Electrical equipment health monitoring method based on big data analysis

Abstract

The invention discloses an electrical equipment health monitoring method based on big data analysis, and relates to the technical field of electrical equipment health management. The electrical equipment health monitoring method based on big data analysis comprises the following steps: motor acquisition speed qualification evaluation; performing sensor vibration signal output qualification evaluation; evaluating motor vibration; and abnormal vibration feedback. According to the method, the motor acquisition speed qualified evaluation value is obtained through the motor acquisition speed related data, whether acquisition speed optimization is carried out is judged, and then whether vibration signal output optimization is carried out is judged through the obtained sensor vibration signal output qualified evaluation value. And finally, based on the obtained motor vibration evaluation value, whether vibration abnormity feedback is carried out is judged, the effect of improving the accuracy of the pushing result in the motor fault monitoring process based on big data analysis is achieved, and the problem that the pushing result is inaccurate in the motor fault monitoring process based on big data analysis in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical equipment health management, and in particular to an electrical equipment health monitoring method based on big data analysis. Background Art

[0002] With the rapid development of science and technology and the widespread application of electrical equipment in all walks of life, the health status of electrical equipment is crucial to ensuring production safety and improving production efficiency. However, the failure and damage of electrical equipment will not only lead to power outages, but may also cause production accidents and economic losses. Therefore, real-time monitoring and prediction of the health status of electrical equipment in order to detect potential faults in time and take corresponding maintenance measures has become the focus of attention of enterprises and research institutions. In recent years, with the rise of big data technology, unprecedented opportunities have been provided for electrical equipment health monitoring. By collecting and analyzing the operating data of electrical equipment, the health status and potential risks of the equipment can be revealed, providing a scientific basis for the maintenance and management of the equipment.

[0003] Existing methods mainly monitor the vibration status of equipment in real time through accelerometers and vibration sensors installed on electrical equipment (such as motors, transformers, generators, etc.). The vibration signal is transmitted to the analysis platform through the data acquisition system for spectrum analysis, time domain analysis, etc. to identify early signs of equipment failure, such as imbalance, looseness, wear, etc.

[0004] For example, the invention patent with announcement number: CN111639842B announces a device health assessment method, an assessment system, and a device health prediction method, including: synchronously collecting vibration data of different vibration collection positions on the device at each vibration collection moment, and synchronously collecting temperature data of different temperature collection positions on the device at each temperature collection moment; obtaining an assessment compensation factor based on all temperature data; based on all vibration data, using a vibration assessment state model to evaluate and obtain an assessment state vector, weighted averaging the probability density corresponding to each assessment state in the assessment state vector, and using the assessment compensation factor to compensate for the weighted average result to complete the equipment health assessment.

[0005] For example, the invention patent with announcement number: CN109102189B announces an electrical equipment health management system and method, including: establishing a mapping relationship through data fitting, inputting operating parameters, analyzing the final result according to the mapping relationship, and continuously correcting the data fitting method according to the final result, and continuously updating the data fitting method. The final result of the analysis is a secondary evaluation prediction result of health assessment and fault prediction; installing various sensors in electrical equipment and obtaining internal data through communication, collecting data through real-time monitoring, regular inspections and other methods.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, in industrial manufacturing production lines, since the motors on high-speed production lines may be affected by various factors, the data acquisition speed of the motors may not meet the needs of high-speed vibration data acquisition, causing the output signal of the sensor to change, leading to the problem of inaccurate push results in the motor fault monitoring process based on big data analysis. Summary of the invention

[0008] The embodiments of the present application solve the problem of inaccurate push results in the motor fault monitoring process based on big data analysis in the prior art by providing an electrical equipment health monitoring method based on big data analysis, and achieve improved accuracy of push results in the motor fault monitoring process based on big data analysis.

[0009] The embodiment of the present application provides an electrical equipment health monitoring method based on big data analysis, comprising the following steps: S1, obtaining motor acquisition speed related data based on an industrial production line monitoring device, obtaining a motor acquisition speed qualified evaluation value through the motor acquisition speed related data, judging whether to optimize the acquisition speed based on the motor acquisition speed qualified evaluation value to obtain a qualified motor acquisition speed value, and the motor acquisition speed qualified evaluation value is used to evaluate the data acquisition qualified condition of the motor during the industrial production line monitoring process; S2, obtaining a sensor vibration signal output qualified evaluation value through the motor acquisition speed qualified evaluation value and the obtained sensor vibration related data, judging whether to optimize the vibration signal output based on the sensor vibration signal output qualified evaluation value to obtain a qualified sensor vibration signal output evaluation value, and the sensor vibration signal output qualified evaluation value is used to evaluate the output qualified condition of the sensor vibration signal during the industrial production line monitoring process; S3, if a qualified sensor vibration signal output evaluation value is obtained, then obtaining a motor vibration evaluation value through the obtained motor vibration related data, and the motor vibration evaluation value is used to evaluate the vibration condition of the motor during the industrial production line monitoring process; S4, judging whether to perform vibration abnormality feedback based on the motor vibration evaluation value, and the vibration abnormality feedback indicates that the motor fault information corresponding to the unqualified vibration evaluation value is pushed to a preset person through a big data analysis method.

[0010] Furthermore, the motor speed acquisition related data includes the average electromagnetic interference power value, the maximum temperature of the production line environment, the average acquisition rate of the vibration signal, the sampling average word length and the sampling average bandwidth; the sensor vibration related data includes the vibration signal fundamental wave amplitude, the vibration signal harmonic amplitude, the vibration signal packet loss rate, the sensor input signal power and the sensor output signal power; the motor vibration related data includes the motor vibration frequency, the motor vibration displacement and the motor vibration acceleration; the sampling average word length represents the average number of bits corresponding to the motor speed acquisition related data within a preset time period; the sampling average bandwidth represents the average bandwidth corresponding to the motor speed acquisition related data within a preset time period.

[0011] Furthermore, the specific process of obtaining the qualified evaluation value of the motor acquisition speed through the motor acquisition speed related data is as follows: V11, analyzing the electromagnetic influence factor through the average electromagnetic interference power value and the preset first acquisition weight value and the preset electromagnetic interference power maximum threshold obtained from the database; V12, analyzing the temperature influence factor through the maximum temperature of the production line environment and the preset second acquisition weight value and the preset production line environment temperature maximum threshold obtained from the database; V13, analyzing the electromagnetic influence factor, the average acquisition rate of the vibration signal and the preset acquisition rate maximum threshold obtained from the database to obtain the electromagnetic-acquisition rate evaluation value; V14, analyzing the electromagnetic influence factor, the average acquisition rate of the vibration signal, the ... The electromagnetic-acquisition speed evaluation value is obtained by analyzing the sample average word length, the sampling average bandwidth and the preset maximum acquisition speed threshold obtained from the database; V15, the temperature-acquisition rate evaluation value is obtained by data processing through the temperature influence factor, the average acquisition rate of the vibration signal and the preset maximum acquisition rate threshold obtained from the database; V16, the temperature-acquisition speed evaluation value is obtained by data processing through the temperature influence factor, the average acquisition rate of the vibration signal, the sampling average word length, the sampling average bandwidth and the preset maximum acquisition speed threshold obtained from the database; V17, the motor acquisition speed qualified evaluation value is obtained through the electromagnetic-acquisition rate evaluation value, the electromagnetic-acquisition speed evaluation value, the temperature-acquisition rate evaluation value and the temperature-acquisition speed evaluation value.

[0012] Furthermore, the limiting expression of the qualified evaluation value of the motor acquisition speed is as follows:

[0013] ;

[0014] In the formula, represents the qualified evaluation value of the motor speed collection in the wth preset time period, , w represents the number of the preset time period, v represents the total number of preset time periods, represents the electromagnetic-collection rate evaluation value in the wth preset time period, represents the electromagnetic-acquisition speed evaluation value in the wth preset time period, represents the temperature-collection rate evaluation value in the wth preset time period, represents the temperature-acquisition speed evaluation value in the wth preset time period, and e represents a natural constant.

[0015] Furthermore, the specific process of obtaining the qualified evaluation value of the sensor vibration signal output through the qualified evaluation value of the motor acquisition speed and the obtained sensor vibration related data is as follows: V21, obtaining the acquisition-distortion evaluation value, the acquisition-distortion evaluation value is obtained by processing the qualified evaluation value of the motor acquisition speed, the harmonic amplitude of the vibration signal, the fundamental amplitude of the vibration signal and the preset signal distortion maximum threshold obtained from the database; V22, obtaining the acquisition-loss evaluation value, the acquisition-loss evaluation value is obtained by processing the qualified evaluation value of the motor acquisition speed, the vibration signal packet loss rate and the preset signal loss maximum threshold obtained from the database; V23, obtaining the acquisition-loss evaluation value, the acquisition-loss evaluation value is obtained by processing the qualified evaluation value of the motor acquisition speed, the sensor input signal power, the sensor output signal power and the preset signal loss maximum threshold obtained from the database; V24, obtaining the qualified evaluation value of the sensor vibration signal output through the acquisition-distortion evaluation value, the acquisition-loss evaluation value and the acquisition-loss evaluation value.

[0016] Furthermore, the specific process of obtaining the motor vibration evaluation value is as follows: V31, judging whether the sensor vibration signal output qualified evaluation value meets the qualified conditions, when the sensor vibration signal output qualified evaluation value meets the qualified conditions, obtaining the motor vibration evaluation value, the qualified condition indicating that the sensor vibration signal output qualified evaluation value is marked as a qualified sensor vibration signal output evaluation value; V32, obtaining the output-vibration frequency evaluation value, the output-vibration frequency evaluation value is obtained by processing the motor vibration frequency; V33, obtaining the output-vibration displacement evaluation value, the output-vibration displacement evaluation value is obtained by processing the motor vibration displacement; V34, obtaining the output-vibration acceleration evaluation value, the output-vibration acceleration evaluation value is obtained by processing the motor vibration acceleration; V35, obtaining the motor vibration evaluation value through the output-vibration frequency evaluation value, the output-vibration displacement evaluation value, the output-vibration acceleration evaluation value and the preset frequency evaluation maximum threshold, preset displacement evaluation maximum threshold and preset acceleration evaluation maximum threshold obtained from the database.

[0017] Furthermore, the specific process of judging whether to perform acquisition speed optimization based on the motor acquisition speed qualified evaluation value to obtain a qualified motor acquisition speed value is as follows: when the difference between the motor acquisition speed qualified evaluation value and the reference acquisition qualified value is not less than 0, the corresponding motor acquisition speed qualified evaluation value is marked as a qualified motor acquisition speed value; when the difference between the motor acquisition speed qualified evaluation value and the reference acquisition qualified value is less than 0, the acquisition speed is optimized; if the difference between the motor acquisition speed qualified evaluation value and the reference acquisition qualified value is still less than 0 after the acquisition speed optimization, an acquisition fault prompt is sent to a preset personnel; the acquisition speed optimization includes differential transmission and temperature compensation; the qualified motor acquisition speed value represents a motor acquisition speed qualified evaluation value that is not less than the reference acquisition qualified value obtained from the database.

[0018] Furthermore, the specific process of judging whether to perform vibration signal output optimization based on the sensor vibration signal output qualified evaluation value to obtain a qualified sensor vibration signal output evaluation value is as follows: when the difference between the sensor vibration signal output qualified evaluation value and the reference vibration signal output qualified value is not less than 0, it indicates that the vibration signal output is qualified, and the corresponding sensor vibration signal output qualified evaluation value is marked as a qualified sensor vibration signal output evaluation value and the motor vibration evaluation value is obtained; when the difference between the sensor vibration signal output qualified evaluation value and the reference vibration signal output qualified value is less than 0, the vibration signal output is optimized; if after the vibration signal output optimization is performed, the difference between the sensor vibration signal output qualified evaluation value and the reference vibration signal output qualified value is still less than 0, indicating that the vibration signal output is unqualified, an output fault prompt is sent to a preset person and the motor vibration evaluation value is not obtained; the vibration signal output optimization includes signal amplification and vibration signal denoising; the qualified sensor vibration signal output evaluation value represents a sensor vibration signal output qualified evaluation value that is not less than the reference vibration signal output qualified value obtained from the database.

[0019] Furthermore, the specific process of judging whether to perform vibration abnormality feedback based on the motor vibration evaluation value is as follows: performing difference calculation on the motor vibration evaluation value and the reference motor vibration qualified value from the database, when the difference between the motor vibration evaluation value and the reference motor vibration qualified value is not greater than 0, it indicates that the motor vibration is qualified, and the corresponding motor vibration evaluation value is marked as a qualified vibration evaluation value; when the difference between the motor vibration evaluation value and the reference motor vibration qualified value is greater than 0, it indicates that the motor vibration is abnormal, and a prompt is sent to a preset person to perform motor vibration optimization, and the motor vibration optimization indicates re-optimizing the acquisition speed and the vibration signal output; when the difference between the monitored motor vibration evaluation value and the reference motor vibration qualified value is not greater than 0, the motor vibration optimization is stopped, otherwise the motor vibration evaluation value corresponding to the difference between the motor vibration evaluation value and the reference motor vibration qualified value is greater than 0 is marked as an unqualified vibration evaluation value.

[0020] Furthermore, the specific process of performing vibration abnormality feedback is as follows: performing visualization processing based on the obtained unqualified vibration evaluation value to obtain a scatter plot of output qualified time period-unqualified vibration evaluation value; pushing the motor fault information corresponding to the scatter plot of output qualified time period-unqualified vibration evaluation value to a preset person; the visualization processing represents extracting the unqualified vibration evaluation value and the corresponding output qualified time period by a big data analysis method; the scatter plot of output qualified time period-unqualified vibration evaluation value is used to reflect the corresponding relationship between the unqualified vibration evaluation value and the output qualified time period.

[0021] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0022] 1. Obtain the qualified evaluation value of the motor acquisition speed through the motor acquisition speed related data and judge whether to optimize the acquisition speed, then judge whether to optimize the vibration signal output through the obtained sensor vibration signal output qualified evaluation value, and finally judge whether to provide vibration abnormality feedback based on the obtained motor vibration evaluation value, thereby improving the reliability of the motor fault information push result, and then achieving the improvement of the accuracy of the push result in the motor fault monitoring process based on big data analysis, effectively solving the problem of inaccurate push results in the motor fault monitoring process based on big data analysis in the prior art.

[0023] 2. The qualified evaluation value of the motor acquisition speed is obtained through the electromagnetic-acquisition rate evaluation value, the electromagnetic-acquisition speed evaluation value, the temperature-acquisition rate evaluation value and the temperature-acquisition speed evaluation value, thereby achieving accurate quantification of the data acquisition qualification of the motor during the monitoring of the industrial production line, and further achieving the improvement of the reliability of obtaining electrical equipment health monitoring data.

[0024] 3. Visualize the obtained unqualified vibration evaluation values ​​to obtain a scatter plot of the output qualified time period-unqualified vibration evaluation values, and then push the motor fault information corresponding to the scatter plot of the output qualified time period-unqualified vibration evaluation values ​​to the preset personnel, thereby realizing the visualization of vibration abnormality feedback, and further realizing the improvement of the effectiveness of the information pushed during the motor fault monitoring process. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flowchart of an electrical equipment health monitoring method based on big data analysis provided in an embodiment of the present application;

[0026] Figure 2 Provide a statistical graph of changes in vibration signal packet loss rate and acquisition-loss evaluation value for the embodiment of the present application;

[0027] Figure 3 An overall flow chart provided for an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiment of the present application solves the problem of inaccurate push results in the motor fault monitoring process based on big data analysis in the prior art by providing an electrical equipment health monitoring method based on big data analysis. A qualified evaluation value of the motor acquisition speed is obtained through motor acquisition speed related data. Based on the qualified evaluation value of the motor acquisition speed, it is determined whether to optimize the acquisition speed to obtain a qualified motor acquisition speed value. Then, a qualified evaluation value of the sensor vibration signal output is obtained through the qualified evaluation value of the motor acquisition speed and the obtained sensor vibration related data. Based on the qualified evaluation value of the sensor vibration signal output, it is determined whether to optimize the vibration signal output to obtain a qualified sensor vibration signal output evaluation value. If a qualified sensor vibration signal output evaluation value is obtained, a motor vibration evaluation value is obtained through the obtained motor vibration related data. Finally, based on the motor vibration evaluation value, it is determined whether to perform vibration abnormality feedback, thereby improving the accuracy of push results in the motor fault monitoring process based on big data analysis.

[0029] The technical solution in the embodiment of the present application is to solve the problem of inaccurate push results in the motor fault monitoring process based on big data analysis. The overall idea is as follows:

[0030] The motor acquisition speed related data is used to obtain the motor acquisition speed qualification evaluation value and determine whether to optimize the acquisition speed. Then, the sensor vibration signal output qualification evaluation value is obtained to determine whether to optimize the vibration signal output. Finally, based on the obtained motor vibration evaluation value, it is determined whether to provide vibration abnormality feedback, thereby achieving the effect of improving the accuracy of the push results in the motor fault monitoring process based on big data analysis.

[0031] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0032] like Figure 1 As shown, it is a flow chart of an electrical equipment health monitoring method based on big data analysis provided by an embodiment of the present application, and the method includes the following steps: S1, motor acquisition speed qualification evaluation: based on the industrial production line monitoring device, the motor acquisition speed related data is obtained, and the motor acquisition speed qualification evaluation value is obtained through the motor acquisition speed related data, and based on the motor acquisition speed qualification evaluation value, it is determined whether to optimize the acquisition speed to obtain a qualified motor acquisition speed value, and the motor acquisition speed qualification evaluation value is used to evaluate the data acquisition qualification of the motor during the industrial production line monitoring process; S2, sensor vibration signal output qualification evaluation: the sensor vibration signal output qualification evaluation value is obtained through the motor acquisition speed qualification evaluation value and the obtained sensor vibration related data, Based on the qualified evaluation value of the sensor vibration signal output, it is judged whether to optimize the vibration signal output to obtain a qualified sensor vibration signal output evaluation value. The qualified evaluation value of the sensor vibration signal output is used to evaluate the qualified output of the sensor vibration signal during the industrial production line monitoring process; S3, motor vibration evaluation: if a qualified sensor vibration signal output evaluation value is obtained, the motor vibration evaluation value is obtained through the obtained motor vibration-related data, and the motor vibration evaluation value is used to evaluate the vibration condition of the motor during the industrial production line monitoring process; S4, vibration abnormality feedback: based on the motor vibration evaluation value, it is judged whether to perform vibration abnormality feedback. Vibration abnormality feedback means that the motor fault information corresponding to the unqualified vibration evaluation value is pushed to the preset personnel through the big data analysis method.

[0033] It should be added that the data related to the motor acquisition speed include the average electromagnetic interference power, the maximum temperature of the production line environment, the average acquisition rate of the vibration signal, the average sampling word length and the average sampling bandwidth; the sensor vibration related data include the vibration signal fundamental wave amplitude, the vibration signal harmonic amplitude, the vibration signal packet loss rate, the sensor input signal power and the sensor output signal power; the motor vibration related data include the motor vibration frequency, the motor vibration displacement and the motor vibration acceleration; the sampling average word length indicates the average number of bits corresponding to the motor acquisition speed related data within the preset time period; the sampling average bandwidth indicates the average bandwidth corresponding to the motor acquisition speed related data within the preset time period.

[0034] In this embodiment, the motor acquisition speed qualified evaluation value, the sensor vibration signal output qualified evaluation value and the motor vibration evaluation value influence each other: the motor acquisition speed qualified evaluation value may affect the output of the sensor vibration signal. When the difference between the motor acquisition speed qualified evaluation value and the reference acquisition qualified value is lower than 0, the acquisition speed needs to be optimized. When the difference between the sensor vibration signal output qualified evaluation value and the reference vibration signal output qualified value is lower than 0, the accuracy of the motor vibration evaluation value obtained based on the sensor vibration signal output qualified evaluation value will also be reduced. The motor vibration evaluation value pays attention to the vibration condition of the motor while indirectly reflecting the operating status of the motor. When the difference between the motor vibration evaluation value and the reference motor vibration qualified value is greater than 0, it indicates that the motor vibration is abnormal and motor vibration optimization is required.

[0035] The present application can process and analyze motor acquisition speed related data, sensor vibration related data, motor vibration related data, motor acquisition speed qualified evaluation value, sensor vibration signal output qualified evaluation value and motor vibration evaluation value through big data analysis technology, and improve the accuracy of motor acquisition speed qualified evaluation value, sensor vibration signal output qualified evaluation value and motor vibration evaluation value by mining the correlation and regularity in the data, thereby achieving the improvement of the accuracy of push results in the motor fault monitoring process based on big data analysis.

[0036] It needs to be explained that the electromagnetic interference power change of the motor within a preset time period is monitored by an electromagnetic interference meter and statistically averaged to obtain the average electromagnetic interference power value. The above-mentioned preset time period is generally the duration corresponding to a complete vibration signal harmonic. The environment of the preset position point of the production line within the preset time period is measured by a temperature sensor and statistically analyzed to obtain the maximum temperature of the production line environment. The number of vibration signal acquisition times within the preset time period is recorded by a counter and a vibration signal analyzer, and statistically averaged to obtain the average acquisition rate of the vibration signal. The number of bits and bandwidth corresponding to the motor acquisition speed related data within the preset time period are recorded by a data acquisition card, and statistically averaged to obtain the sampling average word length and sampling average bandwidth. The industrial production line monitoring device includes an electromagnetic interference meter, a temperature sensor, a counter, a vibration signal analyzer and a data acquisition card.

[0037] The vibration of the motor is monitored during the qualified collection time period by using a vibration sensor and a vibration signal analyzer, and the fundamental component and harmonic component of the vibration signal are extracted and analyzed to obtain the amplitude of the first harmonic and the fundamental amplitude to obtain the vibration signal fundamental amplitude and the vibration signal harmonic amplitude. The number of lost data packets of the vibration signal during the qualified collection time period is counted by using a vibration signal analyzer and a data acquisition card to obtain the vibration signal packet loss rate; the power of the vibration signal received by the sensor (such as an acceleration sensor) at the end of the qualified collection time period is measured by a power meter and analyzed to obtain the sensor input signal power and the sensor output signal power.

[0038] The frequency components of the vibration signal within the qualified output time period are analyzed by a vibration sensor and a vibration signal analyzer to obtain the vibration frequency of the motor. The average value of the displacement change of the motor within the qualified output time period is analyzed and counted by a vibration sensor and a laser rangefinder to obtain the vibration displacement of the motor. The average value of the acceleration change of the motor within the qualified output time period is analyzed and counted by an acceleration sensor to obtain the vibration acceleration of the motor.

[0039] Furthermore, the specific process of obtaining the qualified evaluation value of the motor acquisition speed through the motor acquisition speed related data is as follows: V11, the electromagnetic influence factor is obtained by analyzing the average electromagnetic interference power value and the preset first acquisition weight value and the preset electromagnetic interference power maximum threshold value obtained from the database, and the preset first acquisition weight value is used to reflect the influence of the average electromagnetic interference power value on the electromagnetic influence factor within a preset time period; V12, the temperature influence factor is obtained by analyzing the maximum temperature of the production line environment and the preset second acquisition weight value and the preset production line environment temperature maximum threshold value obtained from the database, and the preset second acquisition weight value is used to reflect the influence of the production line temperature on the electromagnetic influence factor within a preset time period; V13, the electromagnetic-acquisition rate evaluation value is obtained by analyzing the electromagnetic influence factor, the average acquisition rate of the vibration signal and the preset acquisition rate maximum threshold value obtained from the database, that is, , the electromagnetic-acquisition rate evaluation value is used to reflect the influence of the electromagnetic influence factor on the acquisition rate within the preset time period; V14, the electromagnetic-acquisition speed evaluation value is obtained by analyzing the electromagnetic influence factor, the average acquisition rate of the vibration signal, the average sampling word length, the average sampling bandwidth and the preset maximum acquisition speed threshold obtained from the database, that is, ; V15, the temperature-acquisition rate evaluation value is obtained by data processing based on the temperature influence factor, the average acquisition rate of the vibration signal and the preset maximum acquisition rate threshold obtained from the database, that is, ; V16, through the temperature influence factor, the average acquisition rate of the vibration signal, the average sampling word length, the average sampling bandwidth and the preset acquisition speed maximum threshold obtained from the database, the temperature-acquisition speed evaluation value is obtained by data processing, that is, ; V17, obtain the qualified evaluation value of the motor acquisition speed through the electromagnetic-acquisition rate evaluation value, the electromagnetic-acquisition speed evaluation value, the temperature-acquisition rate evaluation value and the temperature-acquisition speed evaluation value.

[0040] Among them, the limiting expression of the qualified evaluation value of the motor acquisition speed is as follows:

[0041] ;

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] ;

[0048] In the formula, represents the qualified evaluation value of the motor speed collection in the wth preset time period, , w represents the number of the preset time period, v represents the total number of preset time periods, represents the electromagnetic-collection rate evaluation value in the wth preset time period, represents the electromagnetic-acquisition speed evaluation value in the wth preset time period, represents the temperature-collection rate evaluation value in the wth preset time period, represents the temperature-collection speed evaluation value in the w-th preset time period, represents the electromagnetic influence factor in the wth preset time period, represents the temperature influence factor in the wth preset time period, represents the average acquisition rate of the vibration signal in the wth preset time period, represents the average word length of samples in the wth preset time period, represents the average sampling bandwidth in the wth preset time period, represents the average electromagnetic interference power in the wth preset time period, represents the maximum temperature of the production line environment in the wth preset time period, Indicates the preset maximum collection rate threshold. Indicates the preset maximum threshold of acquisition speed. Indicates the preset maximum threshold of electromagnetic interference power. Indicates the maximum threshold of the preset production line ambient temperature. Indicates the preset first acquisition weight value, represents the preset second acquisition weight value, and e represents a natural constant.

[0049] In this embodiment, the aforementioned database is a database for storing various types of setting data established before the design of the electrical equipment health monitoring method based on big data analysis provided in the embodiment of the present application. The database includes but is not limited to electromagnetic interference power, vibration frequency, vibration displacement, etc., and the various numerical values ​​therein are directly set by technical personnel; for example, the preset maximum acquisition rate threshold is represented by the maximum value of the acquisition rate in the historical time period in the database, the preset maximum acquisition speed threshold is represented by the maximum value of the acquisition speed in the historical time period in the database, the preset maximum electromagnetic interference power threshold is represented by the maximum value of the electromagnetic interference power in the historical time period in the database, and the preset maximum production line ambient temperature threshold is represented by the maximum value of the production line ambient temperature in the historical time period in the database.

[0050] It should be added that a mapping set that can reflect the mapping relationship between the average electromagnetic interference power value, the maximum temperature of the production line environment and the corresponding weights is pre-set in the database. The mapping relationship between the average electromagnetic interference power value, the maximum temperature of the production line environment and the corresponding weights can be a one-to-one correspondence or a many-to-one relationship. For example, in this embodiment, the preset first acquisition weight value and the preset second acquisition weight value have a value range of 0-1. By inputting the real-time average electromagnetic interference power value and the maximum temperature of the production line environment into the mapping set, the corresponding preset first acquisition weight value and the preset second acquisition weight value can be obtained.

[0051] Specifically, assuming that the electromagnetic-collection rate evaluation value The range is 0.35-0.65, electromagnetic-acquisition speed evaluation value The range is 0.35-0.65, the temperature-collection rate evaluation value The range is 0.35-0.65, temperature-collection speed evaluation value The range is 0.35-0.65, as shown in Table 1, which is a statistical table of changes in the motor speed qualification evaluation value provided in the embodiment of the present application:

[0052] Table 1 Statistics of changes in motor speed qualification assessment values

[0053]

[0054] As can be seen from the above table, with the electromagnetic-collection rate evaluation value , Electromagnetic-Acquisition Speed ​​Evaluation Value , Temperature-collection rate evaluation value and temperature-acquisition speed evaluation value The gradually increases and the qualified evaluation value of the motor acquisition speed gradually decreases, which means that the qualified status of the motor data acquisition during the industrial production line monitoring process gradually decreases.

[0055] It should be understood that the algorithm of this embodiment combines the analysis of the motor acquisition speed related data to obtain the motor acquisition speed qualified evaluation value. The larger the average electromagnetic interference power value and the maximum temperature of the production line environment, the greater the impact on the electromagnetic-acquisition rate evaluation value, the electromagnetic-acquisition speed evaluation value, the temperature-acquisition rate evaluation value and the temperature-acquisition speed evaluation value, resulting in a decrease in the motor acquisition speed qualified evaluation value; the larger the average acquisition rate of the vibration signal, the higher the efficiency of collecting motor-related data, resulting in an increase in the motor acquisition speed qualified evaluation value; the larger the sampling average word length and the sampling average bandwidth, the faster the sampling rate, resulting in an increase in the motor acquisition speed qualified evaluation value.

[0056] It should be understood that the motor acquisition speed related data in the algorithm of this embodiment does not exist independently, but has mutual correlation, and needs comprehensive analysis. The larger the average value of electromagnetic interference power, the more likely it is to affect the stability and accuracy of the data acquisition system, and further affect the average acquisition rate of the vibration signal, the average sampling word length and the average sampling bandwidth, resulting in a decrease in the quality of the collected data, and then a decrease in the qualified evaluation value of the motor acquisition speed; the higher the vibration signal acquisition rate, the higher the sampling word length and bandwidth may be required to support data acquisition and analysis. The higher the average acquisition rate of the vibration signal, the more accurately the vibration information of the motor can be captured, thereby improving the accuracy and reliability of data acquisition, resulting in an increase in the qualified evaluation value of the motor acquisition speed. By analyzing the comprehensive influence between the parameters, the accurate quantification of the qualified data acquisition of the motor in the process of industrial production line monitoring is achieved, and then the accuracy of the push results in the motor fault monitoring process based on big data analysis is improved.

[0057] Further, the specific process of obtaining the qualified evaluation value of the sensor vibration signal output by the motor acquisition speed qualified evaluation value and the acquired sensor vibration related data is as follows: V21, obtain the acquisition-distortion evaluation value, that is, , the acquisition-distortion evaluation value is obtained by processing the motor acquisition speed qualification evaluation value, the vibration signal harmonic amplitude, the vibration signal fundamental amplitude and the preset signal distortion maximum threshold obtained from the database; V22, obtain the acquisition-loss evaluation value, that is, , the acquisition-loss evaluation value is obtained by processing the qualified evaluation value of the motor acquisition speed, the vibration signal packet loss rate and the preset signal loss maximum threshold obtained from the database; V23, obtains the acquisition-loss evaluation value, that is, , the acquisition-loss assessment value is obtained by processing the motor acquisition speed qualified assessment value, the sensor input signal power, the sensor output signal power and the preset signal loss maximum threshold obtained from the database; V24, the sensor vibration signal output qualified assessment value is obtained by the acquisition-distortion assessment value, the acquisition-loss assessment value and the acquisition-loss assessment value; the acquisition qualified time period represents the preset time period corresponding to the motor acquisition speed qualified assessment value; the acquisition qualified time period contains a complete vibration signal harmonic.

[0058] Among them, the qualified evaluation value of the sensor vibration signal output is obtained by the following method:

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] In the formula, represents the qualified evaluation value of the sensor vibration signal output in the mth qualified acquisition time period, , m represents the number of the qualified collection time period, k represents the total number of collection time periods, represents the acquisition-distortion evaluation value in the mth acquisition qualified time period, represents the acquisition-loss evaluation value in the mth acquisition qualified time period, represents the collection-loss assessment value in the mth collection qualified time period, represents the motor acquisition speed qualified evaluation value in the mth acquisition qualified time period. When the motor acquisition speed qualified evaluation value is not lower than the reference acquisition qualified value, the corresponding motor acquisition speed qualified evaluation value is recorded as the motor acquisition speed qualified evaluation value. Indicates the fundamental amplitude of the vibration signal in the mth qualified acquisition time period, represents the harmonic amplitude of the vibration signal in the mth qualified acquisition time period, It represents the vibration signal packet loss rate in the mth qualified collection time period, represents the sensor input signal power in the mth qualified acquisition time period, represents the sensor output signal power in the mth qualified acquisition time period, Indicates the preset maximum threshold of signal distortion. Indicates the preset signal loss maximum threshold. represents the preset maximum signal loss threshold, and e represents a natural constant.

[0064] In this embodiment, the preset signal distortion maximum threshold is represented by the maximum value of the harmonic distortion in the historical time period in the database, the preset signal loss maximum threshold is represented by the maximum value of the vibration signal packet loss rate in the historical time period in the database, and the preset signal loss maximum threshold is represented by the maximum value of the vibration signal attenuation in the historical time period in the database.

[0065] Specifically, assuming that the motor acquisition speed qualification evaluation value Fixed to 1.5, vibration signal packet loss rate The range is 0.35-0.65, and the maximum threshold of signal loss is preset Fixed to 0.65, such as Figure 2 As shown in FIG. 1 , a statistical diagram of the vibration signal packet loss rate and the acquisition-loss evaluation value provided in an embodiment of the present application is shown in FIG. Figure 2 It can be seen that as the vibration signal packet loss rate gradually increases, the acquisition-loss assessment value gradually decreases, which means that the loss degree of the vibration signal gradually increases, resulting in a gradual decrease in the output qualification of the sensor vibration signal.

[0066] It should be understood that the algorithm of this embodiment combines the motor acquisition speed qualified evaluation value and the sensor vibration related data analysis to obtain the sensor vibration signal output qualified evaluation value. The larger the motor acquisition speed qualified evaluation value, the better the data acquisition qualified condition of the motor during the industrial production line monitoring process, resulting in an increase in the sensor vibration signal output qualified evaluation value; the larger the vibration signal harmonic amplitude, the vibration signal packet loss rate, and the sensor input signal power, the smaller the vibration signal fundamental wave amplitude and the sensor output signal power, which means that the vibration signal is distorted, lost, and the degree of loss is greater, resulting in a decrease in the sensor vibration signal output qualified evaluation value.

[0067] It should be understood that the motor acquisition speed qualified evaluation value and the sensor vibration related data in the algorithm of this embodiment do not exist independently, but are interrelated and need to be comprehensively analyzed. The larger the motor acquisition speed qualified evaluation value, the better the motor data acquisition situation, and the more positive the impact on the output of the sensor vibration signal, thereby reflecting the running state of the motor, resulting in an increase in the sensor vibration signal output qualified evaluation value; when the fundamental amplitude of the vibration signal increases, it usually means that the vibration intensity increases, which may cause the harmonic amplitude to increase; an increase in the input signal power may lead to an increase in the output signal power, resulting in an improvement in the signal transmission and recognition capabilities, but an increase in the input signal power may also cause damage to the sensor or performance degradation, resulting in a decrease in the sensor vibration signal output qualified evaluation value. By analyzing the comprehensive impact between the parameters, the accurate quantification of the output qualified situation of the sensor vibration signal in the process of industrial production line monitoring is achieved, and then the accuracy of the push results in the motor fault monitoring process based on big data analysis is improved.

[0068] Furthermore, the specific process of obtaining the motor vibration evaluation value is as follows: V31, judging whether the sensor vibration signal output qualified evaluation value meets the qualified conditions, when the sensor vibration signal output qualified evaluation value meets the qualified conditions, obtaining the motor vibration evaluation value, the qualified conditions indicate that the sensor vibration signal output qualified evaluation value is marked as a qualified sensor vibration signal output evaluation value; V32, obtaining the output-vibration frequency evaluation value, that is, , the output-vibration frequency evaluation value is obtained by processing the motor vibration frequency; V33, obtains the output-vibration displacement evaluation value, that is, , the output-vibration displacement evaluation value is obtained by processing the motor vibration displacement; V34, obtains the output-vibration acceleration evaluation value, that is, , the output-vibration acceleration evaluation value is obtained by processing the motor vibration acceleration; V35, the motor vibration evaluation value is obtained by the output-vibration frequency evaluation value, the output-vibration displacement evaluation value, the output-vibration acceleration evaluation value and the preset frequency evaluation maximum threshold, the preset displacement evaluation maximum threshold and the preset acceleration evaluation maximum threshold obtained from the database; the output qualified time period represents the collection qualified time period corresponding to the qualified sensor vibration signal output evaluation value.

[0069] Among them, the motor vibration evaluation value is obtained by the following method:

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] In the formula, represents the motor vibration evaluation value in the zth output qualified time period, , z represents the number of the qualified output time period, h represents the total number of qualified output time periods, represents the output-vibration frequency evaluation value in the zth output qualified time period, represents the output-vibration displacement evaluation value in the zth output qualified time period, represents the output-vibration acceleration evaluation value in the zth output qualified time period, represents the motor vibration frequency in the zth output qualified time period, represents the motor vibration displacement in the zth output qualified time period, represents the vibration acceleration of the motor during the zth qualified output time period, represents the qualified sensor vibration signal output evaluation value in the zth qualified output time period, that is, when the sensor vibration signal output qualified evaluation value is not lower than the reference vibration signal output qualified value, the corresponding sensor vibration signal output qualified evaluation value is recorded as the qualified sensor vibration signal output evaluation value, Indicates the preset frequency evaluation maximum threshold, Indicates the preset maximum threshold for displacement evaluation. Indicates the preset maximum threshold for acceleration evaluation. It represents the qualified value of the reference vibration signal output, and e represents the natural constant.

[0075] In this embodiment, the preset frequency evaluation maximum threshold is represented by the maximum value of the vibration frequency evaluation value output in the historical time period in the database, the preset displacement evaluation maximum threshold is represented by the maximum value of the vibration displacement evaluation value output in the historical time period in the database, and the preset acceleration evaluation maximum threshold is represented by the maximum value of the vibration acceleration evaluation value output in the historical time period in the database.

[0076] It should be understood that the algorithm of this embodiment combines the motor vibration related data analysis to obtain the motor vibration evaluation value. The larger the motor vibration frequency, motor vibration displacement and motor vibration acceleration, the more severe the motor vibration, resulting in an increase in the motor vibration evaluation value.

[0077] It should be understood that the motor vibration related data in the algorithm of this embodiment does not exist independently, but is interrelated and needs to be comprehensively analyzed. When the motor vibration frequency increases, it means that the motor experiences more vibration cycles per unit time, which may lead to an increase in the cumulative effect of the motor vibration displacement; when the motor vibration frequency increases, the motor vibration acceleration may increase accordingly, because the increase in the change in vibration speed per unit time means that the motor vibrates more violently, which leads to an increase in the motor vibration evaluation value. By analyzing the comprehensive influence between parameters, the accurate quantification of the vibration conditions of the motor in the process of industrial production line monitoring is achieved, and then the accuracy of the push results in the motor fault monitoring process based on big data analysis is improved.

[0078] Furthermore, the specific process of judging whether to perform acquisition speed optimization to obtain a qualified motor acquisition speed value based on the motor acquisition speed qualified evaluation value is as follows: perform difference calculation on the motor acquisition speed qualified evaluation value and the reference acquisition qualified value obtained from the database, and when the difference between the motor acquisition speed qualified evaluation value and the reference acquisition qualified value is not less than 0, mark the corresponding motor acquisition speed qualified evaluation value as a qualified motor acquisition speed value; when the difference between the motor acquisition speed qualified evaluation value and the reference acquisition qualified value is less than 0, perform acquisition speed optimization; if the difference between the motor acquisition speed qualified evaluation value and the reference acquisition qualified value is still less than 0 after the acquisition speed optimization, send an acquisition fault prompt to the preset personnel; the acquisition speed optimization includes differential transmission and temperature compensation; differential transmission means that the transmission is performed by replacing the single-ended signal with a differential signal to improve the anti-interference ability of the vibration signal; temperature compensation means sending a prompt to the preset personnel to stabilize the vibration signal output through the temperature compensation circuit; the qualified motor acquisition speed value represents the motor acquisition speed qualified evaluation value that is not less than the reference acquisition qualified value obtained from the database.

[0079] In this embodiment, the reference acquisition qualified value is represented by the average value of the motor acquisition speed qualified evaluation value in the historical time period in the database; differential transmission has higher anti-interference ability. During single-ended signal transmission, the signal line will be interfered by environmental noise, which may cause signal distortion or misjudgment. Differential transmission can ensure the stability and accuracy of the vibration signal during the transmission process, thereby improving the reliability of the acquisition speed evaluation; when the temperature changes, the temperature compensation circuit will adjust the gain, offset and other parameters of the signal to ensure that the output of the vibration signal remains within a stable range (the difference between the motor acquisition speed qualified evaluation value and the reference acquisition qualified value is not less than 0), thereby achieving the improvement of the accuracy of the push results in the motor fault monitoring process based on big data analysis.

[0080] Furthermore, the specific process of judging whether to perform vibration signal output optimization based on the sensor vibration signal output qualified evaluation value to obtain a qualified sensor vibration signal output evaluation value is as follows: performing a difference operation on the sensor vibration signal output qualified evaluation value and the reference vibration signal output qualified value obtained from the database; when the difference between the sensor vibration signal output qualified evaluation value and the reference vibration signal output qualified value is not less than 0, it indicates that the vibration signal output is qualified, marking the corresponding sensor vibration signal output qualified evaluation value as a qualified sensor vibration signal output evaluation value and obtaining the motor vibration evaluation value; when the difference between the sensor vibration signal output qualified evaluation value and the reference vibration signal output qualified value is less than 0, performing a difference operation on the sensor vibration signal output qualified evaluation value and the reference vibration signal output qualified evaluation value. Vibration signal output optimization is performed; if after vibration signal output optimization, the difference between the sensor vibration signal output qualified evaluation value and the reference vibration signal output qualified value is still lower than 0, indicating that the vibration signal output is unqualified, an output fault prompt is sent to a preset person and the motor vibration evaluation value is not obtained; vibration signal output optimization includes signal amplification and vibration signal denoising; signal amplification means sending a prompt to the preset person to add a signal amplifier at a preset position of the industrial production line; vibration signal denoising means removing nonlinear noise in the vibration signal by wavelet transform; a qualified sensor vibration signal output evaluation value means a sensor vibration signal output qualified evaluation value that is not lower than the reference vibration signal output qualified value obtained from the database.

[0081] In this embodiment, the reference vibration signal output qualified value is represented by the average value of the sensor vibration signal output qualified evaluation value in the historical time period in the database; the signal amplifier can enhance the amplitude of the vibration signal, and the preset personnel install the signal amplifier at the preset position of the production line after receiving the prompt to add the signal amplifier; the wavelet transform can identify and separate the nonlinear noise (such as high-frequency noise or burst noise) in the vibration signal, and can decompose the signal on different time-frequency scales, thereby achieving effective removal of noise and accurate extraction of signal features, thereby achieving improved accuracy of push results in the motor fault monitoring process based on big data analysis.

[0082] Furthermore, the specific process of judging whether to perform abnormal vibration feedback based on the motor vibration evaluation value is as follows: performing difference calculation on the motor vibration evaluation value and the reference motor vibration qualified value from the database; when the difference between the motor vibration evaluation value and the reference motor vibration qualified value is not greater than 0, it indicates that the motor vibration is qualified, and the corresponding motor vibration evaluation value is marked as a qualified vibration evaluation value; when the difference between the motor vibration evaluation value and the reference motor vibration qualified value is greater than 0, it indicates that the motor vibration is abnormal, and a prompt is sent to the preset personnel to perform motor vibration optimization, and the motor vibration optimization indicates re-optimizing the acquisition speed and the vibration signal output; when the difference between the monitored motor vibration evaluation value and the reference motor vibration qualified value is not greater than 0, the motor vibration optimization is stopped, otherwise the motor vibration evaluation value corresponding to the difference between the motor vibration evaluation value and the reference motor vibration qualified value is greater than 0 is marked as an unqualified vibration evaluation value.

[0083] It should be added that the specific process of vibration abnormality feedback is as follows: based on the obtained unqualified vibration evaluation value, visualization processing is performed to obtain a scatter plot of output qualified time period-unqualified vibration evaluation value; the motor fault information corresponding to the scatter plot of output qualified time period-unqualified vibration evaluation value is pushed to the preset personnel; the visualization processing represents the extraction of unqualified vibration evaluation values ​​and corresponding output qualified time periods through big data analysis methods; the scatter plot of output qualified time period-unqualified vibration evaluation value is used to reflect the corresponding relationship between unqualified vibration evaluation values ​​and output qualified time periods.

[0084] In this embodiment, if Figure 3 As shown, it is an overall flow chart provided by an embodiment of the present application; the reference motor vibration qualified value is represented by the average value of the motor vibration evaluation value of the historical time period in the database; the unqualified vibration evaluation value and its corresponding output qualified time period are extracted from the motor vibration related data and the motor vibration evaluation value through a big data analysis method (such as association rule mining); the output qualified time period-unqualified vibration evaluation value scatter plot has the output qualified time period as the horizontal axis and the unqualified vibration evaluation value as the vertical axis, and the correspondence between the unqualified vibration evaluation value and the output qualified time period is intuitively displayed through the distribution of points; when the output qualified time period-unqualified vibration evaluation value scatter plot is generated, the corresponding motor fault information is pushed to the preset personnel, which helps to accurately identify the vibration health status of the motor, thereby achieving an improvement in the accuracy of the push results in the motor fault monitoring process based on big data analysis.

[0085] To summarize, the embodiment of the present application obtains a qualified evaluation value of the motor acquisition speed through motor acquisition speed related data and determines whether to optimize the acquisition speed, then determines whether to optimize the vibration signal output through the obtained qualified evaluation value of the sensor vibration signal output, and finally determines whether to perform vibration abnormality feedback based on the obtained motor vibration evaluation value, thereby achieving an improvement in the reliability of the results of pushing motor fault information, and further achieving an improvement in the accuracy of the push results in the motor fault monitoring process based on big data analysis, effectively solving the problem of inaccurate push results in the motor fault monitoring process based on big data analysis in the prior art.

[0086] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0090] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0091] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An electrical equipment health monitoring method based on big data analysis, characterized in that: The following steps are involved: S1, based on the industrial production line monitoring device, obtains motor acquisition speed related data, obtains the motor acquisition speed qualified evaluation value through the motor acquisition speed related data, and judges whether to optimize the acquisition speed to obtain a qualified motor acquisition speed value based on the motor acquisition speed qualified evaluation value, wherein the motor acquisition speed qualified evaluation value is used to evaluate the data acquisition qualified status of the motor in the industrial production line monitoring process; S2, obtaining a sensor vibration signal output qualified evaluation value through the motor acquisition speed qualified evaluation value and the obtained sensor vibration related data, and judging whether to perform vibration signal output optimization based on the sensor vibration signal output qualified evaluation value to obtain a qualified sensor vibration signal output evaluation value, wherein the sensor vibration signal output qualified evaluation value is used to evaluate the output qualified condition of the sensor vibration signal during the industrial production line monitoring process; S3, if a qualified sensor vibration signal output evaluation value is obtained, a motor vibration evaluation value is obtained by using the obtained motor vibration related data, and the motor vibration evaluation value is used to evaluate the vibration condition of the motor during the industrial production line monitoring process; S4, judging whether to perform vibration abnormality feedback based on the motor vibration evaluation value, wherein the vibration abnormality feedback means pushing the motor fault information corresponding to the unqualified vibration evaluation value to the preset personnel through the big data analysis method.

2. The electrical equipment health monitoring method based on big data analysis as claimed in claim 1, characterized in that: The motor acquisition speed related data include the average electromagnetic interference power, the maximum temperature of the production line environment, the average acquisition rate of the vibration signal, the average sampling word length and the average sampling bandwidth; The sensor vibration related data includes vibration signal fundamental wave amplitude, vibration signal harmonic amplitude, vibration signal packet loss rate, sensor input signal power and sensor output signal power; The motor vibration related data includes motor vibration frequency, motor vibration displacement and motor vibration acceleration; The sampling average word length represents the average number of bits corresponding to the motor speed-related data collected within a preset time period; The sampling average bandwidth represents the average bandwidth corresponding to the motor speed-related data collected within a preset time period.

3. The electrical equipment health monitoring method based on big data analysis as claimed in claim 2, characterized in that: The specific process of obtaining the qualified evaluation value of the motor acquisition speed through the motor acquisition speed related data is as follows: V11, obtaining an electromagnetic influence factor by analyzing an average electromagnetic interference power value, a preset first acquisition weight value obtained from a database, and a preset electromagnetic interference power maximum threshold; V12, analyzing the maximum temperature of the production line environment, the preset second acquisition weight value obtained from the database, and the preset maximum threshold of the production line environment temperature to obtain a temperature influence factor; V13, obtaining an electromagnetic-acquisition rate evaluation value by analyzing the electromagnetic influence factor, the average acquisition rate of the vibration signal and the preset maximum acquisition rate threshold obtained from the database; V14, obtains the electromagnetic-acquisition speed evaluation value by analyzing the electromagnetic influence factor, the average acquisition rate of the vibration signal, the average sampling word length, the average sampling bandwidth and the preset maximum acquisition speed threshold obtained from the database; V15, obtains a temperature-acquisition rate evaluation value by performing data processing based on the temperature influence factor, the average acquisition rate of the vibration signal, and the preset maximum acquisition rate threshold obtained from the database; V16, obtains the temperature-acquisition speed evaluation value by processing the data based on the temperature influence factor, the average acquisition rate of the vibration signal, the average sampling word length, the average sampling bandwidth, and the preset maximum acquisition speed threshold obtained from the database; V17, obtain the qualified evaluation value of the motor acquisition speed through the electromagnetic-acquisition rate evaluation value, the electromagnetic-acquisition speed evaluation value, the temperature-acquisition rate evaluation value and the temperature-acquisition speed evaluation value.

4. The electrical equipment health monitoring method based on big data analysis as claimed in claim 1, characterized in that: The limiting expression of the qualified evaluation value of the motor acquisition speed is as follows: ； In the formula, represents the qualified evaluation value of the motor speed collection in the wth preset time period, , w represents the number of the preset time period, v represents the total number of preset time periods, represents the electromagnetic-collection rate evaluation value in the wth preset time period, represents the electromagnetic-acquisition speed evaluation value in the wth preset time period, represents the temperature-collection rate evaluation value in the wth preset time period, represents the temperature-acquisition speed evaluation value in the wth preset time period, and e represents a natural constant.

5. The electrical equipment health monitoring method based on big data analysis as claimed in claim 2, characterized in that: The specific process of obtaining the sensor vibration signal output qualified evaluation value through the motor acquisition speed qualified evaluation value and the obtained sensor vibration related data is as follows: V21, obtaining an acquisition-distortion evaluation value, wherein the acquisition-distortion evaluation value is obtained by processing the motor acquisition speed qualification evaluation value, the vibration signal harmonic amplitude, the vibration signal fundamental amplitude, and a preset signal distortion maximum threshold obtained from a database; V22, obtaining an acquisition-loss evaluation value, wherein the acquisition-loss evaluation value is obtained by processing the motor acquisition speed qualification evaluation value, the vibration signal packet loss rate, and a preset signal loss maximum threshold obtained from a database; V23, obtaining an acquisition-loss assessment value, wherein the acquisition-loss assessment value is obtained by processing the motor acquisition speed qualification assessment value, the sensor input signal power, the sensor output signal power and a preset signal loss maximum threshold obtained from a database; V24, obtains the sensor vibration signal output qualified evaluation value through the acquisition-distortion evaluation value, the acquisition-loss evaluation value and the acquisition-loss evaluation value.

6. The electrical equipment health monitoring method based on big data analysis as claimed in claim 2, characterized in that: The specific process of obtaining the motor vibration evaluation value is as follows: V31, judging whether the qualified evaluation value of the sensor vibration signal output meets the qualified condition, and obtaining the motor vibration evaluation value when the qualified evaluation value of the sensor vibration signal output meets the qualified condition, wherein the qualified condition indicates that the qualified evaluation value of the sensor vibration signal output is marked as a qualified sensor vibration signal output evaluation value; V32, obtaining an output-vibration frequency evaluation value, wherein the output-vibration frequency evaluation value is obtained by processing the motor vibration frequency; V33, obtaining an output-vibration displacement evaluation value, wherein the output-vibration displacement evaluation value is obtained by processing the motor vibration displacement; V34, obtaining an output-vibration acceleration evaluation value, wherein the output-vibration acceleration evaluation value is obtained by processing the motor vibration acceleration; V35, obtains the motor vibration evaluation value through the output-vibration frequency evaluation value, the output-vibration displacement evaluation value, the output-vibration acceleration evaluation value and the preset frequency evaluation maximum threshold, the preset displacement evaluation maximum threshold and the preset acceleration evaluation maximum threshold obtained from the database.

7. The electrical equipment health monitoring method based on big data analysis as claimed in claim 3, characterized in that: The specific process of judging whether to optimize the acquisition speed to obtain the qualified motor acquisition speed value based on the qualified evaluation value of the motor acquisition speed is as follows: When the difference between the motor acquisition speed qualified evaluation value and the reference acquisition qualified value is not less than 0, the corresponding motor acquisition speed qualified evaluation value is marked as a qualified motor acquisition speed value; When the difference between the motor acquisition speed qualified evaluation value and the reference acquisition qualified value is lower than 0, the acquisition speed is optimized; If the difference between the motor acquisition speed qualified evaluation value and the reference acquisition qualified value is still lower than 0 after the acquisition speed optimization, an acquisition fault prompt is sent to the preset personnel; The acquisition speed optimization includes differential transmission and temperature compensation; The qualified motor acquisition speed value represents a motor acquisition speed qualified evaluation value that is not lower than a reference acquisition qualified value obtained from a database.

8. The electrical equipment health monitoring method based on big data analysis as claimed in claim 5, characterized in that: The specific process of judging whether to perform vibration signal output optimization based on the qualified evaluation value of the sensor vibration signal output to obtain the qualified sensor vibration signal output evaluation value is as follows: When the difference between the sensor vibration signal output qualified evaluation value and the reference vibration signal output qualified value is not less than 0, it indicates that the vibration signal output is qualified, the corresponding sensor vibration signal output qualified evaluation value is marked as a qualified sensor vibration signal output evaluation value and the motor vibration evaluation value is obtained; When the difference between the qualified evaluation value of the sensor vibration signal output and the qualified value of the reference vibration signal output is less than 0, the vibration signal output is optimized; If after the vibration signal output optimization, the difference between the sensor vibration signal output qualified evaluation value and the reference vibration signal output qualified value is still lower than 0, indicating that the vibration signal output is unqualified, an output fault prompt is sent to the preset personnel and the motor vibration evaluation value is not obtained; The vibration signal output optimization includes signal amplification and vibration signal denoising; The qualified sensor vibration signal output evaluation value represents a sensor vibration signal output qualified evaluation value that is not lower than a reference vibration signal output qualified value obtained from a database.

9. The electrical equipment health monitoring method based on big data analysis as claimed in claim 6, characterized in that: The specific process of judging whether to perform vibration abnormality feedback based on the motor vibration evaluation value is as follows: Performing a difference operation on the motor vibration evaluation value and the reference motor vibration qualified value from the database, when the difference between the motor vibration evaluation value and the reference motor vibration qualified value is not greater than 0, it indicates that the motor vibration is qualified, and marking the corresponding motor vibration evaluation value as a qualified vibration evaluation value; When the difference between the motor vibration evaluation value and the reference motor vibration qualified value is greater than 0, it indicates that the motor vibration is abnormal, and a prompt is sent to the preset personnel to optimize the motor vibration, wherein the motor vibration optimization means re-optimizing the acquisition speed and the vibration signal output; When the difference between the monitored motor vibration evaluation value and the reference motor vibration qualified value is not greater than 0, the motor vibration optimization is stopped. Otherwise, when the difference between the motor vibration evaluation value and the reference motor vibration qualified value is greater than 0, the corresponding motor vibration evaluation value is marked as an unqualified vibration evaluation value.

10. The electrical equipment health monitoring method based on big data analysis as claimed in claim 9, characterized in that: The specific process of performing vibration abnormality feedback is as follows: Visualization processing is performed based on the obtained unqualified vibration evaluation values ​​to obtain and output a scatter plot of qualified time period-unqualified vibration evaluation values; Push the motor fault information corresponding to the output qualified time period-unqualified vibration evaluation value scatter diagram to the preset personnel; The visualization processing represents extracting unqualified vibration evaluation values ​​and corresponding output qualified time periods by means of a big data analysis method; The output qualified time period-unqualified vibration evaluation value scatter diagram is used to reflect the corresponding relationship between the unqualified vibration evaluation value and the output qualified time period.

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