Vibrator-based multi-axis vibration detection and analysis method and system

Abstract

The invention relates to a multi-axis vibration detection analysis method and system based on a vibration meter, and the method comprises the steps: obtaining multi-axis vibration signals collected by the vibration meter at different detection positions of to-be-detected equipment, analyzing the single-point vibration direction and the single-point vibration frequency of the multi-axis vibration signals, and carrying out the vibration abnormality analysis. The method comprises the following steps: acquiring single-point vibration signals corresponding to different detection positions, analyzing vibration abnormal characteristics of the single-point vibration signals corresponding to the different detection positions to obtain vibration abnormal characteristic parameters, performing vibration frequency domain analysis on the vibration abnormal characteristic parameters, extracting vibration frequency domain common parameters according to an analysis result to perform data association, and constructing a vibration detection model for analyzing the vibration abnormal positions of the equipment. And inputting the single-point vibration signal with the highest vibration abnormal frequency into the vibration detection model, and carrying out vibration peak point positioning processing on the vibration abnormal state of the current detection position to obtain fault site data of the to-be-detected equipment. The vibration meter has the effect of improving the detection and analysis accuracy of the vibration meter.

Description

Technical Field

[0001] This application relates to the technical field of vibration detection, and in particular to a multi-axis vibration detection and analysis method and system based on a vibration meter. Background Art

[0002] Currently, with the continuous development of the manufacturing industry, continuous operation of production machines has become one of the common means for enterprises to increase output and ensure efficiency. As the power source of machinery and equipment, motor failure can easily affect the normal operation of the entire industrial chain. Therefore, using a vibration meter to detect motor failures to reduce the impact of motor failures has become one of the main detection methods.

[0003] Existing vibration detection methods typically involve collecting vibration frequencies of operating machinery using vibration sensors and then manually assessing whether abnormal vibrations are occurring by personnel observing data such as vibration frequencies from a spectrum analyzer. However, this manual assessment method is susceptible to human error due to the assessor's lack of knowledge and lacks standardized evaluation indicators, which affects the accuracy of vibration assessments. Therefore, there is room for further optimization of vibration detection methods using vibration meters. Summary of the Invention

[0004] To improve the accuracy of vibration meter detection and analysis, this application provides a multi-axis vibration detection and analysis method and system based on a vibration meter.

[0005] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution: A multi-axis vibration detection and analysis method based on a vibration meter includes: The multi-axis vibration signals collected by the vibration meter at different detection positions of the device under test are obtained, and the single-point vibration direction and single-point vibration frequency of the multi-axis vibration signals are analyzed. Vibration anomaly analysis is performed on the vibration direction and vibration frequency of the single point, and the vibration anomaly characteristics of the single point vibration signal corresponding to different detection positions are analyzed to obtain vibration anomaly characteristic parameters. The vibration anomaly characteristic parameters are analyzed in the frequency domain, and common vibration frequency domain parameters are extracted based on the analysis results for data correlation to construct a vibration detection model for analyzing the location of equipment vibration anomalies. The single-point vibration signal with the highest abnormal vibration frequency is input into the vibration detection model to perform vibration peak point localization processing on the abnormal vibration state at the current detection location, thereby obtaining the fault location data of the equipment under test.

[0006] By adopting the above technical solution, multi-axis vibration signals are collected at different detection positions of the equipment under test using a vibration meter for vibration analysis. Analysis of the single-point vibration direction and frequency at each detection position helps to locate the vibration anomaly. Vibration anomaly analysis further analyzes the vibration anomaly characteristics of the single-point vibration signals at different detection positions, and the vibration anomaly location is further determined based on the vibration anomaly characteristic parameters. Combining the vibration frequency domain analysis results of the anomaly characteristic parameters, common vibration frequency domain parameters are selected for data correlation, thereby constructing a vibration detection model for the equipment under test. This facilitates a more convenient and systematic analysis of the equipment's vibration anomaly location. By combining the single-point vibration signal with the highest vibration anomaly frequency from the multi-axis vibration signals collected at each detection position, the vibration peak value of the current detection position is located, thus accurately marking the vibration fault location of the equipment under test and obtaining fault location data. Therefore, the multi-dimensional analysis of multi-axis vibration signals improves the accuracy of the vibration meter's detection and analysis.

[0007] In a preferred embodiment, this application can be further configured as follows: The vibration anomaly analysis of the single-point vibration direction and the single-point vibration frequency, and the analysis of the vibration anomaly characteristics of the single-point vibration signal corresponding to different detection positions to obtain vibration anomaly characteristic parameters, specifically include: Based on the single-point vibration direction and the single-point vibration frequency, vibration analysis is performed on each vibration position of the device to be tested, and the vibration position with the largest vibration amplitude is marked to obtain the vibration anomaly position parameters. Based on the vibration anomaly location parameters, the single-point vibration distance between the vibration instrument and the vibration anomaly location is obtained when the vibration signal is collected at different detection locations. Based on the changes in vibration signals at different detection locations and the corresponding single-point vibration distance, the vibration attenuation from different detection locations to the abnormal vibration location is analyzed to obtain the vibration attenuation coefficient of the device under test. Based on the single-point vibration signals at different detection locations and the corresponding vibration attenuation coefficients, the vibration changes of all single-point vibration signals at the vibration anomaly locations are analyzed to obtain vibration anomaly characteristic parameters.

[0008] By adopting the above technical solution, the vibration direction between each vibration location and the detection location is determined by the single-point vibration direction. Combined with the single-point vibration frequency, the vibration location with the largest vibration amplitude is screened as the suspected vibration anomaly location for preliminary judgment. This helps to reduce the workload of vibration anomaly investigation. By combining the vibration signal changes of the vibrator at different detection locations and the single-point vibration distance between the vibrator and the vibration anomaly location, the vibration attenuation status from different detection locations to the vibration anomaly location is analyzed. Combined with vibration continuity analysis of the vibration attenuation coefficient of the device under test, this helps to jointly analyze the signal attenuation of vibration anomalies detected at different detection locations, improving the accuracy of vibration anomaly signal analysis. By analyzing the single-point vibration signal and corresponding vibration attenuation coefficient of the vibration anomaly location collected at each detection location, the vibration changes of all single-point vibration signals at the vibration anomaly location are analyzed, thereby obtaining the vibration anomaly characteristic parameters at the vibration anomaly location, which helps to improve the accuracy of vibration anomaly characteristic analysis.

[0009] In a preferred embodiment, this application can be further configured as follows: performing vibration frequency domain analysis on the vibration anomaly characteristic parameters, extracting common vibration frequency domain parameters based on the analysis results, and performing data association to construct a vibration detection model for analyzing the location of equipment vibration anomalies, specifically including: The normal vibration characteristic parameters of the equipment under test during normal operation are compared with the vibration abnormality characteristic parameters, and the change in the spectral density amplitude at the vibration abnormality location is calculated based on the comparison results. The single-point vibration signals collected at each detection location are plotted as envelope curves, and the envelope spectral component with the largest amplitude change of each single-point vibration signal is extracted based on the change in spectral density amplitude. The envelope spectral components of each single-point vibration signal are used as common parameters in the vibration frequency domain for data association, and a vibration detection model for analyzing the location of abnormal equipment vibration is constructed based on the association results.

[0010] By adopting the above technical solution, the normal vibration characteristic parameters of the equipment under test during normal operation are used as a reference and compared with the vibration anomaly characteristic parameters. The change in spectral density amplitude at the vibration anomaly location is calculated based on the comparison results, which helps to improve the accuracy of the analysis of vibration anomaly characteristic parameters. Furthermore, the single-point vibration signal at the vibration anomaly location is plotted as an envelope curve, which helps to compare and analyze the vibration signal more intuitively. The envelope spectral component with the largest amplitude change is extracted as a common parameter in the vibration frequency domain for data association, thereby constructing a vibration detection model for the equipment under test. This helps to comprehensively analyze the location of equipment vibration anomalies and improve the accuracy of vibration analysis.

[0011] In a preferred embodiment, this application can be further configured as follows: the single-point vibration signal with the highest abnormal vibration frequency is input into the vibration detection model to perform vibration peak point localization processing on the abnormal vibration state at the current detection location, thereby obtaining fault location data of the device under test, specifically including: Obtain the single-point vibration signal with the highest abnormal vibration frequency collected at each detection location, and calculate the peak difference of the vibration abnormal spectrum in the single-point vibration signal and the adjacent vibration spectrum. Based on the analysis of the peak difference in the spectrum, the vibration abnormality at the current detection location is determined. When the vibration abnormality exceeds the operating vibration range of the device under test, the detection location with the largest peak difference in the spectrum is subjected to vibration peak point localization processing to obtain the fault location data of the device under test.

[0012] By adopting the above technical solution, the single-point vibration signal with the highest abnormal vibration frequency in the vibration signals collected at each detection location is extracted, and the peak difference between the abnormal vibration spectrum of the corresponding single-point vibration signal and the adjacent vibration spectrum is calculated. This helps to accurately analyze the abnormal vibration state. When the abnormal vibration state exceeds the working vibration range of the equipment under test, the vibration peak location of the detection location with the largest peak spectrum is located, which helps to further determine the fault location of the equipment under test and improve the accuracy of fault location.

[0013] In a preferred embodiment, this application can be further configured as follows: inputting the single-point vibration signal with the highest abnormal vibration frequency into the vibration detection model, performing vibration peak point localization processing on the vibration abnormal state at the current detection location, and obtaining fault location data of the device under test, further includes: Acquire historical fault data of the equipment under test, analyze the fault vibration frequency and fault vibration amplitude of different fault types in the historical fault data, and obtain fault vibration reference data of the equipment under test. The fault vibration signal corresponding to the fault location data is compared with the fault vibration reference data, and the fault type corresponding to the current fault vibration signal is analyzed based on the comparison results. Based on the analysis results of the fault type at the current detection location, the corresponding maintenance plan data is retrieved from the historical maintenance database to match the current vibration anomaly with the maintenance plan, thereby obtaining the vibration anomaly maintenance data of the equipment to be tested. By adopting the above technical solution, and using historical fault data of the equipment under test as a fault repair reference, the analysis of the fault vibration frequency and amplitude of different fault types in the historical fault data helps to focus on the analysis of important indicators related to abnormal vibration, thereby forming fault vibration reference data of the equipment under test. By comparing the fault vibration signal related to the fault location data with the fault vibration reference data, the fault type of the current abnormal vibration signal can be located. And by using historical repair data as a repair plan reference, the repair plan matching processing of the current vibration anomaly can be carried out, which helps to provide a repair reference for the vibration anomaly of the equipment under test.

[0014] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions: A multi-axis vibration detection and analysis system based on a vibration meter includes: The data analysis module is used to acquire multi-axis vibration signals collected by the vibrator at different detection positions of the device under test, and to analyze the single-point vibration direction and single-point vibration frequency of the multi-axis vibration signals. The anomaly analysis module is used to perform vibration anomaly analysis on the vibration direction and vibration frequency of the single point, and to analyze the vibration anomaly characteristics of the single point vibration signal corresponding to different detection positions, so as to obtain vibration anomaly characteristic parameters. The model building module is used to perform vibration frequency domain analysis on the vibration anomaly characteristic parameters, and extract common vibration frequency domain parameters based on the analysis results to perform data association and build a vibration detection model for analyzing the location of equipment vibration anomalies. The fault location module is used to input the single-point vibration signal with the highest abnormal vibration frequency into the vibration detection model, perform vibration peak point location processing on the abnormal vibration state at the current detection location, and obtain the fault location data of the equipment under test.

[0015] By adopting the above technical solution, multi-axis vibration signals are collected at different detection positions of the equipment under test using a vibration meter for vibration analysis. Analysis of the single-point vibration direction and frequency at each detection position helps to locate the vibration anomaly. Vibration anomaly analysis further analyzes the vibration anomaly characteristics of the single-point vibration signals at different detection positions, and the vibration anomaly location is further determined based on the vibration anomaly characteristic parameters. Combining the vibration frequency domain analysis results of the anomaly characteristic parameters, common vibration frequency domain parameters are selected for data correlation, thereby constructing a vibration detection model for the equipment under test. This facilitates a more convenient and systematic analysis of the equipment's vibration anomaly location. By combining the single-point vibration signal with the highest vibration anomaly frequency from the multi-axis vibration signals collected at each detection position, the vibration peak value of the current detection position is located, thus accurately marking the vibration fault location of the equipment under test and obtaining fault location data. Therefore, the multi-dimensional analysis of multi-axis vibration signals improves the accuracy of the vibration meter's detection and analysis.

[0016] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described multi-axis vibration detection and analysis method based on a vibration meter.

[0017] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described multi-axis vibration detection and analysis method based on a vibration meter.

[0018] In summary, this application includes at least one of the following beneficial technical effects: 1. By collecting multi-axis vibration signals at different detection positions of the equipment under test using a vibration meter, vibration analysis is performed on the equipment. Analysis of the single-point vibration direction and frequency at each detection position helps to locate the vibration anomaly. Vibration anomaly analysis further analyzes the vibration anomaly characteristics of the single-point vibration signals at different detection positions, and the vibration anomaly location is further determined based on the vibration anomaly characteristic parameters. Combining the vibration frequency domain analysis results of the anomaly characteristic parameters, common vibration frequency domain parameters are selected for data association, thereby constructing a vibration detection model for the equipment under test. This facilitates a more convenient and systematic analysis of the equipment's vibration anomaly location. By combining the single-point vibration signal with the highest vibration anomaly frequency from the multi-axis vibration signals collected at each detection position, the vibration peak value of the current detection position is located, thus accurately marking the vibration fault location of the equipment under test and obtaining fault location data. Therefore, the multi-dimensional analysis of multi-axis vibration signals improves the accuracy of the vibration meter's detection and analysis. 2. By determining the vibration direction between each vibration location and the detection location through single-point vibration direction, and combining it with the single-point vibration frequency, the vibration location with the largest vibration amplitude is selected as the suspected vibration anomaly location for preliminary judgment. This helps to reduce the workload of vibration anomaly investigation. By combining the vibration signal changes of the vibrator at different detection locations and the single-point vibration distance between the vibrator and the vibration anomaly location, the vibration attenuation at different detection locations to the vibration anomaly location is analyzed. Combined with vibration continuity analysis, the vibration attenuation coefficient of the device under test is analyzed, which helps to jointly analyze the signal attenuation of vibration anomalies detected at different detection locations, improving the accuracy of vibration anomaly signal analysis. By analyzing the single-point vibration signal and the corresponding vibration attenuation coefficient of the vibration anomaly location collected at each detection location, the vibration changes of all single-point vibration signals at the vibration anomaly location are analyzed, thereby obtaining the vibration anomaly characteristic parameters at the vibration anomaly location, which helps to improve the accuracy of vibration anomaly characteristic analysis. 3. Using the normal vibration characteristic parameters of the equipment under test during normal operation as a reference, the vibration characteristics are compared with those of abnormal vibration. The comparison results are used to calculate the amplitude change of the spectral density at the abnormal vibration location, which helps to improve the accuracy of the analysis of the abnormal vibration characteristic parameters. The single-point vibration signal at the abnormal vibration location is plotted as an envelope curve, which helps to compare and analyze the vibration signal more intuitively. The envelope spectral component with the largest amplitude change is extracted as a common parameter in the vibration frequency domain for data correlation, thereby constructing a vibration detection model of the equipment under test. This helps to comprehensively analyze the location of abnormal vibration of the equipment and improve the accuracy of vibration analysis. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the implementation of a multi-axis vibration detection and analysis method based on a vibrator in this embodiment.

[0020] Figure 2 This is a flowchart illustrating the implementation of step S20 of the multi-axis vibration detection and analysis method based on a vibrator in this embodiment.

[0021] Figure 3 This is a flowchart illustrating the implementation of step S30 of the multi-axis vibration detection and analysis method based on a vibrator in this embodiment.

[0022] Figure 4 This is a flowchart illustrating the implementation of step S40 of the multi-axis vibration detection and analysis method based on a vibrator in this embodiment.

[0023] Figure 5 This is a flowchart illustrating the implementation of the multi-axis vibration detection and analysis method based on a vibrator for abnormal repair in this embodiment.

[0024] Figure 6 This is a structural block diagram of a multi-axis vibration detection and analysis system based on a vibration meter according to this embodiment.

[0025] Figure 7 This is a schematic diagram of the internal structure of a computer device used to implement a multi-axis vibration detection and analysis method based on a vibration meter. Detailed Implementation

[0026] The present application is further described in detail below with reference to the accompanying drawings.

[0027] In one embodiment, such as Figure 1 As shown, this application discloses a multi-axis vibration detection and analysis method based on a vibration meter, which specifically includes the following steps: S10: Acquire multi-axis vibration signals collected by the vibration meter at different detection positions of the device under test, and analyze the single-point vibration direction and single-point vibration frequency of the multi-axis vibration signals.

[0028] Specifically, a multi-axis vibration signal of the device under test is collected at different test positions of the device under test, including the horizontal, vertical and axial vibration signals of the test point. The vibration direction and vibration frequency of the multi-axis vibration signal at each test position are analyzed to obtain the corresponding single-point vibration direction and single-point vibration frequency. Among them, the single-point vibration direction is the direction of the strongest vibration.

[0029] S20: Perform vibration anomaly analysis on the vibration direction and frequency of a single point, and analyze the vibration anomaly characteristics of the single-point vibration signal corresponding to different detection positions to obtain vibration anomaly characteristic parameters.

[0030] Specifically, such as Figure 2 As shown, step S20 includes: S201: Based on the vibration direction and frequency of a single point, perform vibration analysis on each vibration location of the equipment to be tested, and mark the vibration location with the largest vibration amplitude to obtain the vibration anomaly location parameters.

[0031] Specifically, based on the vibration direction and frequency of a single point, a vibration waveform diagram is drawn along the vibration direction of the single point. Each complete vibration waveform is analyzed, the vibration amplitude of each complete vibration waveform is compared, and the vibration position with the largest vibration amplitude is marked, thereby obtaining the vibration anomaly location parameters.

[0032] S202: Based on the vibration anomaly location parameters, obtain the single-point vibration distance between the vibration instrument and the vibration anomaly location when the vibration signal is collected at different detection locations.

[0033] Specifically, based on the vibration anomaly location parameters, when the vibration signal is collected by the vibration meter at different detection positions, the single-point vibration distance between the component at different detection positions and the vibration anomaly location is measured, such as the axial distance along the central axis of the device under test.

[0034] S203: Based on the changes in vibration signals at different detection locations and the corresponding single-point vibration distance, analyze the vibration attenuation from different detection locations to the abnormal vibration location, and obtain the vibration attenuation coefficient of the device under test.

[0035] Specifically, based on the changes in vibration signals detected at abnormal vibration locations at different detection positions, the changes in adjacent vibration peaks and vibration amplitudes are calculated. Combined with the vibration distance at a single point, a vibration attenuation curve is plotted for each detection location, and the slope of each vibration attenuation curve is used as the vibration attenuation coefficient of the device under test.

[0036] S204: Based on the single-point vibration signals at different detection locations and the corresponding vibration attenuation coefficients, analyze the vibration changes of all single-point vibration signals at the vibration anomaly locations to obtain vibration anomaly characteristic parameters.

[0037] Specifically, based on the single-point vibration signals and corresponding vibration attenuation coefficients at different detection locations, the vibration changes at the detected abnormal vibration locations due to the differences in vibration detection locations are analyzed, including changes in vibration amplitude and vibration peak value. The product of the corresponding vibration amplitude, vibration peak value change and the corresponding vibration attenuation coefficient is used as the vibration abnormality feature value. Thus, the average value of the vibration abnormality features of all single-point vibration signals at the vibration abnormality location is selected as the vibration abnormality feature parameter.

[0038] S30: Perform vibration frequency domain analysis on the characteristic parameters of vibration anomalies, extract common vibration frequency domain parameters based on the analysis results, perform data association, and construct a vibration detection model for analyzing the location of equipment vibration anomalies.

[0039] Specifically, such as Figure 3 As shown, step S30 includes: S301: Compare the normal vibration characteristic parameters of the equipment under test with the vibration abnormal characteristic parameters during normal operation, and calculate the change in the spectral density amplitude at the vibration abnormal location based on the comparison results.

[0040] Specifically, the normal vibration characteristic parameters of the equipment under test during normal operation are compared with the vibration abnormal characteristic parameters. In this embodiment, the vibration amplitude and vibration waveform peak value are mainly compared, and the change in spectral density amplitude at the vibration abnormal location is calculated based on the comparison results.

[0041] S302: Plot the single-point vibration signal at each detection location at the abnormal vibration location into an envelope curve, and extract the envelope spectral component with the largest amplitude change of each single-point vibration signal based on the change in spectral density amplitude.

[0042] Specifically, the single-point vibration signals collected at each detection location are plotted as envelope curves, and the envelope spectral line component with the largest amplitude change for each single-point vibration signal is extracted from the envelope curves in combination with the changes in spectral density amplitude.

[0043] S303: The envelope spectral components of each single-point vibration signal are used as common parameters in the vibration frequency domain for data association, and a vibration detection model for analyzing the location of abnormal equipment vibration is constructed based on the association results.

[0044] Specifically, the envelope spectral components of each single-point vibration signal are used as common parameters in the vibration frequency domain. Data is correlated with the vibration anomaly location as the correlation point. Based on the correlation relationship of the envelope spectral components, a vibration detection model for analyzing the location of equipment vibration anomalies is constructed.

[0045] S40: Input the single-point vibration signal with the highest abnormal vibration frequency into the vibration detection model, perform vibration peak point localization processing on the abnormal vibration state at the current detection location, and obtain the fault location data of the equipment under test.

[0046] Specifically, such as Figure 4 As shown, step S40 includes: S401: Obtain the single-point vibration signal with the highest abnormal vibration frequency collected at each detection location, and calculate the peak difference between the abnormal vibration spectrum and the adjacent vibration spectrum in the single-point vibration signal.

[0047] Specifically, the vibration signal with the highest abnormal vibration frequency collected at each detection location is analyzed, and the peak difference between the abnormal vibration spectrum and the adjacent vibration spectrum is calculated. For example, the peak difference between the maximum peak value of the abnormal vibration spectrum and the maximum peak value of the adjacent vibration spectrum is calculated, and the calculated peak difference is taken as the highest value of the abnormal vibration fluctuation.

[0048] S402: Analyze the vibration anomaly status at the current detection location based on the peak difference of the spectrum. When the vibration anomaly status exceeds the working vibration range of the device under test, perform vibration peak point localization processing on the detection location with the largest peak difference of the spectrum to obtain the fault location data of the device under test.

[0049] Specifically, the vibration anomaly at the current detection location is analyzed based on the peak difference of the spectrum. If the peak difference of the spectrum is within the preset allowable fluctuation range, it indicates that the vibration at the current detection location is in a normal state. When the peak difference of the spectrum exceeds the preset allowable fluctuation range, that is, the vibration anomaly exceeds the working vibration range of the device under test, the vibration peak location processing is performed on the detection location with the largest peak difference of the spectrum to obtain the fault location data of the device under test.

[0050] In this embodiment, if Figure 5 As shown, step S40 further includes: S403: Obtain historical fault data of the equipment under test, analyze the fault vibration frequency and fault vibration amplitude of different fault types in the historical fault data, and obtain fault vibration reference data of the equipment under test.

[0051] Specifically, in this embodiment, the historical fault data of the device under test is used as a reference. If the device under test has no historical maintenance data, the historical fault data of the same model device is used as a reference. The fault vibration frequency and fault vibration amplitude of each fault type in the historical fault data are analyzed to obtain the fault vibration reference data of the device under test.

[0052] S404: Compare the fault vibration signal corresponding to the fault location data with the fault vibration reference data, and analyze the fault type corresponding to the current fault vibration signal based on the comparison results.

[0053] Specifically, the vibration frequency and amplitude of the fault vibration signal corresponding to the fault location data are compared with the fault vibration reference data. When both the vibration frequency and amplitude match the fault vibration frequency and amplitude in the fault vibration reference data, the fault type of the current fault vibration signal is obtained.

[0054] S405: Based on the fault type analysis results of the current detection location, search for corresponding maintenance plan data from the historical maintenance database to perform maintenance plan matching processing on the current vibration anomaly, and obtain the vibration anomaly maintenance data of the equipment to be tested.

[0055] Specifically, based on the fault type analysis results at the current detection location, the corresponding maintenance plan data is retrieved from the historical maintenance database and matched with the current vibration anomaly. The matched maintenance plan is then sent to the maintenance personnel as a maintenance reference to obtain the vibration anomaly maintenance data of the equipment under test.

[0056] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0057] In one embodiment, a multi-axis vibration detection and analysis system based on a vibrator is provided, which corresponds one-to-one with the multi-axis vibration detection and analysis method based on a vibrator described in the above embodiments. For example... Figure 6 As shown, this multi-axis vibration detection and analysis system based on a vibration meter includes a data analysis module, an anomaly analysis module, a model building module, and a fault location module. Detailed descriptions of each functional module are as follows: The data analysis module is used to acquire multi-axis vibration signals collected by the vibrator at different detection positions of the device under test, and to analyze the single-point vibration direction and single-point vibration frequency of the multi-axis vibration signals.

[0058] The anomaly analysis module is used to perform vibration anomaly analysis on the vibration direction and frequency of a single point, and to analyze the vibration anomaly characteristics of the single-point vibration signal corresponding to different detection positions, thereby obtaining vibration anomaly characteristic parameters.

[0059] The model building module is used to perform vibration frequency domain analysis on the characteristic parameters of vibration anomalies, and extract common vibration frequency domain parameters based on the analysis results to perform data association and build a vibration detection model for analyzing the location of equipment vibration anomalies.

[0060] The fault location module is used to input the single-point vibration signal with the highest abnormal vibration frequency into the vibration detection model, perform vibration peak point location processing on the abnormal vibration state at the current detection location, and obtain the fault location data of the equipment under test.

[0061] Preferably, the anomaly analysis module specifically includes: The abnormal location marking submodule is used to perform vibration analysis on each vibration location of the equipment to be tested based on the vibration direction and vibration frequency of a single point, and to mark the vibration location with the largest vibration amplitude to obtain the vibration abnormal location parameters.

[0062] The distance analysis submodule is used to obtain the single-point vibration distance between the vibration meter and the vibration anomaly location when the vibration signal is collected at different detection locations, based on the vibration anomaly location parameters.

[0063] The attenuation analysis submodule is used to analyze the vibration attenuation from different detection positions to the abnormal vibration position based on the changes in vibration signals at different detection positions and the corresponding single-point vibration distance, and to obtain the vibration attenuation coefficient of the device under test.

[0064] The feature analysis submodule is used to analyze the vibration changes of all single-point vibration signals at the vibration anomaly location based on the single-point vibration signals at different detection locations and the corresponding vibration attenuation coefficients, and to obtain vibration anomaly feature parameters.

[0065] Preferably, the model building module specifically includes: The amplitude analysis submodule is used to compare the normal vibration characteristic parameters of the equipment under test with the vibration abnormal characteristic parameters during normal operation, and calculate the change in spectral density amplitude at the vibration abnormality location based on the comparison results.

[0066] The envelope analysis submodule is used to plot the single-point vibration signal at each detection location of the vibration anomaly into an envelope curve, and extract the envelope spectral component with the largest amplitude change of each single-point vibration signal based on the change in spectral density amplitude.

[0067] The model building submodule is used to associate the envelope spectral components of each single-point vibration signal as common parameters in the vibration frequency domain, and to build a vibration detection model for analyzing the location of abnormal vibrations in the equipment based on the association results.

[0068] Preferably, the fault location module specifically includes: The difference calculation submodule is used to obtain the single-point vibration signal with the highest abnormal vibration frequency collected at each detection location, and calculate the peak difference of the vibration abnormal spectrum in the single-point vibration signal and the adjacent vibration spectrum.

[0069] The peak point location submodule is used to analyze the vibration anomaly status of the current detection position based on the peak value difference of the spectrum. When the vibration anomaly status exceeds the working vibration range of the device under test, vibration peak point location processing is performed on the detection position with the largest peak value difference to obtain the fault location data of the device under test.

[0070] Preferably, the fault location module further includes: The reference analysis submodule is used to acquire historical fault data of the device under test, analyze the fault vibration frequency and amplitude of different fault types in the historical fault data, and obtain fault vibration reference data of the device under test.

[0071] The type analysis submodule is used to compare the fault vibration signal corresponding to the fault location data with the fault vibration reference data, and analyze the fault type corresponding to the current fault vibration signal based on the comparison results.

[0072] The scheme matching submodule is used to search for corresponding maintenance scheme data from the historical maintenance database based on the fault type analysis results of the current detection location, and perform maintenance scheme matching processing on the current vibration anomaly to obtain the vibration anomaly maintenance data of the equipment to be tested.

[0073] Specific limitations regarding the multi-axis vibration detection and analysis system based on a vibrator can be found in the limitations of the multi-axis vibration detection and analysis method based on a vibrator mentioned above, and will not be repeated here. Each module in the aforementioned multi-axis vibration detection and analysis system based on a vibrator can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0074] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores vibration detection data from the vibration meter. The network interface allows communication with external terminals via a network connection. When executed by the processor, the computer program implements a multi-axis vibration detection and analysis method based on the vibration meter.

[0075] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described multi-axis vibration detection and analysis method based on a vibration meter.

[0076] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. 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 embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can 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 a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0077] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0078] The above-described 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A multi-axis vibration detection and analysis method based on a vibrometer, characterized in that: include: Acquire multi-axis vibration signals collected by the vibration meter at different detection positions of the device to be detected, and analyze the single-point vibration direction and single-point vibration frequency of the multi-axis vibration signals; Performing vibration anomaly analysis on the single-point vibration direction and the single-point vibration frequency, and analyzing vibration anomaly characteristics of single-point vibration signals corresponding to different detection positions to obtain vibration anomaly characteristic parameters; Performing vibration frequency domain analysis on the vibration abnormality characteristic parameters, extracting vibration frequency domain common parameters for data association based on the analysis results, and constructing a vibration detection model for analyzing the abnormal vibration position of the equipment; The single-point vibration signal with the highest abnormal vibration frequency is input into the vibration detection model, and the vibration peak point positioning processing is performed on the abnormal vibration state of the current detection position to obtain the fault location data of the equipment to be detected.

2. The multi-axis vibration detection and analysis method based on a vibrometer according to claim 1 is characterized in that: The vibration anomaly analysis is performed on the single-point vibration direction and the single-point vibration frequency, and the vibration anomaly characteristics of the single-point vibration signals corresponding to different detection positions are analyzed to obtain vibration anomaly characteristic parameters, specifically including: According to the single-point vibration direction and the single-point vibration frequency, vibration analysis is performed on each vibration position of the device to be detected, and the vibration position with the largest vibration amplitude is marked to obtain vibration abnormality position parameters; According to the vibration abnormality position parameter, a single-point vibration distance between the vibration meter and the vibration abnormality position when collecting vibration signals at different detection positions is obtained; According to the vibration signal changes at different detection positions and the corresponding single-point vibration distance, the vibration attenuation conditions from different detection positions to the vibration abnormality position are analyzed to obtain the vibration attenuation coefficient of the equipment to be detected; According to the single-point vibration signals at different detection positions and the corresponding vibration attenuation coefficients, the vibration changes of all single-point vibration signals at the vibration abnormality positions are analyzed to obtain the vibration abnormality characteristic parameters.

3. The multi-axis vibration detection and analysis method based on a vibrometer according to claim 1 is characterized in that: The vibration abnormality characteristic parameters are analyzed in the vibration frequency domain, and common parameters in the vibration frequency domain are extracted according to the analysis results for data association, so as to construct a vibration detection model for analyzing the abnormal vibration position of the equipment, which specifically includes: Compare the normal vibration characteristic parameters of the equipment to be tested during normal operation with the abnormal vibration characteristic parameters, and calculate the frequency spectrum density amplitude change at the abnormal vibration position according to the comparison result; The single-point vibration signal at the vibration abnormality position collected at each detection position is plotted into an envelope curve, and the envelope spectrum line component with the largest amplitude change of each single-point vibration signal is extracted according to the amplitude change of the spectrum density; The envelope spectrum components of each single-point vibration signal are used as common parameters in the vibration frequency domain for data association, and a vibration detection model for analyzing the abnormal vibration location of the equipment is constructed based on the association results.

4. The multi-axis vibration detection and analysis method based on a vibrometer according to claim 1, characterized in that: The single-point vibration signal with the highest abnormal vibration frequency is input into the vibration detection model, and the vibration peak point positioning processing is performed on the abnormal vibration state of the current detection position to obtain the fault location data of the equipment to be detected, specifically including: Obtain the single-point vibration signal with the highest abnormal vibration frequency collected at each detection position, and calculate the spectrum peak difference between the abnormal vibration spectrum in the single-point vibration signal and the adjacent vibration spectrum; The abnormal vibration state of the current detection position is analyzed according to the spectrum peak difference. When the abnormal vibration state exceeds the working vibration range of the equipment to be detected, the detection position with the largest spectrum peak difference is subjected to vibration peak point positioning processing to obtain the fault location data of the equipment to be detected.

5. The multi-axis vibration detection and analysis method based on a vibration meter according to claim 4 is characterized in that: The single-point vibration signal with the highest abnormal vibration frequency is input into the vibration detection model, and the vibration peak point positioning processing is performed on the abnormal vibration state of the current detection position to obtain the fault location data of the equipment to be detected, and also includes: Obtain historical fault data of the equipment to be tested, analyze the fault vibration frequency and fault vibration amplitude of different fault types in the historical fault data, and obtain fault vibration reference data of the equipment to be tested; Comparing the fault vibration signal corresponding to the fault location data with the fault vibration reference data, and analyzing the fault type corresponding to the current fault vibration signal according to the comparison result; According to the fault type analysis result of the current detection position, the corresponding maintenance plan data is searched from the historical maintenance database to perform maintenance plan matching processing on the current vibration abnormality, and the vibration abnormality maintenance data of the equipment to be detected is obtained.

6. A multi-axis vibration detection and analysis system based on a vibrometer, characterized in that: include: A data analysis module is used to obtain multi-axis vibration signals collected by the vibration meter at different detection positions of the device to be detected, and analyze the single-point vibration direction and single-point vibration frequency of the multi-axis vibration signals; an abnormality analysis module, used to perform vibration abnormality analysis on the single-point vibration direction and the single-point vibration frequency, and analyze the vibration abnormality characteristics of the single-point vibration signals corresponding to different detection positions to obtain vibration abnormality characteristic parameters; A model building module, used to perform vibration frequency domain analysis on the vibration abnormality characteristic parameters, extract common parameters in the vibration frequency domain according to the analysis results, perform data association, and build a vibration detection model for analyzing the abnormal vibration position of the equipment; The fault location module is used to input the single-point vibration signal with the highest vibration abnormality frequency into the vibration detection model, perform vibration peak location processing on the vibration abnormality state of the current detection position, and obtain the fault location data of the equipment to be detected.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the multi-axis vibration detection and analysis method based on a vibration meter as claimed in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the multi-axis vibration detection and analysis method based on a vibration meter as claimed in any one of claims 1 to 5 are implemented.

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