A spindle dynamic balance detection method and system

Abstract

The present application relates to the technical field of precision spindle detection, and particularly relates to a spindle dynamic balance detection method and system, which comprises the following steps: configuring a vibration information collection point, obtaining a vibration component signal related to a balance state in a spindle operation process, the vibration component signal comprising a multi-dimensional vibration component and a spindle dynamic parameter; performing feature extraction and parameterization analysis on the vibration component signal, analyzing the spindle motion state, and if an unbalance state occurs, generating an unbalance feature set describing the spindle unbalance state; establishing a dynamic balance mapping according to the unbalance feature set, and generating a parameterized balance analysis model based on integrated analysis of the dynamic balance mapping; constructing a closed-loop dynamic feedback, monitoring the spindle operation state, and according to the monitoring result, iteratively parameterizing the balance analysis model and correcting the spindle motion until the spindle balance meets a preset target. Through the present application, the problems of poor adaptability, feedback lag and low data utilization of the existing spindle dynamic balance detection method are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of precision spindle detection, and in particular to a spindle dynamic balance detection method and system. Background Art

[0002] In mechanical rotating equipment, the spindle is a key component, and its dynamic balance is crucial to the equipment's operational stability, machining accuracy, and service life. However, due to factors such as manufacturing errors, assembly errors, and material inhomogeneities, the spindle often becomes unbalanced during operation, leading to increased vibration, reduced machining quality, and even equipment failure. Existing spindle dynamic balancing detection methods mostly rely on single static corrections, lack a real-time dynamic feedback mechanism to adapt to complex operating conditions, and have low data utilization, making it difficult to meet the modern industry's demand for high-precision, high-efficiency, and intelligent dynamic balancing detection.

[0003] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0004] The present invention provides a spindle dynamic balance detection method and system, which can effectively solve the problems in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for detecting the dynamic balance of a spindle, the method comprising:

[0007] Configuring vibration information collection points to obtain vibration component signals related to the balance state during the operation of the spindle, wherein the vibration component signals include multi-dimensional vibration components and spindle dynamic parameters;

[0008] Performing feature extraction and parameter analysis on the vibration component signal to analyze the spindle motion state, and if an imbalance state occurs, generating an imbalance feature set describing the spindle imbalance state;

[0009] Establishing a dynamic balance map according to the imbalance feature set, and generating a parameterized balance analysis model based on the integrated analysis of the dynamic balance map;

[0010] A closed-loop dynamic feedback is constructed to monitor the spindle operation status, and the parameterized balance analysis model is iterated based on the monitoring results to correct the spindle motion until the spindle balance meets the preset target.

[0011] Furthermore, generating a parameterized equilibrium analysis model based on the dynamic equilibrium mapping integrated analysis includes:

[0012] performing a vibration mode analysis on the imbalance feature set, associating the spindle dynamic parameters with corresponding vibration modes, and forming a dynamic balance map;

[0013] Based on the dynamic balance map, analyzing the vibration characteristics of the multi-dimensional vibration components, identifying the spatial distribution and intensity of the imbalance sources, and generating the imbalance source spatial matrix;

[0014] Based on the imbalance source spatial matrix, a multi-dimensional imbalance mapping algorithm is used to construct a parametric balance analysis model, spatially analyze the impact of different imbalance sources, quantify their contribution to spindle vibration, and predict the dynamic balance state of the spindle under specific working conditions;

[0015] The parameters of the parameterized balance analysis model are calibrated by comparing the signal with the vibration component signal.

[0016] Furthermore, a spatial analysis of the impact of different imbalance sources is performed, including:

[0017] Performing a frequency-time feature analysis on the imbalance feature set, combining it with the dynamic balance map, identifying the spatial distribution position and position vibration parameters of the spindle imbalance source, and determining the specific contribution of each imbalance source to the dynamic behavior of the spindle;

[0018] Based on the spatial distribution position and position vibration parameters, the unbalance torque generated by different unbalance sources is calculated to quantify the influence of the unbalance source on the spindle motion;

[0019] Based on the unbalanced torque, analyzing the dynamic response characteristics of the unbalanced source during the spindle movement process, and generating an unbalanced source analysis result;

[0020] A balance compensation scheme is established based on the imbalance source analysis result and in combination with the position vibration parameters and the imbalance torque.

[0021] Furthermore, a balanced compensation plan was established, including:

[0022] Based on the imbalance source analysis results and in combination with the dynamic response characteristics, preliminary balance compensation parameters are calculated and generated;

[0023] Simulating the dynamic balance state of the spindle under different operating conditions by using the parameterized balance analysis model, analyzing the adaptability of the preliminary balance compensation parameters under various operating conditions, and generating optimized balance compensation parameters;

[0024] generating a primary balance compensation scheme according to the optimized balance compensation parameters, and verifying the balance compensation scheme through the parameterized balance analysis model to evaluate the effect of suppressing the spindle vibration amplitude;

[0025] If the suppression effect does not reach the preset balance target, the optimized balance compensation parameters are modified according to data feedback until a balance compensation scheme that meets the preset target is generated.

[0026] Furthermore, feature extraction and parameter analysis are performed on the vibration component signal, including:

[0027] Performing transform envelope analysis on the vibration component signal to extract the spindle dynamic parameters, which include energy distribution, frequency band characteristics, and vibration mode indicators;

[0028] Combining the multi-dimensional vibration components, analyzing the dynamic response of the main shaft under different operating conditions, identifying abnormal vibration modes, and generating a dynamic response data set;

[0029] Analyzing the correlation between the parameters of the dynamic response data set, establishing coupling links between the parameters, and establishing a primary dynamic balance mapping of the multidimensional vibration components and the main shaft dynamic parameters based on the coupling links;

[0030] Based on the primary dynamic balance map, the vibration component signals are parameterized and reorganized, and are correlated with the spindle motion state to generate a vibration correlation result.

[0031] Furthermore, coupling links between parameters are established, including:

[0032] selecting the multi-dimensional vibration components and the main shaft dynamic parameters under the abnormal vibration mode from the dynamic response data set;

[0033] Based on the abnormal vibration pattern, performing correlation calculation between the multidimensional vibration components in each of the vibration component signals and the main shaft dynamic parameters, wherein the correlation calculation uses time-delay cross-correlation analysis to determine the correlation strength between the parameters;

[0034] According to the calculation result of the correlation calculation, the vibration component signals are sorted according to the correlation strength, and the core parameter pairs with the highest correlation are screened out;

[0035] The interactive relationship between the core parameter pairs is analyzed, the coupling link between the multi-dimensional vibration components and the main shaft dynamic parameters is established, and the coupling relationship is characterized for constructing a primary dynamic balance map.

[0036] Furthermore, iterating the parameterized balance analysis model and correcting the spindle motion according to the monitoring results includes:

[0037] The vibration component signals of the spindle under the running state are collected through the vibration information collection points, and are integrated with the running timestamps to generate regular monitoring data;

[0038] Comparing the timed monitoring data with the balance prediction result of the parameterized balance analysis model, and calculating the error between the current spindle vibration characteristic and the balance prediction result of the parameterized balance analysis model;

[0039] Preset a balance satisfaction target, and based on the error analysis result, adjust the characteristic prediction parameters in the parameterized balance analysis model and update the parameterized balance analysis model;

[0040] generating a spindle balance correction scheme based on the parameterized balance analysis model, the balance correction scheme including instructions for adjusting the mass and position of the balancing block and optimizing the spindle motion path, and establishing a balance correction database based on the balance correction scheme;

[0041] According to the balance correction plan, a spindle motion adjustment operation is performed, and the balance meets the target by the timed monitoring data of the running spindle.

[0042] Furthermore, establishing a balance correction database according to the balance correction scheme includes:

[0043] Collecting balance correction information included in the balance correction scheme, wherein the balance correction information includes an effective correction scheme during spindle motion and the spindle vibration characteristics;

[0044] Associating the balance correction information with the operating conditions to generate a correction scheme experience record;

[0045] Based on the empirical records of the correction scheme, the characteristic performance of the spindle imbalance under different operating conditions and the effective correction scheme are summarized, and the balance correction empirical rules are extracted;

[0046] Classifying the balance correction empirical rules and the balance correction schemes according to the operating states and storing them in the balance correction experience database, and matching a unique correction scheme for each operating condition;

[0047] After the correction scheme is executed, new data is dynamically added to the balance correction experience library, and the matching between the correction scheme and the operating conditions is further optimized by updating the experience rules.

[0048] A spindle dynamic balance detection system, the system comprising:

[0049] An information acquisition module is configured with vibration information acquisition points to obtain vibration component signals related to the balance state during the operation of the spindle. The vibration component signals include multi-dimensional vibration components and spindle dynamic parameters.

[0050] The parameter analysis module extracts features and performs parameter analysis on the vibration component signal to analyze the spindle motion state. If an imbalance occurs, it generates an imbalance feature set that describes the spindle imbalance state.

[0051] The model generation module establishes a dynamic balance map based on the imbalance feature set, and generates a parameterized balance analysis model based on the integrated analysis of the dynamic balance map;

[0052] The monitoring and correction module builds a closed-loop dynamic feedback loop to monitor the spindle's operating status. Based on the monitoring results, the parameterized balance analysis model is iterated and the spindle motion is corrected until the spindle balance meets the preset target.

[0053] Furthermore, the model generation module includes:

[0054] The balance mapping unit performs vibration mode analysis on the imbalance feature set, associates the spindle dynamic parameters with the corresponding vibration modes, and forms a dynamic balance map;

[0055] The matrix building unit analyzes the vibration characteristics of multi-dimensional vibration components based on dynamic balance mapping, identifies the spatial distribution and intensity of imbalance sources, and generates an imbalance source spatial matrix;

[0056] The spatial analysis unit uses a multi-dimensional imbalance mapping algorithm based on the imbalance source spatial matrix to construct a parametric balance analysis model. It spatially analyzes the impact of different imbalance sources, quantifies their contribution to spindle vibration, and predicts the dynamic balance state of the spindle under specific working conditions.

[0057] Compare the calibration unit with the vibration component signals and calibrate the parameters of the parameterized balance analysis model.

[0058] The technical solution of the present invention can achieve the following technical effects:

[0059] It solves the problems of poor adaptability, feedback lag and low data utilization of existing spindle dynamic balancing detection methods, realizes adaptation to dynamic balancing requirements of different speeds and load conditions, and improves the efficiency, accuracy and intelligence level of detection and correction.

[0060] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0062] Figure 1 Fig. 1 is a flowchart of a main shaft dynamic balance detection method;

[0063] Figure 2 Fig. 2 is a structural diagram of a parameterized balance analysis model;

[0064] Figure 3 Fig. 3 is a flowchart of generating a vibration correlation result;

[0065] Figure 4 Fig. 4 is a flowchart of main shaft motion correction. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0068] Embodiment one

[0069] As shown in the following table, the present application provides a main shaft dynamic balance detection method, the method comprising: Figure 1

[0070] S10: configuring a vibration information collection point, acquiring a vibration component signal related to a balance state in a main shaft running process, the vibration component signal comprising a multi-dimensional vibration component and a main shaft dynamic parameter;

[0071] S20: performing feature extraction and parameterized analysis on the vibration component signal, analyzing a main shaft motion state, and if an unbalance state occurs, generating an unbalance feature set describing the main shaft unbalance state;

[0072] S30: establishing a dynamic balance mapping according to the unbalance feature set, and generating a parameterized balance analysis model based on integrated analysis of the dynamic balance mapping;

[0073] S40: constructing a closed-loop dynamic feedback, monitoring a main shaft running state, and according to a monitoring result, iteratively parameterizing the balance analysis model and correcting the main shaft motion until the main shaft balance meets a preset target.

[0074] ​Specifically, vibration sensors are installed at key positions of the spindle movement (such as the bearing seat, shaft end and shaft body) to collect vibration component signals related to the balance state during the operation of the spindle in real time. The vibration sensors can be three-axis accelerometers and angular velocity sensors, etc., to capture multi-dimensional vibration components. According to the structural characteristics and operating environment of the spindle, the collection points are reasonably arranged to ensure that the collected data fully covers the dynamic behavior of the spindle; the vibration component signals during the operation of the spindle are collected by the vibration sensor. The vibration component signals include the vibration amplitude, frequency, phase difference and acceleration of the spindle, etc. The dynamic parameters of the spindle, such as speed, load and axis trajectory, are collected synchronously to ensure the correspondence between the signal and the operating state; the collected vibration component signals are preprocessed, including denoising, filtering and normalization operations, and the time-frequency analysis method (such as short-time Fourier transform or wavelet transform) is used to extract the time domain and frequency domain characteristic parameters of the signal, analyze the vibration mode of the spindle under different operating conditions, identify the imbalance state, and generate An imbalance feature set is used to describe the imbalance state of the spindle. The imbalance feature set includes parameters such as imbalance amplitude, phase and imbalance source position. Based on the imbalance feature set, a dynamic balance mapping is established to describe the distribution of spindle imbalance sources and their impact on overall vibration, and a parameterized balance analysis model is generated to quantify the contribution of different imbalance sources to the dynamic behavior of the spindle and predict the dynamic balance state of the spindle under different operating conditions. During the operation of the spindle, the vibration component signal is monitored in real time, and the actual monitoring results are compared with the predicted results of the parameterized balance analysis model. If there is an error, a spindle balance correction plan is generated. The spindle balance correction plan includes operations such as adjusting the mass and position of the balancing block and optimizing the spindle motion path. The spindle balance correction operation is performed according to the correction plan, and the correction effect is verified during operation. By monitoring the spindle vibration signal in real time, it is determined whether the balance state meets the preset target. If the balance is insufficient, the model adjustment step is returned and the correction is repeated until the spindle reaches the ideal balance state.

[0075] The technical solution of the present invention solves the problems of poor adaptability, feedback lag and low data utilization of existing spindle dynamic balancing detection methods, realizes adaptation to dynamic balancing requirements of different speeds and load conditions, and improves the efficiency, accuracy and intelligence level of detection and correction.

[0076] Further, if Figure 2 As shown, a parameterized equilibrium analysis model is generated based on the dynamic equilibrium mapping integration analysis, including:

[0077] Perform vibration mode analysis on the imbalance feature set, associate the spindle dynamic parameters with the corresponding vibration modes, and form a dynamic balance map;

[0078] Based on dynamic balance mapping, the vibration characteristics of multi-dimensional vibration components are analyzed, the spatial distribution and intensity of the imbalance source are identified, and the imbalance source space matrix is ​​generated;

[0079] Based on the unbalance source space matrix, a multi-dimensional unbalance mapping algorithm is used to construct a parameterized balance analysis model, the influence of different unbalance sources is analyzed in space, and the contribution to the main shaft vibration is quantified, and the dynamic balance state of the main shaft under a specific working condition is predicted;

[0080] Compared with the vibration component signal, the parameterized balance analysis model parameters are calibrated.

[0081] As a preferred embodiment of the above, the unbalance feature set is extracted from the vibration component signal collected during the operation of the main shaft, the set includes unbalance amplitude, frequency, phase and vibration energy distribution, etc., the vibration mode analysis is performed on the unbalance feature set by using modal analysis (such as modal superposition method or empirical mode decomposition method), the main vibration mode in the operation of the main shaft is identified, including first-order, second-order and high-order modal shapes, the dynamic parameters (such as speed, load and shaft center trajectory) of the main shaft are correlated with the vibration mode results, and a dynamic balance mapping describing the unbalance state is formed, which is used to show the relationship between the unbalance source and the vibration mode; based on the dynamic balance mapping, the spatial characteristics of the main shaft vibration signal are analyzed, the position distribution and intensity of the main unbalance source are identified, and a spatial distribution modeling method (such as finite element modeling or distributed unbalance modeling) is used to generate an unbalance source space matrix, the rows of the unbalance source space matrix represent different unbalance source positions, and the columns represent the amplitude, phase and contribution intensity of the unbalance source to the overall vibration;

[0082] Based on the unbalance source space matrix, a multi-dimensional unbalance mapping algorithm (such as orthogonal decomposition algorithm or multi-objective optimization algorithm) is used to construct a parameterized balance analysis model, the parameterized balance analysis model input is the unbalance source position, vibration amplitude and main shaft dynamic parameter, and the output is the predicted value of the main shaft vibration response, which is used to quantify the contribution of different unbalance sources to the overall vibration behavior of the main shaft. In the process of establishing the model, the accuracy of the parameterized balance analysis model is optimized by piecewise linear fitting or nonlinear regression method, so as to ensure that the dynamic balance state of the main shaft under multiple operating conditions can be reflected; the prediction results of the parameterized balance analysis model are compared with the actually collected vibration component signal, the error between the output of the parameterized balance analysis model and the measured data is calculated, and the error feedback mechanism is used to dynamically calibrate the parameterized balance analysis model by adjusting the key parameters (such as unbalance source intensity coefficient and spatial distribution weight) in the model, so as to ensure that the model can accurately describe the dynamic balance state of the current main shaft.

[0083] Further, the spatial analysis of the influence of different unbalance sources includes:

[0084] The frequency-time characteristic analysis is performed on the unbalance feature set, the spatial distribution position and position vibration parameter of the main shaft unbalance source are identified in combination with the dynamic balance mapping, and the specific contribution of each unbalance source to the dynamic behavior of the main shaft is determined.

[0085] Based on the spatial distribution position and the position vibration parameter, the unbalance torque generated by different unbalance sources is calculated, and the influence of the unbalance sources on the motion of the main shaft is quantified.

[0086] Based on the unbalance torque, the dynamic response characteristics of the unbalance sources in the motion process of the main shaft are analyzed, and an unbalance source analysis result is generated.

[0087] According to the unbalance source analysis result, the position vibration parameter and the unbalance torque are combined to establish a balance compensation scheme.

[0088] As a preferred embodiment of the above embodiment, by performing frequency-time feature analysis on the collected unbalance feature set, the vibration signal is decomposed using short-time Fourier transform (STFT) or continuous wavelet transform (CWT), and the key parameters in the unbalance feature set are extracted, including vibration amplitude, frequency, phase and vibration energy distribution, etc. Combined with dynamic balance mapping, the spatial distribution position of different unbalance sources and the position-related vibration parameters (such as amplitude and phase) are determined, and the main unbalance source area in the main shaft system is marked. For each unbalance source, a geometric relationship model is established according to its spatial distribution position and position vibration parameter, the unbalance torque generated by the unbalance source on the main shaft is calculated, and the following formula is used to calculate the unbalance torque:

[0089] M=r×F

[0090] wherein, M is the unbalance torque, r is the unbalance source position vector, F is the product of the vibration amplitude and the unbalance mass (i.e. the force caused by vibration), and all unbalance torques are summarized to generate the overall unbalance torque distribution of the main shaft. Based on the calculated unbalance torque, the dynamic response characteristics of each unbalance source in the motion process of the main shaft are analyzed, and the modal response analysis method is used to quantify the specific influence of each unbalance source on the vibration behavior of the main shaft under different rotational speeds and load conditions, in combination with the structural characteristics and rotational speed parameters of the main shaft. The results of frequency-time feature analysis, spatial distribution and dynamic response analysis are summarized to form an unbalance source analysis report, which includes the position distribution of each unbalance source, the vibration parameters (amplitude, frequency, phase) of each unbalance source, and the quantitative results of the influence of each unbalance source on the dynamic behavior of the main shaft (such as unbalance torque, vibration energy contribution rate). According to the unbalance source analysis result, the position vibration parameter and the unbalance torque information are combined to design a balance compensation scheme, which includes adjusting the mass and position of the balance block to make the overall vibration of the main shaft reach the preset target, and optimizing the running path and rotational speed of the main shaft for specific unbalance sources to reduce high-frequency vibration in the dynamic response.

[0091] Further, the balance compensation scheme is established, including:

[0092] Based on the unbalance source analysis result, the dynamic response characteristics are combined to calculate and generate preliminary balance compensation parameters.

[0093] The dynamic balance state of the spindle under different operating conditions is simulated by a parametric balance analysis model, the adaptability of the preliminary balance compensation parameters under various operating conditions is analyzed, and the optimized balance compensation parameters are generated;

[0094] Generate a primary balance compensation scheme based on the optimized balance compensation parameters, and verify the balance compensation scheme through a parametric balance analysis model to evaluate the effect of suppressing the spindle vibration amplitude;

[0095] If the suppression effect does not reach the preset balance target, the balance compensation parameters are corrected and optimized according to the data feedback until a balance compensation scheme that meets the preset target is generated.

[0096] As a preferred embodiment of the above, based on the imbalance source analysis results and combined with the dynamic response characteristics of the spindle, preliminary balance compensation parameters are calculated. The parameters include the mass and installation position of the balancing weight and recommended values ​​for adjusting the spindle rotation path or dynamic parameters (such as speed and angular acceleration). The mass and position of the balancing weight are calculated using the following formula:

[0097] ;

[0098] Among them, m 补偿 is the mass of the balance block, M 失衡 is the unbalanced moment, r 补偿 is the radius from the compensation block to the spindle center, ω is the angular velocity of the spindle;

[0099] Using a parameterized balance analysis model, the dynamic balance state of the main shaft under different operating conditions (such as high load, low speed, variable speed operation, etc.) is simulated. In each operating condition, the adaptability of the preliminary balance compensation parameters is analyzed, including the influence on the vibration amplitude, frequency and dynamic response stability. The balance effect and adaptability evaluation index of the preliminary balance compensation parameters under each operating condition are recorded, and the preliminary balance compensation parameters that have a greater impact on the dynamic balance of the main shaft are selected. Based on the operating condition analysis results, the preliminary balance compensation parameters are adjusted through an optimization algorithm (such as genetic algorithm or particle swarm optimization algorithm) to generate optimized balance compensation parameters. The optimization objectives include global minimization of vibration amplitude, minimization of compensation block mass, and maximization of multi-condition adaptability of compensation parameters. According to the optimized balance compensation parameters, a primary balance compensation scheme is generated, including the installation position, mass configuration and operation adjustment scheme of the compensation block. The implementation effect of the primary balance compensation scheme is simulated using the parameterized balance analysis model to evaluate the suppression effect on the vibration amplitude and frequency response of the main shaft. If the vibration suppression effect does not meet the preset balance target, the deficiencies of the compensation scheme are recorded to provide feedback for the next parameter correction. Based on the feedback results of the balance verification, the optimized balance compensation parameters are corrected, including adjusting the position, mass or dynamic parameters of the compensation block. The simulation and verification process is repeated until the final balance compensation scheme that meets the preset balance target is generated, which can significantly reduce the vibration amplitude of the main shaft and improve the dynamic balance performance under each operating condition.

[0100] Further, as shown in Figure 3 characteristic extraction and parameterized analysis of the vibration component signal, including:

[0101] The vibration component signal is processed by envelope analysis transformation to extract the main shaft dynamic parameters, including energy distribution, frequency band characteristics and vibration mode indicators.

[0102] The dynamic response of the main shaft under different operating conditions is analyzed in combination with multi-dimensional vibration components to identify abnormal vibration modes and generate a dynamic response data set.

[0103] The correlation between the parameters in the dynamic response data set is analyzed to establish a coupling link between the parameters. Based on the coupling link, a primary dynamic balance mapping of the multi-dimensional vibration components and the main shaft dynamic parameters is established.

[0104] Based on the primary dynamic balance mapping, the vibration component signal is parameterized and reorganized, and associated analysis is performed with the main shaft motion state to generate a vibration correlation result.

[0105] As a preferred embodiment of the above embodiment, the collected vibration component signal is subjected to transform envelope analysis. First, a bandpass filter is used to preprocess the signal to filter out noise and irrelevant frequency band signals. The Hilbert transform is used to generate an envelope signal for the vibration signal, and the dynamic parameters of the main shaft are extracted, including analyzing the energy change of the signal in the time domain, identifying the energy distribution of the high vibration area of ​​the main shaft, analyzing the spectral distribution of the signal by fast Fourier transform (FFT), extracting the main frequency band characteristics, analyzing the harmonic components of the envelope signal, and extracting the modal eigenvalue representing the imbalance state; combining multi-dimensional vibration components (such as the X, Y, and Z axis components of the acceleration signal), analyzing the dynamic response of the main shaft under different operating conditions (such as speed, load changes, etc.), using the frequency domain feature analysis method to identify abnormal vibration modes, including high-frequency harmonics, low-frequency oscillations, etc., to generate a dynamic response data set, which contains key parameters such as the vibration frequency, amplitude, and phase of the main shaft under different conditions; performing correlation analysis on the various parameters of the dynamic response data set (such as frequency, amplitude, phase, and energy), and using the Pearson correlation coefficient or cross-correlation coefficient to calculate the correlation coefficient. The information volume is used to calculate the correlation between parameters, screen out highly correlated parameter pairs, and establish a coupling link between multidimensional vibration components and spindle dynamic parameters in combination with the spindle motion characteristics (such as the correlation between speed fluctuation and vibration amplitude). Based on the highly correlated parameter pairs, the vibration components are mapped to the spindle dynamic parameters to form a primary dynamic balance map. The primary dynamic balance map is used to describe the preliminary impact of the spindle imbalance source on the overall vibration state, including the correlation between the amplitude change of the vibration component and the dynamic parameters. On the basis of the primary dynamic balance map, the vibration component signals are parametrically decomposed and reorganized, and principal component analysis (PCA) is used to reduce the dimension of the vibration data, extract the main vibration features, and group the high-frequency vibration and low-frequency vibration components separately for subsequent analysis. Combined with the spindle motion state, the decomposed signals are correlated with the spindle operation state to generate vibration correlation results. The vibration correlation results include: the relationship between the vibration mode and the spindle speed and load, as well as the main dynamic characteristics of the imbalance source. The vibration correlation results provide data support for subsequent dynamic balance mapping optimization and correction plan formulation.

[0106] Furthermore, coupling links between parameters are established, including:

[0107] Selecting multi-dimensional vibration components and main shaft dynamic parameters in abnormal vibration modes from the dynamic response data set;

[0108] Based on the abnormal vibration pattern, the correlation between the multi-dimensional vibration components in each vibration component signal and the main shaft dynamic parameters is calculated. The correlation calculation uses time-delay cross-correlation analysis to determine the correlation strength between the parameters.

[0109] According to the results of the correlation calculation, the vibration component signals are sorted by correlation strength, and the core parameter pairs with the highest correlation are screened out;

[0110] The interaction between the core parameter pairs is analyzed, and the coupling links between the multidimensional vibration components and the main shaft dynamic parameters are established to characterize the coupling relationship for constructing the primary dynamic balance map.

[0111] As a preferred embodiment of the above embodiment, data related to abnormal vibration modes are screened from the dynamic response data set, including multidimensional vibration components such as abnormal vibration amplitude, frequency, and phase, as well as spindle dynamic parameters (such as speed, load, and temperature). The abnormal vibration modes are extracted using a time-frequency analysis method (such as short-time Fourier transform or wavelet transform), and the corresponding characteristic data range is marked to ensure that the selected data can fully reflect the characteristics of the abnormal vibration modes. Correlation calculation is performed on the selected multidimensional vibration components and the spindle dynamic parameters, and the correlation strength between different parameters is evaluated using a time-delay cross-correlation analysis method. Specifically, the change in the correlation between two sets of time series with time lag is calculated to identify the lag effect of the spindle dynamic parameters on the vibration components. By constructing a time-delay correlation function, the maximum correlation value and the corresponding lag time at different lag times are determined. The correlation calculation results include the correlation strength, time lag, and directional characteristics between the parameters. Based on the correlation calculation results, all parameter pairs are sorted in descending order of correlation strength, and the core parameter pairs with the highest correlation are screened. The core parameter pairs reflect the key coupling relationship between spindle vibration and dynamic behavior, such as the strong correlation between vibration amplitude and speed fluctuation, and vibration frequency and spindle load. , set a threshold for correlation strength, eliminate parameter pairs with low or insignificant correlation, and optimize the screening results; conduct interaction relationship analysis on the screened core parameter pairs, use partial correlation analysis or multiple regression analysis methods to quantify the direct and indirect effects between parameter pairs, and identify the main interaction patterns between parameter pairs (such as synchronous change relationship or lag-driven relationship). The analysis results show that there may be complex multidimensional interaction characteristics between different core parameter pairs; based on the core parameter pairs and their interaction relationships, construct coupling links between multidimensional vibration components and spindle dynamic parameters to characterize the coupling strength, interaction direction and time lag relationship between parameters. Use a directed weighted network to represent the coupling links, where nodes represent parameters, edge weights represent correlation strength, and directions represent lag relationships. The coupling links are visualized to form an interaction network diagram between parameters. The coupling links describe the intrinsic coupling relationship between multidimensional vibration components and spindle dynamic parameters, providing data support for constructing a primary dynamic balance map; the established coupling links are input into the primary dynamic balance map construction module to describe the influence of the spindle imbalance source on the overall dynamic behavior. The primary dynamic balance map can be further used for imbalance source location, dynamic behavior prediction, and balance compensation scheme optimization.

[0112] Further, if Figure 4 As shown, the parameterized balance analysis model is iterated based on the monitoring results and the spindle motion is corrected, including:

[0113] Through the vibration information collection point, the vibration component signal of the spindle under the running state is collected, and the running timestamp is integrated to generate the regular monitoring data;

[0114] Compare the regular monitoring data with the balance prediction results of the parameterized balance analysis model, and calculate the error between the current spindle vibration characteristics and the balance prediction results of the parameterized balance analysis model;

[0115] The preset balance satisfaction target is set, and based on the error analysis results, the characteristic prediction parameters in the parameterized balance analysis model are adjusted and the parameterized balance analysis model is updated;

[0116] Generate a spindle balance correction plan based on the parametric balance analysis model. The balance correction plan includes adjusting the mass and position of the balance block and optimizing the spindle motion path. Establish a balance correction database based on the balance correction plan.

[0117] According to the balance correction plan, the spindle motion adjustment operation is performed, and the balance meets the target through the regular monitoring data of the running spindle.

[0118] As a preferred embodiment of the above, during the operation of the main spindle, multi-dimensional vibration component signals are collected in real time by configuring vibration sensors at the bearing seat, main spindle end or key support points, etc., and the collected vibration signals are time-stamped synchronously. Combined with the main spindle operation status parameters (such as speed, load, temperature, etc.), the timing monitoring data are integrated to generate the timing monitoring data. The timing monitoring data is recorded in a time series format to provide data support for the subsequent update and correction of the parametric balance analysis model; the collected timing monitoring data is input into the parametric balance analysis model and compared with the main spindle balance state predicted by the parametric balance analysis model. The deviation between the actual vibration characteristics and the prediction results of the parametric balance analysis model is calculated by the error analysis method, including vibration amplitude deviation, frequency deviation and phase deviation. Based on the error calculation results, the characteristic prediction parameters in the parametric balance analysis model (such as imbalance source strength, position and dynamic behavior) are adjusted, and the parametric balance analysis model is dynamically updated to improve the prediction accuracy. The updated The parametric balance analysis model generates a spindle balance correction plan, including balance block adjustment parameters, spindle motion path optimization instructions, and establishes a balance correction database based on the correction plan to record the correction plan and corresponding vibration characteristics under the current operating state for reference in subsequent correction optimization; according to the generated balance correction plan, the spindle balance correction operation is performed, including adjusting the installation position and mass of the balance block and optimizing the dynamic operating parameters of the spindle; during the correction process, the vibration signal is collected in real time to monitor the immediate effect of the correction operation to ensure that the correction steps are executed according to the preset plan; after completing the correction operation, the vibration component signal of the spindle operation is continued to be collected and compared with the prediction results of the parametric balance analysis model to verify whether the corrected spindle vibration amplitude reaches the preset balance target; if the correction effect does not meet the target, the parametric balance analysis model is further adjusted according to the historical records of the correction plan and the current error data, and a new balance correction plan is iteratively generated, and the correction and verification process is repeated until the balance requirements are met.

[0119] Furthermore, a balance correction database is established according to the balance correction scheme, including:

[0120] Collecting balance correction information included in the balance correction plan, the balance correction information includes the effective correction plan during spindle motion and the spindle vibration characteristics;

[0121] Associate the balance correction information with the operating conditions to generate correction scheme experience records;

[0122] Based on the correction scheme experience records, the characteristics of spindle imbalance under different operating conditions and effective correction schemes are summarized, and the balance correction experience rules are extracted;

[0123] The balance correction experience rules and balance correction schemes are stored in the balance correction experience database according to the operating status, and a unique correction scheme is matched for each operating condition;

[0124] After the correction scheme is executed, the new data is dynamically added to the balance correction experience library, and the matching between the correction scheme and the operating conditions is further optimized by updating the experience rules.

[0125] As a preferred embodiment of the above, during the spindle correction process, key information of the balance correction scheme is collected, including an effective correction scheme, that is, parameters adjusted during the correction process, such as the mass, position, installation angle of the balancing block and the spindle motion path optimization instructions, the spindle vibration characteristics before and after correction, including the spindle vibration amplitude, frequency and phase change data, the correction effect is recorded, and the collected information is recorded in the form of structured data to ensure the integrity and traceability of the data; the balance correction information is associated with the operating conditions of the spindle (including dynamic parameters such as speed, load, temperature, etc.) to generate a correction scheme experience record; based on the correction scheme experience record, the characteristic performance of the spindle imbalance under different operating conditions and the corresponding effective correction schemes are summarized, and the balance correction experience rules are extracted. The rule content includes the correlation between the imbalance characteristics and the operating conditions, and the recommended optimal correction parameter configuration for different imbalance characteristics. , prediction of correction effect under specific working conditions; based on the extracted experience rules and correction schemes, the data are classified according to the operating conditions and stored in the balance correction experience library; the balance correction experience library supports rapid retrieval of the best correction scheme according to the working conditions, such as matching the unique correction parameter configuration according to the speed range or load conditions, and marking efficient correction schemes and optimization suggestions in the balance correction experience library to quickly guide subsequent correction operations. After the new correction scheme is executed, the correction information is dynamically collected and updated to the balance correction experience library, and the new data is classified and the rules are optimized, and the experience rules are updated to further improve the matching accuracy and adaptability of the experience library; each correction scheme record stored in the experience library verifies its applicability and correction effect under similar working conditions. For schemes that do not meet the standards, the correction parameters under the corresponding operating conditions are further optimized by analyzing the trends and deviations of the new data to improve the accuracy of the experience library.

[0126] Example 2

[0127] Based on the same inventive concept as the spindle dynamic balancing detection method in the aforementioned embodiment, the present invention further provides a spindle dynamic balancing detection system, the system comprising:

[0128] An information acquisition module is configured with vibration information acquisition points to obtain vibration component signals related to the balance state during the operation of the spindle. The vibration component signals include multi-dimensional vibration components and spindle dynamic parameters.

[0129] The parameter analysis module extracts features and performs parameter analysis on the vibration component signal to analyze the spindle motion state. If an imbalance occurs, it generates an imbalance feature set that describes the spindle imbalance state.

[0130] The model generation module establishes a dynamic balance map based on the imbalance feature set, and generates a parameterized balance analysis model based on the integrated analysis of the dynamic balance map;

[0131] The monitoring and correction module builds a closed-loop dynamic feedback loop to monitor the spindle's operating status. Based on the monitoring results, the parameterized balance analysis model is iterated and the spindle motion is corrected until the spindle balance meets the preset target.

[0132] The above-mentioned adjustment system in the present invention can effectively implement a spindle dynamic balance detection method, and the technical effects that can be achieved are as described in the above-mentioned embodiments and will not be repeated here.

[0133] Specifically, the model generation module includes:

[0134] The balance mapping unit performs vibration mode analysis on the imbalance feature set, associates the spindle dynamic parameters with the corresponding vibration modes, and forms a dynamic balance map;

[0135] The matrix building unit analyzes the vibration characteristics of multi-dimensional vibration components based on dynamic balance mapping, identifies the spatial distribution and intensity of imbalance sources, and generates an imbalance source spatial matrix;

[0136] The spatial analysis unit uses a multi-dimensional imbalance mapping algorithm based on the imbalance source spatial matrix to construct a parametric balance analysis model. It spatially analyzes the impact of different imbalance sources, quantifies their contribution to spindle vibration, and predicts the dynamic balance state of the spindle under specific working conditions.

[0137] Compare the calibration unit with the vibration component signals and calibrate the parameters of the parameterized balance analysis model.

[0138] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the corresponding optimization effects of the method in Example 1, which will not be repeated here.

[0139] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application as defined herein and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.

Claims

1. A spindle dynamic balance detection method, characterized in that: The method comprises: Configuring vibration information collection points to obtain vibration component signals related to the balance state during the operation of the spindle, wherein the vibration component signals include multi-dimensional vibration components and spindle dynamic parameters; Performing feature extraction and parameter analysis on the vibration component signal to analyze the spindle motion state, and if an imbalance state occurs, generating an imbalance feature set describing the spindle imbalance state; Establishing a dynamic balance map according to the imbalance feature set, and generating a parameterized balance analysis model based on the integrated analysis of the dynamic balance map; Constructing a closed-loop dynamic feedback loop to monitor the spindle's operating status, iterating the parameterized balance analysis model based on the monitoring results and correcting the spindle's motion until the spindle's balance meets a preset target; Performing feature extraction and parameter analysis on the vibration component signal, including: Performing transform envelope analysis on the vibration component signal to extract the spindle dynamic parameters, which include energy distribution, frequency band characteristics, and vibration mode indicators; Combining the multi-dimensional vibration components, analyzing the dynamic response of the main shaft under different operating conditions, identifying abnormal vibration modes, and generating a dynamic response data set; Analyzing the correlation between the parameters of the dynamic response data set, establishing coupling links between the parameters, and establishing a primary dynamic balance mapping of the multidimensional vibration components and the main shaft dynamic parameters based on the coupling links; Based on the primary dynamic balance map, the vibration component signal is parameterized and reorganized, and a correlation analysis is performed with the spindle motion state to generate a vibration correlation result; Establish coupling links between parameters, including: Selecting the multi-dimensional vibration components and the main shaft dynamic parameters under the abnormal vibration mode from the dynamic response data set; Based on the abnormal vibration pattern, performing correlation calculation between the multidimensional vibration components in each of the vibration component signals and the main shaft dynamic parameters, wherein the correlation calculation uses time-delay cross-correlation analysis to determine the correlation strength between the parameters; According to the calculation result of the correlation calculation, the vibration component signals are sorted according to the correlation strength, and the core parameter pairs with the highest correlation are screened out; The interactive relationship between the core parameter pairs is analyzed, the coupling link between the multi-dimensional vibration components and the main shaft dynamic parameters is established, and the coupling relationship is characterized for constructing a primary dynamic balance map.

2. A spindle dynamic balance detection method according to claim 1, characterized in that: Generating a parameterized equilibrium analysis model based on the dynamic equilibrium mapping integrated analysis includes: performing a vibration mode analysis on the imbalance feature set, associating the spindle dynamic parameters with corresponding vibration modes, and forming a dynamic balance map; Based on the dynamic balance map, analyzing the vibration characteristics of the multi-dimensional vibration components, identifying the spatial distribution and intensity of the imbalance sources, and generating the imbalance source spatial matrix; Based on the imbalance source spatial matrix, a multi-dimensional imbalance mapping algorithm is used to construct a parametric balance analysis model, spatially analyze the impact of different imbalance sources, quantify their contribution to spindle vibration, and predict the dynamic balance state of the spindle under specific working conditions; By comparing the signal with the vibration component signal, the parameters of the parameterized balance analysis model are calibrated.

3. A spindle dynamic balance detection method according to claim 2, characterized in that: Perform spatial analysis of the impact of the different imbalance sources, including: Performing a frequency-time feature analysis on the imbalance feature set, combining it with the dynamic balance map, identifying the spatial distribution position and position vibration parameters of the spindle imbalance source, and determining the specific contribution of each imbalance source to the dynamic behavior of the spindle; Based on the spatial distribution position and position vibration parameters, the unbalance torque generated by different unbalance sources is calculated to quantify the influence of the unbalance source on the spindle motion; Based on the unbalanced torque, analyzing the dynamic response characteristics of the unbalanced source during the spindle movement process, and generating an unbalanced source analysis result; A balance compensation scheme is established based on the imbalance source analysis result and in combination with the position vibration parameters and the imbalance torque.

4. A spindle dynamic balance detection method according to claim 3, characterized in that: Establish a balanced compensation plan that includes: Based on the imbalance source analysis results and in combination with the dynamic response characteristics, preliminary balance compensation parameters are calculated and generated; Simulating the dynamic balance state of the spindle under different operating conditions by using the parameterized balance analysis model, analyzing the adaptability of the preliminary balance compensation parameters under various operating conditions, and generating optimized balance compensation parameters; generating a primary balance compensation scheme according to the optimized balance compensation parameters, and verifying the balance compensation scheme through the parameterized balance analysis model to evaluate the effect of suppressing the spindle vibration amplitude; If the suppression effect does not reach the preset balance target, the optimized balance compensation parameters are modified according to data feedback until a balance compensation scheme that meets the preset target is generated.

5. A spindle dynamic balance detection method according to claim 1, characterized in that: Iterating the parameterized balance analysis model and correcting the spindle motion according to the monitoring results, including: The vibration component signals of the spindle under the running state are collected through the vibration information collection points, and are integrated with the running timestamps to generate regular monitoring data; Comparing the timed monitoring data with the balance prediction result of the parameterized balance analysis model, and calculating the error between the current spindle vibration characteristic and the balance prediction result of the parameterized balance analysis model; Preset a balance satisfaction target, and based on the error analysis result, adjust the characteristic prediction parameters in the parameterized balance analysis model and update the parameterized balance analysis model; generating a balance correction scheme for the spindle according to the parameterized balance analysis model, the balance correction scheme including instructions for adjusting the mass and position of the balancing block and optimizing the spindle motion path, and establishing a balance correction database according to the balance correction scheme; According to the balance correction plan, a spindle motion adjustment operation is performed, and the balance meets the target by the timed monitoring data of the running spindle.

6. A spindle dynamic balance detection method according to claim 5, characterized in that: Establishing a balance correction database according to the balance correction scheme includes: Collecting balance correction information included in the balance correction scheme, wherein the balance correction information includes an effective correction scheme during spindle motion and the spindle vibration characteristics; Associating the balance correction information with the operating conditions to generate a correction scheme experience record; Based on the empirical records of the correction scheme, the characteristic performance of the spindle imbalance under different operating conditions and the effective correction scheme are summarized, and the balance correction empirical rules are extracted; Classifying the balance correction empirical rules and the balance correction schemes according to the operating states and storing them in the balance correction experience database, and matching a unique correction scheme for each operating condition; After the correction scheme is executed, new data is dynamically added to the balance correction experience library, and the matching between the correction scheme and the operating conditions is further optimized by updating the experience rules.

7. A spindle dynamic balance detection system, characterized in that: The system comprises: An information acquisition module is configured with vibration information acquisition points to obtain vibration component signals related to the balance state during the operation of the spindle. The vibration component signals include multi-dimensional vibration components and spindle dynamic parameters. The parameter analysis module extracts features and performs parameter analysis on the vibration component signal to analyze the spindle motion state. If an imbalance occurs, it generates an imbalance feature set that describes the spindle imbalance state. Feature extraction and parameter analysis of vibration component signals, including: Perform transform envelope analysis on the vibration component signal to extract the spindle dynamic parameters, which include energy distribution, frequency band characteristics and vibration mode indicators; Combine multi-dimensional vibration components to analyze the dynamic response of the spindle under different operating conditions, identify abnormal vibration modes, and generate dynamic response data sets; Analyze the correlation between the parameters of the dynamic response data set, establish coupling links between the parameters, and establish a primary dynamic balance map of multi-dimensional vibration components and main shaft dynamic parameters based on the coupling links; Based on the primary dynamic balance mapping, the vibration component signals are parametrically decomposed and reorganized, and the correlation analysis with the spindle motion state is performed to generate vibration correlation results; Establish coupling links between parameters, including: Selecting multi-dimensional vibration components and main shaft dynamic parameters in abnormal vibration modes from the dynamic response data set; Based on the abnormal vibration pattern, the correlation between the multi-dimensional vibration components in each vibration component signal and the main shaft dynamic parameters is calculated. The correlation calculation uses time-delay cross-correlation analysis to determine the correlation strength between the parameters. According to the results of the correlation calculation, the vibration component signals are sorted by correlation strength, and the core parameter pairs with the highest correlation are selected; Analyze the interaction between core parameter pairs, establish coupling links between multidimensional vibration components and spindle dynamic parameters, and characterize the coupling relationship for constructing a primary dynamic balance map; The system further comprises: a model generation module, which establishes a dynamic balance map according to the imbalance feature set, and generates a parameterized balance analysis model based on the integrated analysis of the dynamic balance map; The monitoring and correction module builds a closed-loop dynamic feedback loop to monitor the spindle's operating status. Based on the monitoring results, the parameterized balance analysis model is iterated and the spindle motion is corrected until the spindle balance meets the preset target.

8. A spindle dynamic balance detection system according to claim 7, characterized in that: The model generation module includes: The balance mapping unit performs vibration mode analysis on the imbalance feature set, associates the spindle dynamic parameters with the corresponding vibration modes, and forms a dynamic balance map; The matrix building unit analyzes the vibration characteristics of multi-dimensional vibration components based on dynamic balance mapping, identifies the spatial distribution and intensity of imbalance sources, and generates an imbalance source spatial matrix; The spatial analysis unit uses a multi-dimensional imbalance mapping algorithm based on the imbalance source spatial matrix to construct a parametric balance analysis model. It spatially analyzes the impact of different imbalance sources, quantifies their contribution to spindle vibration, and predicts the dynamic balance state of the spindle under specific working conditions. Compare the calibration unit with the vibration component signals and calibrate the parameters of the parameterized balance analysis model.

Images