Method and system for comprehensively and intelligently testing performance of high-load bearing

Abstract

The invention discloses a comprehensive intelligent test method and system for the performance of a high-load bearing, and relates to the technical field of mechanical engineering, and the test method comprises the steps: carrying out the loading test of the rigidity performance of a wind power heavy-load industrial bearing according to a loading working condition script parameter constructed according to the working condition specification requirement of the wind power heavy-load bearing, obtaining an original test data set; in combination with a Hilbert transform method, analyzing the phase coupling degree of a micro-seismic signal and a charge signal of the wind power heavy-load industrial bearing within the test duration so as to judge whether the contact behavior of a rolling body in the wind power heavy-load industrial bearing and a raceway is normal or not, and sending out a rigidity attenuation analysis instruction; and analyzing the rigidity attenuation degree of the wind power heavy-load industrial bearing in the test duration by combining the loading working condition script parameters, obtaining a test rigidity attenuation value Zsj to judge whether the rigidity performance of the currently tested wind power heavy-load industrial bearing is qualified or not, and marking the wind power heavy-load industrial bearing as a rigidity performance bearing of a corresponding grade.

Description

Technical Field

[0001] The present invention relates to the field of mechanical engineering technology, and in particular to a comprehensive intelligent testing method and system for high-load bearing performance. Background Art

[0002] With the continuous evolution of mechanical engineering technology towards high reliability, high load-bearing capacity and intelligence, high-load bearings, as core supporting components, play a vital 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 working reliability directly affects the operating efficiency and service life of the whole machine; wind power heavy-duty industrial bearings are key core components connecting the impeller system and the power generation system. Their stiffness performance is not only related to the load-bearing capacity, but also directly determines the mechanical stability and fault threshold during operation; therefore, the construction of a stiffness performance test method for wind power heavy-duty industrial bearings has extremely high engineering value and urgent technical needs.

[0003] In the current stiffness performance test research of heavy-duty industrial bearings for wind power, although some test systems can realize basic loading test response analysis, there are still significant defects in key parameter modeling, micro-contact behavior identification and stiffness evolution trend determination; first, the construction of loading condition script parameters generally relies on manual experience or static typical values, lacks comprehensive consideration of wind speed, impact, start-stop dynamic factors, and is difficult to truly restore the bearing service conditions; at the same time, traditional stiffness determination methods are mostly based on macro load and displacement response curves, which are difficult to reveal the micro-contact stiffness state between rolling elements and raceways under high-speed changing conditions; especially in actual operation, the phase coupling characteristics between the micro-vibration signal caused by rolling contact and the friction charge signal, long-term It is ignored or difficult to analyze effectively, resulting in a lag in the identification of internal contact status and stiffness evolution trend; in addition, in terms of judging the degree of stiffness attenuation, there is a lack of a multi-dimensional evaluation mechanism of coupling and disturbance fusion, which makes it difficult for the test results to accurately determine whether wind power heavy-duty industrial bearings can meet the requirements of use under complex working conditions. Therefore, in order to effectively overcome the problems of rough loading condition modeling, insufficient perception of microscopic contact status and delayed stiffness attenuation evaluation in the existing wind power heavy-duty industrial bearing stiffness performance testing technology, it is urgent to establish a high-load bearing performance comprehensive intelligent testing method and system that is oriented to real working conditions, multi-source signal coordination, and has the ability to identify microscopic stiffness changes, so as to achieve accurate evaluation and dynamic grading of bearing stiffness performance.

[0004] The existence of the above problems is due to the fact that existing stiffness testing methods are mostly based on single-variable drive and single signal acquisition, ignoring the multi-source disturbance characteristics of wind power operating conditions and the microscopic nonlinear nature of contact behavior. In practice, when bearings experience variable wind speeds, start-stop impacts or non-steady-state loads, the internal rolling contact surface may experience stiffness attenuation or structural relaxation. Such changes are often accompanied by subtle phase deviations and charge mismatches. If these microscopic abnormal signals cannot be captured in real time, it will lead to the cumulative amplification of potential fatigue crack propagation, lubrication film failure or local bite hidden faults. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a comprehensive intelligent testing method and system for high-load bearing performance, which solves the problems in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A comprehensive intelligent testing method for high-load 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-load bearing working condition specification, the stiffness performance of the wind power heavy-load industrial bearing is loaded and tested to obtain the original test data set;

[0008] S2. Based on the acquired original test data set, combined with the Hilbert transform method and phase coupling analysis algorithm, the phase coupling degree between the microseismic signal and the charge signal of the wind power heavy-duty industrial bearing during the test period is analyzed when the speed and load change. This is to determine whether the contact behavior between the rolling elements and raceways inside the wind power heavy-duty industrial bearing is normal, and to issue corresponding stiffness attenuation analysis instructions;

[0009] S3. After receiving the stiffness attenuation analysis instruction, the stiffness attenuation degree of the wind power heavy-duty industrial bearing within the test time is analyzed in combination with the loading condition script parameters to obtain the test stiffness attenuation value Zsj;

[0010] S4. Compare and analyze the tested 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 mark it as a bearing with the corresponding grade of stiffness performance.

[0011] Preferably, the specific steps of S1 include:

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

[0013] S12. Based on the extracted characteristics of typical external load conditions and the long-term wind speed variation data measured on site, construct wind speed, speed, and load response curves. Combined with the measured historical load data and digital simulation results, analyze the bearing load time series data of the load, speed, and start-stop impact borne by heavy-duty industrial bearings in wind power. Combined with the time series statistical analysis method, perform feature recognition on the bearing load time series data to construct characteristic parameters of typical conditions.

[0014] S13. Combining and analyzing the constructed typical operating condition characteristic parameters with the load duration and occurrence probability to form a load spectrum characteristic parameter set covering all operating conditions, which is 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, a real-time loading test is performed on the wind power heavy-duty industrial bearing through the servo loading control unit.

[0016] Preferably, the specific step S1 further includes:

[0017] S15. Deploy multiple sets of micro sensors in a non-interference area of ​​a wind power heavy-duty industrial bearing seal structure, wherein the multiple sets of micro sensors include microseismic acceleration sensors and triboelectric charge collection electrodes;

[0018] S16. During the loading test of step S14, based on the deployed microseismic acceleration sensors and the loading test duration, real-time monitoring of high-frequency microseismic signals generated by the rolling elements and raceways within the wind power heavy-duty industrial bearing when the speed and load change, to obtain the microvibration frequency Pzd at each monitoring time point within the test duration;

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

[0020] S18. The micro-vibration frequency Pzd and friction charge Dmc at each monitoring time point within the test duration are constructed as an original test data set, and the original test data set is preprocessed in combination with a wavelet threshold denoising algorithm. The preprocessing process includes denoising, filtering and time series alignment.

[0021] Preferably, the specific steps of S2 include:

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

[0023] ;

[0024] Where, It is expressed as the micro-vibration frequency at the i-th monitoring time point within the test duration, It is expressed as the instantaneous phase coefficient of the micro-vibration frequency at the i-th monitoring time point within the test duration, It is represented by the orthogonal component after Hilbert transform of the micro-vibration frequency at the i-th monitoring time point within the test duration, Expressed as the inverse tangent function;

[0025] S22. By performing feature recognition on the original test data set constructed in step S18, the friction charge Dmc at each monitoring time point within the test duration is extracted. In combination with the Hilbert transform method, the phase information structure of the friction charge generated by the internal rolling elements and raceways of the wind power heavy-duty industrial bearing during the test duration due to friction charging when the speed and load change, the friction charge instantaneous phase coefficient Xdh at each monitoring time point within the test duration is obtained, which is specifically obtained by the following formula:

[0026] ;

[0027] Where, It is expressed as the triboelectric charge at the i-th monitoring time point during the test duration, It is expressed as the instantaneous phase coefficient of the friction charge at the i-th monitoring time point within the test duration, It is represented by the orthogonal component after Hilbert transform of the friction charge at the i-th monitoring time point within the test duration, Expressed as the inverse tangent function.

[0028] Preferably, the specific step S2 further includes:

[0029] S23. Performing a difference processing on the instantaneous phase coefficient Xzd of the microvibration frequency obtained at each monitoring time point during the test duration and the corresponding instantaneous phase coefficient Xdh of the friction charge, analyzing the phase deviation between the microvibration signal and the charge signal of the rolling elements and raceways inside the wind power heavy-duty industrial bearing when the speed and load change during the test duration, and obtaining the phase deviation coefficient Xxp at each monitoring time point during the test duration;

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

[0031] ;

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

[0033] Preferably, the specific step S2 further includes:

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

[0035] If the phase coupling coefficient Xoh is in the range When the test is complete, it indicates that the mechanical behavior and triboelectric effect of the rolling elements and raceways inside the wind power heavy-duty industrial bearing are not highly coupled when the speed and load change. This determines that the stiffness contact between the rolling elements and raceways inside the wind power heavy-duty industrial bearing is abnormal. At this time, a stiffness attenuation analysis command is issued.

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

[0037] Preferably, the specific steps of S3 include:

[0038] S31. After receiving the stiffness attenuation analysis instruction, based on the constructed loading condition script parameters, the load change during the wind power heavy-duty industrial bearing loading test is monitored in real time, and the load value Nzh at each monitoring time point within the test duration is obtained. The average load value Nzh within the test duration is obtained through the statistical averaging algorithm. avg ;

[0039] S32, based on the average load value Nzh within the test duration avg The load fluctuation coefficient Xzh is obtained by correlating it with the load value Nzh at each monitoring time point and analyzing the load disturbance degree during the loading test of the wind power heavy-load industrial bearing after dimensionless processing.

[0040] Preferably, the specific step S3 further includes:

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

[0042] ;

[0043] Where, Expressed as an exponential function, and are expressed as weight values.

[0044] Preferably, the specific steps of S4 include:

[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 wind power heavy-duty industrial bearing currently being tested is qualified, and mark it as a bearing with a corresponding level of stiffness performance. The specific content is as follows:

[0046] If the test stiffness attenuation value Zsj is less than the attenuation threshold S, it indicates that the stiffness performance of the wind power heavy-duty industrial bearing currently being tested is qualified, meeting the requirements for use of the wind power heavy-duty industrial bearing under typical external load conditions, and is marked as a first-level stiffness performance bearing;

[0047] If the test stiffness attenuation value Zsj ≥ attenuation threshold S, it means 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 external typical load conditions, and it is marked as a secondary stiffness performance bearing.

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

[0049] The working condition loading module is used to construct the loading condition script parameters according to the working condition specification requirements of wind power heavy-load bearings, perform loading tests on the stiffness performance of wind power heavy-load industrial bearings, and obtain the original test data set;

[0050] The phase analysis module uses the acquired original test data set, combined with the Hilbert transform method and phase coupling analysis algorithm, to analyze the phase coupling between the microseismic signal and the charge signal of the wind power heavy-duty industrial bearing during the test period when the speed and load change. This determines whether the contact behavior between the rolling elements and raceways inside the wind power heavy-duty industrial bearing is normal and issues corresponding stiffness attenuation analysis instructions.

[0051] The attenuation analysis module is used to analyze the stiffness attenuation degree of the wind power heavy-duty industrial bearing during the test time in combination with the loading condition script parameters after receiving the stiffness attenuation analysis instruction, and obtain the test stiffness attenuation value Zsj;

[0052] The performance judgment 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 mark it as a bearing with the corresponding level of stiffness performance.

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

[0054] (1) The present invention integrates working condition modeling, multi-source signal acquisition and processing, and coupling attenuation evaluation mechanism to achieve accurate evaluation and classification of the stiffness performance of heavy-load industrial bearings for wind power. This solution effectively overcomes the problems of insufficient working condition restoration, insensitive identification of microscopic contact behavior, and delayed response to stiffness degradation trends in 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 ability to identify early failures under complex variable load conditions, significantly improving the operational reliability and safety redundancy level of wind power main shaft systems.

[0055] (2) By combining the working condition specifications of heavy-duty bearings for wind power, wind speed variation curves, historical data of impact loading, and dynamic characteristics of start and stop, a comprehensive and representative set of loading condition script parameters is constructed, avoiding the defects of single static working condition input in traditional test methods; the constructed script parameters not only take into account the typical load paths that bearings may face during their service life, but also conduct multi-dimensional statistical modeling in combination with long-term operation data of wind farms, so that the loading process is highly close to the actual service environment in terms of stress amplitude, rapid impact, and duration distribution, thereby improving the reality matching of the test conditions and the reliability of the stiffness response test.

[0056] (3) The microseismic signal and the friction charge signal are innovatively taken as synchronous observation objects, and the Hilbert transform and phase coupling analysis algorithm are introduced to extract the instantaneous phase structure, and a dynamic coupling identification model of the contact behavior of the rolling element and the raceway is established; compared with the traditional stiffness judgment method based on single-channel displacement or load response, this scheme can capture the consistency of the microscopic contact response inside the bearing during the speed and load changes in real time, especially in the weak contact mismatch and early structural deformation stage, and can achieve high-sensitivity identification through the degree of phase deviation, thereby enhancing the ability to analyze the microscopic stiffness degradation signal.

[0057] (4) By constructing an exponential decay analysis model driven jointly by the phase coupling degree coefficient Xoh and the load fluctuation coefficient Xzh, multi-dimensional modeling and dynamic trend determination of the stiffness degradation process are achieved; the introduced load fluctuation coefficient Xzh can effectively measure the degree of non-steady-state disturbance of the bearing stress environment, and the exponential stiffness mapping formula constructed in combination with the real-time phase coupling results 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 turbine bearings in complex service scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0060] Figure 3 This is the logical thinking diagram of step S1 of the present invention;

[0061] Figure 4 This is the logical thinking diagram of step S2 of the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0063] Example 1

[0064] See also Figure 1 The present invention provides a comprehensive intelligent testing method for high-load 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-load bearing working condition specification, the stiffness performance of the wind power heavy-load industrial bearing is loaded and tested to obtain the original test data set;

[0066] S2. Based on the acquired original test data set, combined with the Hilbert transform method and phase coupling analysis algorithm, the phase coupling degree between the microseismic signal and the charge signal of the wind power heavy-duty industrial bearing during the test period is analyzed when the speed and load change. This is to determine whether the contact behavior between the rolling elements and raceways inside the wind power heavy-duty industrial bearing is normal, and to issue corresponding stiffness attenuation analysis instructions;

[0067] S3. After receiving the stiffness attenuation analysis instruction, the stiffness attenuation degree of the wind power heavy-duty industrial bearing within the test time is analyzed in combination with the loading condition script parameters to obtain the test stiffness attenuation value Zsj;

[0068] S4. Compare and analyze the tested 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 mark it as a bearing with the corresponding grade of stiffness performance.

[0069] In this embodiment, for the stiffness performance test of heavy-duty industrial bearings in wind power under complex working conditions, a multi-step test system with working condition drive, signal coordination and intelligent identification as the core is constructed, which has many significant technical advantages and engineering values. First, by combining wind power operation specifications with historical working condition data to construct loading condition script parameters, the actual service environment of the bearing under variable wind speed, impact load and start-stop state can be truly restored, and a high degree of matching of the test loading scenario can be achieved, overcoming the shortcomings of one-sided working condition setting and high distortion rate in traditional methods. Secondly, Hilbert transform and phase coupling analysis are used to introduce the phase synchronization of microseismic and triboelectric signals as contact for the first time. The behavioral evaluation basis breaks through the limitation of existing test methods that only rely on macroscopic load-displacement data. It can identify signs of microscopic degradation of rolling contact stiffness during operation, and improve the test system's response to potential structural fatigue and hidden problems of lubrication degradation. In addition, the present invention also constructs a comparison mechanism for the threshold of the test stiffness attenuation value Zsj, which can realize the quantitative assessment of the bearing stiffness grade. Overall, this method works together in the three aspects of test modeling, data recognition and stiffness determination, and realizes the accurate assessment, intelligent response and quality grading of the stiffness performance of heavy-duty wind power bearings under complex working conditions, providing high-reliability 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: S1 specific steps include:

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

[0073] S12. Based on the extracted characteristics of typical external load conditions and the long-term wind speed variation data measured on site, construct wind speed, speed, and load response curves. Combined with the measured historical load data and digital simulation results, analyze the bearing load time series data of the load, speed, and start-stop impact borne by heavy-duty industrial bearings in wind power. Combined with the time series statistical analysis method, perform feature recognition on the bearing load time series data to construct characteristic parameters of typical conditions.

[0074] S13. Combining and analyzing the constructed typical operating condition characteristic parameters with the load duration and occurrence probability to form a load spectrum characteristic parameter set covering all operating conditions, which is 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, a real-time loading test is performed on the wind power heavy-duty industrial bearing through the servo loading control unit.

[0076] It should be noted that the external typical load condition characteristics refer to the external operating condition characteristics that often appear in the actual service process of wind power heavy-duty industrial bearings and have a significant impact on the structural response, including working time (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 real mechanical environment of the bearing in different time periods; the typical operating condition characteristic parameters are based on the above-mentioned operating condition characteristics, combined with long-term on-site wind speed monitoring data, historical load records and digital simulation results, by constructing wind speed, rotation speed and load response curves and using time series statistical analysis methods to extract representative and regular parameter sets, including 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 operating status of the bearing throughout its life cycle, as the input basis for the loading condition script, to ensure that the test process highly restores the actual working conditions and improves the authenticity and effectiveness of the stiffness performance test.

[0077] Specifically, the steps of S1 also include:

[0078] S15. Deploy multiple sets of micro sensors in a non-interference area of ​​a wind power heavy-duty industrial bearing seal structure, wherein the multiple sets of micro sensors include microseismic acceleration sensors and triboelectric charge collection electrodes;

[0079] S16. During the loading test of step S14, based on the deployed microseismic acceleration sensors and the loading test duration, real-time monitoring of high-frequency microseismic signals generated by the rolling elements and raceways within the wind power heavy-duty industrial bearing when the speed and load change, to obtain the microvibration frequency Pzd at each monitoring time point within the test duration;

[0080] S17. During the loading test of step S14, based on the deployed triboelectric charge collection electrode and the loading test duration, the instantaneous charge density signal generated by triboelectric charging of the rolling elements and raceways inside the wind power heavy-duty industrial bearing as the speed and load change in real time is monitored by electric field induction 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, slip and impact between the rolling elements and the raceways during the loading test of heavy-duty industrial bearings for wind power, reflecting the changes in the internal stiffness state and contact stability of the bearing; the friction charge Dmc refers to the instantaneous charge density value obtained by electric field induction due to the triboelectric effect generated by the rolling contact interface under high-speed rotation and load under the same working conditions, reflecting the friction behavior and charge accumulation characteristics of the contact surface; these two parameters are respectively collected by micro-vibration acceleration sensors and friction charge collection electrodes deployed in the non-interference area of ​​the bearing seal structure. The micro-vibration frequency Pzd is extracted from the high-frequency acceleration signal through Fourier transform or short-time spectrum analysis, and the friction charge Dmc is obtained through real-time sampling of the charge electrode and a charge density conversion algorithm; the two together constitute the multi-dimensional physical response signal of the rolling contact behavior under the operating state of the bearing, providing a key data basis for subsequent phase coupling analysis and stiffness performance determination.

[0082] S18. The micro-vibration frequency Pzd and friction charge Dmc at each monitoring time point within the test duration are constructed as an original test data set, and the original test data set is preprocessed in combination with a wavelet threshold denoising algorithm. The preprocessing process includes denoising, filtering and time series alignment.

[0083] In this embodiment, by constructing accurate and dynamically responsive loading condition script parameters and deploying micro multi-physics field sensors, the working condition authenticity, data richness and micro-behavior identifiability of the stiffness performance test of heavy-duty industrial bearings in wind power are improved; first, through steps S11-S14, the problem that the loading condition in traditional tests relies on static typical values ​​and ignores wind speed changes and impact responses is effectively solved; by comprehensively extracting the typical load condition characteristics of working time, wind speed disturbance and load mutation, and introducing the bearing load time series data generated by the fusion of on-site wind speed monitoring data, historical load data and digital simulation results, combined with the time series statistical analysis method, a load spectrum parameter set covering all working condition scenarios is constructed; this loading condition construction mechanism can more realistically restore the service state of the bearing and provide highly practical boundary conditions for subsequent stiffness response evaluation; secondly, in steps S15-S18, by Micro-vibration acceleration sensors and friction charge collection electrodes are arranged in the interference area to construct a multi-channel, high-frequency, multi-physical quantity original signal acquisition path, which realizes the synchronous acquisition of high-frequency micro-seismic behavior and friction charging effect 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, the signal preprocessing combined with the wavelet threshold denoising algorithm enables the micro-vibration frequency Pzd and friction charge Dmc to obtain a high-fidelity processing foundation in terms of noise suppression, characteristic frequency band retention and time sequence alignment, providing a complete structure and highly clear data support for subsequent phase coupling analysis and stiffness evolution modeling; therefore, the overall construction of this step not only improves the dynamic simulation capability of the test environment, but also achieves the dual guarantee of physical proximity and data accuracy in the entire process from working condition modeling to signal acquisition, and has unique engineering practicality and integration advantages in the field of wind power heavy-load bearing testing.

[0084] Example 3

[0085] Please refer to Figure 1 and Figure 4 , specifically: S2 specific steps include:

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

[0087] ;

[0088] Where, It is expressed as the micro-vibration frequency at the i-th monitoring time point within the test duration, It is expressed as the instantaneous phase coefficient of the micro-vibration frequency at the i-th monitoring time point within the test duration, It is represented by the orthogonal component after Hilbert transform of the micro-vibration frequency at the i-th monitoring time point within the test duration, Expressed as the inverse tangent 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 inverse tangent function Used to solve the instantaneous phase of the complex analytical form of the vibration signal; this formula directly responds to the technical defect of the traditional method proposed in the background technology that it is difficult to identify the changes in rolling contact stiffness under high-speed dynamic conditions, because the traditional method usually relies on load and displacement response curves, and it is difficult to analyze the subtle changes in micro-vibration characteristics over time; and the instantaneous phase coefficient Xzd of the micro-vibration frequency obtained by this formula can deeply deconstruct the micro-vibration signal in the phase dimension, and capture the tiny synchronization changes between the rolling element and the raceway under load and speed disturbances; its role is not only to describe the value of the micro-vibration frequency at a certain moment, but more importantly, it provides a time and phase structure mapping indicator, which is used to measure the implicit correlation between the periodicity and non-stationarity of the vibration behavior and the contact stiffness state.

[0090] S22. By performing feature recognition on the original test data set constructed in step S18, the friction charge Dmc at each monitoring time point within the test duration is extracted. In combination with the Hilbert transform method, the phase information structure of the friction charge generated by the internal rolling elements and raceways of the wind power heavy-duty industrial bearing during the test duration due to friction charging when the speed and load change, the friction charge instantaneous phase coefficient Xdh at each monitoring time point within the test duration is obtained, which is specifically obtained by the following formula:

[0091] ;

[0092] Where, It is expressed as the triboelectric charge at the i-th monitoring time point during the test duration, It is expressed as the instantaneous phase coefficient of the friction charge at the i-th monitoring time point within the test duration, It is represented by the orthogonal component after Hilbert transform of the friction charge at the i-th monitoring time point within the test duration, Expressed as the inverse tangent function.

[0093] It should be noted that the formula in step S22 is used to calculate the instantaneous phase coefficient Xdh of the friction charge at the i-th monitoring time point. i , It is expressed as an inverse tangent function, which is used to solve the instantaneous phase of the complex analytical form of the charge signal. The introduction of this formula directly responds to the core problem pointed out in the background technology that the traditional test method ignores the correlation between the change of the triboelectric signal and the microscopic contact state. The existing technology generally relies on mechanical quantities (displacement and acceleration) to infer stiffness, but lacks in-depth exploration of the micro-electric signals of the contact interface. Especially under high-speed operation, impact load or poor lubrication conditions, the stiffness decay signal carried by the triboelectric effect is often omitted. The logical role of the friction charge instantaneous phase coefficient Xdh is that it not only reflects the friction The phase state of the friction charge signal at a certain moment, more importantly, reveals the rhythmic and synchronous characteristics of the changes in the friction behavior of the contact interface under dynamic working conditions; the phase of the friction charge is often more sensitive to micro-slip, lubrication film rupture or transient contact imbalance, and can achieve stiffness abnormality response earlier than the vibration signal. Therefore, the formula based on the Hilbert transform to extract the phase angle is not only rigorous in mathematical processing, but also closely fits the real mechanism of contact stiffness evolution of wind power heavy-duty bearings under complex working conditions in a physical sense. It is a key link in realizing the integration of micro-contact perception and multi-dimensional stiffness judgment.

[0094] Specifically, the steps of S2 also include:

[0095] S23. Performing a difference processing on the instantaneous phase coefficient Xzd of the microvibration frequency obtained at each monitoring time point during the test duration and the corresponding instantaneous phase coefficient Xdh of the friction charge, analyzing the phase deviation between the microvibration signal and the charge signal of the rolling elements and raceways inside the wind power heavy-duty industrial bearing when the speed and load change during the test duration, and obtaining the phase deviation coefficient Xxp at each monitoring time point during the test duration;

[0096] It should be noted that the phase deviation coefficient Xxp is an indicator used to measure the degree of phase synchronization deviation between the microvibration signal and the friction charge signal between the rolling element and the raceway at each monitoring time point during the loading test of the wind power heavy-duty industrial bearing. It reflects the consistency of the response of the two under the same physical event, and indirectly reveals the stability of the contact behavior and the stiffness coupling state. Specifically, the phase deviation coefficient Xxp is calculated by performing difference processing on the instantaneous phase coefficient Xzd of the microvibration frequency and the instantaneous phase coefficient Xdh of the friction charge at the corresponding moment. The closer its value is to zero, the more highly synchronized the phases of the two signals are, and the more stable the contact stiffness state is. The larger the deviation, the poorer the synchronization, and the existence of problems such as stiffness degradation or poor lubrication. The phase deviation coefficient Xxp is obtained by performing corresponding processing on the time point after obtaining the instantaneous phase of the microvibration and charge signals through Hilbert transform. It 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 at each monitoring time point during the test duration obtained in step S23, and in combination with the phase coupling analysis algorithm, analyze the phase coupling degree between the microseismic signal and the charge signal of the rolling elements and raceways inside the wind power heavy-duty industrial bearing when the speed and load change during the test duration, and obtain the phase coupling degree coefficient Xoh, which is specifically obtained by the following formula:

[0098] ;

[0099] Where, It is expressed as the phase deviation coefficient of the i-th monitoring time point within the test duration, i=1, 2, 3, ..., n, n represents the monitoring period, where, It is expressed 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 formula is expressed as the cosine value of the phase deviation coefficient at the i-th monitoring time point within the test duration, reflecting the degree of synchronization between the microseismic signal and the triboelectric signal at that moment. This formula is highly correlated with the fact that the phase coupling relationship between microseismic and charge signals has long been ignored, making it difficult to identify changes in microscopic 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 taking the cosine sum of all phase deviations within the test cycle. 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 for converting time-series microphase characteristics into macroscopic contact health assessment, providing accurate, quantitative, and dynamic response criteria for judging the stiffness evolution trend of heavy-duty wind turbine bearings. It is a core innovation that overcomes the problems of macroscopic response lag and insufficient microscopic anomaly identification in existing stiffness assessment methods.

[0101] Specifically, the steps of S2 also include:

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

[0103] If the phase coupling coefficient Xoh is in the range When the test is complete, it indicates that the mechanical behavior and triboelectric effect of the rolling elements and raceways inside the wind power heavy-duty industrial bearing are not highly coupled when the speed and load change. This determines that the stiffness contact between the rolling elements and raceways inside the wind power heavy-duty industrial bearing is abnormal. At this time, a stiffness attenuation analysis command is issued.

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

[0105] In this embodiment, by constructing a multi-dimensional signal coupling analysis mechanism with phase synchronization as the core, high-sensitivity identification of the microscopic contact behavior state of the rolling elements and raceways of heavy-duty industrial bearings for wind power and intelligent judgment of the stiffness evolution trend are achieved, which significantly improves the technical level of stiffness performance testing from macroscopic response to microscopic analysis; first, by performing Hilbert transform on two types of heterogeneous signals, micro-vibration frequency and friction charge, respectively, the corresponding instantaneous phase coefficients are obtained, breaking the limitations of traditional isolated analysis of vibration and electrical parameters, so that the mechanical impact behavior and friction electric behavior generated in the internal rolling contact process of the bearing under dynamic load and speed disturbance can be captured and analyzed synchronously in the time domain; secondly, the 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 friction charge, and then based on the phase difference sequence, the phase coupling degree coefficient Xoh is constructed based on the average summation method of the cosine function, forming a core evaluation index reflecting the degree of consistency of microscopic contact stiffness, which has a clear discriminant edge in terms of numerical value. Boundary: When the phase coupling coefficient Xoh value approaches 1, it indicates that the microseismic and triboelectric responses are highly synchronized, indicating that the contact stiffness between the rolling element and the raceway is good; when the phase coupling coefficient Xoh value deviates significantly from 1 or the fluctuation intensifies, it reveals that the internal contact state has an abnormal trend of instability, relaxation or weakening of the structural stiffness. At this time, a stiffness attenuation analysis instruction will be issued to drive the subsequent stiffness attenuation trend analysis; unlike the traditional method that simply relies on the displacement and load relationship curve or spectrum amplitude comparison, the present invention introduces phase coupling consistency, an indicator with time resolution and physical interpretation power, so that the test can identify early signs of microdamage or lubrication mismatch through phase perturbation before the structure shows obvious stiffness degradation, thereby greatly improving the early warning capability and trend perception accuracy of the stiffness performance test; in summary, the intelligent judgment mechanism based on multi-signal phase fusion constructed in step S2 not only enhances the test system's visual perception capability of microscopic contact stiffness changes, but also provides a high-dimensional judgment support for the stiffness performance test of wind power heavy-duty bearings that transcends the limitations of traditional methods.

[0106] Example 4

[0107] Please refer to Figure 1 , specifically: S3 specific steps include:

[0108] S31. After receiving the stiffness attenuation analysis instruction, based on the constructed loading condition script parameters, the load change during the wind power heavy-duty industrial bearing loading test is monitored in real time, and the load value Nzh at each monitoring time point within the test duration is obtained. The average load value Nzh within the test duration is obtained through the 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 wind power heavy-duty industrial bearing. It is usually expressed in N (Newton) or kN (kilonewton) and is used to reflect the stress state of the bearing under dynamic working conditions. The load value Nzh is applied by the servo loading unit controlled by the loading condition script and is collected in real time by the pressure sensor and force measuring ring arranged in the loading system or outside the bearing. The load value Nzh is the key data for describing the load input condition under the bearing operating environment, and is not only used to calculate the average load value Nzh during the test time. avg It also provides a data basis for the subsequent analysis of load disturbance amplitude and load fluctuation, thereby participating in the identification of stiffness attenuation trends and performance evaluation and judgment.

[0110] S32, based on the average load value Nzh within the test duration avg The load fluctuation coefficient Xzh is obtained by correlating it with the load value Nzh at each monitoring time point and performing dimensionless processing to analyze the load disturbance degree during the loading test of the heavy-duty industrial bearing of wind power. The load fluctuation coefficient Xzh is obtained by the following formula:

[0111] ;

[0112] Where, Expressed as the variance of load values ​​within 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 technology that traditional stiffness evaluation ignores the influence of actual load fluctuation disturbances; the existing technology usually uses the average load as the static input to determine the stiffness response, which is difficult to reflect the actual operating status of the bearing under variable load, impact load and non-steady-state load, thereby causing lag or deviation in the evaluation results; the logical role of the load fluctuation coefficient Xzh is to quantify the 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; otherwise, it indicates that the load borne by the bearing has frequent fluctuations or sudden peaks. These disturbances can easily cause contact stiffness relaxation, lubrication film rupture or rolling fatigue. Therefore, the load fluctuation coefficient Xzh can be used as an important adjustment factor for correcting the stiffness attenuation calculation, so that the stiffness assessment is no longer based on ideal loading, but takes into account the dynamic disturbance influence of actual working conditions, enhancing the adaptability and physical authenticity of the model in complex load environments, and improving the accuracy and engineering value of the stiffness evolution trend analysis of heavy-duty wind power bearings under operation.

[0114] Specifically, the S3 steps also include:

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

[0116] ;

[0117] Where, Expressed as an exponential function, and are expressed as weight values.

[0118] It should be noted that the formula in step S32 is used to calculate the test stiffness attenuation value Zsj. This formula directly responds to the three major pain points raised in the background technology: first, the traditional method lacks a stiffness mapping mechanism for microscopic phase characteristics; second, it does not consider the dynamic amplification effect of load disturbance on structural stiffness; third, the existing stiffness evaluation methods are mostly static linear inferences, which are difficult to accurately reflect the nonlinear stiffness decay trend under complex working conditions; among them, It means that the greater the deviation of contact coupling degree, the easier it is for stiffness to decay; The more severe the disturbance, the more sensitive the attenuation process. That is, when coupling is low and the disturbance is strong, the exponential term approaches 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 methodological transformation from a single mechanical response to a three-in-one intelligent stiffness assessment that combines signal coupling, load perturbation, and standard benchmarks. It is the key numerical basis for realizing the health trend assessment and performance grading of heavy-duty industrial bearings for wind power.

[0119] The core innovation of the formula in step S32 lies in taking the phase coupling degree of two key dynamic signal indicators, microseismic and triboelectric signals, and the load disturbance intensity during loading as coupling variables, and embedding them into the attenuation map of the standard stiffness value through an exponential function to form a dynamic, nonlinear, multi-factor-driven stiffness attenuation model.

[0120] In this embodiment, by constructing a stiffness attenuation evaluation mechanism driven by both the phase coupling degree and the load disturbance state, quantitative modeling and accurate identification of the stiffness degradation trend of heavy-duty industrial bearings in wind power under dynamic service conditions are achieved, which significantly improves 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 during the test duration is obtained statistically. avg , and then conduct a dimensionless comparative analysis 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, and make up for the deficiency of traditional stiffness evaluation that ignores the influence of actual load dynamic fluctuations; secondly, in step S33, the standard reference stiffness K0 is established by introducing the bearing sample data in the manufacturing stage, and the phase coupling degree coefficient Xoh obtained by its real-time coupling analysis is jointly analyzed with the current load fluctuation coefficient Xzh, and the exponential mapping model of the stiffness attenuation value Zsj is further constructed. The model uses the exponential function form to incorporate contact consistency and external disturbance into a unified evaluation framework. , so that the stiffness assessment results include both the intrinsic degradation trend of the structure and the influence of service environment fluctuations; this method breaks through the previous limitation of judging stiffness only based on the load and displacement response slope, and can identify early stiffness attenuation signals through multi-source indicator fusion when the structure has not yet undergone significant deformation, and has high sensitivity and early prediction capabilities; in particular, it not only reflects the degree of stiffness attenuation, but can also serve as a core reference indicator for subsequent graded management and reliability judgment; in summary, step S3 greatly improves the accuracy and scenario adaptability of stiffness performance testing by constructing an attenuation analysis path driven by the coordinated efforts of load disturbance factor, standard stiffness benchmark and phase coupling signal.

[0121] Example 5

[0122] Please refer to Figure 1 , specifically: S4 specific 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 wind power heavy-duty industrial bearing currently being tested is qualified, and mark it as a bearing with a corresponding level of stiffness performance. The specific content is as follows:

[0124] If the test stiffness attenuation value Zsj is less than the attenuation threshold S, it indicates that the stiffness performance of the wind power heavy-duty industrial bearing currently being tested is qualified, meeting the requirements for use of the wind power heavy-duty industrial bearing under typical external load conditions, and is marked as a first-level stiffness performance bearing;

[0125] If the test stiffness attenuation value Zsj ≥ attenuation threshold S, it means 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 external typical 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 for judging whether the stiffness performance of heavy-duty industrial bearings for wind power is qualified. Its acquisition method is usually based on statistical analysis of a large number of samples in the manufacturing stage and retired from operation, engineering experience curve fitting and load condition adaptability standards. Specifically, the threshold S can be obtained by conducting multiple rounds of loading fatigue tests on bearings of the same model under actual working conditions, measuring its failure probability distribution under different stiffness attenuation degrees, and combining the minimum safety stiffness requirements of the bearings under typical wind speed-load coupling environments to determine the safe limit interval 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 the stiffness residual value in the standards of specific wind power main manufacturers or national industry specifications. Its function is to discretize the test stiffness attenuation value Zsj into qualified and unqualified and first- and second-level stiffness grade judgments, provide clear standards for quality grading, reliability screening and on-site installation applications, and enhance the engineering decision-making feasibility and systematic management capabilities of stiffness performance evaluation.

[0127] In this embodiment, by constructing a comparative analysis mechanism based on the stiffness attenuation value Zsj and the preset threshold S, a set of clear and executable stiffness performance grade evaluation system is established, which realizes the quantitative grading management and quality judgment of the stiffness performance of heavy-duty industrial bearings for wind power, and has significant practicality and engineering guidance value; the stiffness attenuation value Zsj obtained by comprehensively considering the phase coupling degree and the load disturbance intensity in step S3 is used as the evaluation benchmark, and the attenuation threshold S preset for the typical service environment of wind power is set. The numerical relationship between the two is used for classification and judgment, which can not only accurately identify whether the bearing has the target The stiffness capability of continuous service under working conditions can also be classified and outputted in the form of grade labels, realizing the key transition from measurement and analysis to judgment and controllability; unlike the traditional fuzzy judgment method, the present invention sets clear performance cutoff values, so that the bearings have reviewable and traceable judgment standards during factory inspection, fault diagnosis and remanufacturing, thereby improving the decision-making value of the stiffness evaluation results; in summary, not only the final closed loop of stiffness evaluation is completed, but also a data quantification basis and engineering implementation path are provided for the reliability control of heavy-duty wind power bearings, so that the test results are transformed from passive data to active decision-making results.

[0128] In a specific embodiment, the stiffness performance test results of the wind power heavy-duty industrial bearing within the test time are displayed accordingly, specifically:

[0129] If the stiffness performance test result of the currently tested wind power heavy-duty industrial bearing is a first-level stiffness performance bearing, the stiffness performance test results of the wind power heavy-duty industrial bearing within the test time will be displayed as qualified stiffness test, for example: "The stiffness performance corresponding to the wind power heavy-duty industrial bearing is qualified";

[0130] If the stiffness performance test result of the currently tested wind power heavy-duty industrial bearing is a second-level stiffness performance bearing, the stiffness performance test result of the wind power heavy-duty industrial bearing within the test time will be displayed as unqualified stiffness test, for example: "The stiffness performance corresponding to the wind power heavy-duty industrial bearing is unqualified and is not recommended for use."

[0131] Example 6

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

[0133] The working condition loading module is used to construct the loading condition script parameters according to the working condition specification requirements of wind power heavy-load bearings, perform loading tests on the stiffness performance of wind power heavy-load industrial bearings, and obtain the original test data set;

[0134] The phase analysis module uses the acquired original test data set, combined with the Hilbert transform method and phase coupling analysis algorithm, to analyze the phase coupling between the microseismic signal and the charge signal of the wind power heavy-duty industrial bearing during the test period when the speed and load change. This determines whether the contact behavior between the rolling elements and raceways inside the wind power heavy-duty industrial bearing is normal and issues corresponding stiffness attenuation analysis instructions.

[0135] The attenuation analysis module is used to analyze the stiffness attenuation degree of the wind power heavy-duty industrial bearing during the test time in combination with the loading condition script parameters after receiving the stiffness attenuation analysis instruction, and obtain the test stiffness attenuation value Zsj;

[0136] The performance judgment 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 mark it as a bearing with the corresponding level of stiffness performance.

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

Claims

1. A comprehensive intelligent testing method for high-load bearing performance, characterized by: The following steps are involved: S1. Based on the loading condition script parameters constructed according to the requirements of the wind power heavy-load bearing working condition specification, the stiffness performance of the wind power heavy-load industrial bearing is loaded and tested to obtain the original test data set; S2. Based on the acquired original test data set, combined with the Hilbert transform method and phase coupling analysis algorithm, the phase coupling degree between the microseismic signal and the charge signal of the wind power heavy-duty industrial bearing during the test period is analyzed when the speed and load change. This is to determine whether the contact behavior between the rolling elements and raceways inside the wind power heavy-duty industrial bearing is normal, and to issue corresponding stiffness attenuation analysis instructions; S3. After receiving the stiffness attenuation analysis instruction, the stiffness attenuation degree of the wind power heavy-duty industrial bearing within the test time is analyzed in combination with the loading condition script parameters to obtain the test stiffness attenuation value Zsj; S4. Compare and analyze the tested 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 mark it as a bearing with the corresponding grade of stiffness performance.

2. A comprehensive intelligent testing method for high-load bearing performance according to claim 1, characterized in that: The specific steps of S1 include: S11. During the stiffness performance test of the wind power heavy-duty industrial bearing, according to the requirements of the wind power heavy-duty bearing operating condition specification, the external typical load conditions of the wind power heavy-duty industrial bearing are identified, and the external typical load condition characteristics of the wind power heavy-duty industrial bearing are extracted. The external typical load condition characteristics include operating time, wind speed changes, and impact load changes; S12. Based on the extracted characteristics of typical external load conditions and the long-term wind speed variation data measured on site, construct wind speed, speed, and load response curves. Combined with the measured historical load data and digital simulation results, analyze the bearing load time series data of the load, speed, and start-stop impact borne by heavy-duty industrial bearings in wind power. Combined with the time series statistical analysis method, perform feature recognition on the bearing load time series data to construct characteristic parameters of typical conditions. S13. Combining and analyzing the constructed typical operating condition characteristic parameters with the load duration and occurrence probability to form a load spectrum characteristic parameter set covering all operating conditions, which is 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, a real-time loading test is performed on the wind power heavy-duty industrial bearing through the servo loading control unit.

3. A comprehensive intelligent testing method for high-load bearing performance according to claim 2, characterized in that: The specific steps of S1 also include: S15. Deploy multiple sets of micro sensors in a non-interference area of ​​a wind power heavy-duty industrial bearing seal structure, wherein the multiple sets of micro sensors include microseismic acceleration sensors and triboelectric charge collection electrodes; S16. During the loading test of step S14, based on the deployed microseismic acceleration sensors and the loading test duration, real-time monitoring of high-frequency microseismic signals generated by the rolling elements and raceways within the wind power heavy-duty industrial bearing when the speed and load change, to obtain the microvibration frequency Pzd at each monitoring time point within the test duration; S17. During the loading test of step S14, based on the deployed triboelectric charge collection electrode and the loading test duration, the instantaneous charge density signal generated by triboelectric charging of the rolling elements and raceways inside the wind power heavy-duty industrial bearing as the speed and load change in real time is monitored by electric field induction to obtain the triboelectric charge Dmc at each monitoring time point within the test duration; S18. The micro-vibration frequency Pzd and friction charge Dmc at each monitoring time point within the test duration are constructed as an original test data set, and the original test data set is preprocessed in combination with a wavelet threshold denoising algorithm. The preprocessing process includes denoising, filtering and time series alignment.

4. A comprehensive intelligent testing method for high-load bearing performance according to claim 3, characterized in that: The specific steps of S2 include: S21. By performing feature recognition on the original test data set constructed in step S18, the micro-vibration frequency Pzd at each monitoring time point within the test duration is extracted. In combination with the Hilbert transform method, the phase information structure of the high-frequency micro-vibration generated by the rolling elements and raceways inside the wind power heavy-duty industrial bearing when the speed and load change during the test duration is analyzed, and the instantaneous phase coefficient Xzd of the micro-vibration frequency at each monitoring time point within the test duration is obtained. The instantaneous phase coefficient Xzd is obtained by the following formula: ； Where, It is expressed as the micro-vibration frequency at the i-th monitoring time point within the test duration, It is expressed as the instantaneous phase coefficient of the micro-vibration frequency at the i-th monitoring time point within the test duration, It is represented by the orthogonal component after Hilbert transform of the micro-vibration frequency at the i-th monitoring time point within the test duration, Expressed as the inverse tangent function; S22. By performing feature recognition on the original test data set constructed in step S18, the friction charge Dmc at each monitoring time point within the test duration is extracted, and combined with the Hilbert transform method, the phase information structure of the friction charge generated by the friction charging of the rolling elements and raceways inside the wind power heavy-duty industrial bearing when the speed and load change during the test duration is analyzed, and the instantaneous phase coefficient Xdh of the friction charge at each monitoring time point within the test duration is obtained.

5. A comprehensive intelligent testing method for high-load bearing performance according to claim 4, characterized in that: The specific steps of S2 also include: S23. Performing a difference processing on the instantaneous phase coefficient Xzd of the microvibration frequency obtained at each monitoring time point during the test duration and the corresponding instantaneous phase coefficient Xdh of the friction charge, analyzing the phase deviation between the microvibration signal and the charge signal of the rolling elements and raceways inside the wind power heavy-duty industrial bearing when the speed and load change during the test duration, and obtaining the phase deviation coefficient Xxp at each monitoring time point during the test duration; S24. Based on the phase deviation coefficient Xxp at each monitoring time point during the test duration obtained in step S23, and in combination with the phase coupling analysis algorithm, analyze the phase coupling degree between the microseismic signal and the charge signal of the rolling elements and raceways inside the wind power heavy-duty industrial bearing when the speed and load change during the test duration, and obtain the phase coupling degree coefficient Xoh, which is specifically obtained by the following formula: ； Where, It is expressed as the phase deviation coefficient at the i-th monitoring time point within the test duration, i=1, 2, 3, ..., n, and n represents the monitoring period.

6. A comprehensive intelligent testing method for high-load bearing performance according to claim 5, characterized in that: The specific steps of S2 also include: S25. Based on the value of the phase coupling coefficient Xoh in step S24, whether the mechanical behavior and triboelectric effect of the rolling elements and raceways inside the wind power heavy-duty industrial bearing are highly coupled when the speed and load change during the test period is determined, so as to determine whether the stiffness contact behavior of the rolling elements and raceways inside the wind power heavy-duty industrial bearing is normal, and issue a corresponding stiffness attenuation analysis instruction. The specific contents are as follows: If the phase coupling coefficient Xoh is in the range When the test is complete, it indicates that the mechanical behavior and triboelectric effect of the rolling elements and raceways inside the wind power heavy-duty industrial bearing are not highly coupled when the speed and load change. This determines that the stiffness contact between the rolling elements and raceways 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 means that the mechanical behavior and triboelectric effect of the rolling elements and raceways inside the wind power heavy-duty industrial bearing are highly coupled when the speed and load change during the test period, so as to determine that the stiffness contact between the rolling elements and raceways inside the wind power heavy-duty industrial bearing is normal. At this time, no additional stiffness attenuation analysis command is issued.

7. A comprehensive intelligent testing method for high-load bearing performance according to claim 6, characterized in that: The specific steps of S3 include: S31. After receiving the stiffness attenuation analysis instruction, based on the constructed loading condition script parameters, the load change during the wind power heavy-duty industrial bearing loading test is monitored in real time, and the load value Nzh at each monitoring time point within the test duration is obtained. The average load value Nzh within the test duration is obtained through the statistical averaging algorithm. avg ; S32, based on the average load value Nzh within the test duration avg The load fluctuation coefficient Xzh is obtained by correlating it with the load value Nzh at each monitoring time point and analyzing the load disturbance degree during the loading test of the wind power heavy-load industrial bearing after dimensionless processing.

8. A comprehensive intelligent testing method for high-load bearing performance according to claim 7, characterized in that: The specific steps of S3 also include: S33. After extracting features from the bearing sample data of the wind power heavy-duty industrial bearing during the manufacturing stage, the standard reference stiffness value K0 of the wind power heavy-duty industrial bearing is obtained, and it is associated with the phase coupling degree coefficient Xoh and the load fluctuation coefficient Xzh. After dimensionless processing, the stiffness attenuation degree of the wind power heavy-duty industrial bearing during the test time is analyzed to obtain the test stiffness attenuation value Zsj, which is specifically obtained by the following formula: ； Where, Expressed as an exponential function, and are expressed as weight values.

9. A comprehensive intelligent testing method for high-load 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 wind power heavy-duty industrial bearing currently being tested is qualified, and mark it as a bearing with a corresponding level of stiffness performance. The specific content is as follows: If the test stiffness attenuation value Zsj is less than the attenuation threshold S, it indicates that the stiffness performance of the wind power heavy-duty industrial bearing currently being tested is qualified, meeting the requirements for use of the wind power heavy-duty industrial bearing under typical external load conditions, and is marked as a first-level stiffness performance bearing; If the test stiffness attenuation value Zsj ≥ attenuation threshold S, it means 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 external typical load conditions, and it is marked as a secondary stiffness performance bearing.

10. A high-load bearing performance comprehensive intelligent testing system, used to implement the high-load bearing performance comprehensive intelligent testing method according to any one of claims 1 to 9, characterized in that: Including working condition loading module, phase analysis module, attenuation analysis module and performance judgment module; The working condition loading module is used to construct the loading condition script parameters according to the working condition specification requirements of wind power heavy-load bearings, perform loading tests on the stiffness performance of wind power heavy-load industrial bearings, and obtain the original test data set; The phase analysis module uses the acquired original test data set, combined with the Hilbert transform method and phase coupling analysis algorithm, to analyze the phase coupling between the microseismic signal and the charge signal of the wind power heavy-duty industrial bearing during the test period when the speed and load change. This determines whether the contact behavior between the rolling elements and raceways inside the wind power heavy-duty industrial bearing is normal and issues corresponding stiffness attenuation analysis instructions. The attenuation analysis module is used to analyze the stiffness attenuation degree of the wind power heavy-duty industrial bearing during the test time in combination with the loading condition script parameters after receiving the stiffness attenuation analysis instruction, and obtain the test stiffness attenuation value Zsj; The performance judgment 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 mark it as a bearing with the corresponding level of stiffness performance.

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