Operation state monitoring method and system for vibration test equipment

Abstract

The invention relates to the technical field of data processing, and discloses an operation state monitoring method and system for vibration test equipment. The method comprises the following steps: generating a multi-dimensional physical characteristic data stream through vibration waveform calibration according to operation parameters collected by each sensor of vibration test equipment; performing frequency response analysis on the data stream to obtain physical response characteristics; based on this, a test calibration reference is established; outputting test state parameters according to the reference analysis real-time response characteristics; and performing performance attenuation analysis according to the parameters, and generating an equipment quality evaluation report. According to the comprehensive analysis method based on the multi-dimensional physical characteristic data flow, real-time monitoring, multi-parameter joint evaluation and performance attenuation trend analysis of the dynamic characteristics of the equipment are achieved, so that the accuracy, comprehensiveness and predictability of state monitoring of the vibration test equipment are improved, and the reliability and consistency of test results are ensured.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a method and system for monitoring the operating state of a vibration test device. Background Art

[0002] Vibration test devices are widely used in fields such as aerospace, automotive, marine, and electronic equipment to simulate the vibration conditions in the actual working environment and evaluate the reliability and durability of products. The traditional monitoring of the operating state of vibration test devices mainly relies on regular calibration and manual inspection, which usually includes regular calibration, simple parameter monitoring, and regular maintenance. These methods are based on engineering experience and equipment operation manuals, and the equipment is inspected and calibrated at fixed intervals. The equipment status is evaluated through manual observation and simple data recording. In practical applications, some advanced vibration test devices have introduced basic self-check functions, which can monitor the working states of key components such as power amplifiers, controllers, and sensors, and issue alarms when obvious abnormalities occur.

[0003] However, there are many deficiencies in the traditional methods for monitoring the operating state of vibration test devices. The methods of regular calibration and manual inspection lack real-time performance and cannot detect the small changes in equipment performance and potential problems in a timely manner. Often, it can only be detected after obvious failures occur in the equipment, increasing the repair cost and downtime. Secondly, the traditional methods lack a comprehensive evaluation of the dynamic characteristics of the equipment and are difficult to capture the performance changes under different frequency bands and working conditions. In particular, the monitoring of key parameters such as resonance frequency bands and dynamic damping characteristics is insufficient, resulting in difficulties in ensuring the reliability and consistency of test results. In addition, the existing monitoring means lack systematicness and intelligence. The data collection is scattered and the parameters are isolated, making it difficult to form a comprehensive evaluation of the overall state of the equipment. Moreover, there is a lack of the ability to predict the performance decay trend, and it cannot provide a scientific basis for preventive maintenance. These problems are particularly prominent in test scenarios with high-precision and high-reliability requirements, affecting the accuracy and credibility of test results. Summary of the Invention

[0004] This application provides a method and system for monitoring the operating state of a vibration test device to solve the technical problems of insufficient real-time performance, incomplete monitoring parameters, and unsystematic performance evaluation in the monitoring of the operating state of vibration test devices. By establishing a comprehensive analysis method based on multi-dimensional physical characteristic data streams, real-time monitoring of the dynamic characteristics of the equipment, joint evaluation of multiple parameters, and analysis of the performance decay trend are realized, thereby improving the accuracy, comprehensiveness, and predictability of the state monitoring of vibration test devices and ensuring the reliability and consistency of test results.

[0005] In a first aspect, the present application provides a method for monitoring the operating state of a vibration testing device. The method for monitoring the operating state of the vibration testing device includes: generating a multi-dimensional physical feature data stream through vibration waveform calibration and signal distortion compensation processing based on the operating parameters collected by the resonant excitation table, servo hydraulic power source, electromagnetic exciter, and dynamic force sensor of the vibration testing device; performing frequency response function analysis and dynamic transfer ratio calculation on the multi-dimensional physical feature data stream to obtain physical response characteristics including excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum, and dynamic response spectrum; establishing a test calibration reference including excitation force vector, resonance frequency band, dynamic damping ratio, and frequency response function based on the physical response characteristics through vibration transfer ratio analysis and dynamic stiffness calculation; performing resonance transfer analysis and resonance bandwidth identification on the physically collected response characteristics based on the test calibration reference, and outputting test state parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics; and generating a quality assessment report including transfer function accuracy, dynamic characteristic matching degree, and equipment reliability level through performance degradation analysis based on the test state parameters in combination with the calibration accuracy level of the vibration test equipment.

[0006] In a second aspect, the present application provides a system for monitoring the operating state of a vibration testing device. The system for monitoring the operating state of the vibration testing device includes: a generation module configured to generate a multi-dimensional physical feature data stream through vibration waveform calibration and signal distortion compensation processing based on the operating parameters collected by the resonant excitation table, servo hydraulic power source, electromagnetic exciter, and dynamic force sensor of the vibration testing device; a calculation module configured to perform frequency response function analysis and dynamic transfer ratio calculation on the multi-dimensional physical feature data stream to obtain physical response characteristics including excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum, and dynamic response spectrum; a establishment module configured to establish a test calibration reference including excitation force vector, resonance frequency band, dynamic damping ratio, and frequency response function based on the physical response characteristics through vibration transfer ratio analysis and dynamic stiffness calculation; an identification module configured to perform resonance transfer analysis and resonance bandwidth identification on the physically collected response characteristics based on the test calibration reference, and output test state parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics; an analysis module configured to generate a quality assessment report including transfer function accuracy, dynamic characteristic matching degree, and equipment reliability level through performance degradation analysis based on the test state parameters in combination with the calibration accuracy level of the vibration test equipment.

[0007] A third aspect of the present invention provides a computer device, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory to cause the computer device to execute the above-mentioned operating state monitoring method for a vibration test device.

[0008] A fourth aspect of the present invention provides a computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer, the computer is caused to execute the above-mentioned operating state monitoring method for a vibration test device.

[0009] In the technical solution provided by this application, through vibration waveform calibration and signal distortion compensation processing of the operating parameters collected by the resonant excitation table, servo hydraulic power source, electromagnetic exciter, and dynamic force sensor, a multi-dimensional physical feature data stream is generated, realizing the comprehensive collection and effective integration of equipment operation data, and providing a high-quality data basis for subsequent analysis; through frequency response function analysis and dynamic transmissibility calculation of the multi-dimensional physical feature data stream, physical response characteristics including excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum, and dynamic response spectrum are obtained, comprehensively characterizing the physical state of the equipment; based on the physical response characteristics, through vibration transmissibility analysis and dynamic stiffness calculation, a test calibration benchmark including excitation force vector, resonance frequency band, dynamic damping ratio, and frequency response function is established, providing a scientific reference basis for equipment performance evaluation; according to the test calibration benchmark, resonant transfer analysis and resonance bandwidth identification are performed on the physically collected response characteristics in real time, and test status parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics are output, realizing the real-time and accurate evaluation of the equipment state; according to the test status parameters, combined with the calibration accuracy level of the vibration test equipment, performance degradation analysis is carried out, and a quality evaluation report including transfer function accuracy, dynamic characteristic matching degree, and equipment reliability level is generated, visualizing, quantifying, and standardizing the equipment health state. This solution has achieved remarkable results particularly in the application of artificial intelligence algorithms, mainly reflected in that the intelligent fusion processing algorithm for multi-source data forms a unified physical feature data stream from discrete signals, enhancing the integrity and relevance of the data; the adaptive spectral decomposition algorithm in frequency response function analysis can automatically adjust analysis parameters according to the signal characteristics of different frequency bands, improving the accuracy and efficiency of spectral analysis; the machine learning enhanced matrix operation method used in dynamic transmissibility calculation can dynamically optimize the transfer characteristics based on historical data, reducing calculation errors; the pattern recognition technology applied in the process of establishing the test calibration benchmark can automatically identify system resonance characteristics and abnormal states, improving the accuracy of benchmark establishment; the online learning algorithm used in the real-time monitoring stage can continuously optimize the judgment threshold as the equipment usage time increases, making the monitoring more accurate; the trend prediction model in performance degradation analysis realizes the prediction function of equipment performance degradation through time series feature extraction and regression analysis. This method of comprehensively applying artificial intelligence algorithms significantly improves the intelligent level and predictive ability of vibration test equipment state monitoring, provides strong support for the scientific management and precise maintenance of the equipment, and at the same time ensures the reliability and consistency of test results, meeting the technical requirements of high-precision testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic diagram of an embodiment of the operation state monitoring method for the vibration test equipment in the embodiments of the present application; Figure 2 It is a schematic diagram of the process of vibration waveform calibration and signal distortion compensation processing in the embodiments of the present application; Figure 3 It is a schematic diagram of an embodiment of the operation state monitoring system for the vibration test equipment in the embodiments of the present application; Figure 4 It is a schematic block diagram of the structure of the computer device in the embodiments of the present invention. Specific Embodiments

[0012] The embodiments of the present application provide an operation state monitoring method and system for vibration test equipment. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims, and above-mentioned drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0013] For ease of understanding, the following describes the specific process of the embodiments of the present application. Please refer to Figure 1 An embodiment of the operation state monitoring method for the vibration test equipment in the embodiments of the present application includes: Step S101: According to the operation parameters collected by the resonant excitation table, servo hydraulic power source, electromagnetic exciter, and dynamic force sensor of the vibration test equipment, generate a multi-dimensional physical feature data stream through vibration waveform calibration and signal distortion compensation processing; Step S102: Perform frequency response function analysis and dynamic transfer ratio calculation on the multi-dimensional physical feature data stream to obtain physical response characteristics including excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum, and dynamic response spectrum; Step S103: Based on the physical response characteristics, establish a test calibration benchmark including the exciting force vector, resonance frequency band, dynamic damping ratio, and frequency response function through vibration transmissibility analysis and dynamic stiffness calculation; Step S104: According to the test calibration benchmark, perform harmonic transfer analysis and resonance bandwidth identification on the real-time collected physical response characteristics, and output test status parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics; Step S105: Based on the test status parameters, perform performance degradation analysis in combination with the calibration accuracy level of the vibration test equipment, and generate a quality assessment report including transfer function accuracy, dynamic characteristic matching degree, and equipment reliability level.

[0014] It can be understood that the execution subject of this application can be the operating status monitoring system for vibration test equipment, or it can also be a terminal or a server, and specific limitations are not made here. In this embodiment of the application, the server is used as the execution subject for illustration.

[0015] Specifically, the direction control data is partitioned in time according to the characteristics of the material hardening stage to obtain the hardening stage parameters. Using the direction control data collected by the sensor, through time series analysis methods, the hardening characteristics of the material in different time periods are identified. The characteristics of the material hardening stage refer to the physical state characteristics of the material in the mold box at different time points, including indicators such as hardness, fluidity, and plasticity. By dividing the continuous time data into several discrete time windows, each window corresponding to a specific hardening stage of the material, a set of hardening stage parameters is formed. For example, when the material in the mold box gradually solidifies from a liquid state, it will go through an initial flow stage, a semi-solidification stage, and a complete solidification stage, and the steering control requirements corresponding to each stage are different. Perform steering sensitivity analysis on the hardening stage parameters, and obtain the steering sensitivity matrix by calculating the degree of influence of the direction change on the operation stability of the equipment within each hardening stage. The steering sensitivity matrix is a multi-dimensional data structure, where each element represents the response sensitivity of the equipment to different angle steering inputs under a specific hardening stage, and the larger the value, the higher the steering sensitivity in this stage. Extract the key turning points from the steering sensitivity matrix to obtain the steering segment identification. The key turning points are the points where the numerical values change significantly in the steering sensitivity matrix, and these points usually represent the transition nodes of the material hardening state. By setting a sensitivity change threshold, when the difference between adjacent elements in the matrix exceeds this threshold, the corresponding time point is marked as a key turning point. These key turning points are arranged continuously to form the steering segment identification, dividing the entire steering process into multiple different steering intervals. Construct a steering limit table according to the steering segment identification to obtain the angle limit value. The steering limit table is a mapping structure that associates each steering interval with its corresponding maximum allowable steering angle. The angle limit value represents the maximum steering angle that the self-propelled mold box can safely execute within a specific interval, and this value is determined comprehensively based on the material hardening state and equipment stability.

[0016] Calculate the steering compensation curve based on the angle limit value to obtain the steering correction sequence. The steering compensation curve is a mathematical function that describes the strategy for adjusting the steering input according to the actual steering requirement. By applying different compensation coefficients at different steering angles, a smooth and continuous steering correction sequence is generated. The steering correction sequence is a set of time-series data indicating how to adjust the steering input at each time point to ensure the stability of the self-propelled mold box during the hardening process of the material. Correlate the steering correction sequence with the material curing temperature to obtain the temperature correction coefficient. The material curing temperature refers to the actual temperature of the material in the mold box during the hardening process, which directly affects the hardening rate and physical properties of the material. By establishing a mapping relationship between the steering correction sequence and the temperature, the temperature correction coefficient is calculated and used to dynamically adjust the steering strategy under different temperature conditions. Dynamically divide the steering interval according to the temperature correction coefficient to obtain the interval division table. The interval division table is a data structure that records the boundaries of the steering intervals after temperature correction. By multiplying the boundary values of the original steering interval by the corresponding temperature correction coefficients, the dynamic adjustment of the interval is realized, so that under different temperature conditions, the division of the steering interval is more in line with the actual hardening state of the material. Obtain the interval adjustment amount through the material state verification of the interval division table. The material state verification refers to the process of evaluating whether the current interval division is reasonable by real-time monitoring the actual hardening state of the material. If it is found that the interval division does not match the actual material state, the offset amount that needs to be adjusted is calculated to form the interval adjustment amount.

[0017] Fuse the interval adjustment amount with the steering control interval to generate the segmented steering parameters. Parameter fusion refers to the process of applying the interval adjustment amount to the original steering control interval to correct the interval boundaries and internal parameters. The segmented steering parameters after fusion more accurately reflect the relationship between the material hardening state and the steering control requirements, and can guide the self-propelled mold box to perform safer and more effective steering operations at different hardening stages.

[0018] For example, during the intelligent obstacle detection of a self-propelled mold box, 10 minutes of direction control data was collected, including information such as steering angle, steering rate, and steering torque. Through time-partition analysis, these 10 minutes were divided into three hardening stages: the initial stage (0 - 3 minutes), the intermediate stage (3 - 7 minutes), and the final stage (7 - 10 minutes). Steering sensitivity analysis was performed on these three stages, and the calculated steering sensitivity matrix was a 3×5 matrix, where the rows represented the hardening stages and the columns represented different steering angle inputs (10°, 20°, 30°, 40°, 50°). The values in the matrix represented the sensitivity coefficients under the corresponding conditions. For example, the sensitivity to a 30° steering input in the initial stage was 0.85, in the intermediate stage was 1.22, and in the final stage was 1.76, indicating that as the material hardens, the same steering input has a greater impact. Key turning points were extracted from the steering sensitivity matrix, and points with a sensitivity change exceeding 0.4 were identified as turning points. The steering segmentation identifier was obtained as [3 minutes, 7 minutes], dividing the entire process into three intervals. Based on these intervals, a steering limit table was constructed, determining that the maximum allowable steering angle in the initial stage was 45°, in the intermediate stage was 30°, and in the final stage was 15°. Based on these angle limit values, a steering compensation curve was calculated, obtaining a set of 60-element steering correction sequences corresponding to the steering adjustment coefficients every 10 seconds within 10 minutes. At the same time, the change in the material curing temperature was recorded, rising from an initial 25°C to a final 35°C. The steering correction sequence was correlated with the temperature data for calculation, obtaining a temperature correction coefficient of [1.0, 0.92, 0.85], indicating that as the temperature rises, the allowable steering angle needs to be reduced. Based on the temperature correction coefficient, the steering intervals were dynamically divided, adjusting the original intervals [0 - 3, 3 - 7, 7 - 10] to [0 - 3, 3 - 6.44, 6.44 - 8.5]. By real-time monitoring of the material hardening state, it was found that the material hardening speed in the final stage exceeded expectations. Therefore, the interval division table was verified, and the interval adjustment amount was obtained as [-0.2, -0.5] minutes, indicating that it was necessary to enter a more restrictive steering control stage in advance. Finally, the interval adjustment amount was integrated with the steering control intervals, generating segmented steering parameters [0 - 2.8, 2.8 - 5.94, 5.94 - 8.5], and the corresponding maximum steering angles [45°, 30°, 15°]. In this way, the self-propelled mold box can dynamically adjust the steering strategy according to the actual hardening state of the material, ensuring safe and effective obstacle detection and avoidance operations at different stages.

[0019] In the embodiment of the present application, by performing vibration waveform calibration and signal distortion compensation processing on the operating parameters collected by the resonant excitation table, servo hydraulic power source, electromagnetic exciter, and dynamic force sensor, a multi-dimensional physical feature data stream is generated, realizing the comprehensive collection and effective integration of equipment operation data, providing a high-quality data basis for subsequent analysis; by performing frequency response function analysis and dynamic transfer ratio calculation on the multi-dimensional physical feature data stream, physical response characteristics including excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum, and dynamic response spectrum are obtained, comprehensively characterizing the physical state of the equipment; based on the physical response characteristics, through vibration transfer ratio analysis and dynamic stiffness calculation, a test calibration benchmark including excitation force vector, resonance frequency band, dynamic damping ratio, and frequency response function is established, providing a scientific reference basis for equipment performance evaluation; according to the test calibration benchmark, resonant transfer analysis and resonance bandwidth identification are performed on the physically collected response characteristics in real time, and test status parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics are output, realizing the real-time and accurate evaluation of the equipment status; according to the test status parameters, combined with the calibration accuracy level of the vibration test equipment, performance degradation analysis is performed, and a quality evaluation report including transfer function accuracy, dynamic characteristic matching degree, and equipment reliability level is generated, visualizing, quantifying, and standardizing the equipment health state. This solution has achieved remarkable results especially in the application of artificial intelligence algorithms, mainly reflected in that the intelligent fusion processing algorithm for multi-source data forms a unified physical feature data stream from discrete signals, enhancing the integrity and relevance of the data; the adaptive spectrum decomposition algorithm in frequency response function analysis can automatically adjust analysis parameters according to the signal characteristics of different frequency bands, improving the accuracy and efficiency of spectrum analysis; the machine learning enhanced matrix operation method used in dynamic transfer ratio calculation can dynamically optimize transfer characteristics based on historical data, reducing calculation errors; the pattern recognition technology applied in the process of establishing the test calibration benchmark can automatically identify system resonance characteristics and abnormal states, improving the accuracy of benchmark establishment; the online learning algorithm used in the real-time monitoring stage can continuously optimize the judgment threshold as the equipment usage time increases, making the monitoring more accurate; the trend prediction model in performance degradation analysis realizes the prediction function of equipment performance degradation through time series feature extraction and regression analysis. This method of comprehensively applying artificial intelligence algorithms significantly improves the intelligent level and predictive ability of vibration test equipment status monitoring, provides strong support for the scientific management and precise maintenance of the equipment, and at the same time ensures the reliability and consistency of test results, meeting the technical requirements of high-precision tests.

[0020] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) The vibration acceleration signal collected by the triaxial acceleration sensor installed on the resonant excitation tabletop, the hydraulic characteristic signal collected by the pressure and flow sensor installed on the servo hydraulic power source, the excitation current signal collected by the current sensor installed on the electromagnetic exciter, and the dynamic force signal collected by the dynamic force sensor are input into the signal acquisition device; (2) The signal acquisition device performs waveform distortion detection on the vibration acceleration signal to generate a vibration amplitude correction coefficient and a phase correction coefficient; (3) Using the vibration amplitude correction coefficient and the phase correction coefficient, amplitude compensation and phase calibration are performed on the vibration acceleration signal to obtain a calibrated vibration characteristic signal; (4) Pressure fluctuation analysis and flow uniformity calculation are performed on the hydraulic characteristic signal to obtain a hydraulic state characteristic signal; (5) Harmonic component extraction and drive characteristic analysis are performed on the current signal to obtain an excitation characteristic signal; (6) According to the calibrated vibration characteristic signal, the hydraulic state characteristic signal, and the excitation characteristic signal, multi-source data synchronization fusion is performed to generate a multi-dimensional physical characteristic data stream.

[0021] Specifically, as Figure 2As shown in the figure, it is a schematic flowchart of the vibration waveform calibration and signal distortion compensation process in the embodiment of the present application. Signals are collected through multiple sensors and multi-dimensional physical feature data streams are generated through data processing. The resonance excitation table is a test device used to simulate real environmental vibration conditions. It generates controllable vibration excitation through electromagnetic or hydraulic drive methods and is used to evaluate the reliability, durability, and performance characteristics of the item under test in a vibration environment. This method inputs the vibration acceleration signals collected by the triaxial acceleration sensor installed on the resonance excitation tabletop, the hydraulic characteristic signals collected by the pressure and flow sensor installed on the servo hydraulic power source, the excitation current signals collected by the current sensor installed on the electromagnetic exciter, and the dynamic force signals collected by the dynamic force sensor into the signal acquisition device. The triaxial acceleration sensor can simultaneously measure the vibration accelerations in the X, Y, and Z directions, providing comprehensive vibration state monitoring; the servo hydraulic power source is the power source of the resonance excitation table, and its pressure and flow sensor can monitor the working state of the hydraulic system; the electromagnetic exciter is the core component that generates vibration, and the current sensor can monitor the change in its working current; the dynamic force sensor directly measures the magnitude and direction of the force generated during vibration. These four types of signals are converted into digital signals through a dedicated analog-to-digital converter and then input into the signal acquisition device. The signal acquisition device is a hardware device integrated with multi-channel synchronous acquisition functions, which can ensure the time synchronization of signals from different sources. The signal acquisition device performs waveform distortion detection on the vibration acceleration signals to generate a vibration amplitude correction coefficient and a phase correction coefficient. Waveform distortion detection refers to the process of quantitatively analyzing the difference between the actual waveform of the collected vibration acceleration signal and the ideal sine waveform. Ideally, the vibration generated by the resonance excitation table should be a pure sine wave, but due to the influence of factors such as the non-linear characteristics of the mechanical structure, assembly errors, and material fatigue, the actual vibration often has distortion. Waveform distortion detection converts the time-domain signal into a frequency-domain signal through the fast Fourier transform and analyzes its spectral characteristics; then, by calculating the total harmonic distortion rate, it evaluates the influence degree of the non-fundamental frequency components on the fundamental frequency components; finally, through phase spectrum analysis, it detects the stability and consistency of the signal phase. Based on the results of the waveform distortion detection, a vibration amplitude correction coefficient is calculated. This coefficient reflects the proportional relationship between the actual vibration amplitude and the ideal amplitude and is used for subsequent calibration of the vibration amplitude; at the same time, a phase correction coefficient is calculated. This coefficient reflects the deviation between the actual vibration phase and the ideal phase and is used for subsequent calibration of the vibration phase.

[0022] Using the vibration amplitude correction coefficient and the phase correction coefficient, amplitude compensation and phase calibration are performed on the vibration acceleration signal to obtain the calibrated vibration characteristic signal. Amplitude compensation refers to the process of multiplying the amplitude of the original vibration acceleration signal by the amplitude correction coefficient to make it closer to the true vibration level. Phase calibration refers to the process of adjusting the phase of the original vibration acceleration signal to make it consistent with the ideal phase. These two processes are usually realized through digital signal processing technologies such as filter design and signal reconstruction. The calibrated vibration characteristic signal not only contains the basic characteristics of vibration such as frequency, amplitude and phase, but also contains richer vibration mode information such as resonance frequency, damping ratio and mode shape. These characteristic information are important bases for evaluating the working state of the resonant excitation table. Pressure fluctuation analysis and flow rate uniformity calculation are performed on the hydraulic characteristic signal to obtain the hydraulic state characteristic signal. Pressure fluctuation analysis is the process of quantitatively analyzing the law of the working pressure of the hydraulic system changing with time, including the extraction of characteristics such as pressure fluctuation amplitude, frequency and periodicity. Flow rate uniformity calculation is the evaluation of the flow rate stability of the hydraulic system. By calculating statistical indicators such as the variance, peak factor and imbalance degree of the flow rate, the uniformity degree of the flow rate is quantified. The hydraulic state characteristic signal is a comprehensive description of the working state of the hydraulic system, containing multiple indicators such as pressure stability, flow rate uniformity and system response characteristics. These indicators directly reflect the working state and performance level of the servo hydraulic power source.

[0023] Extract the harmonic components of the current signal and analyze the driving characteristics to obtain the excitation characteristic signal. Harmonic component extraction is the process of separating the fundamental wave and each harmonic component from the current signal through spectrum analysis technology, which is used to evaluate the power quality and the linearity of the exciter. Driving characteristic analysis is the study of the relationship between the current signal and the vibration response, including the identification of the current-vibration transfer function, the measurement of the response time, and the calculation of the driving efficiency, etc. The excitation characteristic signal comprehensively reflects the working state of the electromagnetic exciter, including multiple aspects such as the driving force generation ability, the energy conversion efficiency, and the temperature stability, etc., providing a basis for evaluating the performance of the exciter and predicting potential failures. According to the calibrated vibration characteristic signal, the hydraulic state characteristic signal, and the excitation characteristic signal, perform multi-source data synchronous fusion to generate a multi-dimensional physical characteristic data stream. Multi-source data synchronous fusion is a technology that aligns the time, matches the features, and comprehensively analyzes the data from different sensors and different physical quantities. Time alignment ensures the consistency of data from different sources in the time dimension, usually achieved through timestamp matching or interpolation resampling; feature matching is the process of identifying the correlated feature points or feature intervals in different data sources, such as the correspondence between the vibration peak and the current peak; comprehensive analysis is based on the results of time alignment and feature matching, and conducts unified analysis and processing on multi-source data to extract more comprehensive and in-depth information. The multi-dimensional physical characteristic data stream is a structured data form that continuously records and updates the physical characteristics of the system at a predetermined time interval and data format, including but not limited to vibration characteristics, hydraulic state, electrical performance, and thermodynamic characteristics, etc., providing comprehensive data support for the condition monitoring, performance evaluation, and fault diagnosis of the vibration test equipment.

[0024] Suppose that during an aviation component vibration test, it is necessary to monitor the operating state of the resonance excitation table in real time to ensure the reliability and accuracy of the test process. A triaxial acceleration sensor is installed on the excitation table surface to collect vibration acceleration signals in the X, Y, and Z directions. The sampling frequency is set to 5 kHz to ensure that high-frequency vibration characteristics can be captured; a pressure and flow sensor is installed at the output end of the servo hydraulic power source to collect working pressure and flow data, and the sampling frequency is 1 kHz; a current sensor is installed in the power supply circuit of the electromagnetic exciter to collect excitation current signals, and the sampling frequency is 10 kHz; a dynamic force sensor is installed at the connection between the exciter and the table surface to collect dynamic force signals during vibration, and the sampling frequency is 5 kHz. These signals are amplified, filtered, and analog-to-digital converted through their respective signal conditioning circuits and then synchronously input into the signal acquisition device. The signal acquisition device performs waveform distortion detection on the vibration acceleration signals, converts the time-domain signals into frequency-domain signals through fast Fourier transform, and analyzes their spectral characteristics. It is detected that in the vibration acceleration signal in the X direction, in addition to the set fundamental frequency component of 100 Hz, there are also harmonic components of 200 Hz and 300 Hz, and the total harmonic distortion rate reaches 8%, indicating obvious waveform distortion in the X direction; the vibration acceleration signal in the Y direction is relatively pure, and the total harmonic distortion rate is only 2%; there is obvious phase jitter in the vibration acceleration signal in the Z direction, and the phase stability is poor. Based on these analysis results, the vibration amplitude correction coefficient in the X direction is calculated to be 1.12, indicating that the actual vibration amplitude is 12% smaller than the ideal value and needs to be amplified; the phase correction coefficient in the Z direction is -15°, indicating that the actual phase lags behind the ideal phase by 15° and needs to be advanced.

[0025] Using these correction coefficients, amplitude compensation and phase calibration are performed on the original vibration acceleration signal. For the vibration acceleration signal in the X direction, the value of each sampling point is multiplied by 1.12 to achieve amplitude compensation; for the vibration acceleration signal in the Z direction, it is converted into an analytic signal through Hilbert transform, and its phase angle is adjusted to achieve a 15° phase advance. After these processes, the calibrated vibration characteristic signal is obtained, whose waveform is closer to the ideal sine wave, and the frequency stability and phase consistency are significantly improved. At the same time, pressure fluctuation analysis and flow rate uniformity calculation are performed on the hydraulic characteristic signal. The pressure fluctuation analysis shows that during the test, the working pressure of the hydraulic system fluctuates around 10 MPa, the maximum fluctuation amplitude is ±0.5 MPa, and the fluctuation period is about 50 ms, which is close to the reciprocal of the excitation frequency, indicating that there is a certain correlation between the pressure fluctuation and the vibration process; the calculation results of flow rate uniformity show that the coefficient of variation of the hydraulic flow rate is 0.06, the peak factor is 3.2, and the unbalance is 4%. These indicators are all within the normal range, indicating that the flow rate supply of the hydraulic system is relatively uniform and stable. Based on these analysis results, a hydraulic state characteristic signal is formed, which includes multiple hydraulic state characteristic parameters such as the mean pressure, pressure fluctuation amplitude, pressure fluctuation frequency, mean flow rate, and coefficient of variation of flow rate. Harmonic component extraction and drive characteristic analysis are performed on the current signal. Through harmonic component extraction, it is found that the fundamental wave component in the current signal accounts for 92% of the total energy, the 3rd harmonic accounts for 5%, the 5th harmonic accounts for 2%, and the remaining harmonic components account for 1%, indicating that the power supply quality is good but there is a certain harmonic interference; the drive characteristic analysis shows that the time delay between the current change and the vibration response is 5 ms, the gain of the current-vibration transfer function near 100 Hz is 2.5 g / A, and the phase difference is 60°. These parameters reflect the dynamic response characteristics of the electromagnetic exciter. Based on these analysis results, an excitation characteristic signal is formed, which describes the working state and performance characteristics of the electromagnetic exciter. According to the calibrated vibration characteristic signal, hydraulic state characteristic signal, and excitation characteristic signal, multi-source data synchronous fusion is performed. The data from different sources are aligned by timestamps to ensure their consistency on the time axis; then through correlation analysis, the correlation relationships between different signals are identified, such as the correspondence between the vibration acceleration peak and the current peak, and the relationship between the pressure fluctuation and the vibration frequency, etc.; finally, through feature extraction and data fusion algorithms, such as principal component analysis, Kalman filtering, etc., the information from different sources is integrated to generate a multi-dimensional physical characteristic data stream. This data stream continuously outputs the physical state parameters of the system at an update frequency of 100 Hz, including vibration acceleration in each direction, vibration frequency, phase relationship, harmonic content, hydraulic pressure, hydraulic flow rate, drive current, power consumption, temperature change, and other indicators, providing comprehensive data support for the monitoring, analysis, and evaluation of the equipment operating state.

[0026] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Separate the multi-dimensional physical feature data stream according to the signal type to obtain the corresponding vibration signal stream, hydraulic signal stream, excitation signal stream, and dynamic force signal stream; (2) Process the vibration signal stream through Fourier transform, perform amplitude statistics and phase extraction on the transformed spectrum data to generate a vibration acceleration spectrum; (3) Conduct pressure pulsation analysis on the hydraulic signal stream, and obtain a hydraulic pressure spectrum through pulsation waveform decomposition and amplitude reconstruction; (4) Conduct excitation force characteristic analysis on the excitation signal stream, and form an excitation force spectrum through force vector decomposition and amplitude recombination; (5) Conduct response characteristic decomposition on the dynamic force signal stream, and obtain a dynamic response spectrum through signal reconstruction and frequency domain mapping; (6) Based on the vibration acceleration spectrum, hydraulic pressure spectrum, excitation force spectrum, and dynamic response spectrum, conduct frequency domain correlation analysis and transfer characteristic calculation to obtain physical response characteristics.

[0027] Specifically, the multi-dimensional physical feature data stream refers to the comprehensive data stream formed after vibration waveform calibration and signal distortion compensation processing collected from the resonant excitation table, servo hydraulic power source, electromagnetic exciter, and dynamic force sensor. This data stream contains various physical characteristic information such as vibration, hydraulics, excitation, and dynamic force, and needs to be separated and processed according to different signal types. During the signal type separation process, the data identification algorithm is applied to classify the multi-dimensional physical feature data stream according to signal characteristics, and identify the vibration signal stream, hydraulic signal stream, excitation signal stream, and dynamic force signal stream. The vibration signal stream mainly contains acceleration, velocity, and displacement information; the hydraulic signal stream contains pressure fluctuation and flow rate change characteristics; the excitation signal stream contains drive current and excitation force information; the dynamic force signal stream contains information about the force on the test table and transfer feedback.

[0028] For the processing of the vibration signal stream, the time-domain signal is converted into a frequency-domain signal through Fourier transform to obtain spectrum data. In spectrum analysis, amplitude statistics are performed on each frequency point to extract its peak value, mean value, and variance, and at the same time, the phase angle of each frequency point is calculated to form a vibration acceleration spectrum. The vibration acceleration spectrum intuitively reflects the vibration intensity and phase characteristics of the test equipment at different frequency points, and is the basic data for evaluating the vibration performance of the equipment. The processing of the hydraulic signal stream involves pressure pulsation analysis. The hydraulic pressure signal is decomposed into different frequency components through discrete wavelet transform to identify the main frequency component and harmonic components, and then the hydraulic pressure spectrum is reconstructed according to the energy distribution of the frequency components. The hydraulic pressure spectrum reflects the working state of the hydraulic system and can effectively identify abnormal fluctuations and pulsation characteristics of the hydraulic system.

[0029] The processing of the excitation signal flow mainly conducts the analysis of the excitation force characteristics. The excitation force is decomposed into three components along the X-axis, Y-axis, and Z-axis according to the direction, and the amplitude and phase relationships of the components in each direction are analyzed. Then, the excitation force spectrum is formed through vector synthesis and recombination. The excitation force spectrum characterizes the excitation ability and direction characteristics of the testing equipment and is an important indicator for evaluating the excitation performance of the equipment. The processing of the dynamic force signal flow decomposes the dynamic force signal into three parts: elastic response, damping response, and inertial response through response characteristic decomposition. Through frequency domain transformation and reconstruction technology, the dynamic response spectrum is obtained. The dynamic response spectrum reflects the response characteristics of the testing equipment to external excitation and shows the dynamic performance of the equipment at different frequencies.

[0030] The vibration acceleration spectrum, hydraulic pressure spectrum, excitation force spectrum, and dynamic response spectrum are comprehensively analyzed. The frequency domain correlation coefficients between the spectra are calculated, and the transfer relationship between the input and output is analyzed to obtain the physical response characteristics. The physical response characteristics include key indicators such as the transfer function, coherence function, resonance point distribution, and frequency response function of the testing equipment, which comprehensively reflect the operating state of the vibration testing equipment.

[0031] Taking a certain model of vibration testing equipment as an example, when monitoring the operating state of the equipment, the data of the three-axis acceleration sensors installed on the resonant excitation tabletop are collected. After distortion compensation, this data forms a vibration signal flow. Applying the fast Fourier transform to the vibration signal flow, spectral data in the frequency range of 5 - 2000 Hz is obtained. Through spectral analysis, obvious vibration peaks are identified at 78 Hz, 126 Hz, and 513 Hz, with amplitudes of 2.5 g, 1.8 g, and 0.9 g respectively, and phase angles of 27°, 43°, and 85° respectively, thus generating the vibration acceleration spectrum. At the same time, after wavelet decomposition of the pressure signal collected from the servo hydraulic power source, the pressure pulsation with the main frequency component at 12 Hz is identified, with an amplitude of 1.2 MPa, and the hydraulic pressure spectrum is formed through reconstruction. After the current signal collected from the electromagnetic exciter is converted into an excitation force signal, it is decomposed into a horizontal force of 0.8 kN and a vertical force of 1.5 kN, and the excitation force spectrum is formed through recombination. The data of the dynamic force sensor undergoes response characteristic decomposition to identify the resonant response of the system near 150 Hz, forming the dynamic response spectrum. These four spectral diagrams are comprehensively analyzed, and the frequency domain correlation coefficient matrix is calculated. The correlation coefficient between the vibration acceleration spectrum and the excitation force spectrum is 0.87, indicating a high correlation between the two; while the correlation coefficient between the hydraulic pressure spectrum and the dynamic response spectrum is 0.62, indicating that the state of the hydraulic system has a certain influence on the dynamic response. Through the calculation of the transfer function, the frequency response function of the equipment in the frequency band of 20 - 1000 Hz is obtained, and resonance points are identified in the frequency bands of 75 - 85 Hz and 510 - 520 Hz.

[0032] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1)Compare the input and output of the excitation force spectrum and the dynamic response spectrum in the physical response characteristics, and extract the vibration transfer rate data through transfer function analysis; (2)Perform band segmentation statistics on the vibration transfer rate data, and obtain the excitation force vector of each frequency band through bandwidth analysis; (3)Decompose the hydraulic pressure spectrum in the frequency domain, and determine the system resonance frequency band through pressure pulsation characteristic analysis; (4)Extract the peak characteristics of the vibration acceleration spectrum, and calculate the dynamic damping ratio through resonance characteristic calculation; (5)Conduct correlation analysis on the vibration transfer rate data and the dynamic damping ratio, and generate the frequency response function through dynamic stiffness calculation; (6)Comprehensively analyze the system characteristics based on the excitation force vector, resonance frequency band, dynamic damping ratio and frequency response function to obtain the test calibration reference.

[0033] Specifically, comparing the input and output of the excitation force spectrum and the dynamic response spectrum in the physical response characteristics means taking the excitation force spectrum as the system input signal and the dynamic response spectrum as the system output signal, and calculating the transfer function through the ratio relationship between the two. The transfer function describes the response characteristics of the system to the input signal. By calculating the ratio of the output to the input at each frequency point, the vibration transfer rate data is obtained. The vibration transfer rate data reflects the transmission ability of the vibration test equipment to the excitation signal at each frequency point and is a basic index for evaluating the equipment performance. Performing band segmentation statistics on the vibration transfer rate data is to divide the full-band transfer rate data into low-frequency bands (usually 5 - 100 Hz), medium-frequency bands (100 - 500 Hz) and high-frequency bands (500 - 2000 Hz), and calculate the average transfer rate, standard deviation and fluctuation coefficient of each frequency band respectively. Through bandwidth analysis, calculate the effective bandwidth and cut-off frequency of each frequency band, and combine the directional characteristics of the transfer rate data to obtain the excitation force vector of each frequency band. The excitation force vector contains the magnitude and direction information of the force value in each frequency band and reflects the excitation characteristics of the equipment in different frequency intervals.

[0034] The frequency-domain decomposition of the hydraulic pressure spectrum refers to the spectral analysis of the pressure fluctuation signal of the hydraulic system to identify the main frequency components and harmonic components. By analyzing the frequency distribution and energy concentration region of the pressure pulsation, combined with the inherent characteristics of the hydraulic system, the resonance frequency band of the system is determined. The resonance frequency band refers to the frequency band in which the equipment is prone to resonance phenomena within a specific frequency range, usually manifested as a significant increase in the amplitude of the pressure fluctuation and a sharp change in the phase. Identifying the resonance frequency band is of great significance for preventing resonance damage to the equipment. The peak feature extraction of the vibration acceleration spectrum refers to identifying the peak points in the vibration acceleration spectrum and analyzing the frequency position, amplitude size, and bandwidth characteristics of these peak points. By using the half-power point method to calculate the quality factor and bandwidth of the peak points, combined with the inherent frequency characteristics of the system, the dynamic damping ratio is calculated. The dynamic damping ratio characterizes the ability of the system to dissipate energy. The larger the damping ratio, the faster the vibration attenuation of the system and the better the stability.

[0035] The correlation analysis of the vibration transmissibility data and the dynamic damping ratio refers to establishing the mathematical relationship between the two and analyzing the correlation between the change of the transmissibility and the damping characteristics. By calculating the dynamic stiffness parameters at each frequency point, including the composite stiffness based on mass, stiffness, and damping, the frequency response function is generated. The frequency response function comprehensively describes the dynamic characteristics of the system in the frequency domain, including the amplitude-frequency characteristic and the phase-frequency characteristic, and is a comprehensive index for evaluating the dynamic performance of the system. The comprehensive system characteristics based on the exciting force vector, resonance frequency band, dynamic damping ratio, and frequency response function refer to the weighted fusion of these parameters according to a certain weight to establish a multi-dimensional evaluation system. By calculating the standardized values and weight coefficients of each parameter, the comprehensive score of the system characteristics is formed, and combined with the calibration accuracy requirements of the equipment, the test calibration reference is determined. The test calibration reference includes the performance indicators and allowable deviation ranges of the equipment in each frequency band and is a reference for real-time monitoring of the equipment operation status.

[0036] Taking a certain model of vibration testing equipment as an example, when establishing the test calibration reference, the excitation force spectrum data (for example, the excitation force at 50 Hz is 1.2 kN and the phase is 30°) is compared with the dynamic response spectrum data (the response at the same frequency point is 0.9 g and the phase is 45°), and the transmissibility is calculated to be 0.75 g / kN and the phase difference is 15°. By statistically analyzing the transmissibility data of the entire frequency band, it is found that the average transmissibility in the low-frequency band (5 - 100 Hz) is 0.80 g / kN and the standard deviation is 0.05; the average transmissibility in the middle-frequency band (100 - 500 Hz) is 0.65 g / kN and the standard deviation is 0.08; the average transmissibility in the high-frequency band (500 - 2000 Hz) is 0.45 g / kN and the standard deviation is 0.12. Through bandwidth analysis, the effective bandwidth of the low-frequency band is determined to be 85 Hz, the effective bandwidth of the middle-frequency band is 350 Hz, and the effective bandwidth of the high-frequency band is 1200 Hz, thereby obtaining the excitation force vector characteristics of each frequency band. By analyzing the hydraulic pressure spectrum, it is identified that there are obvious pressure pulsation peaks in two frequency bands of 35 - 45 Hz and 120 - 140 Hz, and the energy proportions are 28% and 35% respectively, and these two frequency bands are determined as the main resonance frequency bands of the system. Peak characteristics are extracted from the vibration acceleration spectrum. The peak value is 2.3 g at 38 Hz and the bandwidth is 4 Hz; the peak value is 1.8 g at 132 Hz and the bandwidth is 6 Hz, and the corresponding dynamic damping ratios are calculated to be 0.053 and 0.068 respectively.

[0037] By correlatively analyzing the vibration transmissibility data and the dynamic damping ratio, it is found that the transmissibility and the damping ratio are negatively correlated near the resonance frequency points. The dynamic stiffness at 38 Hz is calculated to be 15.6 kN / mm, and the dynamic stiffness at 132 Hz is 20.3 kN / mm, and a frequency response function covering the 5 - 2000 Hz frequency band is generated. Finally, based on the comprehensive evaluation of the excitation force vector, resonance frequency band, dynamic damping ratio, and frequency response function, the calibration reference of the equipment is determined, including the standard values and allowable deviation ranges of the transmissibility in the low, middle, and high frequency bands, the standard value of the damping ratio at the resonance frequency point, and the reference value of the dynamic stiffness, providing a scientific reference for real-time monitoring of the equipment operation status.

[0038] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Compare the frequency response function in the test calibration reference with the physical response characteristics collected in real time, and obtain the vibration transmission accuracy through the analysis of the transfer function deviation; (2) Conduct real-time response analysis on the resonance frequency band, and obtain the dynamic response bandwidth through frequency band scanning; (3) Compare the phase of the excitation force vector with the physical response characteristics in real time, and determine the resonance frequency offset through the analysis of the resonance characteristics; (4) Compare the signals based on the dynamic damping ratio and the physical response characteristics in real time, and obtain the phase delay characteristics through the calculation of the phase characteristics; (5) Conduct systematic characteristic analysis and state quantification on vibration transmission accuracy, dynamic response bandwidth, resonance frequency shift, and phase delay characteristics to obtain test state parameters.

[0039] Specifically, comparing the frequency response function in the test calibration reference with the physical response characteristics collected in real time means using the preset frequency response function in the calibration reference as a reference standard and making a quantitative comparison with the physical response characteristics collected during the operation of the current device. The frequency response function is a mathematical expression describing the relationship between the input and output of a system, characterizing the response characteristics of the device at different frequency points. Transfer function deviation analysis is to evaluate the transfer performance of the current device by calculating the difference between the real-time frequency response function and the calibration reference frequency response function. Vibration transmission accuracy refers to the degree of conformity between the actual transmission characteristics of the device and the calibration reference, usually expressed by the root mean square error or the percentage of relative deviation. Conducting real-time response analysis on the resonance frequency band means dynamically monitoring the response of the device within the resonance frequency band determined in the calibration reference. The resonance frequency band refers to the frequency band within which the device is prone to resonance phenomena at specific frequencies, usually manifested as a significant increase in the response amplitude. Band scanning is a method for analyzing the response characteristics of each frequency point within the resonance frequency band point by point. By calculating the response amplitude and phase of each frequency point, the actual bandwidth of the current resonance frequency band is determined. The dynamic response bandwidth refers to the effective response frequency range of the device in the resonance state, reflecting the frequency selectivity and filtering characteristics of the device.

[0040] Comparing the excitation force vector with the physical response characteristics in real time and conducting resonance characteristic analysis is an important step in determining the resonance frequency shift. The excitation force vector contains information about the magnitude and direction of the excitation force, and phase comparison is to analyze the phase relationship between the input excitation signal and the output response signal. Resonance characteristic analysis is a special analysis based on the phase characteristics of the system near the resonance point and can be expressed by the following mathematical model:

[0041] where represents the resonance frequency shift, with the unit of Hz; represents the number of analysis frequency points; represents the weight coefficient of the i-th frequency point; represents the phase angle of the real-time physical response at the frequency point in degrees; represents the phase angle of the calibration reference at the frequency point in degrees; represents the phase sensitivity coefficient at the frequency point reflecting the sensitivity of the phase change to the resonance frequency.

[0042] Signal comparison based on the dynamic damping ratio and real-time physical response characteristics is achieved by comparing the damping characteristics of the current system with those in the calibration reference to analyze the change in the system's energy dissipation capacity. The dynamic damping ratio is a dimensionless parameter characterizing the vibration attenuation characteristics of the system. The larger the value, the faster the system decays. The phase characteristic calculation is an analysis based on the phase delay characteristics of the system response, used to evaluate the degree of phase delay of the system. The phase delay characteristic refers to the degree of phase lag of the system output response relative to the input excitation, reflecting the time-domain response delay characteristic of the system. Systematic characteristic analysis and state quantification of vibration transfer accuracy, dynamic response bandwidth, resonance frequency shift, and phase delay characteristics involve comprehensively considering the above four parameters and processing them through specific mathematical models or statistical methods to obtain a quantitative index that can comprehensively reflect the current operating state of the device. Systematic characteristic analysis is a comprehensive evaluation of the transfer characteristics, frequency band characteristics, resonance characteristics, and phase characteristics of the device, while state quantification is to convert the analysis results into numerically quantifiable comparison indicators. The test state parameters refer to the set of quantitative parameters that can reflect the current operating state of the device and are the data basis for subsequent performance evaluation.

[0043] Taking a certain type of electromagnetic vibration table as an example, during real-time condition monitoring, the frequency response function data is extracted from the calibration reference. This function has specific amplitude-frequency and phase-frequency characteristic curves in the frequency band of 20 - 2000 Hz. At the same time, the physical response characteristics during the current operation of the device are collected, including the response amplitudes and phase angles at each frequency point. By calculating the differences between the two at each frequency point, the vibration transfer accuracy data is obtained. Specifically, the relative deviation at each frequency point is calculated to form a deviation distribution curve, and then the average value and maximum value of the deviation are statistically calculated to obtain the overall transfer accuracy evaluation index. For the resonance band analysis, the resonance bands determined in the calibration reference are two intervals: 75 - 85 Hz and 510 - 520 Hz. During real-time monitoring, these two bands are scanned in detail, and the response data is collected every 0.5 Hz to analyze the change trend of the response amplitude, determine the current half-power bandwidth points, and calculate the dynamic response bandwidth. If the resonance bandwidth of 75 - 85 Hz in the calibration reference is 8 Hz, while the bandwidth measured in real-time is 10 Hz, it indicates that the frequency band selectivity of the device has changed. In the resonance characteristic analysis, the phase characteristics of the excitation force vector in the calibration reference are compared with the physically collected response characteristics in real-time. The resonance frequency shift is calculated through the above mathematical model, where the weight coefficient is set according to the importance of the frequency points, and the phase sensitivity coefficient Derivation based on the system resonance characteristics. For example, if the resonance frequency in the calibration reference is 80 Hz and the offset is calculated to be 2.5 Hz through phase comparison analysis, then the current resonance frequency is 82.5 Hz, indicating a change in the device characteristics. For the phase delay characteristics, by comparing the dynamic damping ratio in the calibration reference (such as 0.05) with the damping ratio measured in real time (such as 0.06) and combining the phase angle change trend, the phase delay characteristics are calculated. If at a specific frequency point, the phase angle in the calibration reference is 30° while the real-time phase angle is 40°, the phase delay increases by 10°, reflecting the extension of the system response time. Through comprehensive analysis of the four parameters of vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics, by setting the weight coefficients of each parameter, the comprehensive score is calculated and quantified as the test state parameter. For example, the relative deviation of vibration transfer accuracy, the change rate of dynamic response bandwidth, the offset percentage of resonance frequency, and the increment of phase delay are weighted and summed to generate the comprehensive index of device state, which is used to evaluate the overall operating state of the current device and provide data basis for device maintenance and performance adjustment.

[0044] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Conduct deviation analysis on the vibration transfer accuracy in the test state parameters and the calibration accuracy level of the vibration test equipment, and obtain the transfer function accuracy through transfer function error calculation; (2) Conduct bandwidth matching degree analysis on the dynamic response bandwidth and resonance frequency offset, and obtain the dynamic characteristic matching degree through frequency response characteristic comparison; (3) Conduct time series stability analysis on the phase delay characteristics, and obtain the time series matching parameter through phase offset statistics; (4) Classify the transfer function accuracy into grades, and obtain the functional reliability index through accuracy level analysis; (5) Conduct comprehensive evaluation on the dynamic characteristic matching degree and the time series matching parameter, and obtain the performance reliability index through system characteristic analysis; (6) Conduct comprehensive analysis based on the functional reliability index and the performance reliability index, and generate a quality evaluation report including transfer function accuracy, dynamic characteristic matching degree, and device reliability level.

[0045] Specifically, the deviation analysis of the vibration transfer accuracy in the test status parameters and the calibration accuracy level of the vibration test equipment refers to comparing the vibration transfer accuracy data obtained from real-time monitoring with the accuracy level standard determined at the time of equipment factory shipment or regular calibration. The vibration transfer accuracy refers to the degree of conformity between the actual transfer characteristics of the equipment and the ideal transfer characteristics, while the calibration accuracy level refers to the accuracy standard level that the equipment should reach, usually divided into different levels such as A, B, C, etc., and each level corresponds to a different allowable error range. The transfer function error calculation quantifies and evaluates the current transfer performance of the equipment by comparing the difference between the actual transfer function and the standard transfer function. The transfer function accuracy is an index reflecting the accuracy of the equipment transfer function, usually expressed by relative error or absolute error. The bandwidth matching degree analysis of the dynamic response bandwidth and resonance frequency offset is to compare and analyze the dynamically monitored response bandwidth and resonance frequency offset with the standard values. The dynamic response bandwidth refers to the effective response frequency range of the equipment in the resonance state, and the resonance frequency offset refers to the difference between the current resonance frequency and the standard resonance frequency. The bandwidth matching degree analysis refers to evaluating the stability of the equipment frequency response characteristics by calculating the change rate of the dynamic response bandwidth and the offset degree of the resonance frequency. The frequency response characteristic comparison is to compare the current frequency response characteristics of the equipment with the standard characteristics point by point and analyze their consistency. The dynamic characteristic matching degree refers to the degree of conformity between the current dynamic characteristics of the equipment and the standard characteristics, which is an important index for evaluating the frequency response performance of the equipment.

[0046] The timing stability analysis of the phase delay characteristic is to quantitatively evaluate the time stability of the equipment phase delay characteristic. The phase delay characteristic refers to the degree of phase lag of the system output response relative to the input excitation, reflecting the time-domain response delay characteristic of the system. The timing stability analysis evaluates the stability degree of the equipment timing characteristic by statistically analyzing the changes of the phase delay characteristic at different time points. The phase offset statistics statistically processes the phase offset data at different frequency points and different times to obtain statistical parameters reflecting the overall phase stability. The timing matching parameter refers to a parameter that can quantitatively represent the matching degree between the equipment timing characteristic and the standard characteristic, usually including indicators such as the phase stability coefficient and time delay consistency. The index classification of the transfer function accuracy divides it into different grade intervals according to the numerical range of the transfer function accuracy. The accuracy level analysis determines the evaluation criteria and allowable error range corresponding to different accuracy levels according to the actual application requirements of the equipment and industry standards. The functional reliability index refers to a quantitative index that can reflect the reliable degree of the basic functions of the equipment, usually determined based on the transfer function accuracy level, including aspects such as functional integrity, stability, and consistency.

[0047] The comprehensive evaluation of the dynamic characteristic matching degree and the timing matching parameter means comprehensively considering two key indicators representing the dynamic performance and the timing performance of the device, and obtaining a comprehensive evaluation reflecting the overall performance of the device through a specific mathematical model or weight allocation method. The system characteristic analysis is a multi-dimensional and multi-angle comprehensive analysis of the dynamic characteristics and the timing characteristics of the device, evaluating the mutual relationship and the overall coordination among the characteristics. The performance reliability index refers to a quantitative index that can reflect the lasting stability degree of the device performance, usually determined based on the dynamic characteristic matching degree and the timing matching parameter, including aspects such as performance stability, durability, and adaptability. The comprehensive analysis based on the function reliability index and the performance reliability index is to integrate the indexes reflecting the two dimensions of the device function and performance, and obtain a comprehensive evaluation comprehensively reflecting the quality state of the device. The quality evaluation report refers to a comprehensive report containing key indicators such as the transfer function accuracy, the dynamic characteristic matching degree, and the device reliability level, which is used to guide the device maintenance and use decisions.

[0048] Taking a certain model of large hydraulic vibration table as an example, when conducting the performance decay analysis, the vibration transfer accuracy data obtained by real-time monitoring is compared with the calibration accuracy level of this device. The calibration accuracy level of this device is Class A, requiring that the relative error of the transfer function does not exceed ±5% in the frequency band of 20 - 1000 Hz, and does not exceed ±8% in the frequency band of 1000 - 2000 Hz. By calculating the error between the actual transfer function and the standard transfer function at each frequency point, an error distribution curve is formed. It is statistically obtained that the average relative error in the frequency band of 20 - 1000 Hz is 4.8%, and the maximum relative error of 6.2% appears at 950 Hz; the average relative error in the frequency band of 1000 - 2000 Hz is 7.3%, and the maximum relative error of 9.1% appears at 1850 Hz. According to the error statistical results, it is determined that the overall transfer function accuracy of the current device reaches the level of Class A- (slightly lower than Class A). For the analysis of the dynamic characteristic matching degree, the current dynamic response bandwidth and the resonance frequency offset are compared with the standard values. Under the standard state, the resonance bandwidth of this device at 80 Hz is 5 Hz, while the currently measured bandwidth is 6.2 Hz, and the bandwidth change rate is 24%; the standard resonance frequency is 80 Hz, and the currently measured value is 82.3 Hz, with a frequency offset of 2.3 Hz and a relative offset rate of 2.88%. Through the comparative analysis of the frequency response characteristics, the frequency response characteristic matching coefficients in the low frequency band (20 - 200 Hz), the middle frequency band (200 - 800 Hz), and the high frequency band (800 - 2000 Hz) are calculated to be 0.92, 0.87, and 0.83 respectively. The comprehensive dynamic characteristic matching degree is 0.89, indicating that the overall dynamic characteristics of the device remain good but there has been a slight decay.

[0049] In the timing stability analysis, statistical processing is performed on the phase delay characteristics. Under standard conditions, the average phase delay of the device across the entire frequency band is 15°, while the currently measured average phase delay is 18.5°, an increase of 3.5°. Through the statistical analysis of the phase offsets at different frequency points, the phase stability coefficient is calculated to be 0.85, the time delay consistency coefficient is 0.88, and the comprehensive timing matching parameter is 0.86, indicating that the timing characteristics of the device have slightly decreased but are still within an acceptable range. Based on the index classification of the transfer function accuracy, the accuracy level is divided into four levels according to industry standards: S level (excellent), A level (good), B level (general), and C level (poor). According to the evaluation results of the transfer function accuracy, the device is currently at the A- level. The function integrity coefficient is 0.92, the stability coefficient is 0.88, and the consistency coefficient is 0.90. The comprehensive function reliability index is 0.90, indicating that the basic functions of the device still maintain a high level of reliability.

[0050] The dynamic characteristic matching degree (0.89) and the timing matching parameter (0.86) are comprehensively evaluated. Considering that the importance of dynamic characteristics to the overall performance is slightly higher, the weight is assigned as 6:4. The calculated performance reliability index is 0.878, indicating that the overall performance state of the device is good but there is a slight attenuation trend. Based on the comprehensive analysis of the function reliability index (0.90) and the performance reliability index (0.878), considering that their contributions to the device quality are equivalent, the arithmetic mean is used to obtain the comprehensive quality index of the device as 0.889. According to this index, a quality assessment report including the transfer function accuracy (A- level), the dynamic characteristic matching degree (0.89), and the device reliability level (A level) is generated, and it is recommended to perform device calibration and parameter adjustment within the next 3 months to ensure that the device continuously maintains a good working state.

[0051] In a specific embodiment, the process of performing the step of grading the transfer function accuracy index may specifically include the following steps: (1) Separate the three-dimensional data of the transfer function accuracy into low-frequency band, medium-frequency band, and high-frequency band according to the standard frequency interval, and obtain the full-band accuracy characteristic diagram through spectrum distribution statistics; (2) Perform a uniform scan of the full-band accuracy characteristic diagram. Through frequency point density calculation and amplitude fluctuation analysis, obtain the frequency band consistency index; (3) Perform transfer characteristic analysis according to the frequency band consistency index. Through frequency band response curve fitting and phase offset calculation, obtain the frequency band stability parameter; (4) Dynamically compare the frequency band stability parameter with the calibration benchmark of the vibration test equipment. Through accuracy level mapping and deviation cumulative statistics, obtain the reliability quantification index; (5) Evaluate the performance loss of the reliability quantification index, and obtain the functional integrity coefficient through attenuation characteristic analysis and stability verification; (6) Perform weighted fusion processing on the frequency band consistency index, frequency band stability parameter, and functional integrity coefficient, and obtain the functional reliability index through comprehensive reliability evaluation.

[0052] Specifically, separate the transfer function accuracy into three-dimensional data in the low-frequency band, middle-frequency band, and high-frequency band according to the standard frequency interval, and obtain the full-band accuracy feature map through spectral distribution statistics. The transfer function accuracy refers to the degree of coincidence between the actual transfer function and the standard transfer function, reflecting the performance state of the device; the three-dimensional data separation is the process of classifying and organizing the transfer function accuracy data according to the frequency dimension, amplitude dimension, and phase dimension; the low-frequency band usually refers to 0 - 100 Hz, the middle-frequency band is 100 - 500 Hz, and the high-frequency band is 500 - 2000 Hz; the spectral distribution statistics is to statistically analyze the accuracy data within each frequency band to obtain characteristic quantities such as the average value, standard deviation, and maximum deviation; the full-band accuracy feature map is a graphical representation that intuitively shows the distribution characteristics of the transfer function accuracy in different frequency bands. In actual operation, for the vibration test of a certain aircraft engine blade, obtain the transfer function accuracy data, and then divide the test frequency band of 10 - 2000 Hz into three intervals: low-frequency (10 - 100 Hz), middle-frequency (100 - 500 Hz), and high-frequency (500 - 2000 Hz). Respectively, statistically analyze the average value and standard deviation of the transfer function accuracy within each interval to form a three-dimensional dataset of frequency-accuracy-standard deviation, and intuitively show the accuracy distribution characteristics of the full frequency band in the form of a heat map. Perform a frequency band uniformity scan on the full-band accuracy feature map, and obtain the frequency band consistency index through frequency point density calculation and amplitude fluctuation analysis. The frequency band uniformity scan refers to scanning and analyzing the accuracy feature map to evaluate the uniformity of the accuracy distribution within each frequency band; the frequency point density calculation is the process of determining the number of effective test points within a unit frequency band; the amplitude fluctuation analysis is a method for evaluating the change range of the accuracy amplitude within the frequency band; the frequency band consistency index is a parameter that measures the distribution consistency of the transfer function accuracy within each frequency band. Taking the test of the spacecraft hatch hinge as an example, the accuracy mean value in the low-frequency band (20 - 120 Hz) is 0.95, and the standard deviation is 0.03, indicating that the accuracy distribution is relatively stable; the frequency point density in the middle-frequency band (120 - 600 Hz) is uneven, the density decreases in the interval of 340 - 420 Hz, and at the same time the accuracy fluctuation increases, and the standard deviation rises to 0.08. By calculation, the overall frequency band consistency index is 0.88, reflecting certain performance anomalies in the middle-frequency band.

[0053] Conduct transfer characteristic analysis based on the band consistency index. By fitting the frequency response curve and calculating the phase shift, the frequency band stability parameter is obtained. Transfer characteristic analysis is a process of in-depth study of the system transfer characteristics; fitting the frequency response curve is to describe the law of the response characteristics changing with frequency using a mathematical model; calculating the phase shift is to determine the trend and amplitude of the phase change; the frequency band stability parameter is an index characterizing the response stability of the system. In the vibration test of the helicopter drive shaft, an anomaly is found in the 400 - 550 Hz interval according to the band consistency index. A fine analysis is carried out on this interval. A fifth-order polynomial is used to fit the frequency response curve, the fitting error is calculated to be 0.056, and the phase shift is 23 degrees. Through comprehensive evaluation, the frequency band stability parameter is 0.82, which is lower than the standard value of 0.90, indicating that there are stability problems in this frequency band and further inspection is needed. The frequency band stability parameter is dynamically compared with the calibration benchmark of the vibration test equipment. Through accuracy level mapping and deviation accumulation statistics, a reliability quantification index is obtained. Dynamic comparison is a process of real-time comparison of the current parameter with the standard parameter; accuracy level mapping is a method of mapping continuous accuracy values to discrete levels; deviation accumulation statistics is a method of calculating the total sum of deviations at each frequency point; the reliability quantification index is a parameter for quantitatively evaluating the reliability of the equipment. In the test of the landing gear shock absorber of a commercial aircraft, the measured frequency band stability parameter of 0.87 is compared with the standard value of 0.93, and the relative deviation is calculated to be 6.5%, corresponding to accuracy level B (allowable deviation range 5% - 10%); by accumulating the deviations at 35 key frequency points, the total deviation sum is 2.31 and the average deviation is 0.066. Based on this, the reliability quantification index is calculated to be 0.91, which reaches the equipment maintenance threshold but does not reach the alarm threshold, indicating that the equipment is in an acceptable but attention-required state.

[0054] Evaluate the performance loss of reliability quantification indicators. Through attenuation characteristic analysis and stability verification, the functional integrity coefficient is obtained. Performance loss evaluation is the process of analyzing the degree of equipment performance degradation; attenuation characteristic analysis is the method of studying the change of system damping characteristics; stability verification is the method of verifying system stability; the functional integrity coefficient is a parameter characterizing the functional integrity of equipment. In the vibration test of satellite solar panels, through time series analysis, it is found that the reliability quantification indicator has dropped from 0.94 to 0.89 in the past 30 days, and the calculated performance loss rate is 0.17% per day; through step response test, the system stability is evaluated, and the overshoot increases by 12% and the settling time extends by 0.8 seconds; considering these factors comprehensively, the calculated functional integrity coefficient is 0.86, which is lower than the standard threshold of 0.90, indicating that the equipment needs maintenance and adjustment, especially the control system and hydraulic system need to be inspected key points. The frequency band consistency index, frequency band stability parameter and functional integrity coefficient are weighted and fused, and the functional reliability index is obtained through comprehensive reliability evaluation. Weighted fusion processing is the process of combining multiple parameters according to different weights; comprehensive reliability evaluation is the method of multi-dimensional evaluation of system reliability; the functional reliability index is a comprehensive index characterizing the overall reliability level of the system. In the vibration durability test of the automotive chassis, the frequency band consistency index is 0.91, the frequency band stability parameter is 0.85, and the functional integrity coefficient is 0.88. Considering that the middle frequency band (200 - 400 Hz) has the greatest impact on the test results in this test, a higher weight of 0.5 is assigned to the frequency band stability parameter, and the frequency band consistency index and functional integrity coefficient are assigned weights of 0.3 and 0.2 respectively. The calculated functional reliability index through weighted average is 0.875, which is in the "good" grade but close to the "to be maintained" threshold. It is recommended to perform equipment calibration and necessary maintenance after completing the current test to ensure the accuracy and reliability of the next round of tests. Through this method, not only can the functional state of the vibration test equipment be comprehensively evaluated, but also potential problems can be discovered in time, avoiding test errors caused by equipment performance degradation, and improving the credibility and consistency of test results.

[0055] The above describes the operation state monitoring method for the vibration test equipment in the embodiments of the present application. Next, the operation state monitoring system for the vibration test equipment in the embodiments of the present application will be described. Please refer to Figure 3 , an embodiment of the operation state monitoring system for the vibration test equipment in the embodiments of the present application includes: A generation module, configured to generate a multi-dimensional physical feature data stream through vibration waveform calibration and signal distortion compensation processing based on the operation parameters collected by the resonant excitation table, servo hydraulic power source, electromagnetic exciter, and dynamic force sensor of the vibration test equipment; A calculation module, configured to perform frequency response function analysis and dynamic transfer rate calculation on the multi-dimensional physical feature data stream, so as to obtain physical response features including excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum and dynamic response spectrum; An establishment module, configured to establish a test calibration benchmark including excitation force vector, resonance frequency band, dynamic damping ratio and frequency response function based on the physical response features through vibration transfer rate analysis and dynamic stiffness calculation; An identification module, configured to perform resonance transfer analysis and resonance bandwidth identification on the physically collected response features in real time according to the test calibration benchmark, and output test status parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset and phase delay characteristics; An analysis module, configured to perform performance attenuation analysis according to the test status parameters in combination with the calibration accuracy level of the vibration test equipment, and generate a quality assessment report including transfer function accuracy, dynamic characteristic matching degree and equipment reliability level.

[0056] Through the collaborative cooperation of the above-mentioned various components, by calibrating the vibration waveform and compensating for signal distortion of the operating parameters collected by the resonant excitation table, servo hydraulic power source, electromagnetic exciter, and dynamic force sensor, a multi-dimensional physical feature data stream is generated, realizing the comprehensive collection and effective integration of equipment operation data, providing a high-quality data basis for subsequent analysis; by performing frequency response function analysis and dynamic transmissibility calculation on the multi-dimensional physical feature data stream, physical response characteristics including excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum, and dynamic response spectrum are obtained, comprehensively characterizing the physical state of the equipment; based on the physical response characteristics, through vibration transmissibility analysis and dynamic stiffness calculation, a test calibration benchmark including excitation force vector, resonance frequency band, dynamic damping ratio, and frequency response function is established, providing a scientific reference basis for equipment performance evaluation; according to the test calibration benchmark, resonance transfer analysis and resonance bandwidth identification are performed on the real-time collected physical response characteristics, and test status parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics are output, realizing the real-time and accurate evaluation of the equipment status; according to the test status parameters, combined with the calibration accuracy level of the vibration test equipment, performance degradation analysis is carried out, and a quality evaluation report including transfer function accuracy, dynamic characteristic matching degree, and equipment reliability level is generated, visualizing, quantifying, and standardizing the equipment health status. This solution has achieved remarkable results especially in the application of artificial intelligence algorithms. It is mainly reflected in that the intelligent fusion processing algorithm for multi-source data enables discrete signals to form a unified physical feature data stream, enhancing the integrity and relevance of the data; the adaptive spectrum decomposition algorithm in frequency response function analysis can automatically adjust analysis parameters according to the signal characteristics of different frequency bands, improving the accuracy and efficiency of spectrum analysis; the machine learning enhanced matrix operation method used in dynamic transmissibility calculation can dynamically optimize the transfer characteristics according to historical data, reducing calculation errors; the pattern recognition technology applied in the process of establishing the test calibration benchmark can automatically identify the resonance characteristics and abnormal states of the system, improving the accuracy of benchmark establishment; the online learning algorithm used in the real-time monitoring stage can continuously optimize the judgment threshold as the equipment usage time increases, making the monitoring more accurate; the trend prediction model in performance degradation analysis realizes the prediction function of equipment performance degradation through time series feature extraction and regression analysis. This method of comprehensively applying artificial intelligence algorithms significantly improves the intelligent level and predictability of vibration test equipment status monitoring, provides strong support for the scientific management and precise maintenance of equipment, and at the same time ensures the reliability and consistency of test results, meeting the technical requirements of high-precision tests.

[0057] Referring to Figure 4 , in the embodiment of the present invention, a computer device is further provided. This computer device can be a server, and its internal structure can be as Figure 4As shown in the figure. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected via a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0058] Those skilled in the art can understand that Figure 4 the structure shown in the figure is only a block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0059] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0060] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0061] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0062] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0063] The above is the case. The above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for monitoring the operating state of a vibration test device, characterized in that, The method for monitoring the operating state of the vibration test equipment includes: Based on the operating parameters collected by the resonant excitation table, servo-hydraulic power source, electromagnetic exciter, and dynamic force sensor of the vibration test equipment, through vibration waveform calibration and signal distortion compensation processing, a multi-dimensional physical feature data stream is generated; Perform frequency response function analysis and dynamic transfer rate calculation on the multi-dimensional physical feature data stream to obtain physical response characteristics including excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum, and dynamic response spectrum; Based on the physical response characteristics, through vibration transfer rate analysis and dynamic stiffness calculation, establish a test calibration reference including excitation force vector, resonance frequency band, dynamic damping ratio, and frequency response function; According to the test calibration reference, perform resonance transfer analysis and resonance bandwidth identification on the physically collected response characteristics in real time, and output test state parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics; According to the test state parameters, combined with the calibration accuracy level of the vibration test equipment, perform performance degradation analysis, and generate a quality assessment report including transfer function accuracy, dynamic characteristic matching degree, and equipment reliability level.

2. The operating state monitoring method for a vibration test device according to claim 1, characterized in that, The step of generating a multi-dimensional physical feature data stream based on the operating parameters collected by the resonant excitation table, servo-hydraulic power source, electromagnetic exciter, and dynamic force sensor of the vibration test equipment through vibration waveform calibration and signal distortion compensation processing includes: Input the vibration acceleration signal collected by the triaxial acceleration sensor installed on the resonant excitation table, the hydraulic characteristic signal collected by the pressure and flow sensor installed on the servo-hydraulic power source, the excitation current signal collected by the current sensor installed on the electromagnetic exciter, and the dynamic force signal collected by the dynamic force sensor into the signal acquisition device; Detect waveform distortion of the vibration acceleration signal through the signal acquisition device to generate a vibration amplitude correction coefficient and a phase correction coefficient; Use the vibration amplitude correction coefficient and the phase correction coefficient to perform amplitude compensation and phase calibration on the vibration acceleration signal to obtain a calibrated vibration characteristic signal; Perform pressure fluctuation analysis and flow uniformity calculation on the hydraulic characteristic signal to obtain a hydraulic state characteristic signal; Extract harmonic components and analyze drive characteristics of the current signal to obtain an excitation characteristic signal; According to the calibrated vibration characteristic signal, hydraulic state characteristic signal, and excitation characteristic signal, perform multi-source data synchronization fusion to generate the multi-dimensional physical feature data stream.

3. The operating state monitoring method for the vibration test equipment according to claim 1, characterized in that, The step of performing frequency response function analysis and dynamic transfer rate calculation on the multi-dimensional physical feature data stream to obtain physical response characteristics including excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum, and dynamic response spectrum includes: Separate the multi-dimensional physical feature data stream according to signal types to obtain corresponding vibration signal stream, hydraulic signal stream, excitation signal stream, and dynamic force signal stream; Perform Fourier transform processing on the vibration signal stream, perform amplitude statistics and phase extraction on the transformed spectrum data to generate a vibration acceleration spectrum; Perform pressure pulsation analysis on the hydraulic signal stream, and through pulsation waveform decomposition and amplitude reconstruction, obtain a hydraulic pressure spectrum; Perform an analysis of the excitation force characteristics on the excitation signal flow, and form an excitation force spectrum through force vector decomposition and amplitude recombination; Perform a decomposition of the response characteristics on the dynamic force signal flow, and obtain a dynamic response spectrum through signal reconstruction and frequency-domain mapping; According to the vibration acceleration spectrum, hydraulic pressure spectrum, excitation force spectrum, and dynamic response spectrum, perform frequency-domain correlation analysis and transfer characteristic calculation to obtain the physical response characteristics.

4. The method for monitoring the operating state of the vibration test equipment according to claim 1, wherein Based on the physical response characteristics, through vibration transmissibility analysis and dynamic stiffness calculation, establish a test calibration benchmark including the excitation force vector, resonance frequency band, dynamic damping ratio, and frequency response function, including: Compare the input and output of the excitation force spectrum and the dynamic response spectrum in the physical response characteristics, and extract vibration transmissibility data through transfer function analysis; Perform frequency-band segmented statistics on the vibration transmissibility data, and obtain the excitation force vector of each frequency band through bandwidth analysis; Perform frequency-domain decomposition on the hydraulic pressure spectrum, and determine the system resonance frequency band through pressure pulsation characteristic analysis; Extract the peak characteristics of the vibration acceleration spectrum, and obtain the dynamic damping ratio through resonance characteristic calculation; Perform correlation analysis on the vibration transmissibility data and the dynamic damping ratio, and generate a frequency response function through dynamic stiffness calculation; Perform system characteristic synthesis based on the excitation force vector, resonance frequency band, dynamic damping ratio, and frequency response function to obtain the test calibration benchmark.

5. The method for monitoring the operating state of the vibration testing equipment according to claim 1, characterized in that, Based on the test calibration benchmark, perform resonance transfer analysis and resonance bandwidth identification on the physically responsive characteristics collected in real time, and output test status parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics, including: Compare the frequency response function in the test calibration benchmark with the physically responsive characteristics collected in real time, and obtain the vibration transfer accuracy through transfer function deviation analysis; Perform real-time response analysis on the resonance frequency band, and obtain the dynamic response bandwidth through frequency-band scanning; Compare the phase of the excitation force vector with the real-time physical response characteristics, and determine the resonance frequency offset through resonance characteristic analysis; Compare the signal of the dynamic damping ratio with the real-time physical response characteristics, and obtain the phase delay characteristic through phase characteristic calculation; Perform system characteristic analysis and state quantification on the vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics to obtain the test status parameters.

6. The method for monitoring the operating state of a vibration test device according to claim 1, characterized in that, According to the test status parameters, perform performance degradation analysis in combination with the calibration accuracy level of the vibration test equipment, and generate a quality assessment report including transfer function accuracy, dynamic characteristic matching degree, and equipment reliability level, including: Perform deviation analysis on the vibration transfer accuracy in the test status parameters and the calibration accuracy level of the vibration test equipment, and obtain the transfer function accuracy through transfer function error calculation; Perform bandwidth matching degree analysis on the dynamic response bandwidth and resonance frequency offset, and obtain the dynamic characteristic matching degree through frequency response characteristic comparison; Perform time-series stability analysis on the phase delay characteristic, and obtain the time-series matching parameter through phase offset statistics; Perform index grading on the transfer function accuracy, and obtain the functional reliability index through accuracy level analysis; Comprehensively evaluate the dynamic characteristic matching degree and the timing matching parameter, and obtain the performance reliability index through system characteristic analysis; Conduct comprehensive analysis based on the functional reliability index and the performance reliability index to generate the quality evaluation report including the transfer function accuracy, dynamic characteristic matching degree, and equipment reliability level.

7. The operating state monitoring method for the vibration test equipment according to claim 6, characterized in that, Grade the transfer function accuracy, and obtain the functional reliability index through accuracy level analysis, including: Separate the three-dimensional data of the transfer function accuracy into low-frequency band, middle-frequency band, and high-frequency band according to the standard frequency interval, and obtain the full-band accuracy feature map through spectral distribution statistics; Perform frequency band uniformity scanning on the full-band accuracy feature map, and obtain the frequency band consistency index through frequency point density calculation and amplitude fluctuation analysis; Conduct transfer characteristic analysis based on the frequency band consistency index, and obtain the frequency band stability parameter through frequency band response curve fitting and phase shift calculation; Conduct dynamic comparison between the frequency band stability parameter and the calibration reference of the vibration test equipment, and obtain the reliability quantification index through accuracy level mapping and deviation accumulation statistics; Conduct performance loss evaluation on the reliability quantification index, and obtain the function integrity coefficient through attenuation characteristic analysis and stability verification; Conduct weighted fusion processing on the frequency band consistency index, frequency band stability parameter, and function integrity coefficient, and obtain the functional reliability index through reliability comprehensive evaluation.

8. An operating state monitoring system for a vibration testing device, which is used to implement the operating state monitoring method for a vibration testing device as described in any one of claims 1-7, characterized in that, The operation status monitoring system for the vibration test equipment includes: A generation module, configured to generate a multi-dimensional physical feature data stream through vibration waveform calibration and signal distortion compensation processing based on the operation parameters collected by the resonant excitation table, servo hydraulic power source, electromagnetic exciter, and dynamic force sensor of the vibration test equipment; A calculation module, configured to conduct frequency response function analysis and dynamic transfer ratio calculation on the multi-dimensional physical feature data stream to obtain physical response features including excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum, and dynamic response spectrum; A establishment module, configured to establish a test calibration reference including excitation force vector, resonance frequency band, dynamic damping ratio, and frequency response function based on the physical response features through vibration transfer ratio analysis and dynamic stiffness calculation; An identification module, configured to conduct resonance transfer analysis and resonance bandwidth identification on the physically collected response features in real time according to the test calibration reference, and output test status parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics; An analysis module, configured to conduct performance attenuation analysis in combination with the calibration accuracy level of the vibration test equipment based on the test status parameters, and generate a quality evaluation report including transfer function accuracy, dynamic characteristic matching degree, and equipment reliability level.

9. A computer device, characterized in that, It includes a memory and a processor, and the memory stores a computer program that can run on the processor. It is characterized in that when the processor executes the computer program, it implements the operation status monitoring method for the vibration test equipment according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, and when the computer program is run by a processor, the processor is caused to execute the operating state monitoring method for a vibration testing device according to any one of claims 1 to 7.

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