A method and system for monitoring the operating status of vibration test equipment

Abstract

The present application relates to the field of data processing technology, and discloses a method and system for monitoring the operating status of vibration test equipment. The method includes: generating a multi-dimensional physical characteristic data stream based on the operating parameters collected by each sensor of the vibration test equipment through vibration waveform calibration; performing frequency response analysis on the data stream to obtain physical response characteristics; establishing a test calibration benchmark based on this; outputting test status parameters based on the real-time response characteristics of the benchmark analysis; performing performance attenuation analysis based on the parameters, and generating an equipment quality assessment report. The present application establishes a comprehensive analysis method based on a multi-dimensional physical characteristic data stream to achieve real-time monitoring of the dynamic characteristics of the equipment, multi-parameter joint evaluation, and performance attenuation trend analysis, thereby improving the accuracy, comprehensiveness, and predictability of the status monitoring of the vibration test equipment and ensuring the reliability and consistency of the test results.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for monitoring the operating status of vibration testing equipment. Background Art

[0002] Vibration testing equipment is widely used in aerospace, automotive, marine, and electronic equipment industries to simulate the vibration conditions found in actual operating environments and assess product reliability and durability. Traditional vibration testing equipment operating status monitoring relies primarily on periodic calibration and manual inspection, typically involving regular calibration, simple parameter monitoring, and scheduled maintenance. These methods, based on engineering experience and equipment manuals, inspect and calibrate the equipment at fixed intervals and assess equipment status through manual observation and simple data logging. In actual applications, some advanced vibration testing equipment has introduced basic self-test functions that can monitor the operating status of key components such as power amplifiers, controllers, and sensors, and issue alarms when significant anomalies occur.

[0003] However, traditional methods for monitoring the operating status of vibration test equipment have many shortcomings. Regular calibration and manual inspection methods lack real-time performance and cannot promptly detect subtle changes and potential problems in equipment performance. They are often not detected until obvious equipment failures occur, increasing repair costs and downtime. Secondly, traditional methods lack a comprehensive assessment of the dynamic characteristics of the equipment, making it difficult to capture performance changes in different frequency bands and under different operating conditions. In particular, the monitoring of key parameters such as the resonance frequency band and dynamic damping characteristics is insufficient, making it difficult to ensure the reliability and consistency of the test results. In addition, existing monitoring methods lack systematicity and intelligence, with scattered data collection and isolated parameters, making it difficult to form a comprehensive assessment of the overall status of the equipment. There is also a lack of predictive capabilities for performance degradation trends, and no 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 the test results. Summary of the Invention

[0004] This application provides a method and system for monitoring the operating status of vibration test equipment, designed to address the technical issues of insufficient real-time performance, incomplete monitoring parameters, and unsystematic performance evaluation in vibration test equipment operating status monitoring. By establishing a comprehensive analysis method based on multidimensional physical characteristic data streams, real-time monitoring of equipment dynamic characteristics, multi-parameter joint evaluation, and performance degradation trend analysis are achieved, thereby improving the accuracy, comprehensiveness, and predictability of vibration test equipment status monitoring and ensuring the reliability and consistency of test results.

[0005] In a first aspect, the present application provides an operating status monitoring method for vibration testing equipment, the operating status monitoring method comprising: generating a multidimensional physical characteristic data stream through vibration waveform calibration and signal distortion compensation processing based on operating parameters collected from a resonant excitation table, a servo hydraulic power source, an electromagnetic exciter, and a dynamic force sensor of the vibration testing equipment; performing frequency response function analysis and dynamic transmissibility calculation on the multidimensional physical characteristic data stream to obtain physical response characteristics including an excitation force spectrum, a hydraulic pressure spectrum, a vibration acceleration spectrum, and a dynamic response spectrum; establishing a test calibration benchmark including an excitation force vector, a resonant frequency band, a dynamic damping ratio, and a frequency response function based on the physical response characteristics through vibration transmissibility analysis and dynamic stiffness calculation; performing resonant transfer analysis and resonant bandwidth identification on the physical response characteristics collected in real time according to the test calibration benchmark, and outputting test status parameters including vibration transfer accuracy, dynamic response bandwidth, resonant frequency offset, and phase delay characteristics; performing performance degradation analysis based on the test status parameters in combination with the calibration accuracy level of the vibration testing equipment to generate a quality assessment report including transfer function accuracy, dynamic characteristic matching, and equipment reliability level.

[0006] In a second aspect, the present application provides an operating status monitoring system for a vibration test device, the operating status monitoring system for the vibration test device comprising:

[0007] A generation module is used to generate 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;

[0008] a calculation module, configured to perform frequency response function analysis and dynamic transmissibility calculation on the multi-dimensional physical characteristic data stream to obtain physical response characteristics including an excitation force spectrum, a hydraulic pressure spectrum, a vibration acceleration spectrum, and a dynamic response spectrum;

[0009] An establishment module is used to establish a test calibration benchmark including an exciting force vector, a resonance frequency band, a dynamic damping ratio, and a frequency response function based on the physical response characteristics through vibration transmissibility analysis and dynamic stiffness calculation;

[0010] an identification module for performing resonance transfer analysis and resonance bandwidth identification on the physical response characteristics collected in real time according to the test calibration benchmark, and outputting test state parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics;

[0011] The analysis module is used to perform performance degradation analysis based on the test state 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.

[0012] A third aspect of the present invention provides a computer device comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned operating status monitoring method for vibration testing equipment.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for monitoring the operating status of vibration testing equipment.

[0014] In the technical solution provided by 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 characteristic data stream is generated, thereby realizing the comprehensive collection and effective integration of equipment operation data, and providing a high-quality data foundation for subsequent analysis; by performing frequency response function analysis and dynamic transmission rate calculation on the multi-dimensional physical characteristic data stream, the physical response characteristics including the excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum and dynamic response spectrum are obtained, so that the physical state of the equipment is fully characterized; based on the physical response characteristics, through vibration transmission rate analysis and dynamic stiffness calculation, a system including the vibration characteristics is established. The test calibration benchmark, which includes the excitation force vector, resonance frequency band, dynamic damping ratio, and frequency response function, provides a scientific reference for equipment performance evaluation. Based on the test calibration benchmark, the real-time collected physical response characteristics are subjected to resonance transfer analysis and resonance bandwidth identification, and the test state parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics are output, realizing real-time and accurate evaluation of the equipment status. Based on the test state parameters, combined with the calibration accuracy level of the vibration test equipment, performance attenuation analysis is performed to generate a quality assessment report including transfer function accuracy, dynamic characteristic matching, and equipment reliability level, so that the equipment health status can be visualized, quantified, and standardized. This solution has achieved significant results in the application of artificial intelligence algorithms. This is primarily reflected in the intelligent fusion processing algorithm for multi-source data, which transforms discrete signals into a unified physical characteristic data stream, enhancing data integrity and relevance. The adaptive spectrum decomposition algorithm in frequency response function analysis automatically adjusts analysis parameters based on signal characteristics in different frequency bands, improving the accuracy and efficiency of spectrum analysis. The machine learning-enhanced matrix operation method used in dynamic transmissibility calculation dynamically optimizes transmissibility characteristics based on historical data, reducing calculation errors. Pattern recognition technology applied during the establishment of the test calibration benchmark automatically identifies system resonance characteristics and abnormal conditions, improving benchmark accuracy. The online learning algorithm used in the real-time monitoring phase continuously optimizes judgment thresholds as equipment age increases, making monitoring more accurate. The trend prediction model in performance degradation analysis, through time series feature extraction and regression analysis, predicts equipment performance degradation. This integrated application of artificial intelligence algorithms significantly enhances the intelligence and predictive capabilities of vibration test equipment condition monitoring, providing strong support for scientific management and precise maintenance of equipment while ensuring the reliability and consistency of test results, meeting the technical requirements of high-precision testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A schematic diagram of an embodiment of a method for monitoring the operating status of a vibration test device in an embodiment of the present application;

[0017] Figure 2 This is a flow chart of vibration waveform calibration and signal distortion compensation processing in an embodiment of the present application;

[0018] Figure 3 A schematic diagram of an embodiment of an operation status monitoring system for vibration testing equipment in an embodiment of the present application;

[0019] Figure 4 It is a schematic block diagram of the structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present application provide an operating status monitoring method and system for a vibration test device. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, a method for monitoring the operating status of a vibration test device includes:

[0022] Step S101: Generate 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;

[0023] Step S102: performing frequency response function analysis and dynamic transmissibility calculation on the multi-dimensional physical characteristic data stream to obtain physical response characteristics including an excitation force spectrum, a hydraulic pressure spectrum, a vibration acceleration spectrum, and a dynamic response spectrum;

[0024] Step S103: Based on the physical response characteristics, a test calibration benchmark including an exciting force vector, a resonance frequency band, a dynamic damping ratio, and a frequency response function is established through vibration transmissibility analysis and dynamic stiffness calculation;

[0025] Step S104: Perform resonance transfer analysis and resonance bandwidth identification on the real-time collected physical response characteristics according to the test calibration benchmark, and output test state parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics;

[0026] Step S105: Perform performance degradation analysis based on the test state parameters and 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.

[0027] It is understandable that the execution subject of the present application can be a vibration test equipment operation status monitoring system, or a terminal or a server, which is not limited here. The embodiment of the present application is described by taking a server as the execution subject as an example.

[0028] 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 characteristic data stream is generated, thereby achieving comprehensive collection and effective integration of equipment operation data, and providing a high-quality data foundation for subsequent analysis; by performing frequency response function analysis and dynamic transmission rate calculation on the multi-dimensional physical characteristic data stream, physical response characteristics including the excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum and dynamic response spectrum are obtained, so that the physical state of the equipment is fully characterized; based on the physical response characteristics, a vibration transmission rate analysis and dynamic stiffness calculation are performed to establish a dynamic response spectrum including the excitation force spectrum. The test calibration benchmarks for vibration force vector, resonance frequency band, dynamic damping ratio and frequency response function provide a scientific reference for equipment performance evaluation. Based on the test calibration benchmark, the real-time collected physical response characteristics are subjected to resonance transfer analysis and resonance bandwidth identification, and the test state parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset and phase delay characteristics are output, realizing real-time and accurate evaluation of the equipment status. Based on the test state parameters, combined with the calibration accuracy level of the vibration test equipment, performance attenuation analysis is carried out to generate a quality assessment report including transfer function accuracy, dynamic characteristic matching and equipment reliability level, so that the equipment health status can be visualized, quantified and standardized. This solution has achieved significant results in the application of artificial intelligence algorithms. This is primarily reflected in the intelligent fusion processing algorithm for multi-source data, which transforms discrete signals into a unified physical characteristic data stream, enhancing data integrity and relevance. The adaptive spectrum decomposition algorithm in frequency response function analysis automatically adjusts analysis parameters based on signal characteristics in different frequency bands, improving the accuracy and efficiency of spectrum analysis. The machine learning-enhanced matrix operation method used in dynamic transmissibility calculation dynamically optimizes transmissibility characteristics based on historical data, reducing calculation errors. Pattern recognition technology applied during the establishment of the test calibration benchmark automatically identifies system resonance characteristics and abnormal conditions, improving benchmark accuracy. The online learning algorithm used in the real-time monitoring phase continuously optimizes judgment thresholds as equipment age increases, making monitoring more accurate. The trend prediction model in performance degradation analysis, through time series feature extraction and regression analysis, predicts equipment performance degradation. This integrated application of artificial intelligence algorithms significantly enhances the intelligence and predictive capabilities of vibration test equipment condition monitoring, providing strong support for scientific management and precise maintenance of equipment while ensuring the reliability and consistency of test results, meeting the technical requirements of high-precision testing.

[0029] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0030] (1) The three-axis acceleration sensor installed on the resonant excitation table collects the vibration acceleration signal, the pressure flow sensor installed on the servo hydraulic power source collects the hydraulic characteristic signal, the current sensor installed on the electromagnetic exciter collects the excitation current signal, and the dynamic force sensor collects the dynamic force signal, and inputs them into the signal acquisition device;

[0031] (2) Perform waveform distortion detection on the vibration acceleration signal through the signal acquisition device to generate the vibration amplitude correction coefficient and phase correction coefficient;

[0032] (3) Using the vibration amplitude correction coefficient and phase correction coefficient, the vibration acceleration signal is amplitude compensated and the phase calibrated to obtain the calibrated vibration characteristic signal;

[0033] (4) Perform pressure fluctuation analysis and flow uniformity calculation on the hydraulic characteristic signal to obtain the hydraulic state characteristic signal;

[0034] (5) Extract the harmonic components of the current signal and analyze the driving characteristics to obtain the excitation characteristic signal;

[0035] (6) Based on the calibrated vibration characteristic signal, hydraulic state characteristic signal and excitation characteristic signal, multi-source data is synchronously fused to generate a multi-dimensional physical characteristic data stream.

[0036] Specifically, if Figure 2The figure shows a flow chart of vibration waveform calibration and signal distortion compensation in an embodiment of the present application. Signals are collected through multiple sensors, and multi-dimensional physical characteristic data streams are generated through data processing. A resonant vibration table is a test device used to simulate real-world vibration conditions. It generates controllable vibration excitation through electromagnetic or hydraulic drive and is used to evaluate the reliability, durability, and performance characteristics of the tested object in a vibration environment. In this method, a triaxial accelerometer mounted on the resonant vibration table collects vibration acceleration signals, a pressure flow sensor mounted on the servo hydraulic power source collects hydraulic characteristic signals, a current sensor mounted on the electromagnetic exciter collects excitation current signals, and a dynamic force sensor collects dynamic force signals, which are input into a signal acquisition device. The triaxial accelerometer can simultaneously measure vibration acceleration in the X, Y, and Z directions, providing comprehensive vibration status monitoring. The servo hydraulic power source is the power source of the resonant vibration table, and its pressure flow sensor can monitor the operating status of the hydraulic system. The electromagnetic exciter is the core component that generates vibration, and the current sensor can monitor changes in its operating current. The dynamic force sensor directly measures the magnitude and direction of the force generated during vibration. These four types of signals are converted to digital signals via a dedicated analog-to-digital converter and then input into a signal acquisition device. This device is a hardware device with integrated multi-channel synchronous acquisition capabilities, ensuring the time synchronization of signals from different sources. The signal acquisition device performs waveform distortion detection on the vibration acceleration signal to generate vibration amplitude and phase correction coefficients. Waveform distortion detection is the process of quantitatively analyzing the difference between the actual waveform of the acquired vibration acceleration signal and the ideal sinusoidal waveform. Ideally, the vibration generated by a resonant excitation table should be a pure sine wave. However, due to factors such as the nonlinear characteristics of the mechanical structure, assembly errors, and material fatigue, the actual vibration often exhibits distortion. Waveform distortion detection converts the time-domain signal into the frequency domain through a fast Fourier transform and analyzes its spectral characteristics. The total harmonic distortion ratio is then calculated to assess the impact of non-fundamental frequency components on the fundamental frequency component. Finally, phase spectrum analysis is used to test the stability and consistency of the signal phase. Based on the results of waveform distortion detection, the 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, the 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.

[0037] Using the vibration amplitude correction coefficient and phase correction coefficient, the vibration acceleration signal is amplitude compensated and phase calibrated to obtain a 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 bring it closer to the actual vibration level. Phase calibration refers to the process of adjusting the phase of the original vibration acceleration signal to align it with the ideal phase. Both processes are typically implemented using digital signal processing techniques, such as filter design and signal reconstruction. The calibrated vibration characteristic signal not only contains basic vibration characteristics such as frequency, amplitude, and phase, but also contains more comprehensive vibration modal information, such as resonant frequency, damping ratio, and mode shape. This characteristic information is an important basis for evaluating the operating condition of the resonant vibration table. Pressure fluctuation analysis and flow 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 temporal variation of the hydraulic system's operating pressure, including the extraction of pressure fluctuation amplitude, frequency, and periodicity. Flow uniformity calculation assesses the stability of hydraulic system flow. It quantifies flow uniformity by calculating statistical indicators such as flow variance, peak factor, and imbalance. The hydraulic state characteristic signal comprehensively describes the operating state of the hydraulic system, encompassing multiple indicators such as pressure stability, flow uniformity, and system response characteristics. These indicators directly reflect the operating state and performance level of the servo hydraulic power source.

[0038] The current signal is subjected to harmonic component extraction and drive characteristic analysis to obtain an excitation characteristic signal. Harmonic component extraction is the process of separating the fundamental and harmonic components from the current signal using spectrum analysis technology to evaluate the power supply quality and exciter linearity. Drive 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 drive efficiency. The excitation characteristic signal comprehensively reflects the operating status of the electromagnetic exciter, including multiple aspects such as the driving force generation capability, energy conversion efficiency, and temperature stability, providing a basis for evaluating exciter performance and predicting potential failures. Based on the calibrated vibration characteristic signal, hydraulic state characteristic signal, and excitation characteristic signal, multi-source data is synchronously fused to generate a multi-dimensional physical characteristic data stream. Multi-source data synchronous fusion is a technology that time-aligns, features, and comprehensively analyzes data from different sensors and different physical quantities. Time alignment ensures the temporal consistency of data from different sources, typically achieved through timestamp matching or interpolation resampling. Feature matching is the process of identifying interrelated feature points or feature intervals in different data sources, such as the correspondence between vibration peaks and current peaks. Comprehensive analysis, based on the results of time alignment and feature matching, uniformly analyzes and processes multi-source data to extract more comprehensive and in-depth information. A multidimensional physical feature data stream is a structured data format that continuously records and updates various physical characteristics of the system, including but not limited to vibration characteristics, hydraulic status, electrical properties, and thermodynamic properties, at predetermined time intervals and data formats. This provides comprehensive data support for condition monitoring, performance evaluation, and fault diagnosis of vibration test equipment.

[0039] Suppose that during a vibration test of an aviation component, the operating status of a resonant vibration table needs to be monitored in real time to ensure the reliability and accuracy of the test process. A triaxial accelerometer is installed on the vibration table surface to collect vibration acceleration signals in the X, Y, and Z directions, with a sampling frequency of 5 kHz to ensure that high-frequency vibration characteristics are captured. A pressure and flow sensor is installed at the output of the servo hydraulic power source to collect operating pressure and flow data, with a sampling frequency of 1 kHz. A current sensor is installed in the power supply circuit of the electromagnetic exciter to collect the excitation current signal, with a sampling frequency of 10 kHz. A dynamic force sensor is installed at the connection between the exciter and the table surface to collect the dynamic force signal during the vibration process, with a sampling frequency of 5 kHz. These signals are amplified, filtered, and converted to analog-to-digital by their respective signal conditioning circuits before being synchronously input to a signal acquisition device. The signal acquisition device performs waveform distortion detection on the vibration acceleration signals, converts the time-domain signals into the frequency domain using a fast Fourier transform, and analyzes their spectral characteristics. Testing revealed that the X-direction vibration acceleration signal, in addition to the intended 100Hz fundamental frequency component, also contained harmonic components at 200Hz and 300Hz, with a total harmonic distortion (THD) of 8%, indicating significant waveform distortion in the X direction. The Y-direction vibration acceleration signal was relatively pure, with a THD of only 2%. The Z-direction vibration acceleration signal exhibited significant phase jitter, indicating poor phase stability. Based on these analysis results, the X-direction vibration amplitude correction factor was calculated to be 1.12, indicating that the actual vibration amplitude was 12% smaller than the ideal value and required amplification. The Z-direction phase correction factor was -15°, indicating that the actual phase lagged 15° from the ideal and required advancement.

[0040] Using these correction coefficients, the original vibration acceleration signal was amplitude compensated and phase calibrated. For the X-direction vibration acceleration signal, each sampling point was multiplied by 1.12 to achieve amplitude compensation. For the Z-direction vibration acceleration signal, a Hilbert transform was used to convert it into an analytical signal, and its phase angle was adjusted to achieve a 15° phase advance. This process yielded a calibrated vibration characteristic signal, whose waveform more closely resembled an ideal sine wave, with significantly improved frequency stability and phase consistency. Simultaneously, pressure fluctuation analysis and flow uniformity calculations were performed on the hydraulic characteristic signal. The pressure fluctuation analysis revealed that during the test, the hydraulic system's operating pressure fluctuated around 10 MPa, with a maximum fluctuation amplitude of ±0.5 MPa and a fluctuation period of approximately 50 ms, which is close to the inverse of the excitation frequency, indicating a correlation between pressure fluctuations and the vibration process. Flow uniformity calculations revealed a coefficient of variation of 0.06, a peak factor of 3.2, and an imbalance of 4% for the hydraulic flow rate. These indicators were all within normal ranges, indicating that the hydraulic system's flow supply was relatively uniform and stable. Based on these analysis results, a hydraulic state characteristic signal is generated. This signal includes multiple hydraulic state characteristic parameters, including pressure mean, pressure fluctuation amplitude, pressure fluctuation frequency, flow mean, and flow rate variation coefficient. Harmonic component extraction and drive characteristic analysis are performed on the current signal. Harmonic component extraction reveals that the fundamental component of the current signal accounts for 92% of the total energy, the third harmonic accounts for 5%, the fifth harmonic accounts for 2%, and the remaining harmonic components account for 1%, indicating good power quality but with some harmonic interference. Drive characteristic analysis reveals a 5ms time delay between current change and vibration response, a 2.5g / A gain, and a 60° phase difference in the current-vibration transfer function around 100Hz. These parameters reflect the dynamic response characteristics of the electromagnetic exciter. Based on these analysis results, an excitation characteristic signal is generated, which describes the operating state and performance characteristics of the electromagnetic exciter. Multi-source data synchronization is performed based on the calibrated vibration characteristic signal, hydraulic state characteristic signal, and excitation characteristic signal. Data from different sources is aligned using timestamps to ensure consistency along the timeline. Correlation analysis then identifies relationships between different signals, such as the correspondence between vibration acceleration peaks and current peaks, and the relationship between pressure fluctuations and vibration frequency. Finally, feature extraction and data fusion algorithms, such as principal component analysis and Kalman filtering, combine information from different sources to generate a multidimensional physical feature data stream. This data stream continuously outputs the system's physical state parameters at a 100Hz update rate, including vibration acceleration in all directions, vibration frequency, phase relationships, harmonic content, hydraulic pressure, hydraulic flow, drive current, power consumption, temperature changes, and other indicators, providing comprehensive data support for monitoring, analysis, and evaluation of equipment operating status.

[0041] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0042] (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;

[0043] (2) The vibration signal stream is processed by Fourier transform, and the amplitude statistics and phase extraction of the transformed spectrum data are performed to generate the vibration acceleration spectrum;

[0044] (3) Perform pressure pulsation analysis on the hydraulic signal flow and obtain the hydraulic pressure spectrum by decomposing the pulsation waveform and reconstructing the amplitude;

[0045] (4) Analyze the excitation force characteristics of the excitation signal stream and form an excitation force spectrum through force vector decomposition and amplitude reorganization;

[0046] (5) Decompose the response characteristics of the dynamic force signal flow and obtain the dynamic response spectrum through signal reconstruction and frequency domain mapping;

[0047] (6) Based on the vibration acceleration spectrum, hydraulic pressure spectrum, excitation force spectrum and dynamic response spectrum, frequency domain correlation analysis and transfer characteristics calculation are performed to obtain the physical response characteristics.

[0048] Specifically, the multidimensional physical characteristic data stream refers to a comprehensive data stream collected from the resonant excitation table, servo hydraulic power source, electromagnetic exciter, and dynamic force sensor, after vibration waveform calibration and signal distortion compensation. This data stream contains a variety of physical characteristic information such as vibration, hydraulic pressure, 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 multidimensional physical characteristic 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 fluctuations and flow change characteristics; the excitation signal stream contains driving current and excitation force information; and the dynamic force signal stream contains the force applied to the test table and the transmission feedback information.

[0049] For processing vibration signal streams, the time domain signal is converted into a frequency domain signal through Fourier transform to obtain spectrum data. In spectrum analysis, the amplitude of each frequency point is statistically analyzed, and its peak value, mean, and variance are extracted. 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 hydraulic signal streams involves pressure pulsation analysis. The hydraulic pressure signal is decomposed into different frequency components through discrete wavelet transform, and the main frequency component and harmonic components are identified. The hydraulic pressure spectrum is then reconstructed based on the energy distribution of the frequency components. The hydraulic pressure spectrum reflects the operating status of the hydraulic system and can effectively identify abnormal fluctuations and pulsation characteristics of the hydraulic system.

[0050] Excitation signal stream processing primarily analyzes the characteristics of the excitation force, decomposing the excitation force into three components along the X, Y, and Z axes according to their direction. The amplitude and phase relationships of the components in each direction are analyzed, and then the excitation force spectrum is formed through vector synthesis and recombination. The excitation force spectrum characterizes the excitation capability and directional characteristics of the test equipment and is an important indicator for evaluating the excitation performance of the equipment. Dynamic force signal stream processing decomposes the dynamic force signal into three components: elastic response, damping response, and inertial response through response characteristic decomposition. The dynamic response spectrum is then obtained through frequency domain transformation and reconstruction techniques. The dynamic response spectrum reflects the response characteristics of the test equipment to external excitation and demonstrates the dynamic performance of the equipment at different frequencies.

[0051] 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 input and output is analyzed to obtain the physical response characteristics. The physical response characteristics include key indicators such as the test equipment's transfer function, coherence function, resonance point distribution, and frequency response function, and comprehensively reflect the operating status of the vibration test equipment.

[0052] Taking a certain type of vibration testing equipment as an example, during equipment operational status monitoring, data from a triaxial accelerometer mounted on a resonant excitation table is collected. After distortion compensation, this data forms a vibration signal stream. Fast Fourier transform (FFT) is applied to the vibration signal stream to obtain spectral data in the frequency range of 5-2000Hz. Spectral analysis identifies significant vibration peaks at 78Hz, 126Hz, and 513Hz, with amplitudes of 2.5g, 1.8g, and 0.9g, respectively, and phase angles of 27°, 43°, and 85°, respectively. This generates a vibration acceleration spectrum. Simultaneously, wavelet decomposition is performed on the pressure signal collected from the servo hydraulic power source, identifying a pressure pulsation with a dominant frequency component at 12Hz and an amplitude of 1.2MPa. This is then reconstructed to form a hydraulic pressure spectrum. The current signal collected from the electromagnetic exciter is converted into an excitation force signal, decomposed into a horizontal force of 0.8kN and a vertical force of 1.5kN, and reconstructed to form an excitation force spectrum. The dynamic force sensor data was decomposed through response characteristics, identifying the system's resonant response near 150 Hz and forming a dynamic response spectrum. A comprehensive analysis of these four spectra was conducted to calculate a frequency domain correlation coefficient matrix. The correlation coefficient between the vibration acceleration spectrum and the excitation force spectrum was 0.87, indicating a high correlation between the two. The correlation coefficient between the hydraulic pressure spectrum and the dynamic response spectrum was 0.62, indicating that the hydraulic system state has a certain influence on the dynamic response. Transfer function calculations were used to determine the device's frequency response function in the 20-1000 Hz frequency range, identifying resonance points in the 75-85 Hz and 510-520 Hz frequency bands.

[0053] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0054] (1) Compare the input and output of the excitation force spectrum and dynamic response spectrum in the physical response characteristics, and extract the vibration transmissibility data through transfer function analysis;

[0055] (2) Perform frequency band segmentation statistics on the vibration transmissibility data and obtain the exciting force vector of each frequency band through bandwidth analysis;

[0056] (3) Decompose the hydraulic pressure spectrum in the frequency domain and determine the system resonance frequency band through pressure pulsation characteristic analysis;

[0057] (4) Extract the peak features of the vibration acceleration spectrum and calculate the dynamic damping ratio through the resonance characteristics;

[0058] (5) Correlation analysis is performed between the vibration transmissibility data and the dynamic damping ratio, and the frequency response function is generated through dynamic stiffness calculation;

[0059] (6) The system characteristics are synthesized based on the exciting force vector, resonance frequency band, dynamic damping ratio and frequency response function to obtain the test calibration benchmark.

[0060] Specifically, comparing the excitation force spectrum and dynamic response spectrum in the physical response characteristics involves using the excitation force spectrum as the system input signal and the dynamic response spectrum as the system output signal, and calculating the transfer function based on the ratio of the two. The transfer function describes the system's response to the input signal. Vibration transmissibility data is obtained by calculating the output-to-input ratio at each frequency point. Vibration transmissibility data reflects the vibration test equipment's ability to transmit the excitation signal at each frequency point and is a fundamental indicator for evaluating equipment performance. Frequency band segmentation of vibration transmissibility data involves dividing the full-band transmissibility data into low-frequency bands (typically 5-100Hz), mid-frequency bands (100-500Hz), and high-frequency bands (500-2000Hz). The average transmissibility, standard deviation, and coefficient of variation are calculated for each frequency band. Bandwidth analysis calculates the effective bandwidth and cutoff frequency of each frequency band. Combined with the directional characteristics of the transmissibility data, the excitation force vector for each frequency band is obtained. The excitation force vector contains information about the force magnitude and direction in each frequency band, reflecting the excitation characteristics of the equipment in different frequency ranges.

[0061] Frequency-domain decomposition of the hydraulic pressure spectrum involves performing spectral analysis on the hydraulic system's pressure fluctuation signal to identify the primary and harmonic components. By analyzing the frequency distribution and energy concentration areas of the pressure pulsations, combined with the inherent characteristics of the hydraulic system, the system's resonant frequency band is determined. The resonant frequency band refers to the frequency range within which equipment is prone to resonance, typically manifested by a significant increase in the pressure fluctuation amplitude and a sharp phase shift. Identifying the resonant frequency band is crucial for preventing equipment damage caused by resonance. Peak feature extraction from the vibration acceleration spectrum involves identifying peak points within the spectrum and analyzing their frequency location, amplitude, and bandwidth. The quality factor and bandwidth of the peak points are calculated using the half-power point method. Combined with the system's inherent frequency characteristics, the dynamic damping ratio is then calculated. The dynamic damping ratio characterizes the system's ability to dissipate energy. A larger damping ratio results in faster vibration damping and improved stability.

[0062] Correlating vibration transmissibility data with dynamic damping ratios involves establishing a mathematical relationship between the two and analyzing the correlation between changes in transmissibility and damping characteristics. By calculating dynamic stiffness parameters at each frequency point, including composite stiffness based on mass, stiffness, and damping, a frequency response function is generated. The frequency response function comprehensively describes the system's dynamic characteristics in the frequency domain, encompassing both amplitude-frequency and phase-frequency characteristics, and serves as a comprehensive indicator for evaluating the system's dynamic performance. System performance synthesis based on the excitation force vector, resonant frequency band, dynamic damping ratio, and frequency response function involves weighting and integrating these parameters to establish a multidimensional evaluation system. By calculating the standardized values ​​and weighting coefficients of each parameter, a comprehensive system performance score is generated. This score is then combined with the calibration accuracy requirements of the equipment to determine a test calibration benchmark. This benchmark includes the equipment's performance indicators and allowable deviation ranges in each frequency band and serves as a reference for real-time monitoring of the equipment's operating status.

[0063] Taking a certain type of vibration testing equipment as an example, when establishing a test calibration benchmark, the excitation force spectrum data (for example, an excitation force of 1.2kN and a phase of 30° at 50Hz) was compared with the dynamic response spectrum data (a response of 0.9g and a phase of 45° at the same frequency point). The transmissibility was calculated to be 0.75g / kN, with a phase difference of 15°. Statistics of the transmissibility data across all frequency bands revealed an average transmissibility of 0.80g / kN with a standard deviation of 0.05 in the low-frequency range (5-100Hz); 0.65g / kN with a standard deviation of 0.08 in the mid-frequency range (100-500Hz); and 0.45g / kN with a standard deviation of 0.12 in the high-frequency range (500-2000Hz). Bandwidth analysis determined the effective bandwidth of the low-frequency band to be 85Hz, the effective bandwidth of the mid-frequency band to be 350Hz, and the effective bandwidth of the high-frequency band to be 1200Hz. The excitation force vector characteristics of each frequency band were then derived. Analysis of the hydraulic pressure spectrum identified significant pressure pulsation peaks in the 35-45Hz and 120-140Hz frequency bands, accounting for 28% and 35% of the energy, respectively. These two frequency bands were identified as the system's primary resonant frequency bands. Peak features were extracted from the vibration acceleration spectrum, with a peak of 2.3g at 38Hz and a bandwidth of 4Hz; and a peak of 1.8g at 132Hz and a bandwidth of 6Hz. The corresponding dynamic damping ratios were calculated to be 0.053 and 0.068, respectively.

[0064] Correlating vibration transmissibility data with dynamic damping ratio revealed a negative correlation between transmissibility and damping ratio near the resonant frequency. Dynamic stiffness at 38 Hz was calculated to be 15.6 kN / mm, and at 132 Hz to be 20.3 kN / mm, generating a frequency response function covering the 5-2000 Hz frequency range. Ultimately, based on a comprehensive evaluation of the excitation force vector, resonant frequency band, dynamic damping ratio, and frequency response function, the equipment's calibration benchmark was determined. This included standard transmissibility values ​​and allowable deviation ranges for low, medium, and high frequency bands, as well as standard damping ratio values ​​and dynamic stiffness reference values ​​at the resonant frequency. This provided a scientific benchmark for real-time monitoring of equipment operating status.

[0065] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0066] (1) Compare the frequency response function in the test calibration benchmark with the physical response characteristics collected in real time, and obtain the vibration transfer accuracy through transfer function deviation analysis;

[0067] (2) Perform real-time response analysis on the resonance frequency band and obtain the dynamic response bandwidth by frequency band scanning;

[0068] (3) Compare the phase of the excitation force vector with the real-time physical response characteristics, and determine the resonant frequency offset through resonance characteristic analysis;

[0069] (4) Compare the signals based on the dynamic damping ratio and the real-time physical response characteristics, and calculate the phase delay characteristics through the phase characteristics;

[0070] (5) Perform system characteristic analysis and state quantification on the vibration transmission accuracy, dynamic response bandwidth, resonance frequency offset and phase delay characteristics to obtain the test state parameters.

[0071] Specifically, comparing the frequency response function in the test calibration benchmark with the real-time collected physical response characteristics involves using the preset frequency response function in the calibration benchmark as a reference standard and performing a quantitative comparison with the physical response characteristics collected during the current device operation. A frequency response function is a mathematical expression that describes the relationship between a system's input and output, characterizing the device's response characteristics at different frequencies. Transfer function deviation analysis evaluates the current device's transfer performance by calculating the difference between the real-time frequency response function and the calibration benchmark's frequency response function. Vibration transfer accuracy refers to the degree of conformity between the device's actual transfer characteristics and the calibration benchmark, typically expressed as root mean square error (RMS) or relative deviation percentage. Real-time resonant frequency band response analysis dynamically monitors the device's response within the resonant frequency band defined in the calibration benchmark. A resonant frequency band is a frequency range within which a device is prone to resonance, typically manifested as a significant increase in response amplitude. Frequency band scanning analyzes the response characteristics of each frequency point within the resonant frequency band point by point, determining the actual bandwidth of the current resonant frequency band by calculating the response amplitude and phase at each frequency point. The dynamic response bandwidth refers to the effective response frequency range of the device in a resonant state, reflecting the device's frequency selectivity and filtering characteristics.

[0072] Comparing the phase of the excitation force vector with the real-time physical response characteristics and performing resonance characteristic analysis is an important step in determining the resonant frequency shift. The excitation force vector contains the magnitude and direction of the excitation force, while phase comparison analyzes the phase relationship between the input excitation signal and the output response signal. Resonance characteristic analysis is a specialized analysis based on the phase characteristics of the system near the resonant point, which can be expressed using the following mathematical model:

[0073]

[0074] in, Indicates the resonant frequency offset in Hz; Indicates the number of analysis frequency points; represents the weight coefficient of the i-th frequency point; Indicates real-time physical response at frequency The phase angle at , in degrees; Indicates the calibration reference mid-frequency point The phase angle at , in degrees; Indicates frequency The phase sensitivity coefficient at reflects the sensitivity of the phase change to the resonance frequency.

[0075] Signal comparison based on the dynamic damping ratio and real-time physical response characteristics analyzes changes in the system's energy dissipation capacity by comparing the current system's damping characteristics with those of the calibration benchmark. The dynamic damping ratio is a dimensionless parameter that characterizes the system's vibration attenuation characteristics; a larger value indicates faster system attenuation. Phase characteristic calculation analyzes the phase delay characteristics of the system response and is used to assess the system's phase delay. Phase delay refers to the phase lag of the system's output response relative to the input stimulus, reflecting the system's time-domain response delay characteristics. System characteristic analysis and state quantification of vibration transfer accuracy, dynamic response bandwidth, resonant frequency offset, and phase delay characteristics comprehensively consider these four parameters and process them using specific mathematical models or statistical methods to obtain quantitative indicators that comprehensively reflect the device's current operating status. System characteristic analysis comprehensively evaluates the device's transfer characteristics, frequency band characteristics, resonance characteristics, and phase characteristics, while state quantification converts the analysis results into quantifiable and comparable numerical indicators. Test state parameters are a set of quantitative parameters that reflect the device's current operating status and serve as the data foundation for subsequent performance evaluation.

[0076] Taking a certain model of electromagnetic vibration table as an example, during real-time condition monitoring, frequency response function data is extracted from the calibration benchmark. This function exhibits specific amplitude-frequency and phase-frequency characteristics within the 20-2000Hz frequency range. Simultaneously, the physical response characteristics of the current device are collected, including the response amplitude and phase angle at each frequency point. The difference between the two at each frequency point is calculated to obtain vibration transmission accuracy data. Specifically, the relative deviation at each frequency point is calculated to form a deviation distribution curve. The average and maximum deviations are then statistically analyzed to obtain an overall transmission accuracy evaluation index. For resonant frequency band analysis, the calibration benchmark defines two resonant frequency bands: 75-85Hz and 510-520Hz. During real-time monitoring, these two frequency bands are subdivided and scanned, with response data collected every 0.5Hz. The response amplitude trends are analyzed to determine the current half-power bandwidth (HPFB) and calculate the dynamic response bandwidth. If the 75-85Hz resonant bandwidth in the calibration benchmark is 8Hz, while the real-time measured bandwidth is 10Hz, this indicates a change in the device's frequency band selectivity. In the resonance characteristic analysis, the phase characteristics of the excitation force vector in the calibration benchmark are compared with the physical response characteristics collected in real time. The resonant frequency shift is calculated using the above mathematical model, where the weight coefficient According to the importance of frequency setting, phase sensitivity coefficient The derivation is based on the system's resonant characteristics. For example, if the resonant frequency in the calibration benchmark is 80 Hz, and the offset calculated through phase contrast analysis is 2.5 Hz, the current resonant frequency is 82.5 Hz, indicating a change in the device characteristics. Regarding the phase delay characteristic, the dynamic damping ratio in the calibration benchmark (e.g., 0.05) is compared with the damping ratio measured in real time (e.g., 0.06), and the phase angle trend is combined to calculate the phase delay characteristic. If, at a specific frequency point, the calibration benchmark phase angle is 30°, while the real-time phase angle is 40°, the phase delay has increased by 10°, reflecting an increase in system response time. A comprehensive analysis is performed on four parameters: vibration transmission accuracy, dynamic response bandwidth, resonant frequency offset, and phase delay characteristics. By assigning weights to each parameter, a comprehensive score is calculated and quantified as a test status parameter. For example, the relative deviation of vibration transmission accuracy, the rate of change of dynamic response bandwidth, the percentage offset of resonant frequency, and the increment of phase delay are weighted and summed to generate a comprehensive device status index. This index is used to assess the overall operating status of the current device and provide data for equipment maintenance and performance adjustments.

[0077] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0078] (1) Analyze the deviation between the vibration transfer accuracy in the test state parameters and the calibration accuracy level of the vibration test equipment, and calculate the transfer function accuracy through the transfer function error;

[0079] (2) Analyze the bandwidth matching of the dynamic response bandwidth and the resonant frequency offset, and obtain the dynamic characteristic matching by comparing the frequency response characteristics;

[0080] (3) Perform timing stability analysis on the phase delay characteristics and obtain timing matching parameters through phase offset statistics;

[0081] (4) Grading the transfer function accuracy and obtaining the functional reliability index through accuracy grade analysis;

[0082] (5) Comprehensively evaluate the dynamic characteristics matching degree and timing matching parameters, and obtain the performance reliability index through system characteristics analysis;

[0083] (6) Conduct a comprehensive analysis based on the functional reliability index and performance reliability index to generate a quality assessment report that includes transfer function accuracy, dynamic characteristics matching and equipment reliability level.

[0084] Specifically, analyzing the deviation between the vibration transfer accuracy in the test state parameters and the calibration accuracy level of the vibration test equipment involves comparing the vibration transfer accuracy data obtained from real-time monitoring with the accuracy level standards determined at the time of factory shipment or during regular calibration. Vibration transfer accuracy refers to the degree of conformity between the equipment's actual transfer characteristics and its ideal transfer characteristics, while the calibration accuracy level refers to the accuracy standard the equipment should meet. These levels are typically categorized as A, B, and C, each corresponding to a different allowable error range. Transfer function error calculation quantitatively evaluates the equipment's current transfer performance by comparing the difference between the actual transfer function and the standard transfer function. Transfer function accuracy is a metric reflecting the accuracy of the equipment's transfer function, typically expressed as relative error or absolute error. Bandwidth matching analysis of the dynamic response bandwidth and resonant frequency offset involves comparing the dynamic response bandwidth and resonant frequency offset obtained from real-time monitoring with their standard values. The dynamic response bandwidth refers to the effective response frequency range of the equipment in its resonant state, while the resonant frequency offset refers to the difference between the current resonant frequency and the standard resonant frequency. Bandwidth matching analysis evaluates the stability of the equipment's frequency response characteristics by calculating the rate of change of the dynamic response bandwidth and the degree of resonant frequency offset. Frequency response comparison is a point-by-point comparison of a device's current frequency response characteristics with the standard characteristics to analyze their consistency. Dynamic characteristics matching refers to the degree of conformity between a device's current dynamic characteristics and the standard characteristics, and is a key metric for evaluating a device's frequency response performance.

[0085] Performing timing stability analysis on phase delay characteristics quantitatively assesses the time stability of a device's phase delay characteristics. Phase delay refers to the phase lag of a system's output response relative to the input stimulus, reflecting the system's time-domain response delay characteristics. Timing stability analysis assesses the stability of a device's timing characteristics by statistically analyzing the changes in phase delay characteristics at different time points. Phase offset statistics process phase offset data at different frequencies and times to generate statistical parameters reflecting overall phase stability. Timing matching parameters quantify the degree to which a device's timing characteristics match standard characteristics and typically include metrics such as phase stability coefficient and time delay consistency. Transfer function accuracy is graded into different levels based on the transfer function's accuracy range. Accuracy grade analysis determines the evaluation criteria and allowable error ranges for different accuracy grades based on the device's actual application requirements and industry standards. Functional reliability indicators are quantitative indicators that reflect the reliability of a device's basic functions. They are typically determined based on the transfer function accuracy grade and include aspects such as functional integrity, stability, and consistency.

[0086] Comprehensively evaluating the dynamic characteristics matching degree and timing matching parameters refers to comprehensively considering these two key indicators that characterize the dynamic and timing performance of a device, and obtaining a comprehensive evaluation that reflects the overall performance of the device through a specific mathematical model or weight distribution method. System characteristics analysis is a comprehensive, multi-dimensional, and multi-angle analysis of the dynamic and timing characteristics of a device, evaluating the interrelationships and overall coordination between these characteristics. Performance reliability indicators are quantitative indicators that reflect the long-term stability of device performance. They are typically determined based on the dynamic characteristics matching degree and timing matching parameters, and include aspects such as performance stability, durability, and adaptability. Comprehensive analysis based on functional reliability indicators and performance reliability indicators integrates indicators reflecting the two dimensions of device function and performance to obtain a comprehensive evaluation that fully reflects the quality status of the device. A quality assessment report is a comprehensive report that includes key indicators such as transfer function accuracy, dynamic characteristics matching degree, and device reliability level, and is used to guide equipment maintenance and usage decisions.

[0087] Taking a large hydraulic shaker as an example, during performance degradation analysis, the vibration transfer accuracy data obtained from real-time monitoring was compared with the device's calibration accuracy level. The device's calibration accuracy level is Class A, requiring a transfer function relative error of no more than ±5% in the 20-1000Hz frequency band and no more than ±8% in the 1000-2000Hz band. By calculating the error between the actual transfer function and the standard transfer function at each frequency point, an error distribution curve was generated. The statistical results showed an average relative error of 4.8% in the 20-1000Hz band, with a maximum relative error of 6.2% occurring at 950Hz. The average relative error in the 1000-2000Hz band was 7.3%, with a maximum relative error of 9.1% occurring at 1850Hz. Based on these error statistics, the current device's transfer function accuracy was determined to be A- (slightly below Class A). For dynamic characteristics matching analysis, the current dynamic response bandwidth and resonant frequency offset were compared with the standard values. Under standard conditions, the device's resonant bandwidth at 80Hz is 5Hz, while the currently measured bandwidth is 6.2Hz, with a bandwidth variation rate of 24%. The standard resonant frequency is 80Hz, but the current measured value is 82.3Hz, with a frequency offset of 2.3Hz and a relative offset rate of 2.88%. Comparative analysis of frequency response characteristics revealed matching coefficients of 0.92, 0.87, and 0.83 for the low-frequency band (20-200Hz), mid-frequency band (200-800Hz), and high-frequency band (800-2000Hz), respectively, for a combined dynamic characteristics matching degree of 0.89, indicating that the device's overall dynamic characteristics remain good, though with slight attenuation.

[0088] In the timing stability analysis, phase delay characteristics were statistically analyzed. Under standard conditions, the device's average phase delay across the entire frequency band was 15°. The current measured average phase delay was 18.5°, an increase of 3.5°. Statistical analysis of phase offsets at different frequencies resulted in a calculated phase stability coefficient of 0.85 and a delay consistency coefficient of 0.88. The resulting timing matching parameter was 0.86, indicating a slight degradation in the device's timing characteristics, but still within acceptable limits. Based on the transfer function accuracy grading system, industry standards categorize the accuracy levels into S (Excellent), A (Good), B (Fair), and C (Poor). Based on the transfer function accuracy assessment results, the device currently ranks at the A- level, with a functional integrity coefficient of 0.92, a stability coefficient of 0.88, and a consistency coefficient of 0.90. The resulting functional reliability index is 0.90, indicating that the device's basic functions remain highly reliable.

[0089] A comprehensive evaluation of the dynamic characteristics matching degree (0.89) and the timing matching parameter (0.86) was conducted. Considering the slightly higher importance of dynamic characteristics to overall performance, a weighting of 6:4 was assigned, resulting in a performance reliability index of 0.878, indicating that the device's overall performance is good but showing a slight decline. A comprehensive analysis of the functional reliability index (0.90) and the performance reliability index (0.878) was conducted. Considering their comparable contributions to device quality, the arithmetic average was used to obtain a comprehensive device quality index of 0.889. Based on this index, a quality assessment report was generated, including transfer function accuracy (A-grade), dynamic characteristics matching degree (0.89), and device reliability grade (A grade). Device calibration and parameter adjustment are recommended within the next three months to ensure continued good performance.

[0090] In a specific embodiment, the process of performing the step of index grading of the transfer function accuracy may specifically include the following steps:

[0091] (1) Separate the three-dimensional data of the transfer function accuracy into low-frequency, mid-frequency, and high-frequency bands according to the standard frequency range, and obtain the full-band accuracy feature map through spectrum distribution statistics;

[0092] (2) Perform a frequency band uniformity scan on the full-band accuracy characteristic diagram, and obtain the frequency band consistency index through frequency point density calculation and amplitude fluctuation analysis;

[0093] (3) Perform transfer characteristic analysis based on the frequency band consistency index, and obtain the frequency band stability parameters through frequency band response curve fitting and phase offset calculation;

[0094] (4) Dynamically compare the frequency band stability parameters with the vibration test equipment calibration benchmark, and obtain the reliability quantitative index through accuracy level mapping and deviation accumulation statistics;

[0095] (5) Evaluate the performance loss of the reliability quantitative indicators and obtain the functional integrity coefficient through attenuation characteristic analysis and stability verification;

[0096] (6) 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.

[0097] Specifically, the transfer function accuracy is separated into three-dimensional data for low, mid, and high frequency bands according to standard frequency ranges, and a full-band accuracy characteristic diagram is obtained through spectrum distribution statistics. Transfer function accuracy refers to the degree of agreement between the actual transfer function and the standard transfer function, reflecting the performance status of the device. Three-dimensional data separation is the process of classifying and organizing transfer function accuracy data according to frequency, amplitude, and phase dimensions. The low frequency band generally refers to 0-100Hz, the mid frequency band is 100-500Hz, and the high frequency band is 500-2000Hz. Spectral distribution statistics are statistical analysis of the accuracy data within each frequency band to obtain characteristic quantities such as mean, standard deviation, and maximum deviation. The full-band accuracy characteristic diagram is a graphical representation that intuitively displays the distribution characteristics of transfer function accuracy in different frequency bands. In practice, for a vibration test of an aircraft engine blade, transfer function accuracy data was obtained. The test frequency band of 10-2000Hz was then divided into three intervals: low frequency (10-100Hz), mid frequency (100-500Hz), and high frequency (500-2000Hz). The mean and standard deviation of the transfer function accuracy within each interval were calculated, forming a three-dimensional frequency-accuracy-standard deviation dataset. The accuracy distribution characteristics across the entire frequency band were visually displayed as a heat map. A frequency band uniformity scan was performed on the full-band accuracy characteristic map, and the frequency band consistency index was obtained through frequency point density calculation and amplitude fluctuation analysis. Frequency band uniformity scanning involves scanning and analyzing the accuracy characteristic map to assess the uniformity of the accuracy distribution within each frequency band. Frequency point density calculation is the process of determining the number of valid test points within a frequency band. Amplitude fluctuation analysis is a method for evaluating the variation of the accuracy amplitude within the frequency band. The frequency band consistency index is a parameter that measures the consistency of the transfer function accuracy distribution within each frequency band. Taking the spacecraft hatch hinge test as an example, the mean accuracy in the low-frequency band (20-120Hz) is 0.95, and the standard deviation is 0.03, indicating that the accuracy distribution is relatively stable; the frequency point density in the mid-frequency band (120-600Hz) is uneven, and the density decreases in the 340-420Hz range. At the same time, the accuracy fluctuation increases, and the standard deviation rises to 0.08. The overall frequency band consistency index is calculated to be 0.88, reflecting certain performance anomalies in the mid-frequency band.

[0098] Transfer characteristic analysis is performed based on the frequency band consistency index. Band response curve fitting and phase offset calculation are used to obtain the band stability parameter. Transfer characteristic analysis is the process of conducting an in-depth study of the system's transfer characteristics. Band response curve fitting uses a mathematical model to describe how the response characteristics vary with frequency. Phase offset calculation determines the trend and magnitude of phase variation. Band stability parameters are indicators of system response stability. During vibration testing of a helicopter drive shaft, band consistency indicators revealed anomalies in the 400-550Hz range. A detailed analysis of this range revealed an anomaly using a fifth-order polynomial to fit the frequency response curve. The fitting error was calculated to be 0.056, and the phase offset was 23 degrees. A comprehensive evaluation revealed a band stability parameter of 0.82, below the standard value of 0.90, indicating stability issues in this frequency band and requiring further investigation. The band stability parameters were dynamically compared with the vibration test equipment calibration benchmark. Reliability metrics were then derived through accuracy level mapping and cumulative deviation statistics. Dynamic comparison is the process of comparing current parameters with standard parameters in real time; precision level mapping is the method of mapping continuous precision values ​​to discrete levels; deviation accumulation statistics is the method of calculating the sum of deviations at each frequency point; and reliability quantification is a parameter used to quantitatively evaluate equipment reliability. In a commercial aircraft landing gear shock absorber test, the measured frequency band stability parameter of 0.87 was compared with the standard value of 0.93, and the calculated relative deviation was 6.5%, corresponding to an accuracy level of B (allowable deviation range of 5%-10%). By accumulating deviations at 35 key frequency points, the total deviation was 2.31, with an average deviation of 0.066. Based on this, the reliability quantification index was calculated to be 0.91, which met the equipment maintenance threshold but did not reach the alarm threshold, indicating that the equipment is in an acceptable state but requires attention.

[0099] Reliability metrics are evaluated for performance degradation, and the functional integrity coefficient is derived through attenuation characteristic analysis and stability verification. Performance degradation assessment is the process of analyzing the degree of equipment performance degradation; attenuation characteristic analysis is a method for studying changes in a system's damping characteristics; stability verification is a method for verifying system stability; and the functional integrity coefficient is a parameter that characterizes the functional integrity of a device. During vibration testing of satellite solar panels, time series analysis revealed that the reliability metric decreased from 0.94 to 0.89 over the past 30 days, resulting in a calculated performance degradation rate of 0.17% per day. System stability was assessed through step response testing, revealing a 12% increase in overshoot and a 0.8-second increase in stabilization time. Taking these factors into account, the functional integrity coefficient was calculated to be 0.86, below the standard threshold of 0.90, indicating that the equipment requires maintenance and adjustment, particularly for the control and hydraulic systems. The frequency band consistency index, frequency band stability parameters, and functional integrity coefficient are weighted and integrated to generate a comprehensive reliability evaluation to determine the functional reliability index. Weighted fusion processing combines multiple parameters according to different weights; comprehensive reliability evaluation is a multi-dimensional method for assessing system reliability; and the functional reliability index is a comprehensive indicator that characterizes the overall reliability level of a system. In the automotive chassis vibration durability test, the frequency band consistency index was 0.91, the frequency band stability parameter was 0.85, and the functional integrity coefficient was 0.88. Considering that the mid-frequency band (200-400Hz) has the greatest impact on the test results, the frequency band stability parameter was given a higher weight of 0.5, while the frequency band consistency index and the functional integrity coefficient were given weights of 0.3 and 0.2, respectively. The weighted average calculation yielded a functional reliability index of 0.875, which is in the "good" category but close to the "need maintenance" threshold. It is recommended that equipment calibration and necessary maintenance be performed after the current test to ensure the accuracy and reliability of the next round of testing. This method not only comprehensively assesses the functional status of vibration test equipment but also promptly identifies potential issues, avoids test errors caused by equipment performance degradation, and improves the credibility and consistency of test results.

[0100] The above describes the operating status monitoring method for the vibration test equipment in the embodiment of the present application. The following describes the operating status monitoring system for the vibration test equipment in the embodiment of the present application. Figure 3 In one embodiment of the present application, an operating status monitoring system for a vibration test device includes:

[0101] A generation module is used to generate 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;

[0102] a calculation module, configured to perform frequency response function analysis and dynamic transmissibility calculation on the multi-dimensional physical characteristic data stream to obtain physical response characteristics including an excitation force spectrum, a hydraulic pressure spectrum, a vibration acceleration spectrum, and a dynamic response spectrum;

[0103] An establishment module is used to establish a test calibration benchmark including an exciting force vector, a resonance frequency band, a dynamic damping ratio, and a frequency response function based on the physical response characteristics through vibration transmissibility analysis and dynamic stiffness calculation;

[0104] an identification module for performing resonance transfer analysis and resonance bandwidth identification on the physical response characteristics collected in real time according to the test calibration benchmark, and outputting test state parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics;

[0105] The analysis module is used to perform performance degradation analysis based on the test state 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.

[0106] Through the coordinated cooperation of the above components, the vibration waveform calibration and signal distortion compensation processing are performed on the operating parameters collected by the resonant excitation table, servo hydraulic power source, electromagnetic exciter and dynamic force sensor to generate a multi-dimensional physical characteristic data stream, which realizes the comprehensive collection and effective integration of equipment operation data, and provides a high-quality data foundation for subsequent analysis; by performing frequency response function analysis and dynamic transmissibility calculation on the multi-dimensional physical characteristic data stream, the physical response characteristics including the excitation force spectrum, hydraulic pressure spectrum, vibration acceleration spectrum and dynamic response spectrum are obtained, so that the physical state of the equipment is fully characterized; based on the physical response characteristics, through vibration transmissibility analysis and dynamic stiffness calculation, a physical response spectrum is established. A test calibration benchmark including the exciting force vector, resonance frequency band, dynamic damping ratio and frequency response function is developed, providing a scientific reference for equipment performance evaluation. Based on the test calibration benchmark, the real-time collected physical response characteristics are subjected to resonance transfer analysis and resonance bandwidth identification, and the test state parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset and phase delay characteristics are output, realizing real-time and accurate evaluation of the equipment status. Based on the test state parameters, the performance attenuation analysis is carried out in combination with the calibration accuracy level of the vibration test equipment, and a quality assessment report including transfer function accuracy, dynamic characteristic matching and equipment reliability level is generated, so that the equipment health status can be visualized, quantified and standardized. This solution has achieved significant results in the application of artificial intelligence algorithms. This is primarily reflected in the intelligent fusion processing algorithm for multi-source data, which transforms discrete signals into a unified physical characteristic data stream, enhancing data integrity and relevance. The adaptive spectrum decomposition algorithm in frequency response function analysis automatically adjusts analysis parameters based on signal characteristics in different frequency bands, improving the accuracy and efficiency of spectrum analysis. The machine learning-enhanced matrix operation method used in dynamic transmissibility calculation dynamically optimizes transmissibility characteristics based on historical data, reducing calculation errors. Pattern recognition technology applied during the establishment of the test calibration benchmark automatically identifies system resonance characteristics and abnormal conditions, improving benchmark accuracy. The online learning algorithm used in the real-time monitoring phase continuously optimizes judgment thresholds as equipment age increases, making monitoring more accurate. The trend prediction model in performance degradation analysis, through time series feature extraction and regression analysis, predicts equipment performance degradation. This integrated application of artificial intelligence algorithms significantly enhances the intelligence and predictive capabilities of vibration test equipment condition monitoring, providing strong support for scientific management and precise maintenance of equipment while ensuring the reliability and consistency of test results, meeting the technical requirements of high-precision testing.

[0107] Reference Figure 4 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4As shown. The computer device includes a processor, memory, display screen, input device, network interface and database connected via a system bus. 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 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.

[0108] Those skilled in the art will understand that Figure 4 The structure shown in the figure is merely a block diagram of a portion of the structure 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.

[0109] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-described method when executed by a processor. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0110] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0111] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0112] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0113] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring the operating status of a vibration test device, characterized in that: The vibration test equipment operation status monitoring method includes: Based on the operating parameters collected from the resonant excitation table, servo hydraulic power source, electromagnetic exciter and dynamic force sensor of the vibration test equipment, a multi-dimensional physical characteristic data stream is generated through vibration waveform calibration and signal distortion compensation processing; Performing frequency response function analysis and dynamic transmissibility calculation on the multi-dimensional physical characteristic data stream to obtain physical response characteristics including an excitation force spectrum generated by an electromagnetic exciter, a hydraulic pressure spectrum of a servo hydraulic power source, a vibration acceleration spectrum of a resonant excitation table, and a dynamic response spectrum detected by a dynamic force sensor; Based on the physical response characteristics, a test calibration benchmark including an exciting force vector, a resonance frequency band, a dynamic damping ratio and a frequency response function is established through vibration transmissibility analysis and dynamic stiffness calculation, including: performing input and output comparison on the exciting force spectrum and the dynamic response spectrum in the physical response characteristics, and extracting vibration transmissibility data through transfer function analysis; performing frequency band segmentation statistics on the vibration transmissibility data, and obtaining the exciting force vector of each frequency band through bandwidth analysis; performing frequency domain decomposition on the hydraulic pressure spectrum, and determining the system resonance frequency band through pressure pulsation characteristic analysis; performing peak feature extraction on the vibration acceleration spectrum, and obtaining the dynamic damping ratio through resonance characteristic calculation; performing correlation analysis on the vibration transmissibility data and the dynamic damping ratio, and generating a frequency response function through dynamic stiffness calculation; performing system characteristic synthesis based on the exciting force vector, resonance frequency band, dynamic damping ratio and frequency response function, and obtaining the test calibration benchmark; According to the test calibration benchmark, the physical response characteristics collected in real time are subjected to resonance transfer analysis and resonance bandwidth identification, and the test state parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset and phase delay characteristics are output, including: comparing the frequency response function in the test calibration benchmark with the physical response characteristics collected in real time, and obtaining the vibration transfer accuracy through transfer function deviation analysis; performing real-time response analysis on the resonance frequency band, and obtaining the dynamic response bandwidth through frequency band scanning; performing phase comparison between the excitation force vector and the real-time physical response characteristics, and determining the resonance frequency offset through resonance characteristic analysis; performing signal comparison based on the dynamic damping ratio and the real-time physical response characteristics, and obtaining the phase delay characteristics through phase characteristic calculation; performing system characteristic analysis and state quantification on the vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset and phase delay characteristics, and obtaining the test state parameters; According to the test state parameters, combined with the calibration accuracy level of the vibration test equipment, a performance attenuation analysis is performed to generate a quality assessment report including transfer function accuracy, dynamic characteristic matching and equipment reliability level, including: performing deviation analysis on the vibration transfer accuracy in the test state parameters and the calibration accuracy level of the vibration test equipment, and obtaining the transfer function accuracy through transfer function error calculation; performing bandwidth matching analysis on the dynamic response bandwidth and resonant frequency offset, and obtaining the dynamic characteristic matching through frequency response characteristic comparison; performing timing stability analysis on the phase delay characteristics, and obtaining the timing matching parameters through phase offset statistics; performing index grading on the transfer function accuracy, and obtaining the functional reliability index through accuracy level analysis; performing a comprehensive evaluation on the dynamic characteristic matching and timing matching parameters, and obtaining the performance reliability index through system characteristic analysis; performing a comprehensive analysis based on the functional reliability index and the performance reliability index, and generating the quality assessment report including transfer function accuracy, dynamic characteristic matching and equipment reliability level.

2. The operating status monitoring method for vibration testing equipment according to claim 1, characterized in that: The method generates a multi-dimensional physical feature data stream based on the operating parameters collected from 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, including: Inputting the vibration acceleration signal collected by the triaxial acceleration sensor installed on the resonant excitation table, the hydraulic characteristic signal collected by the pressure 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; Performing waveform distortion detection on the vibration acceleration signal through a signal acquisition device to generate a vibration amplitude correction coefficient and a phase correction coefficient; Using the vibration amplitude correction coefficient and the phase correction coefficient, the vibration acceleration signal is amplitude compensated and the phase is calibrated to obtain a calibrated vibration characteristic signal; Performing pressure fluctuation analysis and flow uniformity calculation on the hydraulic characteristic signal to obtain a hydraulic state characteristic signal; Extracting harmonic components and analyzing driving 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, multi-source data is synchronously fused to generate the multi-dimensional physical characteristic data stream.

3. The method for monitoring the operating status of vibration testing equipment according to claim 1, wherein: The performing of frequency response function analysis and dynamic transmissibility calculation on the multi-dimensional physical characteristic data stream to obtain physical response characteristics including an excitation force spectrum, a hydraulic pressure spectrum, a vibration acceleration spectrum, and a dynamic response spectrum includes: Separating the multi-dimensional physical feature data stream according to signal type to obtain corresponding vibration signal stream, hydraulic signal stream, excitation signal stream and dynamic force signal stream; The vibration signal stream is processed by Fourier transform, and amplitude statistics and phase extraction are performed on the transformed spectrum data to generate a vibration acceleration spectrum; Performing pressure pulsation analysis on the hydraulic signal flow, and obtaining a hydraulic pressure spectrum by pulsation waveform decomposition and amplitude reconstruction; Performing an excitation force characteristic analysis on the excitation signal stream, and forming an excitation force spectrum through force vector decomposition and amplitude recombination; Decomposing the dynamic force signal flow into response characteristics, and obtaining a dynamic response spectrum through signal reconstruction and frequency domain mapping; Frequency domain correlation analysis and transfer characteristic calculation are performed based on the vibration acceleration spectrum, hydraulic pressure spectrum, exciting force spectrum and dynamic response spectrum to obtain the physical response characteristics.

4. The method for monitoring the operating status of vibration testing equipment according to claim 1, wherein: The step of grading the transfer function accuracy and obtaining a functional reliability index through accuracy grade analysis includes: Separate the transfer function accuracy into three-dimensional data of low frequency band, mid frequency band and high frequency band according to the standard frequency range, and obtain the full-band accuracy feature map through spectrum distribution statistics; Performing a frequency band uniformity scan on the full-band accuracy characteristic graph, and obtaining a frequency band consistency index through frequency point density calculation and amplitude fluctuation analysis; Performing transfer characteristic analysis based on the frequency band consistency index, and obtaining frequency band stability parameters through frequency band response curve fitting and phase shift calculation; Dynamically compare the frequency band stability parameters with the vibration test equipment calibration benchmark, and obtain the reliability quantitative index through accuracy level mapping and deviation accumulation statistics; Performing performance loss evaluation on the reliability quantitative indicators, and obtaining a functional integrity coefficient through attenuation characteristic analysis and stability verification; The frequency band consistency index, frequency band stability parameter and function integrity coefficient are weighted and fused, and the function reliability index is obtained through comprehensive reliability evaluation.

5. A vibration test equipment operation status monitoring system, used to implement the vibration test equipment operation status monitoring method according to any one of claims 1 to 4, characterized in that: The vibration test equipment operation status monitoring system includes: A generation module is used to generate a multi-dimensional physical feature data stream based on the operating parameters collected from 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 calculation module for performing frequency response function analysis and dynamic transmissibility calculation on the multi-dimensional physical characteristic data stream to obtain physical response characteristics including an excitation force spectrum generated by an electromagnetic exciter, a hydraulic pressure spectrum of a servo hydraulic power source, a vibration acceleration spectrum of a resonant excitation table, and a dynamic response spectrum detected by a dynamic force sensor; Establish a module for establishing a test calibration benchmark including an exciting force vector, a resonance frequency band, a dynamic damping ratio and a frequency response function based on the physical response characteristics through vibration transmissibility analysis and dynamic stiffness calculation, including: performing input and output comparison on the exciting force spectrum and the dynamic response spectrum in the physical response characteristics, and extracting vibration transmissibility data through transfer function analysis; performing frequency band segmentation statistics on the vibration transmissibility data, and obtaining the exciting force vector of each frequency band through bandwidth analysis; performing frequency domain decomposition on the hydraulic pressure spectrum, and determining the system resonance frequency band through pressure pulsation characteristic analysis; performing peak feature extraction on the vibration acceleration spectrum, and obtaining the dynamic damping ratio through resonance characteristic calculation; performing correlation analysis on the vibration transmissibility data and the dynamic damping ratio, and generating the frequency response function through dynamic stiffness calculation; and performing system characteristic synthesis based on the exciting force vector, resonance frequency band, dynamic damping ratio and frequency response function to obtain the test calibration benchmark; an identification module, configured to perform resonance transfer analysis and resonance bandwidth identification on the physical response characteristics collected in real time according to the test calibration benchmark, and output test state parameters including vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics, including: comparing the frequency response function in the test calibration benchmark with the physical response characteristics collected in real time, and obtaining vibration transfer accuracy through transfer function deviation analysis; performing real-time response analysis on the resonance frequency band, and obtaining dynamic response bandwidth through frequency band scanning; performing phase comparison between the excitation force vector and the real-time physical response characteristics, and determining the resonance frequency offset through resonance characteristic analysis; performing signal comparison between the dynamic damping ratio and the real-time physical response characteristics, and obtaining the phase delay characteristics through phase characteristic calculation; and performing system characteristic analysis and state quantification on the vibration transfer accuracy, dynamic response bandwidth, resonance frequency offset, and phase delay characteristics, and obtaining the test state parameters; An analysis module is used to perform performance attenuation analysis based on the test state 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 and equipment reliability level, including: performing deviation analysis on the vibration transfer accuracy in the test state parameters and the calibration accuracy level of the vibration test equipment, and obtaining the transfer function accuracy through transfer function error calculation; performing bandwidth matching analysis on the dynamic response bandwidth and resonant frequency offset, and obtaining the dynamic characteristic matching through frequency response characteristic comparison; performing timing stability analysis on the phase delay characteristics, and obtaining the timing matching parameter through phase offset statistics; performing index grading on the transfer function accuracy, and obtaining the functional reliability index through accuracy level analysis; performing comprehensive evaluation on the dynamic characteristic matching and timing matching parameters, and obtaining the performance reliability index through system characteristic analysis; performing comprehensive analysis based on the functional reliability index and the performance reliability index to generate the quality assessment report including transfer function accuracy, dynamic characteristic matching and equipment reliability level.

6. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, the method for monitoring the operating status of the vibration test equipment according to any one of claims 1 to 4 is implemented. 7 . A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor is caused to execute the method for monitoring the operating status of vibration testing equipment according to claim 1 .

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