CN110542474A - Method, system, medium, and apparatus for detecting vibration signal of device - Google Patents
Method, system, medium, and apparatus for detecting vibration signal of device Download PDFInfo
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- CN110542474A CN110542474A CN201910831580.1A CN201910831580A CN110542474A CN 110542474 A CN110542474 A CN 110542474A CN 201910831580 A CN201910831580 A CN 201910831580A CN 110542474 A CN110542474 A CN 110542474A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
- G01H1/12—Measuring characteristics of vibrations in solids by using direct conduction to the detector of longitudinal or not specified vibrations
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Abstract
The invention provides a method, a system, a medium and a device for detecting a vibration signal of a device, wherein the method comprises the following steps: acquiring a time domain vibration signal of the device based on the vibration sensor; acquiring a time sequence of the amplitude of the characteristic frequency point changing along with time by the time-domain vibration signal based on a time-frequency domain joint analysis algorithm; and analyzing the time sequence through a preset time sequence analysis model to obtain a judgment result of whether the device is abnormal or not. The method, the system, the medium and the device for detecting the vibration signal of the device are used for improving the accuracy of a judgment result based on a time-frequency domain joint analysis algorithm and a preset time sequence analysis model.
Description
Technical Field
the present invention relates to the field of vibration signal detection technologies, and in particular, to a method, a system, a medium, and an apparatus for detecting a vibration signal of a device.
background
Most of the traditional abnormal detection methods based on vibration are modeled by performing stress analysis on the device.
In engineering science, the gravity center position influences the balance and stability of the device, and the gravity center position always reflects the condition of stress distribution inside the device. The vibration in the device is transmitted to the surface of the device, and the position of the vibration center of gravity of the vibration can reflect the distribution rule of the vibration on the surface of the device. For a device which normally and stably works, when the internal mechanical structure is unchanged, the vibration distribution on the surface of the device is basically unchanged, and when the inside of the device is in fault, the vibration distribution on the surface of the device is changed, so that the position of the vibration center of gravity is also changed. Therefore, the state of the device can be monitored through the change of the vibration center and the position of the surface of the device. The key point is to find out the stress model of the current device, analyze the vibration characteristics of the current device in detail from the aspect of mechanics, and judge whether the state is abnormal or not according to the vibration characteristics. In practice, however, the working environment of the device is often not ideal, and other environmental disturbances and noises are often generated.
The vibration signal is acquired by means of a vibration sensor mounted on the surface of the device, which means that the mixed vibrations of the device and the surroundings are recorded. Therefore, it is not practical to accurately interpret the vibration signal of the device by an analytical method after mixing the harmonic and the fundamental in the vibration spectrum. In addition, each device has its own vibration mode, and the slight component difference causes the vibration mode of each device to be different, so that it is difficult to cover all the vibration modes of a single model. It is therefore almost impossible to implement a comprehensive vibration analysis model suitable for all devices.
In the traditional method, the abnormal detection of the vibration analysis of the device is realized by analyzing and judging a surface vibration signal when the device works, and the method obtains a better result under certain conditions. However, the theoretical basis of the method, namely stress analysis modeling, is based on an ideal state, and in the actual detection process, the vibration sensor may collect the vibration of other equipment and the vibration of environmental noise in the working environment at that time, so that the judgment result is influenced.
Therefore, it is desirable to solve the problem of how to improve the accuracy of the determination result.
disclosure of Invention
in view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a method, a system, a medium and a device for detecting a vibration signal of a device, which are used to solve the problem of how to improve the accuracy of a determination result in the prior art.
To achieve the above and other related objects, the present invention provides a method for detecting a vibration signal of a device, comprising the steps of: acquiring a time domain vibration signal of the device based on the vibration sensor; acquiring a time sequence of the amplitude of the characteristic frequency point changing along with time by the time-domain vibration signal based on a time-frequency domain joint analysis algorithm; and analyzing the time sequence through a preset time sequence analysis model to obtain a judgment result of whether the device is abnormal or not.
In an embodiment of the present invention, the obtaining a time sequence of amplitude variation of characteristic frequency points along with time by using the time-domain vibration signal based on a time-frequency domain joint analysis algorithm includes the following steps: converting the time domain vibration signal into a frequency domain signal by Fourier transform; obtaining the amplitude of the characteristic frequency point in the frequency domain signal; a time series of the amplitude changes over time is recorded.
In an embodiment of the present invention, the method further includes sending the determination result to a preset mobile terminal.
In an embodiment of the invention, the predetermined time series analysis model is a long-term and short-term memory network model.
In order to achieve the above object, the present invention also provides a vibration signal detection system of a device, including: the device comprises a first acquisition module, a second acquisition module and a judgment module; the first acquisition module is used for acquiring a time domain vibration signal of the device based on the vibration sensor; the second acquisition module is used for acquiring a time sequence of the amplitude of the characteristic frequency point changing along with time by using the time domain vibration signal based on a time-frequency domain joint analysis algorithm; the judging module is used for analyzing the time sequence through a preset time sequence analysis model to obtain a judging result of whether the device is abnormal or not.
In an embodiment of the invention, the second obtaining module is further configured to: converting the time domain vibration signal into a frequency domain signal by Fourier transform; obtaining the amplitude of the characteristic frequency point in the frequency domain signal; a time series of the amplitude changes over time is recorded.
in an embodiment of the present invention, the present invention further includes a sending module; and the sending module is used for sending the judgment result to a preset mobile terminal.
to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a vibration signal detection method of any of the above devices.
In order to achieve the above object, the present invention also provides a vibration signal detecting apparatus of a device, including: a processor and a memory; the memory is used for storing a computer program; the processor is connected with the memory and is used for executing the computer program stored in the memory so as to enable the vibration signal detection device of the device to execute the vibration signal detection method of any one of the devices.
Finally, the invention also provides a vibration signal detection system of the device, which comprises a vibration signal detection device of the device, the cloud server, the mobile terminal and the controller; the vibration signal detection device comprises a vibration sensor for acquiring a time domain vibration signal of the device; the cloud server is used for receiving a judgment result sent by the vibration signal detection device; the mobile terminal is used for receiving a judgment result sent by the cloud server and sending an instruction to the controller based on the judgment result; the controller is configured to control switching of the device based on the command.
As described above, the method, system, medium, and apparatus for detecting a vibration signal of a device according to the present invention have the following advantages: the method is used for improving the accuracy of the judgment result based on the time-frequency domain joint analysis algorithm and the preset time sequence analysis model.
drawings
FIG. 1 is a flow chart illustrating a vibration signal detection method of the device according to an embodiment of the present invention;
FIG. 2a is a schematic diagram of a long term memory network model according to the present invention;
FIG. 2b is a schematic diagram of a vibration signal detection system of the device of the present invention in one embodiment;
FIG. 3a is a schematic structural diagram of a vibration signal detection apparatus of the device according to an embodiment of the present invention;
FIG. 3b is a schematic structural diagram of a vibration signal detection apparatus of the device according to another embodiment of the present invention;
Fig. 4 is a schematic structural diagram of a vibration signal detection system of the device of the present invention in another embodiment.
Description of the element reference numerals
21 first acquisition module
22 second acquisition module
23 judging module
31 processor
32 memory
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, so that the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, the type, quantity and proportion of the components in actual implementation can be changed freely, and the layout of the components can be more complicated.
the method, the system, the medium and the device for detecting the vibration signal of the device are used for improving the accuracy of a judgment result based on a time-frequency domain joint analysis algorithm and a preset time sequence analysis model.
As shown in fig. 1, in an embodiment, the method for detecting a vibration signal of a device of the present invention includes the following steps:
And step S11, acquiring a time-domain vibration signal of the device based on the vibration sensor.
Specifically, the vibration sensor is configured to acquire a vibration signal of the device so as to acquire a time-domain vibration signal of the device. The time domain vibration signal is a process in which the amplitude of the vibration signal changes with time.
And step S12, obtaining a time sequence of the amplitude of the characteristic frequency point changing along with time by the time-domain vibration signal based on a time-frequency domain joint analysis algorithm.
Specifically, the step of obtaining a time sequence of amplitude values of characteristic frequency points changing with time based on a time-frequency domain joint analysis algorithm by the time-domain vibration signal includes the following steps: converting the time domain vibration signal into a frequency domain signal by Fourier transform; obtaining the amplitude of the characteristic frequency point in the frequency domain signal; a time series of the amplitude changes over time is recorded. Specifically, the characteristic frequency point is a frequency point at which a regular change in amplitude occurs with time at the same frequency. For example, the amplitude changes from 0.5 to 200 at 200MHZ, and changes from 200 to 0.5 at 200MHZ, so that the frequency point of 200MHZ is the characteristic frequency point. The time sequence is a sequence formed by arranging numerical values of a certain statistical index of a certain phenomenon on different times according to time sequence. The time series method is a quantitative prediction method, and is also called a simple epitaxy method. Is widely applied as a commonly used prediction means in statistics. The time sequence in this application refers to the corresponding relationship between the amplitude of the characteristic frequency point and the time.
and step S13, analyzing the time sequence through a preset time sequence analysis model to obtain a judgment result of whether the device is abnormal.
specifically, the preset time series analysis model is a long-short term memory network model, and the structure of the long-short term memory network model is shown in fig. 2 a. The Long Short Term Memory Network (LSTM) successfully solves the defects of the original recurrent neural Network, becomes the most popular RNN at present, and is successfully applied to many fields such as voice recognition, picture description, natural language processing and the like. h (t) represents the network agent, x (t) is the network input, o (t) is the network output, and the loop structure allows information to pass from the current output to the next network input. And inputting the time sequence of the amplitude of the characteristic frequency point changing along with the time into the long-short term memory network model, wherein the long-short term memory network model can continuously input the time sequence of the amplitude of the characteristic frequency point changing along with the time and has the capability of judging whether the device is abnormal or not so as to judge whether the device is abnormal or not.
Specifically, when the long-short term memory network model is applied, it is found that the time domain data is not applied to the long-short term memory network model to a good effect, because the time domain data has a large amount of information which is not needed, and the direct application of the data can make the network very complex, so that the system is very redundant. In vibration analysis, our attention is focused more on frequency domain analysis, and the amplitude of the characteristic frequency point is the most important information. The data in the time domain is subjected to fourier transform (FFT) to convert into the frequency domain. And then, recording the time sequence of the amplitude of the characteristic frequency point along with the change of time, and analyzing by using a long-term and short-term memory network model to obtain a good result. In the process, the time-domain data analysis is abandoned, and the time-domain and frequency-domain joint analysis is adopted instead, so that a lot of disordered noise information in the time domain is eliminated, useful information in the frequency domain is reserved, and the time continuity is kept, so that the long-term and short-term memory network model can run smoothly. And the long-short term memory network model is based on: and (3) obtaining a judgment result of whether the device works abnormally or not by analyzing the time sequence of the amplitude of the characteristic frequency point along with the time change. For example, if the amplitude of the characteristic frequency point is greatly different from that of other time periods in a certain time period, a judgment result of the abnormal operation of the device is obtained.
Specifically, the method further comprises the step of sending the judgment result to a preset mobile terminal. The mobile terminal includes but is not limited to: smart phones, computers, smart tablets.
As shown in fig. 2b, in an embodiment, the vibration signal detection system of the device of the present invention includes a first obtaining module 21, a second obtaining module 22 and a determining module 23.
The first obtaining module 21 is configured to obtain a time-domain vibration signal of the device based on the vibration sensor.
Specifically, the vibration sensor is configured to acquire a vibration signal of the device so as to acquire a time-domain vibration signal of the device. The time domain vibration signal is a process in which the amplitude of the vibration signal changes with time.
the second obtaining module 22 is configured to obtain a time sequence of amplitude values of the characteristic frequency points changing with time based on a time-frequency domain joint analysis algorithm from the time-domain vibration signal.
Specifically, the second obtaining module 22 is further configured to: converting the time domain vibration signal into a frequency domain signal by Fourier transform; obtaining the amplitude of the characteristic frequency point in the frequency domain signal; a time series of the amplitude changes over time is recorded. Specifically, the characteristic frequency point is a frequency point at which a regular change in amplitude occurs with time at the same frequency. For example, the amplitude changes from 0.5 to 200 at 200MHZ, and changes from 200 to 0.5 at 200MHZ, so that the frequency point of 200MHZ is the characteristic frequency point. The time sequence is a sequence formed by arranging numerical values of a certain statistical index of a certain phenomenon on different times according to time sequence. The time series method is a quantitative prediction method, and is also called a simple epitaxy method. Is widely applied as a commonly used prediction means in statistics. The time sequence in this application refers to the corresponding relationship between the amplitude of the characteristic frequency point and the time.
The judging module 23 is configured to analyze the time sequence through a preset time sequence analysis model to obtain a judgment result of whether the device is abnormal.
Specifically, the preset time series analysis model is a long-short term memory network model, and the structure of the long-short term memory network model is shown in fig. 2 a. The Long Short Term Memory Network (LSTM) successfully solves the defects of the original recurrent neural Network, becomes the most popular RNN at present, and is successfully applied to many fields such as voice recognition, picture description, natural language processing and the like. h (t) represents the network agent, x (t) is the network input, o (t) is the network output, and the loop structure allows information to pass from the current output to the next network input. And inputting the time sequence of the amplitude of the characteristic frequency point changing along with the time into the long-short term memory network model, wherein the long-short term memory network model can continuously input the time sequence of the amplitude of the characteristic frequency point changing along with the time and has the capability of judging whether the device is abnormal or not so as to judge whether the device is abnormal or not.
Specifically, when the long-short term memory network model is applied, it is found that the time domain data is not applied to the long-short term memory network model to a good effect, because the time domain data has a large amount of information which is not needed, and the direct application of the data can make the network very complex, so that the system is very redundant. In vibration analysis, our attention is focused more on frequency domain analysis, and the amplitude of the characteristic frequency point is the most important information. The data in the time domain is subjected to fourier transform (FFT) to convert into the frequency domain. And then, recording the time sequence of the amplitude of the characteristic frequency point along with the change of time, and analyzing by using a long-term and short-term memory network model to obtain a good result. In the process, the time-domain data analysis is abandoned, and the time-domain and frequency-domain joint analysis is adopted instead, so that a lot of disordered noise information in the time domain is eliminated, useful information in the frequency domain is reserved, and the time continuity is kept, so that the long-term and short-term memory network model can run smoothly. And the long-short term memory network model is based on: and (3) obtaining a judgment result of whether the device works abnormally or not by analyzing the time sequence of the amplitude of the characteristic frequency point along with the time change. For example, if the amplitude of the characteristic frequency point is greatly different from that of other time periods in a certain time period, a judgment result of the abnormal operation of the device is obtained.
Specifically, the method further comprises the step of sending the judgment result to a preset mobile terminal. The mobile terminal includes but is not limited to: smart phones, computers, smart tablets.
It should be noted that the division of the modules of the above system is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the x module may be a processing element that is set up separately, or may be implemented by being integrated in a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the function of the x module may be called and executed by a processing element of the apparatus. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when one of the above modules is implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
In an embodiment of the present invention, the present invention further includes a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the vibration signal detection method of any of the above devices.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the above method embodiments may be performed by hardware associated with a computer program. The aforementioned computer program may be stored in a computer readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
as shown in fig. 3a, in an embodiment, the vibration signal detection apparatus of the device of the present invention includes: a processor 31 and a memory 32; the memory 32 is for storing a computer program; the processor 31 is connected to the memory 32 and is configured to execute the computer program stored in the memory 32, so as to enable the device vibration signal detection apparatus to execute any one of the device vibration signal detection methods.
Specifically, the memory 32 includes: various media that can store program codes, such as ROM, RAM, magnetic disk, U-disk, memory card, or optical disk.
Preferably, the Processor 31 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components.
As shown in fig. 3b, in an embodiment, the vibration signal detection apparatus of the device of the present invention includes: vibration sensor the vibration sensor obtains the time domain vibration signal of device, is used for obtaining the incoming of original vibration signal thereby obtains the time domain vibration signal. And the programmable logic (FPGA) is connected with the vibration sensor and used for performing time-frequency domain joint analysis, namely the programmable logic is used for acquiring a time sequence of the amplitude of the characteristic frequency point changing along with time based on a time-frequency domain joint analysis algorithm by the time-domain vibration signal and storing the time sequence as data in a format of 32-bit single-precision floating point number. And the multi-channel DMA is connected with the programmable logic (FPGA) and is used for sending the time sequence of the amplitude of the characteristic frequency point changing along with the time to a DDR memory (memorizer). And the ARM core (processor) is connected with the DDR memory and is used for reading data from the DDR memory, converting the 32-bit single-precision floating point number into decimal data and performing deep learning LSTM network calculation, wherein the deep learning LSTM network calculation is used for analyzing the decimal data through a preset long-short term memory network model to obtain a judgment result of whether the device is abnormal or not.
As shown in fig. 4, in an embodiment, the vibration signal detection system of the device of the present invention includes the vibration signal detection apparatus (soft and hard cooperative monitoring analysis) of the device, the device (to-be-monitored device), the cloud server (cloud storage), the mobile terminal (portable device) and the controller; the vibration signal detection device comprises a vibration sensor for acquiring a time domain vibration signal of the device; the cloud server is used for receiving a judgment result sent by the vibration signal detection device; the mobile terminal is used for receiving a judgment result sent by the cloud server and sending an instruction to the controller based on the judgment result; the controller is configured to control switching of the device based on the command. By sending the judgment result to the cloud server, the method and the device have the advantages that the reliability and the usability of dynamic monitoring, 24-hour global monitoring and visibility, the cost of a vibration signal detection system of the device is reduced, the aspects such as data access of a user of the mobile terminal and the like are greatly simplified, and the working efficiency of workers is greatly improved. The method further comprises the steps of uploading all data of the vibration signal detection method of the device to the cloud server, establishing proper backup and calculation on the cloud server, transmitting the data to a mobile terminal such as a mobile phone or a computer through a protocol, and accessing and monitoring the state and the data of the vibration signal detection device of the device at any time and any place. And if the abnormal condition or the early warning of the abnormal condition exists, the early warning can be timely sent to the mobile terminal, so that the staff is reminded. The staff can send an instruction to the controller through the mobile terminal; the controller is configured to control switching of the device based on the command.
In summary, the method, the system, the medium and the device for detecting the vibration signal of the device are used for improving the accuracy of the judgment result based on the time-frequency domain joint analysis algorithm and the preset time sequence analysis model. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A vibration signal detection method of a device, characterized by comprising the steps of:
acquiring a time domain vibration signal of the device based on the vibration sensor;
Acquiring a time sequence of the amplitude of the characteristic frequency point changing along with time by the time-domain vibration signal based on a time-frequency domain joint analysis algorithm;
and analyzing the time sequence through a preset time sequence analysis model to obtain a judgment result of whether the device is abnormal or not.
2. The method for detecting the vibration signal of the device according to claim 1, wherein the step of obtaining the time series of the amplitude of the characteristic frequency points changing with time based on the time-frequency domain joint analysis algorithm by the time-domain vibration signal comprises the following steps:
Converting the time domain vibration signal into a frequency domain signal by Fourier transform;
obtaining the amplitude of the characteristic frequency point in the frequency domain signal;
a time series of the amplitude changes over time is recorded.
3. The method for detecting the vibration signal of the device according to claim 1, further comprising sending the determination result to a preset mobile terminal.
4. The method of claim 1, wherein the predetermined time series analysis model is a long-short term memory network model.
5. A vibration signal detection system for a device, comprising: the device comprises a first acquisition module, a second acquisition module and a judgment module;
The first acquisition module is used for acquiring a time domain vibration signal of the device based on the vibration sensor;
the second acquisition module is used for acquiring a time sequence of the amplitude of the characteristic frequency point changing along with time by using the time domain vibration signal based on a time-frequency domain joint analysis algorithm;
the judging module is used for analyzing the time sequence through a preset time sequence analysis model to obtain a judging result of whether the device is abnormal or not.
6. The system of claim 5, wherein the second acquisition module is further configured to:
Converting the time domain vibration signal into a frequency domain signal by Fourier transform;
Obtaining the amplitude of the characteristic frequency point in the frequency domain signal;
a time series of the amplitude changes over time is recorded.
7. The system for detecting a vibration signal of a device according to claim 5, further comprising a transmission module; and the sending module is used for sending the judgment result to a preset mobile terminal.
8. A computer-readable storage medium on which a computer program is stored, the computer program being executed by a processor to implement the vibration signal detection method of the device according to any one of claims 1 to 4.
9. A vibration signal detecting apparatus of a device, comprising: a processor and a memory;
The memory is used for storing a computer program;
The processor is connected to the memory for executing the computer program stored in the memory to cause the vibration signal detection apparatus of the device to perform the vibration signal detection method of the device according to any one of claims 1 to 4.
10. A vibration signal detection system of a device, comprising the vibration signal detection apparatus of the device of claim 9, the device, a cloud server, a mobile terminal, and a controller;
The vibration signal detection device comprises a vibration sensor for acquiring a time domain vibration signal of the device;
the cloud server is used for receiving a judgment result sent by the vibration signal detection device;
The mobile terminal is used for receiving a judgment result sent by the cloud server and sending an instruction to the controller based on the judgment result;
The controller is configured to control switching of the device based on the command.
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| CN112697267A (en) * | 2020-11-11 | 2021-04-23 | 杭州未名信科科技有限公司 | Abnormal vibration detection device for industrial equipment |
| CN113591165A (en) * | 2021-08-11 | 2021-11-02 | 平安普惠企业管理有限公司 | Hard disk read-write protection method and device, computer equipment and storage medium |
| CN113807268A (en) * | 2021-09-22 | 2021-12-17 | 北京夸克创智科技有限公司 | Cylindrical object vibration detection method and device and computer storage medium |
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Application publication date: 20191206 |