes. Especially in the weak contact mismatch and early structural deformation stage, high sensitivity identification can be achieved by the degree of phase deviation, which enhances the ability to analyze micro-stiffness degradation signals.

[0057] (4) By constructing an exponential decay analysis model jointly driven by the phase coupling coefficient Xoh and the load fluctuation coefficient Xzh, multidimensional modeling and dynamic trend determination of stiffness degradation process are realized. The introduced load fluctuation coefficient Xzh can effectively measure the degree of unsteady disturbance of bearing stress environment. Combined with the exponential stiffness mapping formula constructed by real-time phase coupling results, it can not only determine whether the stiffness has decreased, but also accurately quantify its stiffness level. This mechanism improves the early warning capability of structural fatigue expansion and contact stiffness relaxation, and provides data support for the reliability verification and operation and maintenance strategy optimization of wind power bearings in complex service scenarios. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the comprehensive intelligent testing method for high load-bearing bearing performance according to the present invention;

[0059] Figure 2 This is a block diagram of a comprehensive intelligent testing system for high load-bearing bearing performance according to the present invention;

[0060] Figure 3 This is a logic diagram for step S1 of the present invention;

[0061] Figure 4 This is a logic diagram for step S2 of the present invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Example 1

[0064] Please see Figure 1 This invention provides a comprehensive intelligent testing method for high load-bearing bearing performance, comprising the following steps:

[0065] S1. Based on the loading condition script parameters constructed according to the requirements of the wind power heavy-duty bearing operating condition specification, load test the stiffness performance of the wind power heavy-duty industrial bearing and obtain the original test dataset.

[0066] S2. Based on the acquired original test dataset, combined with the Hilbert transform method and phase coupling analysis algorithm, analyze the phase coupling degree between the micro-vibration signal and the charge signal of the wind power heavy-duty industrial bearing when the speed and load change during the test period, so as to determine whether the contact behavior between the rolling elements and the raceway inside the wind power heavy-duty industrial bearing is normal, and issue corresponding stiffness attenuation analysis commands.

[0067] S3. After receiving the stiffness attenuation analysis command, combine the loading condition script parameters to analyze the degree of stiffness attenuation of the wind power heavy-duty industrial bearing during the test period and obtain the test stiffness attenuation value Zsj.

[0068] S4. Compare and analyze the test stiffness attenuation value Zsj with the preset attenuation threshold S to determine whether the stiffness performance of the wind power heavy-duty industrial bearing under test is qualified, and mark it as a bearing with the corresponding stiffness performance level.

[0069] In this embodiment, a multi-step testing system based on operating condition driving, signal coordination, and intelligent recognition is constructed for the stiffness performance testing of heavy-duty industrial bearings in wind power under complex operating conditions. This system has several significant technical advantages and engineering value. First, by combining wind power operation specifications and historical operating condition data to construct loading condition script parameters, the actual service environment of the bearing under varying wind speeds, impact loads, and start-stop states can be realistically reproduced, achieving a high degree of matching of the test loading scenario and overcoming the shortcomings of traditional methods, such as one-sided operating condition settings and high distortion rates. Second, using Hilbert transform and phase coupling analysis, the phase synchronization of micro-vibration and triboelectric signals is introduced for the first time as a contact... The behavioral evaluation criteria break through the limitations of existing testing methods that rely solely on macroscopic load displacement data. It can identify microscopic degradation signs of rolling contact stiffness during operation, improving the testing system's response to potential structural fatigue and lubrication deterioration issues. Furthermore, this invention constructs a comparison mechanism for the threshold of the test stiffness attenuation value Zsj, enabling quantitative evaluation of bearing stiffness levels. Overall, this method works synergistically in test modeling, data identification, and stiffness determination, achieving accurate assessment, intelligent response, and quality grading of the stiffness performance of heavy-duty wind turbine bearings under complex operating conditions, providing highly reliable technical support for ensuring the long-term stable operation of wind power systems.

[0070] Example 2

[0071] Please refer to Figure 1 and Figure 3 Specifically, the steps in S1 include:

[0072] S11. During the stiffness performance test of wind power heavy-duty industrial bearings, the typical external load conditions of wind power heavy-duty industrial bearings are identified according to the requirements of the wind power heavy-duty bearing operating condition specifications, and the characteristics of the typical external load conditions of wind power heavy-duty industrial bearings are extracted. The characteristics of the typical external load conditions include working time, wind speed change and impact load change.

[0073] S12. Based on the extracted external typical load condition characteristics and through long-term wind speed change data measured on-site, wind speed, rotational speed and load response curves are constructed. At the same time, combined with the measured historical load data and digital simulation results, the bearing load time series data of the load, rotational speed and start-stop impact borne by the heavy-duty industrial bearing of wind power are analyzed. Combined with time series statistical analysis, the bearing load time series data is characterized to construct typical working condition characteristic parameters.

[0074] S13. Combine and analyze the constructed typical working condition characteristic parameters with the duration and probability of occurrence of the load to form a load spectrum characteristic parameter set covering the entire working condition, which can be used as the loading condition script parameters for the performance test of wind power heavy-duty industrial bearings. The loading condition script parameters include load, speed and test duration.

[0075] S14. Based on the loading condition script parameters constructed in step S13, the wind power heavy-duty industrial bearing is subjected to real-time loading test through the servo loading control unit.

[0076] It should be noted that typical external load conditions refer to the external operating conditions that frequently occur in the actual service of wind power heavy-duty industrial bearings and have a significant impact on structural response. These include operating duration (long-term continuous operation), wind speed changes (gusts and wind shear), and impact load changes (start-stop or sudden load disturbances). These characteristics reflect the actual mechanical environment in which the bearing is located at different time periods. Typical operating condition characteristic parameters are a set of representative and regular parameters extracted by constructing wind speed, rotational speed, and load response curves and using time-series statistical analysis methods, based on the above operating condition characteristics and combined with long-term on-site wind speed monitoring data, historical load records, and digital simulation results. These parameters include the duration of different load steps, the amplitude of frequently occurring impact fluctuations, and the repetition period of typical start-stop events. These parameters are used to generate a load spectrum characteristic parameter set covering the entire life cycle of the bearing, serving as the input basis for the loading condition script, ensuring that the testing process highly replicates the actual operating conditions, and improving the authenticity and effectiveness of stiffness performance testing.

[0077] Specifically, the S1 steps also include:

[0078] S15. Deploy multiple sets of micro sensors in the non-interference area of ​​the sealing structure of the heavy-duty industrial bearing for wind power. Among them, the multiple sets of micro sensors include micro-vibration acceleration sensors and triboelectric charge collection electrode sheets.

[0079] S16. During the loading test in step S14, based on the deployed micro-vibration acceleration sensor and the loading test duration, the high-frequency micro-vibration signal generated by the rolling elements and raceway inside the wind power heavy-duty industrial bearing when the rotational speed and load change is monitored in real time, so as to obtain the micro-vibration frequency Pzd at each monitoring time point within the test duration.

[0080] S17. During the loading test in step S14, based on the deployed triboelectric charge collection electrode and the test duration, the instantaneous charge density signal generated by the internal rolling elements and raceway of the wind power heavy-duty industrial bearing due to triboelectric charging is monitored in real time by electric field induction when the rotational speed and load change, so as to obtain the triboelectric charge Dmc at each monitoring time point within the test duration.

[0081] It should be noted that the micro-vibration frequency Pzd refers to the frequency characteristics of the high-frequency micro-vibration signal generated by the microscopic interaction of contact, slippage, and impact between the rolling elements and raceway during the loading test of the wind power heavy-duty industrial bearing. It reflects the changes in the internal stiffness state and contact stability of the bearing. The triboelectric charge Dmc refers to the instantaneous charge density value obtained by electric field induction under the same working conditions due to the triboelectric effect generated by the rolling contact interface under high-speed rotation and load. It reflects the friction behavior and charge accumulation characteristics of the contact surface. These two parameters are obtained by a micro-vibration acceleration sensor and a triboelectric charge collection electrode plate deployed in the non-interference area of ​​the bearing sealing structure, respectively. The micro-vibration frequency Pzd is extracted from the high-frequency acceleration signal by Fourier transform or short-time spectrum analysis, while the triboelectric charge Dmc is obtained by real-time sampling by the charge electrode and by the charge density conversion algorithm. Together, they constitute a multi-dimensional physical response signal of the rolling contact behavior under the bearing operating state, providing a key data foundation for subsequent phase coupling analysis and stiffness performance determination.

[0082] S18. The micro-vibration frequency Pzd and triboelectric charge Dmc at each monitoring time point within the obtained test duration are used to construct the original test dataset. The original test dataset is then preprocessed using a wavelet threshold denoising algorithm. The preprocessing process includes denoising, filtering, and time-series alignment.

[0083] In this embodiment, by constructing precise and dynamically responsive loading condition script parameters and deploying miniature multiphysics sensors, the realism of the loading conditions, the richness of the data, and the identifiability of the microscopic behavior in the stiffness performance test of heavy-duty industrial bearings for wind power are improved. First, steps S11-S14 effectively solve the problem that traditional tests rely on static typical values ​​for loading conditions and ignore wind speed changes and impact responses. By comprehensively extracting typical load condition characteristics of working duration, wind speed disturbance, and load mutation, and introducing bearing load time series data generated by fusing on-site wind speed monitoring data, historical load data, and digital simulation results, combined with time series statistical analysis methods, a load spectrum parameter set covering all loading conditions is constructed. This loading condition construction mechanism can more realistically restore the bearing service state and provide highly realistic boundary conditions for subsequent stiffness response evaluation. Second, in steps S15-S18, by using bearing seal non- Micro-vibration accelerometers and triboelectric charge collection electrodes were deployed in the interference area to construct a multi-channel, high-frequency, and multi-physical-quantity original signal acquisition path. This enabled the synchronous acquisition of high-frequency micro-vibration behavior and triboelectric effects of the rolling elements and raceways inside the bearing under different loading stages, effectively breaking the limitations of traditional single displacement / load response testing. In addition, signal preprocessing combined with wavelet threshold denoising algorithm ensured high-fidelity processing of the micro-vibration frequency Pzd and triboelectric charge Dmc in terms of noise suppression, characteristic frequency band preservation, and time sequence alignment. This provided structurally complete and highly clear data support for subsequent phase coupling analysis and stiffness evolution modeling. Therefore, the overall construction of this step not only improved the dynamic simulation capability of the test environment but also achieved dual guarantees of physical proximity and data accuracy throughout the entire process from working condition modeling to signal acquisition, demonstrating unique engineering practicality and integration advantages in the field of wind power heavy-duty bearing testing.

[0084] Example 3

[0085] Please refer to Figure 1 and Figure 4 Specifically, the steps in S2 include:

[0086] S21. By performing feature recognition on the original test dataset constructed in step S18, the micro-vibration frequency Pzd at each monitoring time point within the test duration is extracted. Combined with the Hilbert transform method, the phase information structure of the high-frequency micro-vibrations generated by the rolling elements and raceways inside the heavy-duty wind turbine industrial bearing during changes in speed and load within the test duration is analyzed. The instantaneous phase coefficient Xzd of the micro-vibration frequency at each monitoring time point within the test duration is obtained, specifically through the following formula:

[0087] ;

[0088] In the formula, Let represent the micro-vibration frequency at the i-th monitoring time point within the test duration. It is represented as the instantaneous phase coefficient of the micro-vibration frequency at the i-th monitoring time point within the test duration. This is represented as the orthogonal component of the micro-vibration frequency at the i-th monitoring time point within the test duration after Hilbert transformation. Represented as the arctangent function;

[0089] It should be noted that the formula in step S21 is used to calculate the instantaneous phase coefficient Xzd of the micro-vibration frequency at the i-th monitoring time point within the test duration. i , where the arctangent function This formula is used to solve for the instantaneous phase of the complex analytical form of vibration signals. It directly addresses the technical deficiency of traditional methods mentioned in the background, which are unable to identify changes in rolling contact stiffness under high-speed dynamic conditions. This is because traditional methods usually rely on load and displacement response curves, making it difficult to analyze the subtle changes in micro-vibration characteristics over time. The instantaneous phase coefficient Xzd of the micro-vibration frequency obtained by this formula can perform in-depth deconstruction of the phase dimension of the micro-vibration signal, capturing the subtle synchronous changes between the rolling element and the raceway under load and speed disturbances. Its role is not only to describe the magnitude of the micro-vibration frequency at a certain moment, but more importantly, to provide a time-phase structure mapping index for measuring the implicit correlation between the periodicity, non-stationarity, and contact stiffness state of vibration behavior.

[0090] S22. By performing feature recognition on the original test dataset constructed in step S18, the friction charge Dmc at each monitoring time point within the test duration is extracted. Combined with the Hilbert transform method, the phase information structure of the friction charge generated by frictional charging of the rolling elements and raceways inside the heavy-duty wind turbine industrial bearing during changes in speed and load within the test duration is analyzed. The instantaneous phase coefficient Xdh of the friction charge at each monitoring time point within the test duration is obtained using the following formula:

[0091] ;

[0092] In the formula, The triboelectric charge at the i-th monitoring time point within the test duration is represented as . It is represented as the instantaneous phase coefficient of the triboelectric charge at the i-th monitoring time point within the test duration. It is represented as the orthogonal component of the triboelectric charge at the i-th monitoring time point within the test duration after performing a Hilbert transform. It is represented as the arctangent function.

[0093] It should be noted that the formula in step S22 is used to calculate the instantaneous phase coefficient Xdh of the triboelectric charge at the i-th monitoring time point. i , Represented as the arctangent function, it is used to solve for the instantaneous phase of the complex analytical form of the charge signal. The introduction of this formula directly addresses the core issue pointed out in the background technology: traditional testing methods neglect the correlation between changes in triboelectric signals and microscopic contact states. Existing technologies generally rely on mechanical quantities (displacement and acceleration) for stiffness inference, lacking in-depth analysis of the micro-electric signals at the contact interface. Especially under high-speed operation, impact loads, or poor lubrication conditions, the stiffness degradation signal carried by the triboelectric effect is often overlooked. The logical role of the instantaneous phase coefficient Xdh of the triboelectric charge is that it not only reflects the frictional charge but also... The phase state of the triboelectric signal at a certain instant reveals, more importantly, the rhythmic and synchronous characteristics of the frictional behavior changes of the contact interface under dynamic conditions. The phase of the triboelectric charge is often more sensitive to micro-slippage, lubricating film rupture, or transient contact imbalance, and can achieve an earlier stiffness anomaly response than vibration signals. Therefore, the formula based on the Hilbert transform to extract the phase angle is not only mathematically rigorous, but also closely fits the real mechanism of contact stiffness evolution of wind turbine heavy-duty bearings under complex conditions in a physical sense. It is a key link in realizing the integration of micro-contact perception and multi-dimensional stiffness determination.

[0094] Specifically, the S2 steps also include:

[0095] S23. Subtract the instantaneous phase coefficient Xzd of the micro-vibration frequency at each monitoring time point within the test duration from the corresponding instantaneous phase coefficient Xdh of the friction charge, analyze the phase deviation between the micro-vibration signal and the charge signal when the internal rolling elements and raceways of the wind power heavy-duty industrial bearing change speed and load within the test duration, and obtain the phase deviation coefficient Xxp at each monitoring time point within the test duration.

[0096] It should be noted that the phase deviation coefficient Xxp is an index used to measure the degree of phase synchronization deviation between the micro-vibration signal and the triboelectric charge signal between the rolling elements and raceway at various monitoring time points during the loading test of heavy-duty wind power industrial bearings. It reflects the consistency of their responses under the same physical event and indirectly reveals the stability of contact behavior and stiffness coupling state. Specifically, the phase deviation coefficient Xxp is calculated by subtracting the instantaneous phase coefficient Xzd of the micro-vibration frequency from the instantaneous phase coefficient Xdh of the triboelectric charge at the corresponding moment. The closer its value is to zero, the higher the phase synchronization of the two signals and the more stable the contact stiffness state. The larger the deviation, the poor the synchronization and the existence of stiffness degradation or poor lubrication. The phase deviation coefficient Xxp is obtained by performing time-point corresponding processing after obtaining the instantaneous phase of the micro-vibration and charge signals through Hilbert transform, and is an important basic data for realizing subsequent phase coupling analysis and stiffness change trend identification.

[0097] S24. Based on the phase deviation coefficient Xxp obtained in step S23 for each monitoring time point within the test duration, and combined with the phase coupling analysis algorithm, analyze the phase coupling degree between the micro-vibration signal and the charge signal when the internal rolling elements and raceways of the wind power heavy-duty industrial bearing change speed and load within the test duration, and obtain the phase coupling degree coefficient Xoh, which is specifically obtained through the following formula:

[0098] ;

[0099] In the formula, Let represent the phase deviation coefficient at the i-th monitoring time point within the test duration, where i = 1, 2, 3, ..., n, and n represents the monitoring period. It is represented as the cosine value of the phase deviation coefficient at the i-th monitoring time point within the test duration.

[0100] It should be noted that the formula in step S24 is used to calculate the phase coupling coefficient Xoh. The phase deviation coefficient cosine value at the i-th monitoring time point within the test duration is represented, reflecting the synchronization degree between the micro-vibration signal and the triboelectric signal at that moment. This formula is highly related to the shortcomings of the prior art, which has long neglected the phase coupling relationship between micro-vibration and charge signals, making it difficult to identify changes in micro-contact stiffness. Traditional stiffness assessment techniques generally lack modeling and quantification of the relative phase behavior between signals, making it difficult to reveal the coupling degradation trend between the rolling element and the raceway under complex load disturbances. This formula constructs a dimensionless phase coupling coefficient Xoh in the range of [-1, 1] by summing and averaging the cosines of all phase deviations within the test period. This formula has good physical interpretation; the stronger the coupling, the better the synchronization response, indicating that the actual stiffness contact state between the rolling element and the raceway is good. In summary, the phase coupling coefficient Xoh is a key bridge to transform time-series micro-phase characteristics into macro-contact health assessment, providing accurate, quantitative, and dynamic response criteria for judging the stiffness evolution trend of wind power heavy-duty bearings. It is the core innovation point that makes up for the problems of macro-response lag and insufficient micro-anomaly identification in existing stiffness assessment methods.

[0101] Specifically, the S2 steps also include:

[0102] S25. Based on the value of the phase coupling coefficient Xoh in step S24, indicate whether the mechanical behavior and triboelectric effect of the rolling elements and raceways inside the heavy-duty wind turbine industrial bearing are highly coupled during the test period when the rotational speed and load change. This is to determine whether the stiffness contact behavior of the rolling elements and raceways inside the heavy-duty wind turbine industrial bearing is normal, and to issue a corresponding stiffness attenuation analysis command. The specific content is as follows:

[0103] If the phase coupling coefficient Xoh is within the range If the mechanical behavior and triboelectric effect of the rolling elements and raceway inside the heavy-duty industrial bearing of the wind power are not highly coupled when the speed and load change during the test period, it can be determined that the stiffness contact between the rolling elements and raceway inside the heavy-duty industrial bearing of the wind power is abnormal. At this time, a stiffness attenuation analysis command is issued.

[0104] If the phase coupling coefficient Xoh=1, it indicates that the mechanical behavior and triboelectric effect of the rolling elements and raceways inside the heavy-duty industrial bearing of the wind power are highly coupled when the speed and load change during the test period. This indicates that the stiffness contact between the rolling elements and raceways inside the heavy-duty industrial bearing of the wind power is normal, and no additional stiffness attenuation analysis command is issued at this time.

[0105] In this embodiment, by constructing a multi-dimensional signal coupling analysis mechanism with phase synchronization as its core, a highly sensitive identification of the microscopic contact behavior of the rolling elements and raceways of heavy-duty wind power industrial bearings and an intelligent judgment of stiffness evolution trends are achieved, significantly improving the technical level of stiffness performance testing from macroscopic response to microscopic analysis. First, by performing Hilbert transforms on two heterogeneous signals—micro-vibration frequency and triboelectric charge—the corresponding instantaneous phase coefficients are obtained, breaking the limitations of traditional isolated analysis of vibration and electrical parameters. This allows the mechanical impact behavior and triboelectric behavior generated during the internal rolling contact process of the bearing under dynamic loads and speed disturbances to be simultaneously captured and analyzed in the time domain. Second, a phase deviation coefficient Xxp is constructed by the phase difference between the instantaneous phase coefficient Xzd of the micro-vibration frequency and the corresponding instantaneous phase coefficient Xdh of the triboelectric charge. Then, based on this phase difference sequence, a phase coupling degree coefficient Xoh is constructed using the cosine function average summation method, forming a core evaluation index reflecting the consistency of microscopic contact stiffness, which has a clear numerical discrimination boundary. Boundary: When the phase coupling coefficient Xoh value approaches 1, it indicates that the micro-vibration and triboelectric response are highly synchronized, characterizing good contact stiffness between the rolling element and the raceway. However, when the phase coupling coefficient Xoh value deviates significantly from 1 or fluctuates more, it reveals an abnormal trend of instability, relaxation, or weakening of structural stiffness in the internal contact state. At this time, a stiffness decay analysis command will be issued to drive subsequent stiffness decay trend analysis. Unlike traditional methods that rely solely on displacement and load relationship curves or spectral amplitude comparisons, this invention introduces phase coupling consistency, an index with both time resolution and physical interpretation, enabling the test to identify early micro-damage or lubrication mismatch signs through phase perturbation before the structure shows obvious stiffness decay. This significantly improves the early warning capability and trend perception accuracy of stiffness performance testing. In summary, the intelligent judgment mechanism based on multi-signal phase fusion constructed in step S2 not only enhances the test system's ability to visualize changes in micro-contact stiffness but also provides a high-dimensional criterion support for the stiffness performance testing of wind power heavy-duty bearings that transcends the limitations of traditional methods.

[0106] Example 4

[0107] Please refer to Figure 1 Specifically, the S3 steps include:

[0108] S31. Upon receiving the stiffness attenuation analysis command, based on the constructed loading condition script parameters, the load changes during the wind power heavy-duty industrial bearing loading test are monitored in real time to obtain the load value Nzh at each monitoring time point within the test duration. The average load value Nzh within the test duration is then obtained using a statistical averaging algorithm. avg ;

[0109] It should be noted that the load value Nzh refers to the instantaneous load value actually borne by the bearing at each monitoring time point during the loading test of the heavy-duty industrial bearing for wind power. It is usually expressed in N (Newtons) or kN (kilonewtons) and is used to reflect the stress state of the bearing under dynamic working conditions. The load value Nzh is obtained in real time by the servo loading unit controlled by the loading condition script to apply the actual load, and is collected in real time by pressure sensors and force measuring rings deployed on the loading system or outside the bearing. As a key data point describing the load input under the bearing's operating environment, the load value Nzh is not only used to calculate the average load value Nzh over the test duration. avg It also provides a data basis for subsequent analysis of load disturbance amplitude and load fluctuation, thereby participating in stiffness attenuation trend identification and performance evaluation.

[0110] S32. Based on the average load value Nzh acquired during the test duration. avg The load value Nzh at each monitoring time point is correlated and, after dimensionless processing, the load disturbance degree during the heavy-duty industrial bearing loading test of wind power is analyzed to obtain the load fluctuation coefficient Xzh, which is specifically obtained through the following formula:

[0111] ;

[0112] In the formula, Expressed as the variance of the load values ​​over the test duration;

[0113] It should be noted that the formula in step S32 is used to calculate the load fluctuation coefficient Xzh. This formula proposes a solution to the problem pointed out in the background art that traditional stiffness assessment ignores the influence of actual load fluctuation disturbance. Existing technologies usually use the average load as a static input for stiffness response determination, which is difficult to reflect the actual operating state of the bearing under variable load, impact load and unsteady load, thus causing the assessment results to be lagging or biased. The logical role of the load fluctuation coefficient Xzh is to quantify the degree of load instability of the bearing during the loading test in a dimensionless form, that is, the disturbance intensity under unit load. When the load fluctuation coefficient Xzh approaches zero, it indicates that the load process is stable and the structural response is predictable. Conversely, it indicates that the load borne by the bearing fluctuates frequently or has sudden peaks. These disturbances can easily lead to contact stiffness relaxation, lubricating film rupture, or rolling fatigue. Therefore, the load fluctuation coefficient Xzh can be used as an important adjustment factor to correct stiffness attenuation calculations. This allows stiffness assessment to no longer be based on ideal loading, but to take into account the dynamic disturbance effects of actual working conditions. This enhances the adaptability and physical realism of the model under complex load environments and improves the accuracy and engineering value of stiffness evolution trend analysis of wind power heavy-duty bearings under operating conditions.

[0114] Specifically, the S3 steps also include:

[0115] S33. After extracting features from bearing sample data during the manufacturing stage of heavy-duty wind power industrial bearings, the standard reference stiffness value K0 of the heavy-duty wind power industrial bearings is obtained. This value is then correlated with the phase coupling coefficient Xoh and the load fluctuation coefficient Xzh. After dimensionless processing, the stiffness attenuation degree of the heavy-duty wind power industrial bearings during the test period is analyzed to obtain the test stiffness attenuation value Zsj, which is specifically obtained using the following formula:

[0116] ;

[0117] In the formula, Represented as an exponential function, and All are represented as weight values.

[0118] It should be noted that the formula in step S32 is used to calculate the test stiffness decay value Zsj. This formula directly addresses the three major pain points raised in the background technology: first, traditional methods lack a stiffness mapping mechanism based on microscopic phase characteristics; second, they do not consider the dynamic amplification effect of load disturbances on structural stiffness; and third, existing stiffness evaluation methods are mostly static linear inferences, which are difficult to accurately reflect the nonlinear stiffness decay trend under complex working conditions. This indicates that the greater the deviation in the degree of contact coupling, the easier it is for the stiffness to decrease; This indicates that the more severe the disturbance, the more sensitive the attenuation process; that is, under the conditions of low coupling and strong disturbance, the exponential term tends to 0, causing the test stiffness attenuation value Zsj to approach the standard reference stiffness value K0, and the stiffness decreases significantly; conversely, the stiffness remains good. The introduction of the test stiffness attenuation value Zsj not only solves the problem of difficulty in quantifying and predicting stiffness changes in the background, but also realizes the transformation from a single mechanical response to a three-in-one intelligent stiffness assessment of signal coupling, load disturbance and standard benchmark at the method level. It is the key numerical basis for realizing the health trend assessment and performance level classification of wind power heavy-duty industrial bearings.

[0119] The core innovation of the formula in step S32 lies in using the phase coupling degree of the two key dynamic signal indicators, micro-vibration and triboelectric signal, and the load disturbance intensity during loading as coupling variables. These variables are then embedded into the attenuation mapping of the standard stiffness value through an exponential function, forming a dynamic, nonlinear, multi-factor driven stiffness attenuation model.

[0120] In this embodiment, by constructing a stiffness attenuation assessment mechanism driven by both phase coupling degree and load disturbance state, quantitative modeling and accurate identification of stiffness degradation trends of heavy-duty wind power industrial bearings under dynamic service environments are achieved, significantly improving the trend insight capability and engineering practical value of bearing performance testing. First, in steps S31-S32, the load value Nzh at each moment during the loading test is monitored in real time, and the average load value Nzh over the test duration is statistically obtained. avg Then, a dimensionless comparative analysis is performed with the load at each time point to construct the load fluctuation coefficient Xzh, which can effectively quantify the load stability and external disturbance intensity of the wind turbine main shaft bearing during service, making up for the shortcomings of traditional stiffness assessment that ignores the influence of actual load dynamic fluctuations. Secondly, in step S33, a standard reference stiffness K0 is established by introducing bearing sample data from the manufacturing stage, and the phase coupling degree coefficient Xoh obtained from its real-time coupling analysis is jointly analyzed with the current load fluctuation coefficient Xzh to further construct an exponential mapping model of stiffness attenuation value Zsj. This model uses an exponential function to incorporate contact consistency and external disturbances into a unified evaluation framework. This approach allows stiffness assessment results to encompass both the inherent degradation trend of the structure and the influence of service environment fluctuations. It overcomes the limitations of previous methods that relied solely on load and displacement response slopes to determine stiffness, enabling the identification of early stiffness attenuation signals through multi-source index fusion before significant structural deformation occurs. This demonstrates high sensitivity and predictive capability. Furthermore, it not only reflects the degree of stiffness attenuation but also serves as a core reference indicator for subsequent hierarchical management and reliability assessment. In summary, step S3, by constructing an attenuation analysis path driven by the load disturbance factor, standard stiffness benchmark, and phase coupling signal, significantly improves the accuracy and scenario adaptability of stiffness performance testing.

[0121] Example 5

[0122] Please refer to Figure 1 Specifically, the S4 steps include:

[0123] S41. Based on the test stiffness attenuation value Zsj obtained in step S33, compare and analyze it with the preset attenuation threshold S to determine whether the stiffness performance of the currently tested wind power heavy-duty industrial bearing is qualified, and mark it as a bearing of the corresponding stiffness performance level. The specific content is as follows:

[0124] If the test stiffness attenuation value Zsj < attenuation threshold S, it means that the stiffness performance of the wind power heavy-duty industrial bearing under the current test is qualified, meets the use of the wind power heavy-duty industrial bearing under typical external load conditions, and is marked as a first-class stiffness performance bearing.

[0125] If the test stiffness attenuation value Zsj ≥ attenuation threshold S, it indicates that the stiffness performance of the currently tested wind power heavy-duty industrial bearing is unqualified and cannot meet the use of wind power heavy-duty industrial bearings under typical external load conditions, and it is marked as a secondary stiffness performance bearing.

[0126] It should be noted that the attenuation threshold S is a reference judgment limit value used to determine whether the stiffness performance of heavy-duty industrial bearings for wind power is qualified. It is usually obtained based on statistical analysis of a large number of manufacturing and operational decommissioning samples, engineering experience curve fitting, and load condition adaptability standards. Specifically, the threshold S can be determined by conducting multiple rounds of fatigue tests on the same type of bearing under actual working conditions, measuring its failure probability distribution under different stiffness attenuation levels, and combining it with the minimum safe stiffness requirements of the bearing under typical wind speed-load coupling environment to determine the safe limit range that the stiffness attenuation should not exceed. In addition, it can also be set as a preset value based on the qualified lower limit of stiffness residual value in the standards of specific wind turbine manufacturers or national industry specifications. Its function is to discretize the tested stiffness attenuation value Zsj into qualified and unqualified, and first-level and second-level stiffness level judgments, providing clear standards for quality classification, reliability screening, and on-site installation applications, and enhancing the feasibility and systematic management capabilities of engineering decisions for stiffness performance evaluation.

[0127] In this embodiment, a clear and executable stiffness performance level evaluation system is established by constructing a comparative analysis mechanism based on the stiffness attenuation value Zsj and a preset threshold S. This enables quantitative classification management and quality qualification judgment of the stiffness performance of heavy-duty wind power industrial bearings, demonstrating significant practicality and engineering guidance value. Using the stiffness attenuation value Zsj obtained in step S3, which comprehensively considers the phase coupling degree and load disturbance intensity, as the evaluation benchmark, and setting a preset attenuation threshold S for typical wind power service environments, the numerical relationship between the two is used for classification and judgment. This not only accurately identifies whether the bearing meets the target requirements but also... The stiffness capability under continuous service conditions can be classified and output in the form of grade labels, realizing a key leap from measurement and analysis to judgment and control. Unlike traditional fuzzy judgment methods, this invention sets clear performance thresholds, enabling bearings to have verifiable and traceable judgment standards during factory inspection, fault diagnosis, and remanufacturing, thereby improving the decision-making value of stiffness assessment results. In summary, it not only completes the final closed loop of stiffness assessment, but also provides a data quantification foundation and engineering implementation path for the reliability control of heavy-duty wind power bearings, transforming test results from passive data to proactive decision-making results.

[0128] In a specific embodiment, the stiffness performance test results of the heavy-duty industrial bearing for wind power during the test period are displayed accordingly, specifically as follows:

[0129] If the stiffness performance test result of the wind power heavy-duty industrial bearing being tested is a first-class stiffness performance bearing, then the stiffness performance test result of the wind power heavy-duty industrial bearing within the test period will be displayed as qualified, for example: "The stiffness performance of the wind power heavy-duty industrial bearing is qualified".

[0130] If the stiffness performance test result of the current wind power heavy-duty industrial bearing is a level 2 stiffness performance bearing, then the stiffness performance test result of the wind power heavy-duty industrial bearing within the test period will be displayed as a stiffness test failure, for example: "The stiffness performance of the wind power heavy-duty industrial bearing is unqualified and its use is not recommended".

[0131] Example 6

[0132] Please refer to Figure 1 and Figure 2 Specifically: a comprehensive intelligent testing system for high load-bearing bearing performance, including a working condition loading module, a phase analysis module, an attenuation analysis module, and a performance determination module;

[0133] The loading condition module is used to perform loading tests on the stiffness performance of heavy-duty wind power industrial bearings based on the loading condition script parameters constructed according to the requirements of the wind power heavy-duty bearing specifications, and to obtain the original test dataset.

[0134] Based on the acquired original test dataset, the phase analysis module combines the Hilbert transform method and phase coupling analysis algorithm to analyze the phase coupling degree between the micro-vibration signal and the charge signal of the wind power heavy-duty industrial bearing when the speed and load change during the test period. This is to determine whether the contact behavior between the rolling elements and the raceway inside the wind power heavy-duty industrial bearing is normal and to issue corresponding stiffness attenuation analysis commands.

[0135] The stiffness attenuation analysis module is used to analyze the degree of stiffness attenuation of the wind power heavy-duty industrial bearing within the test duration and obtain the test stiffness attenuation value Zsj after receiving the stiffness attenuation analysis command and combining the loading condition script parameters.

[0136] The performance determination module is used to compare and analyze the test stiffness attenuation value Zsj with the preset attenuation threshold S to determine whether the stiffness performance of the currently tested wind power heavy-duty industrial bearing is qualified, and to mark it as a bearing with the corresponding stiffness performance level.

[0137] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A comprehensive intelligent testing method for high load-bearing bearing performance, characterized in that: Includes the following steps: S1. Based on the loading condition script parameters constructed according to the requirements of the wind power heavy-duty bearing operating condition specification, load test the stiffness performance of the wind power heavy-duty industrial bearing and obtain the original test dataset. S2. Based on the acquired original test dataset, combined with the Hilbert transform method and phase coupling analysis algorithm, analyze the phase coupling degree between the micro-vibration signal and the charge signal of the wind power heavy-duty industrial bearing when the speed and load change during the test period, so as to determine whether the contact behavior between the rolling elements and the raceway inside the wind power heavy-duty industrial bearing is normal, and issue corresponding stiffness attenuation analysis commands. S3. After receiving the stiffness attenuation analysis command, combine the loading condition script parameters to analyze the degree of stiffness attenuation of the wind power heavy-duty industrial bearing during the test period and obtain the test stiffness attenuation value Zsj. S4. Compare and analyze the test stiffness attenuation value Zsj with the preset attenuation threshold S to determine whether the stiffness performance of the wind power heavy-duty industrial bearing under test is qualified, and mark it as a bearing with the corresponding stiffness performance level.

2. The comprehensive intelligent testing method for high load-bearing bearing performance according to claim 1, characterized in that: The specific steps in S1 include: S11. During the stiffness performance test of wind power heavy-duty industrial bearings, the typical external load conditions of wind power heavy-duty industrial bearings are identified according to the requirements of the wind power heavy-duty bearing operating condition specifications, and the characteristics of the typical external load conditions of wind power heavy-duty industrial bearings are extracted. The characteristics of the typical external load conditions include working time, wind speed change and impact load change. S12. Based on the extracted external typical load condition characteristics and through long-term wind speed change data measured on-site, wind speed, rotational speed and load response curves are constructed. At the same time, combined with the measured historical load data and digital simulation results, the bearing load time series data of the load, rotational speed and start-stop impact borne by the heavy-duty industrial bearing of wind power are analyzed. Combined with time series statistical analysis, the bearing load time series data is characterized to construct typical working condition characteristic parameters. S13. Combine and analyze the constructed typical working condition characteristic parameters with the duration and probability of occurrence of the load to form a load spectrum characteristic parameter set covering the entire working condition, which can be used as the loading condition script parameters for the performance test of wind power heavy-duty industrial bearings. The loading condition script parameters include load, speed and test duration. S14. Based on the loading condition script parameters constructed in step S13, the wind power heavy-duty industrial bearing is subjected to real-time loading test through the servo loading control unit.

3. The comprehensive intelligent testing method for high load-bearing bearing performance according to claim 2, characterized in that: The specific steps in S1 also include: S15. Deploy multiple sets of micro sensors in the non-interference area of ​​the sealing structure of the heavy-duty industrial bearing for wind power. Among them, the multiple sets of micro sensors include micro-vibration acceleration sensors and triboelectric charge collection electrode sheets. S16. During the loading test in step S14, based on the deployed micro-vibration acceleration sensor and the loading test duration, the high-frequency micro-vibration signal generated by the rolling elements and raceway inside the wind power heavy-duty industrial bearing when the rotational speed and load change is monitored in real time, so as to obtain the micro-vibration frequency Pzd at each monitoring time point within the test duration. S17. During the loading test in step S14, based on the deployed triboelectric charge collection electrode and the test duration, the instantaneous charge density signal generated by the internal rolling elements and raceway of the wind power heavy-duty industrial bearing due to triboelectric charging is monitored in real time by electric field induction when the rotational speed and load change, so as to obtain the triboelectric charge Dmc at each monitoring time point within the test duration. S18. The micro-vibration frequency Pzd and triboelectric charge Dmc at each monitoring time point within the obtained test duration are used to construct the original test dataset. The original test dataset is then preprocessed using a wavelet threshold denoising algorithm. The data preprocessing process includes denoising, filtering, and time-series alignment.

4. The comprehensive intelligent testing method for high load-bearing bearing performance according to claim 3, characterized in that: The specific steps in S2 include: S21. By performing feature recognition on the original test dataset constructed in step S18, the micro-vibration frequency Pzd at each monitoring time point within the test duration is extracted. Combined with the Hilbert transform method, the phase information structure of the high-frequency micro-vibrations generated by the rolling elements and raceways inside the heavy-duty wind turbine industrial bearing during changes in speed and load within the test duration is analyzed. The instantaneous phase coefficient Xzd of the micro-vibration frequency at each monitoring time point within the test duration is obtained, specifically through the following formula: ； In the formula, Pzd i Let Xzd represent the micro-vibration frequency at the i-th monitoring time point within the test duration. i H(Pzd) represents the instantaneous phase coefficient of the micro-vibration frequency at the i-th monitoring time point within the test duration. i The expression represents the orthogonal component of the micro-vibration frequency at the i-th monitoring time point within the test duration after performing a Hilbert transform. Represented as the arctangent function; S22. By performing feature recognition on the original test dataset constructed in step S18, the friction charge Dmc at each monitoring time point within the test duration is extracted. Combined with the Hilbert transform method, the phase information structure of the friction charge generated by frictional charging of the rolling elements and raceways inside the wind power heavy-duty industrial bearing during the test duration when the rotational speed and load change is analyzed, and the instantaneous phase coefficient Xdh of the friction charge at each monitoring time point within the test duration is obtained.

5. The comprehensive intelligent testing method for high load-bearing bearing performance according to claim 4, characterized in that: S2 also includes the following specific steps: S23. Subtract the instantaneous phase coefficient Xzd of the micro-vibration frequency at each monitoring time point within the test duration from the corresponding instantaneous phase coefficient Xdh of the friction charge, analyze the phase deviation between the micro-vibration signal and the charge signal when the internal rolling elements and raceways of the wind power heavy-duty industrial bearing change speed and load within the test duration, and obtain the phase deviation coefficient Xxp at each monitoring time point within the test duration. S24. Based on the phase deviation coefficient Xxp obtained in step S23 for each monitoring time point within the test duration, and combined with the phase coupling analysis algorithm, analyze the phase coupling degree between the micro-vibration signal and the charge signal when the internal rolling elements and raceways of the wind power heavy-duty industrial bearing change speed and load within the test duration, and obtain the phase coupling degree coefficient Xoh, which is specifically obtained through the following formula: ； In the formula, Xxp i It represents the phase deviation coefficient at the i-th monitoring time point within the test duration, where i = 1, 2, 3, ..., n, and n represents the monitoring period.

6. The comprehensive intelligent testing method for high load-bearing bearing performance according to claim 5, characterized in that: S2 also includes the following specific steps: S25. Based on the value of the phase coupling coefficient Xoh in step S24, indicate whether the mechanical behavior and triboelectric effect of the rolling elements and raceways inside the heavy-duty wind turbine industrial bearing are highly coupled during the test period when the rotational speed and load change. This is to determine whether the stiffness contact behavior of the rolling elements and raceways inside the heavy-duty wind turbine industrial bearing is normal, and to issue a corresponding stiffness attenuation analysis command. The specific content is as follows: If the phase coupling coefficient Xoh is in the range [-1, 1), it indicates that the mechanical behavior and triboelectric effect of the rolling elements and raceway inside the wind power heavy-duty industrial bearing are not highly coupled when the speed and load change during the test period. This indicates that the stiffness contact between the rolling elements and raceway inside the wind power heavy-duty industrial bearing is abnormal. At this time, a stiffness attenuation analysis command is issued. If the phase coupling coefficient Xoh=1, it indicates that the mechanical behavior and triboelectric effect of the rolling elements and raceways inside the heavy-duty industrial bearing of the wind power are highly coupled when the speed and load change during the test period. This indicates that the stiffness contact between the rolling elements and raceways inside the heavy-duty industrial bearing of the wind power is normal, and no additional stiffness attenuation analysis command is issued at this time.

7. The comprehensive intelligent testing method for high load-bearing bearing performance according to claim 6, characterized in that: The specific steps in S3 include: S31. Upon receiving the stiffness attenuation analysis command, based on the constructed loading condition script parameters, the load changes during the wind power heavy-duty industrial bearing loading test are monitored in real time to obtain the load value Nzh at each monitoring time point within the test duration. The average load value Nzh within the test duration is then obtained using a statistical averaging algorithm. avg ; S32. Based on the average load value Nzh acquired during the test duration. avg The load value Nzh at each monitoring time point was correlated with the load value Nzh. After dimensionless processing, the load disturbance degree during the heavy-duty industrial bearing loading test of wind power was analyzed to obtain the load fluctuation coefficient Xzh.

8. The comprehensive intelligent testing method for high load-bearing bearing performance according to claim 7, characterized in that: The specific steps in S3 also include: S33. After extracting features from bearing sample data during the manufacturing stage of heavy-duty wind power industrial bearings, the standard reference stiffness value K0 of the heavy-duty wind power industrial bearings is obtained. This value is then correlated with the phase coupling coefficient Xoh and the load fluctuation coefficient Xzh. After dimensionless processing, the stiffness attenuation degree of the heavy-duty wind power industrial bearings during the test period is analyzed to obtain the test stiffness attenuation value Zsj, which is specifically obtained using the following formula: ； In the formula, exp(*) represents the exponential function, and α and β both represent weight values.

9. The comprehensive intelligent testing method for high load-bearing bearing performance according to claim 8, characterized in that: The specific steps of S4 include: S41. Based on the test stiffness attenuation value Zsj obtained in step S33, compare and analyze it with the preset attenuation threshold S to determine whether the stiffness performance of the currently tested wind power heavy-duty industrial bearing is qualified, and mark it as a bearing of the corresponding stiffness performance level. The specific content is as follows: If the test stiffness attenuation value Zsj < attenuation threshold S, it means that the stiffness performance of the wind power heavy-duty industrial bearing under the current test is qualified, meets the use of the wind power heavy-duty industrial bearing under typical external load conditions, and is marked as a first-class stiffness performance bearing. If the test stiffness attenuation value Zsj ≥ attenuation threshold S, it indicates that the stiffness performance of the currently tested wind power heavy-duty industrial bearing is unqualified and cannot meet the use of wind power heavy-duty industrial bearings under typical external load conditions, and it is marked as a secondary stiffness performance bearing.

10. A comprehensive intelligent testing system for high-load bearing performance, used to implement the comprehensive intelligent testing method for high-load bearing performance as described in any one of claims 1 to 9, characterized in that: It includes a working condition loading module, a phase analysis module, an attenuation analysis module, and a performance determination module; The loading condition module is used to perform loading tests on the stiffness performance of heavy-duty wind power industrial bearings based on the loading condition script parameters constructed according to the requirements of the wind power heavy-duty bearing specifications, and to obtain the original test dataset. Based on the acquired original test dataset, the phase analysis module combines the Hilbert transform method and phase coupling analysis algorithm to analyze the phase coupling degree between the micro-vibration signal and the charge signal of the wind power heavy-duty industrial bearing when the speed and load change during the test period. This is to determine whether the contact behavior between the rolling elements and the raceway inside the wind power heavy-duty industrial bearing is normal and to issue corresponding stiffness attenuation analysis commands. The stiffness attenuation analysis module is used to analyze the degree of stiffness attenuation of the wind power heavy-duty industrial bearing within the test duration and obtain the test stiffness attenuation value Zsj after receiving the stiffness attenuation analysis command and combining the loading condition script parameters. The performance determination module is used to compare and analyze the test stiffness attenuation value Zsj with the preset attenuation threshold S to determine whether the stiffness performance of the currently tested wind power heavy-duty industrial bearing is qualified, and to mark it as a bearing with the corresponding stiffness performance level.

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