A comprehensive testing system and method for electric spindles using multi-sensor data

Abstract

This invention proposes a comprehensive testing system and method for electric spindles using multi-sensor data, belonging to the field of equipment testing technology. It includes: a multi-sensor acquisition module for acquiring real-time data and uploading it to a host computer data processing module; a host computer data processing module for constructing a global temperature field of the electric spindle to obtain theoretical thermal elongation, analyzing the vibration spectrum and identifying natural frequencies, comparing and analyzing the actual thermal elongation with the theoretical thermal elongation, analyzing the coupling influence of preload, rotational speed, and temperature, and generating test results based on the analysis results; and a drive control module for generating dynamic operating condition optimization and adjustment commands based on the test results generated by the host computer data processing module, responding to and adjusting the operating conditions in real time according to the commands, continuously collecting optimized operating condition data, and forming a closed-loop test. This invention can accurately simulate the dynamic changes of preload under actual operating conditions and can dynamically adjust the test conditions based on real-time analysis results.

Description

Technical Field

[0001] This invention belongs to the field of equipment testing technology, and in particular relates to a comprehensive testing system and method for electric spindles based on multi-sensor data. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] As a core functional component of machine tools, the electric spindle's operating accuracy, stability, and service life directly determine the machine tool's machining quality and production efficiency. Preload is a key parameter affecting the overall performance of the electric spindle. Reasonable preload can improve spindle rigidity and rotational accuracy, while excessive preload will lead to excessive bearing temperature rise and accelerated wear, while insufficient preload can easily cause vibration and increased positioning errors.

[0004] Existing electric spindle preload testing equipment and methods have weak data analysis capabilities, lack coupled analysis of multiple physical quantities, and preload testing is mostly static loading, making it difficult to simulate dynamic changes under actual working conditions. Data acquisition and analysis are disconnected from operating condition adjustment, failing to accurately capture the dynamic performance of spindles with variable preload. This results in an inability to dynamically optimize operating conditions based on real-time analysis results, and makes it difficult to provide accurate experimental data support for intelligent preload adjustment algorithms. Summary of the Invention

[0005] To overcome the shortcomings of the existing technologies, this invention proposes a multi-sensor data integrated testing system and method for electric spindles, which solves problems such as non-standard sensor layout, disconnect between data acquisition and analysis, insufficient integrated analysis capabilities of the host computer, and poor coordination between dynamic testing and control in existing electric spindle preload testing equipment.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: In a first aspect, the present invention discloses a multi-sensor data integrated testing system for electric spindles, comprising: The multi-sensor acquisition module is used to acquire real-time data and upload it to the host computer data processing module, including temperature sensor, triaxial accelerometer, eddy current displacement sensor and pressure sensor; The host computer data processing module is used to receive real-time data uploaded by the multi-sensor acquisition module, construct the global temperature field of the electric spindle based on the data of the temperature sensor to obtain the theoretical thermal elongation, analyze the vibration spectrum and identify the natural frequency based on the data of the triaxial accelerometer, compare and analyze the actual thermal elongation collected by the eddy current displacement sensor with the theoretical thermal elongation, analyze the coupling influence relationship between preload, rotational speed and temperature based on the data of the pressure sensor, and generate test results based on the analysis results. The drive control module is used to generate dynamic operating condition optimization and adjustment instructions based on the test results generated by the host computer data processing module, respond and adjust the operating conditions in real time according to the instructions, continuously collect optimized operating condition data, and form a closed-loop test.

[0007] Secondly, this invention discloses a method for comprehensive testing of electric spindle data from multiple sensors, including: Acquire real-time data and upload it to the host computer data processing module, including temperature sensor, triaxial accelerometer, eddy current displacement sensor and pressure sensor; The system receives real-time data uploaded by a multi-sensor acquisition module, constructs a global temperature field of the electric spindle based on the data from the temperature sensor to obtain the theoretical thermal elongation, analyzes the vibration spectrum and identifies the natural frequency based on the data from the triaxial accelerometer, compares and analyzes the actual thermal elongation collected by the eddy current displacement sensor with the theoretical thermal elongation, analyzes the coupling influence relationship between preload, rotational speed, and temperature based on the data from the pressure sensor, and generates test results based on the analysis results. Based on the test results generated by the host computer data processing module, dynamic operating condition optimization and adjustment instructions are generated. The operating conditions are adjusted in real time according to the instructions, and the optimized operating condition data is continuously collected to form a closed-loop test.

[0008] Thirdly, the present invention discloses an electronic device, including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when run by the processor, complete the steps of the above-mentioned method for comprehensive testing of electric spindle data based on multi-sensor data.

[0009] Fourthly, the present invention discloses a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the above-mentioned method for integrated testing of electric spindle data using multi-sensor data.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention integrates advanced analysis functions such as temperature field construction, spectrum analysis, and thermo-mechanical coupling iteration into a host computer. Based on real-time analysis results, it dynamically adjusts rotational speed and preload, forming a closed-loop control chain of testing, analysis, feedback, and optimization. This makes the testing process more closely resemble the dynamic response characteristics under real-world machining scenarios. Through full-condition testing and coupled iterative convergence analysis, a comprehensive decision-making optimization model for preload, encompassing the coupling laws of multiple physical fields, is constructed, improving the accuracy of preload adjustment.

[0011] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0012] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0013] Figure 1 This is a schematic diagram of the multi-sensor data integrated testing system for electric spindles described in Embodiment 1 of the present invention. Detailed Implementation

[0014] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, 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 invention pertains.

[0015] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0016] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0017] With the increasing demand for high-speed, precision, and intelligent electric spindles in high-end manufacturing, variable preload electric spindles are gradually replacing traditional fixed preload spindles. They can dynamically adjust the preload according to different speeds and load conditions to achieve a balance between accuracy and lifespan.

[0018] Existing electric spindle preload testing equipment and methods have significant limitations: First, the layout of multiple sensors lacks a unified standard, with unclear installation positions and ambiguous data acquisition types. Furthermore, the lack of a dedicated data acquisition system to support simultaneous uploading of multiple parameters results in insufficient accuracy of raw data, asynchronous transmission, and difficulty in conducting multi-physical quantity coupling analysis. Second, the host computer's data analysis capabilities are weak, failing to integrate functions such as temperature field construction, comparison of theoretical and actual thermal elongation values, vibration spectrum analysis, and preload coupling effect analysis, resulting in insufficient data processing depth. Third, preload testing is mostly static loading, making it difficult to simulate dynamic changes under actual working conditions. Moreover, data acquisition and working condition adjustment are disconnected, making it impossible to accurately capture the dynamic performance of spindles with variable preload. Fourth, the integration of data processing and control is low, with poor coordination between the host computer and various modules, making it impossible to dynamically optimize working conditions based on real-time analysis results and providing accurate experimental data support for intelligent preload adjustment algorithms.

[0019] Therefore, this invention proposes a comprehensive testing system and method for a variable preload electric spindle with precise multi-sensor layout, integrated data acquisition and analysis, and dynamically adjustable working conditions. It relies on a dedicated data acquisition system to achieve synchronous uploading of multiple parameters, and uses the integrated analysis function of the host computer to explore the coupling law of each parameter, providing a reliable experimental basis for performance optimization and intelligent adjustment algorithm training of the variable preload electric spindle.

[0020] Example 1 In one or more embodiments, a multi-sensor data integrated testing system for electric spindles is disclosed, including a multi-sensor acquisition module, a drive control module, a host computer data processing module, and a user interaction module. Each module works collaboratively through a dedicated data acquisition system and communication link to achieve comprehensive performance testing of the variable preload electric spindle. Figure 1 As shown, specifically: The multi-sensor acquisition module is used to acquire real-time data and upload it to the host computer data processing module. It includes a temperature sensor, a triaxial accelerometer, an eddy current displacement sensor, and a pressure sensor.

[0021] The temperature sensors adopt a patch structure and are respectively attached to the inner and outer rings of the front bearing, the inner and outer rings of the rear bearing, and the surface of the motor stator winding to collect real-time temperature data of each key part, providing a basic data source for the subsequent construction of the full-domain temperature field of the spindle.

[0022] The triaxial accelerometer is installed in the center area of ​​the front end cap of the spindle by thread fastening, and collects radial and axial vibration acceleration signals of the spindle, which serve as the core data support for vibration spectrum analysis and natural frequency identification.

[0023] The eddy current displacement sensor is fixed on a special bracket at the front end of the spindle housing. The probe maintains a preset constant gap with the outer circular surface of the journal at the front end of the spindle, and collects the dynamic thermal displacement data of the front end of the spindle relative to the housing, which is used as the measured data of thermal elongation.

[0024] The pressure sensor is embedded in the pre-reserved mounting hole of the preload spacer ring of the outer ring of the front and rear bearings of the spindle. It collects real-time data of the actual preload force on the outer ring of the bearing and is used to analyze the coupling effect between preload force and speed and temperature.

[0025] This embodiment provides accurate, multi-dimensional raw data for testing. The installation location, acquisition type, and function of each sensor are clearly defined. All acquired data is uploaded to the host computer data processing module in real time through a dedicated data acquisition system.

[0026] By standardizing sensor installation locations and clarifying data acquisition types, and using a dedicated data acquisition system to achieve real-time synchronous uploading of multi-dimensional data, the integrity, accuracy, and timeliness of raw data are ensured. This lays a solid foundation for the coupled analysis of multiple physical quantities, fills the gap in the coordination of sensor layout and data transmission in existing equipment, and improves the accuracy and synchronization of multi-parameter acquisition.

[0027] The host computer data processing module is used to receive real-time data uploaded by the multi-sensor acquisition module, construct the global temperature field of the electric spindle based on the data from the temperature sensor to obtain the theoretical thermal elongation, analyze the vibration spectrum and identify the natural frequency based on the data from the triaxial accelerometer, compare and analyze the actual thermal elongation collected by the eddy current displacement sensor with the theoretical thermal elongation, analyze the coupling influence relationship between preload, rotational speed and temperature based on the data from the pressure sensor, and generate test results and dynamic working condition optimization adjustment commands based on the analysis results.

[0028] The theoretical thermal elongation is obtained by constructing the global temperature field of the electric spindle based on temperature sensor data. Specifically: Substituting real-time temperature data from key components collected by temperature sensors as boundary conditions, a global temperature field across the principal axis is constructed using the finite element method, specifically including: First, a three-dimensional geometric model of the electric spindle is established and meshed. The measured temperatures of the inner and outer rings of the front and rear bearings, as well as the stator windings of the motor, are applied as first-type boundary conditions to the corresponding nodes of the model. The heat conduction equation is solved to obtain the temperature distribution across the entire spindle. The theoretical thermal elongation is calculated using an approximate model. The spindle center can be considered as a hollow steel tube structure made of homogeneous material. Therefore, there is a linear proportional relationship between temperature change and thermal elongation, and its physical law follows the thermal form of Hooke's Law, that is, the change in material length is proportional to the change in temperature. The formula for calculating the theoretical thermal elongation is as follows:

[0029] In the formula, This is the thermal elongation, expressed in mm. The coefficient of thermal expansion of the material is expressed in 1 / °C or 1 / K. This represents the original length of the object, in mm. This refers to the temperature difference, which is the difference between the operating temperature and the installation / reference temperature, in °C.

[0030] The vibration spectrum and natural frequency identification based on data analysis from a triaxial accelerometer are as follows: First, the vibration acceleration signal is preprocessed through filtering and spectrum analysis to extract characteristic frequency components. Then, the natural frequency of the spindle is accurately identified, and the resonant speed range and critical speed value are determined.

[0031] Identifying the natural frequency of a spindle typically requires a combination of static experimental modal analysis and dynamic operational spectrum analysis. This invention ultimately uses the frequency components identified during dynamic testing that are consistent with the static test results as the natural frequency of the spindle.

[0032] Static testing includes: placing an accelerometer in a stationary spindle state, obtaining the frequency response function by striking the spindle with a hammer, and the peak frequency of the frequency response function is the static natural frequency. The static natural frequency is used as a reference to help confirm the natural frequency line in the dynamic spectrum and eliminate interference from the rotational frequency and its harmonics.

[0033] Dynamic testing includes: controlling the spindle to accelerate uniformly from low speed to maximum speed while rotating, and continuously acquiring vibration signals, generating a spectrum diagram through fast Fourier transform.

[0034] In the spectrum diagram, identify the frequency components that do not change with the rotational speed, that is, the vertical lines perpendicular to the time or rotational speed axis, and compare and verify them with the static natural frequency to finally determine the natural frequency of the spindle. The frequencies in the spectrum diagram are mainly divided into excitation frequency and natural frequency. The dynamic natural frequency is represented by the vertical line perpendicular to the time or rotational speed axis. This frequency component always exists regardless of the rotational speed, and its amplitude may be amplified at a specific rotational speed.

[0035] Furthermore, observe the change in amplitude corresponding to the natural frequency with rotational speed to identify the rotational speed range where the amplitude is amplified, i.e., the resonant rotational speed range.

[0036] The critical speed value for the change in rotational speed is obtained by dividing the product of the natural frequency and the constant parameter at a certain stage by the excitation order, where the constant parameter is usually taken as 60.

[0037] Preferably, the preprocessed vibration acceleration signal is subjected to a Fast Fourier Transform (FFT) and the power spectral density (PSD) is calculated to obtain a power spectral density map. Finding the frequency point with the largest amplitude and a drastic phase change in the PSD map is the most reliable basis for determining the natural frequency. The natural frequency is verified by measuring the frequency width at which the amplitude drops to 1 / √2 of the peak value, and the damping ratio is calculated. The damping ratio is used to evaluate the vibration damping capability of the spindle system. The smaller the damping ratio, the larger the vibration amplitude at resonance, and the worse the system stability. This invention uses the damping ratio as one of the important indicators for preload optimization. A reasonable preload can improve the system damping ratio and effectively suppress vibration. The damping ratio is calculated using the half-power bandwidth method, and the formula is:

[0038] In the formula, f 0 represents the natural frequency. f 1. f 2 represents the lower cutoff frequency and upper cutoff frequency, respectively, when the power spectral density amplitude drops to 1 / √2 of the peak value.

[0039] The actual thermal expansion measured by the eddy current displacement sensor was compared with the theoretical thermal expansion for calibration. Linear regression analysis was then used to quantify the influence of temperature on thermal expansion and the range of deviation. Specifically, the dynamic thermal displacement of the spindle front end measured by the eddy current displacement sensor was used as the actual thermal expansion, and a point-by-point comparison and calibration was performed with the theoretical thermal expansion calculated based on the global temperature field. A multiple linear regression equation was established with the temperature at each measurement point as the independent variable and the actual thermal expansion as the dependent variable to obtain the influence coefficient of temperature at each measurement point on thermal expansion. Simultaneously, the root mean square error (RMSE) and maximum absolute deviation between the theoretical and actual thermal expansion were calculated to determine the prediction error range of thermal expansion under different operating conditions, providing data support for thermal error compensation of the electric spindle.

[0040] Based on the data analysis of the pressure sensor, the coupling influence between preload, rotational speed, and temperature is analyzed. Specifically, the pressure sensor data is first subjected to trend fitting and anomaly removal to capture the preload response time. The signal of the piezoelectric force sensor is highly susceptible to mechanical shock and electrical interference. Therefore, a multi-level anomaly signal removal strategy is typically employed. The first level filters physical limit isolation, i.e., the physical constraint range is specified according to the spindle design parameters. Preferably, the preload cannot be negative, nor can it exceed 1.5 times the bearing's rated load. The second level filters the rate of change threshold, which is reflected in the fact that the preload is limited by material stiffness and damping, and cannot undergo abrupt changes within milliseconds. The difference is calculated as follows:

[0041] In the formula, ΔF is the rate of change of preload, in N / ms; F t F t-1 Δt represents the preload force at time t and time t-1, respectively, in N; Δt is the time interval between two adjacent sampling points, in ms; V max The maximum allowable rate of change of preload is set according to the stiffness characteristics of the spindle bearing, and is usually taken as 10 N / ms.

[0042] If ΔF>V max If the noise is identified as impact noise (such as tool change impact), the point and its neighborhood are marked as invalid. The third-level filtering statistical outlier detection calculates the mean and standard deviation within one second and removes all outliers. The point, among which, Here, μ is the pressure sensor value, σ is the mean, and σ is the standard deviation.

[0043] Next, a low-pass filter is applied. Preload changes are low-frequency signals, typically less than 5Hz. A Butterworth low-pass filter with a cutoff frequency of 5-10Hz is used to filter out the high-frequency vibration modulation signals caused by the spindle rotation frequency (1X, 2X...) and its harmonics, while preserving the quasi-static trend.

[0044] Then, the coupling of spindle speed, temperature and preload is analyzed. This coupling is a thermo-dynamic multi-field coupling, including sequential changes in speed, frictional heat generation, temperature rise, thermal deformation, preload change, friction and stiffness change, and temperature and speed characteristics change, forming a closed-loop coupling.

[0045] The analysis primarily utilizes mechanical and thermal equilibrium to establish a thermo-dynamic coupled model. Mechanical equilibrium is defined as the sum of bearing contact force, preload, and external load being zero. Thermal equilibrium is defined as the heat generation rate equal to the sum of heat conduction, convection, and radiation heat dissipation rates. Through coupled iteration, when the relative error between the calculated temperature and preload parameters is less than a preset threshold (typically 10), the model is considered complete. - When the system reaches a state of thermo-mechanical equilibrium (³), the convergence value at this point is considered the overall state parameter of the system. Based on its coupling effect mechanism and according to the stiffness and speed requirements of actual machining, the bearing temperature rise rate and contact stress under different preloads are calculated through a coupling model. The expected bearing life is then calculated using the L10 life formula for rolling bearings, thereby determining the impact of preload on spindle temperature rise and service life, which is used to formulate preload control strategies for specific machining processes. Finally, a test result report is generated, and a comprehensive preload decision model is constructed to generate dynamic working condition optimization adjustment commands. All analysis results are compiled into a standardized monitoring report, including raw data, analysis charts, and reports. Simultaneously, the comprehensive preload decision optimization model is trained based on full-dimensional test data, providing algorithmic support for intelligent adjustment of electric spindle preload.

[0046] In this embodiment, the construction of the preload comprehensive decision optimization model includes collecting full-parameter test data under different speeds, preloads, and load conditions. The host computer then calls the algorithm model for training to construct the preload comprehensive decision optimization model. This model can be adapted to the electric spindle model under test, realizing intelligent adjustment of preload under different working conditions. The algorithm model can be trained using an existing neural network model to ultimately output the optimized preload; the network model is not specifically limited.

[0047] This embodiment features outstanding integrated analysis capabilities of the host computer, integrating functions such as chart plotting, temperature field construction, thermal elongation comparison, vibration spectrum analysis, and preload coupling analysis. It achieves integrated processing from raw data to in-depth analysis results, significantly improving the depth, efficiency, and visualization level of data processing.

[0048] The drive control module is used to generate dynamic working condition optimization and adjustment commands based on the test results generated by the host computer data processing module, and respond to and adjust the working conditions in real time according to the commands, including preload pressure adjustment and speed adjustment, and continuously collect the optimized working condition data to form a closed-loop test.

[0049] This embodiment uses a programmable logic controller (PLC) as the core drive and control unit of the entire experimental platform, integrating servo hydraulic loading control functions. A stable bidirectional communication link is established with the host computer data processing module via a network port, while simultaneously linking with the user interaction module to achieve integrated control of precise test parameter adjustment, stable operating condition maintenance, and dynamic feedback adjustment. Specifically, it includes: Parameter Reception and Execution: The system receives control commands from the host computer in real time. These commands are primarily output by the preload comprehensive decision optimization model and also include adjustment commands manually issued by the user. Specific control commands include: target spindle speed, target preload, preload loading mode (static / dynamic), loading rate, and test duration. The system synchronously drives the electric spindle speed adjustment and precisely controls the opening of the servo hydraulic valve to achieve continuous adjustment of the preload loading value and switching between static / dynamic loading modes, keeping the loading accuracy error within a preset threshold.

[0050] Closed-loop control assurance: Real-time acquisition of feedback signals such as actual values ​​of electric spindle speed and preload force, comparison and calibration with preset parameters, dynamic correction of control commands, ensuring that the test conditions are stable within the set range, and providing loading and control conditions that conform to the actual machining scenario for dynamic performance testing.

[0051] In this embodiment, dynamic working condition optimization and adjustment is adopted. Dynamic correction control commands are used. Based on the analysis results of the host computer, if it is found that the spindle enters the resonance speed range or there is a mismatch between the preload and temperature rise and vibration, the speed or preload parameters are adjusted through the user interface. The PLC module responds and adjusts the working condition in real time, continuously collects the optimized working condition data, and forms a closed-loop test.

[0052] Specifically, the resonant speed range generates a significant vibration acceleration signal, with a marked increase in vibration amplitude at a specific frequency. An amplitude threshold is set for this specific frequency range for assessment. Based on the above analysis, a reasonable temperature rise range can be obtained for different operating conditions; exceeding this range triggers an alarm. Similarly, the effective time-domain (RMS) value is monitored to ensure it exceeds a preset time-domain range; insufficient rigidity results in a significantly larger RMS. Furthermore, in other specific situations, mismatches such as normal preload but abnormally high temperature rise (excessive friction), preload and vibration "decoupling" (abnormal stiffness), or normal temperature rise but excessively rapid preload decrease (abnormal looseness) will trigger a fault alarm.

[0053] This embodiment enhances the synergy between dynamic testing and closed-loop control. Through deep collaboration between the servo hydraulic loading module, PLC, and host computer, it can accurately simulate the dynamic changes of preload under actual working conditions and dynamically adjust the test conditions based on real-time analysis results, forming a complete closed-loop testing system and providing high-precision experimental data support for the intelligent preload adjustment algorithm.

[0054] Human-computer interaction linkage: Responding to user commands such as spindle speed range, target preload, and test duration issued through the interactive interface, the system executes control actions after verification by the host computer, enabling convenient switching of test conditions.

[0055] The user interaction module provides a visual interface for staff to set test parameters, export data, view reports, and adjust operating conditions.

[0056] The visual operation interface serves as the core carrier of human-computer interaction, supporting users to export raw data, analysis charts, and complete monitoring reports. Core parameters such as spindle speed, target preload, test duration, and preload loading mode can be set directly and manually. After the parameter commands are verified by the host computer, they are transmitted to the PLC control module for execution, enabling convenient control of the testing process and flexible adaptation to different testing needs.

[0057] As one implementation method, the testing of the above system includes the following steps: Step 101: Build and debug the platform, accurately position and fix the variable preload electric spindle to be tested, install each sensor and servo hydraulic loading device according to the preset standard, complete the network communication connection between the multi-sensor acquisition module and the dedicated data acquisition system, and between the drive control module and the host computer; debug the operating status of each module, calibrate the sensor accuracy, and ensure that the data acquisition, signal transmission and loading adjustment functions are stable and reliable.

[0058] Step 102: Input test parameters through the user interface, including spindle speed range, preload loading mode (static / dynamic), preload adjustment range, test duration, etc. The host computer verifies the parameters and sends them to the PLC control module to complete the test condition initialization.

[0059] Step 3: Synchronously collect multiple parameters, start the electric spindle and servo hydraulic loading device, and the PLC module adjusts the spindle speed and preload according to preset parameters; the multi-sensor acquisition module works synchronously to collect data such as temperature of various parts, spindle vibration acceleration, front thermal displacement, and bearing preload in real time, and uploads them to the host computer data processing module through a dedicated data acquisition system.

[0060] Step 104: The host computer integrates, filters, and removes anomalies from the uploaded multi-dimensional raw data, automatically completes operations such as temperature field construction, vibration spectrum analysis, and preload trend analysis, generates various visualization charts, organizes them into standardized monitoring reports, and supports data and report export for subsequent in-depth analysis.

[0061] Step 105: Dynamic working condition optimization and adjustment. Based on the analysis results of the host computer, if it is found that the spindle enters the resonance speed range or there is a mismatch between the preload and temperature rise and vibration, the speed or preload parameters are adjusted through the user interface. The PLC module responds and adjusts the working condition in real time, continuously collects the optimized working condition data, and forms a closed-loop test.

[0062] Simultaneously, a preload optimization model is constructed, and full-parameter test data under different speeds, preloads, and load conditions are collected. The host computer calls the algorithm model for training and constructs a comprehensive decision optimization model for preload. This model can be adapted to the electric spindle model under test and realize intelligent adjustment of preload under different working conditions.

[0063] Step 106: Test completion and data archiving. After completing the preset working condition test, stop the equipment operation, export all test data, visualization reports and optimization model parameters, classify and archive them to provide complete experimental data support for the performance evaluation and improvement of the electric spindle.

[0064] This embodiment is widely applicable and practical. The sensor layout scheme, data processing logic, and control strategy can be adapted to different models of variable preload electric spindles. The test report and optimization model can directly serve the design improvement of electric spindles and the iteration of intelligent control strategies, fully covering the electric spindle performance testing needs in the high-end manufacturing field.

[0065] Example 2 In one or more embodiments, a method for comprehensive testing of electric spindle data based on multi-sensor data is disclosed, comprising: Acquire real-time data and upload it to the host computer data processing module, including temperature sensor, triaxial accelerometer, eddy current displacement sensor and pressure sensor; The system receives real-time data uploaded by a multi-sensor acquisition module, constructs a global temperature field of the electric spindle based on the data from the temperature sensor to obtain the theoretical thermal elongation, analyzes the vibration spectrum and identifies the natural frequency based on the data from the triaxial accelerometer, compares and analyzes the actual thermal elongation collected by the eddy current displacement sensor with the theoretical thermal elongation, analyzes the coupling influence relationship between preload, rotational speed, and temperature based on the data from the pressure sensor, and generates test results based on the analysis results. Based on the test results generated by the host computer data processing module, dynamic operating condition optimization and adjustment instructions are generated. The operating conditions are adjusted in real time according to the instructions, and the optimized operating condition data is continuously collected to form a closed-loop test.

[0066] Next, a variable preload electric spindle of model HSK-A63 was selected as the test object to further illustrate the invention in detail. It should be noted that this embodiment is only for illustrating the invention and is not intended to limit the scope of protection of the invention.

[0067] The HSK-A63 variable preload electric spindle has a rated speed of 12000 r / min, a rated power of 22 kW, and a preload adjustment range of 500-3000 N. The hardware selection and installation requirements for its supporting modules are as follows: The temperature sensor is a PT100 surface mount platinum resistance sensor, which is attached to the outer ring of the front angular contact ball bearing, the outer ring of the rear angular contact ball bearing, and the surface of the motor stator winding.

[0068] The triaxial vibration sensor is a triaxial piezoelectric sensor, which is fastened to the upper end of the front end cap of the spindle with M5 hex bolts.

[0069] The eddy current displacement sensor is a non-contact eddy current sensor, which is fixed on a special bracket at the front end of the spindle housing and is used to read the displacement of the spindle center.

[0070] The host computer data processing module uses an industrial computer with an Intel Core i5 processor, 16GB of RAM, a 512GB solid-state drive, and a gigabit Ethernet interface to meet the requirements of parallel data processing and high-speed transmission. The software uses LabVIEW 2025 to develop data processing and control programs, integrating data acquisition, signal analysis, chart plotting, report generation, and model training functions. It also utilizes the ANSYS finite element analysis module to construct the temperature field and employs a BP neural network algorithm to build a preload decision model.

[0071] The user interaction module includes four functional areas: interface setting parameter setting area, real-time monitoring area, data export area, and operating condition adjustment area. Parameter settings support manual input and preset template calling.

[0072] Platform debugging includes: After the platform is built, module linkage debugging is performed. First, each module is started individually to check the stability of sensor data acquisition, the accuracy of PLC instruction execution, and the running status of the host computer software. Then, linkage debugging is performed, with the spindle speed set to 5000 r / min and the preload force set to 1500 N (static loading mode). The system is run for 30 minutes to verify the accuracy of data acquisition and PLC closed-loop control, ensuring that each module works together normally without data loss, instruction delay, or other issues.

[0073] The test implementation steps include: Step 201, Parameter Preset: Input test parameters through the industrial touch screen (user interaction module), set the spindle speed range to 1000-11000 r / min (set a test node every 1000 r / min, not exceeding the rated speed of 12000 r / min), the preload adjustment range to 800-2800 N (adapting to an adjustment range of 500-3000 N), the loading mode to dynamic loading (the preload changes linearly with the speed, and the preload increases by 200 N for every 1000 r / min increase in speed), the test duration to 20 min per node, and the data sampling frequency to 1 kHz. The host computer verifies the parameters (determines whether the parameters are within the rated range of the electric spindle and the testing capability of the equipment). After verification, the parameters are sent to the PLC control module. After receiving the instruction, the PLC completes the test condition initialization, and the servo hydraulic loading device and spindle driver enter the standby state.

[0074] Step 202, Multi-parameter synchronous acquisition: The test is started through the user interface. The PLC control module drives the spindle to gradually increase the speed according to the preset speed node, while controlling the servo hydraulic valve to adjust the preload to the corresponding value to maintain the dynamic loading state. The multi-sensor acquisition module works synchronously. The PT100 sensor collects the temperature data of the inner and outer rings of the front and rear bearings and the stator winding of the motor in real time. The vibration sensor collects the radial and axial vibration acceleration signals of the spindle. The eddy current displacement sensor collects the thermal displacement data of the front end of the spindle. The pressure sensor collects the bearing preload data. All data are synchronously uploaded to the host computer data processing module through the data acquisition system. The host computer stores the raw data in real time for subsequent analysis.

[0075] Step 203, Data Processing and Analysis: The host computer preprocesses the uploaded raw data, uses a mean filtering algorithm to remove outlier data, and then performs the following analysis operations: Plot the time-temperature change curve, the preload change curve with rotational speed, the vibration spectrum, and the temperature rise-thermal elongation relationship graph. The temperature rise calculation is based on the initial test temperature (room temperature 25℃).

[0076] The finite element physical model of the spindle is called, and the temperature data is substituted to construct the global temperature field of the spindle. The theoretical thermal elongation of the front end of the spindle at each speed node is calculated and compared with the actual thermal elongation collected by the eddy current displacement sensor to obtain the influence coefficient of temperature on thermal elongation and the deviation range.

[0077] Spectral analysis of the vibration acceleration signal is performed to extract characteristic frequency components, identify the natural frequency of the main shaft, and obtain the corresponding resonant speed range and critical speed.

[0078] Trend fitting was performed on the preload data to determine the preload response time. The changes in preload value caused by increased bearing temperature rise during speed increase were also analyzed.

[0079] Step 204, Multi-condition data acquisition and model construction: Complete all speed node tests according to the above steps, and collect full parameter test data under different speed and preload conditions; the host computer calls the BP neural network algorithm, with speed, temperature, vibration acceleration, and thermal elongation as input parameters and optimal preload as output parameter, to train the data and build a comprehensive decision optimization model for preload.

[0080] Step 205, Test Completion and Data Archiving: After completing all preset working condition tests, the test is stopped through the user interface. The PLC controls the spindle to gradually reduce speed until it stops, the servo hydraulic loading device unloads the preload, and all modules return to standby mode. The host computer generates test data, analysis charts, and a standardized monitoring report, which includes test parameters, data statistics, etc. Users can directly export raw data, analysis charts, and monitoring reports through the user interface module, providing experimental data support for performance optimization and intelligent preload adjustment algorithm iteration of this model of electric spindle.

[0081] Example 3 This embodiment provides an electronic device, including a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When the computer instructions are executed by the processor, they complete the steps of the above-mentioned method for comprehensive testing of electric spindle data based on multi-sensor data.

[0082] Example 4 This embodiment provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the above-described method for comprehensive testing of electric spindle data based on multi-sensor data.

[0083] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, whereby a series of operational steps are performed to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0087] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A comprehensive testing system for electric spindles based on multi-sensor data, characterized in that, include: The multi-sensor acquisition module is used to acquire real-time data and upload it to the host computer data processing module, including temperature sensor, triaxial accelerometer, eddy current displacement sensor and pressure sensor; The host computer data processing module is used to receive real-time data uploaded by the multi-sensor acquisition module, construct the global temperature field of the electric spindle based on the data of the temperature sensor to obtain the theoretical thermal elongation, analyze the vibration spectrum and identify the natural frequency based on the data of the triaxial accelerometer, compare and analyze the actual thermal elongation collected by the eddy current displacement sensor with the theoretical thermal elongation, analyze the coupling influence relationship between preload, rotational speed and temperature based on the data of the pressure sensor, and generate test results based on the analysis results. The drive control module is used to generate dynamic working condition optimization and adjustment instructions based on the test results generated by the host computer data processing module, respond and adjust the working condition in real time according to the instructions, and continuously collect the optimized working condition data to form a closed-loop test. A multi-level outlier removal strategy is adopted: the first level filters physical limit isolation, the second level filters change rate threshold, and the third level filters statistical outlier detection. Based on the data analysis of the pressure sensor, the coupling influence between preload, rotational speed, and temperature is as follows: A thermo-dynamic coupled model is established using mechanical equilibrium and thermal equilibrium for coupled analysis. In mechanical equilibrium, the sum of bearing contact force, preload, and external load is zero. In thermal equilibrium, the heat generation rate is equal to the sum of heat conduction, convection, and radiation heat dissipation rates. When the relative error between the temperature and preload parameters calculated iteratively is less than a preset threshold, a thermo-dynamic equilibrium state is reached. The convergence value at this point is the overall state parameter of the system. The temperature sensor adopts a patch structure and is respectively attached to the inner and outer rings of the front bearing of the electric spindle, the inner and outer rings of the rear bearing, and the surface of the motor stator winding; the pressure sensor is embedded in the reserved mounting hole of the preload spacer ring of the outer ring of the front and rear bearings of the spindle. The dynamic working condition optimization adjustment command is generated by using a preload comprehensive decision optimization model. The input of the preload comprehensive decision optimization model includes full parameter test data of rotational speed, temperature, vibration acceleration, and thermal elongation, and the output is the optimized preload.

2. The multi-sensor data integrated testing system for electric spindles as described in claim 1, characterized in that, Based on the data from the temperature sensor, a global temperature field of the electric spindle is constructed to obtain the theoretical thermal elongation, specifically: Substituting real-time temperature data of key components collected by temperature sensors as boundary conditions, a global temperature field of the principal axis is constructed based on the finite element method. The change in length of a material is directly proportional to the change in temperature; the theoretical thermal elongation is: In the formula, This refers to thermal elongation. The coefficient of thermal expansion of the material; This represents the original length of the object; This refers to the temperature difference.

3. The multi-sensor data integrated testing system for electric spindles as described in claim 1, characterized in that, Based on the data from the triaxial accelerometer, the vibration spectrum is analyzed and the natural frequency is identified, specifically as follows: The vibration acceleration signal is preprocessed by filtering and spectral analysis to extract characteristic frequency components; An accelerometer is placed in the spindle when it is stationary to obtain the frequency response function. The peak frequency of the frequency response function is the static natural frequency. The static natural frequency is used as a reference to help confirm the natural frequency line in the dynamic spectrum. Vibration signals are continuously acquired under different rotational speeds of the spindle. A spectrum is generated by fast Fourier transform. Frequency components that do not change with rotational speed are identified in the spectrum and compared with the static natural frequency for verification, thus obtaining the spindle's natural frequency.

4. The multi-sensor data integrated testing system for electric spindles as described in claim 1, characterized in that, The analysis is based on a comparison between the actual thermal elongation collected by the eddy current displacement sensor and the theoretical thermal elongation, specifically as follows: The dynamic thermal displacement of the spindle front end collected by the eddy current displacement sensor is used as the actual thermal elongation and compared and calibrated point by point with the theoretical thermal elongation calculated based on the global temperature field. Linear regression analysis was used to establish a regression equation with the temperature value of each temperature measurement point as the independent variable and the actual thermal elongation as the dependent variable. The influence of temperature on thermal elongation was quantitatively analyzed, and the influence coefficient of temperature on thermal elongation at each temperature measurement point was obtained. Calculate the root mean square error and maximum absolute deviation between the theoretical and actual thermal elongation to determine the prediction error range of thermal elongation under different speed and preload conditions.

5. The multi-sensor data integrated testing system for electric spindles as described in claim 1, characterized in that, The dynamic operating condition optimization and adjustment instructions generated based on the test results produced by the host computer data processing module are as follows: The system employs dynamic correction control commands. If the spindle is found to be entering the resonance speed range or if there is a mismatch between the preload and temperature rise or vibration, the speed or preload parameters can be adjusted through the user interface. The PLC module responds in real time and adjusts the working conditions, continuously collecting optimized working condition data to form a closed-loop test.

6. A method for comprehensive testing of electric spindle data using multi-sensor data from the system described in claim 1, characterized in that, include: Acquire real-time data and upload it to the host computer data processing module, including temperature sensor, triaxial accelerometer, eddy current displacement sensor and pressure sensor; The system receives real-time data uploaded by a multi-sensor acquisition module, constructs a global temperature field of the electric spindle based on the data from the temperature sensor to obtain the theoretical thermal elongation, analyzes the vibration spectrum and identifies the natural frequency based on the data from the triaxial accelerometer, compares and analyzes the actual thermal elongation collected by the eddy current displacement sensor with the theoretical thermal elongation, analyzes the coupling influence relationship between preload, rotational speed, and temperature based on the data from the pressure sensor, and generates test results based on the analysis results. Based on the test results generated by the host computer data processing module, dynamic operating condition optimization and adjustment instructions are generated. The operating conditions are adjusted in real time according to the instructions, and the optimized operating condition data is continuously collected to form a closed-loop test.

7. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When the processor executes the computer instructions, it completes the electric spindle integrated testing method for multi-sensor data as described in claim 6.

8. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by the processor, complete the electric spindle integrated testing method for multi-sensor data as described in claim 6.

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