CN112345250A - Bearing vibration monitoring method and system and computer readable storage medium - Google Patents
Bearing vibration monitoring method and system and computer readable storage medium Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
- G01M13/045—Acoustic or vibration analysis
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
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Abstract
The embodiment of the invention provides a bearing vibration monitoring method and system and a computer readable storage medium. The method is applied to a wind generating set and comprises the following steps: obtaining vibration data of the wind generating set; converting the vibration data into frequency domain data; resampling the frequency domain data to obtain multi-scale frequency domain data; acquiring a corresponding bearing fault frequency energy state index under multiple scales based on the multi-scale frequency domain data; and processing the bearing fault frequency energy state index under the multi-scale condition to obtain the health index of the bearing so as to monitor the bearing. The bearing vibration monitoring method provided by the embodiment of the invention can be used for more accurately monitoring the bearing.
Description
Technical Field
The embodiment of the invention relates to the technical field of wind power generation, in particular to a bearing vibration monitoring method and system applied to a wind generating set and a computer readable storage medium.
Background
With the gradual depletion of energy sources such as coal and petroleum, human beings increasingly pay more attention to the utilization of renewable energy sources. Wind energy is increasingly gaining attention as a clean renewable energy source in all countries of the world. With the continuous development of wind power technology, the application of wind generating sets in power systems is increasing day by day. Wind generating sets are large-scale devices that convert wind energy into electrical energy, and are usually installed in areas with abundant wind energy resources. In order to find out potential faults of the wind generating set in advance and ensure normal operation of the wind generating set, the state of the wind generating set, particularly the vibration condition, needs to be monitored.
At present, the operating condition of a wind turbine generator set is generally monitored based on vibration data of the wind turbine generator set. After FFT (Fast Fourier Transform) is used for vibration data of the wind generating set, the failure natural frequency of a bearing component corresponding to the wind generating set can be found. However, the multi-scale FFT setting may cause the energy allocated on the corresponding frequency not to achieve a significant effect, and thus may cause erroneous judgment and missed judgment.
Disclosure of Invention
The embodiment of the invention aims to provide a bearing vibration monitoring method, a system and a computer readable storage medium thereof, which are applied to a wind generating set and can accurately monitor a bearing of the wind generating set.
One aspect of the embodiment of the invention provides a bearing vibration monitoring method, which is applied to a wind generating set. The method comprises the following steps: obtaining vibration data of the wind generating set; converting the vibration data into frequency domain data; resampling the frequency domain data to obtain multi-scale frequency domain data; acquiring a bearing fault frequency energy state index corresponding to the multi-scale based on multi-scale frequency domain data; and processing the bearing fault frequency energy state index under the multi-scale to obtain the health index of the bearing so as to monitor the bearing.
Another aspect of an embodiment of the present invention also provides a bearing vibration monitoring system. The bearing vibration monitoring system includes one or more processors for implementing the bearing vibration monitoring method as described above.
Yet another aspect of an embodiment of the present invention also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a program which, when executed by a processor, implements a bearing vibration monitoring method as described above.
The bearing vibration monitoring method, the bearing vibration monitoring system and the computer readable storage medium of the embodiment of the invention acquire multi-scale frequency data by resampling the vibration frequency data of the wind generating set, thereby improving the quality of FFT data.
Drawings
FIG. 1 is a schematic view of a wind turbine generator system;
FIG. 2 is a flow chart of a bearing vibration monitoring method according to an embodiment of the present invention;
FIG. 3 is a detailed step of obtaining a multi-scale corresponding bearing fault frequency energy status indicator according to the calculated bearing fault frequency based on multi-scale frequency domain data according to an embodiment of the present invention;
FIG. 4 is a detailed step of obtaining a multi-scale corresponding bearing fault frequency energy status indicator based on multi-scale frequency domain data and according to a calculated bearing fault frequency according to another embodiment of the present invention;
FIG. 5 is a schematic block diagram of a bearing vibration monitoring system in accordance with one embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus consistent with certain aspects of the invention, as detailed in the appended claims.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Unless otherwise defined, technical or scientific terms used in the embodiments of the present invention should have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. The use of "first," "second," and similar terms in the description and in the claims does not indicate any order, quantity, or importance, but rather is used to distinguish one element from another. Also, the use of the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one. "plurality" or "a number" means two or more. Unless otherwise indicated, "front", "rear", "lower" and/or "upper" and the like are for convenience of description and are not limited to one position or one spatial orientation. The word "comprising" or "comprises", and the like, means that the element or item listed as preceding "comprising" or "includes" covers the element or item listed as following "comprising" or "includes" and its equivalents, and does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Fig. 1 discloses a perspective view of a wind park 100. As shown in fig. 1, a wind park 100 comprises a plurality of blades 101, a nacelle 102, a hub 103 and a tower 104. A tower 104 extends upwardly from a foundation (not shown), a nacelle 102 is mounted on top of the tower 104, a hub 103 is mounted at one end of the nacelle 102, and a plurality of blades 101 are mounted on the hub 103.
FIG. 2 discloses a flow chart of a bearing vibration monitoring method according to an embodiment of the invention. As shown in fig. 2, the bearing vibration monitoring method according to an embodiment of the present invention is applied to the wind turbine generator system 100 shown in fig. 1, and may include steps S11 to S15.
In step S11, vibration data of the wind turbine generator system 100 is acquired.
In one embodiment, a CMS (Condition Monitoring System) vibration sensor (not shown) may be mounted on the bearing of the wind turbine generator System 100, and the CMS vibration sensor may be used to acquire vibration data of the bearing of the wind turbine generator System 100.
The CMS vibration sensors may comprise acceleration sensors and the vibration data of the wind park 100 comprises the vibration amplitude of the acceleration.
Optionally, the vibration data of the bearings of the wind park 100 may comprise vibration data of the bearings of the power section provided by the main controller of the wind park 100.
Because the wind power of the external environment may change in real time during the actual operation period of the wind turbine generator system 100, the operation state of the wind turbine generator system 100 may also be different, for example, at a medium and high wind speed, the wind turbine generator system 100 operates in a full-power state, and at this time, the vibration energy of the wind turbine generator system 100 is large; at low wind speeds, the wind turbine generator system 100 is operated in a non-full-power mode, and at this time, the vibration energy of the wind turbine generator system 100 is small. Therefore, by dividing the vibration data of the bearings into power segments provided by the main controller of the wind turbine generator system 100, the vibration data of the bearings can be grouped, for example, the vibration data of the bearings of the wind turbine generator system 100 with high power can be grouped into one group, and the vibration data of the bearings of the wind turbine generator system 100 with low power can be grouped into one group, so that the operation data of the wind turbine generator system 100 and the CMS hardware of the wind turbine generator system 100 can be well combined, and the strong coupling of the CMS hardware and the wind turbine generator system 100 is increased.
In step S12, the vibration data is converted into frequency domain data.
In one embodiment, the vibration data may be converted to frequency domain data by a Fast Fourier Transform (FFT).
In step S13, the frequency domain data is resampled to obtain multi-scale frequency domain data, so that the quality of the FFT frequency data can be improved.
In one embodiment, the logic corresponding to resampling the frequency domain data in step S13 is as follows:
the sampling frequency of the CMS vibration sensor, sampling _ rate, 256000, where 256000HZ is the raw sampling frequency commonly used by CMS vibration sensors,
length datasize len (data1) of data collected by CMS vibration sensor,
the corresponding FFT x-axis data t is np.
For example, the resampling is X ═ 4,16,32,64,128, i.e., under the data of t, the corresponding X-axis (time), y-axis (FFT magnitude data) is recorded every X points.
Therefore, the original 1 piece of FFT data can be changed into 5 pieces of FFT data of multi-scale by the above method.
It should be noted that the above resampling interval X points and the number thereof are merely illustrated as an illustrative example, and are not intended to limit the present invention, and the resampling of the frequency domain data in step S13 in the embodiment of the present invention is not limited to the above 5 pieces of FFT data, and in other embodiments, the resampling of the frequency domain data in step S13 may obtain more pieces of FFT data at more scales. These simple variations are intended to be covered by the scope of the present invention.
In step S14, a corresponding bearing fault frequency energy status Index (Condition Index, CI) at multiple scales may be obtained based on the multi-scale frequency domain data.
In step S15, the bearing fault frequency energy status indicator at multiple scales may be processed to obtain a Health Index (HI) of the bearing, so as to monitor the bearing.
The bearing vibration monitoring method provided by the embodiment of the invention obtains multi-scale frequency data by resampling the vibration frequency data of the wind generating set 100, so that the quality of FFT data can be improved, and generates a new more accurate monitoring variable, namely a health index of the bearing, by correspondingly processing the bearing fault frequency energy state index obtained under multi-scale, so that an alarm index with higher latitude can be created, the bearing can be more accurately monitored, the accuracy of judging the running condition of the wind generating set 100 is improved, and the misjudgment rate of the wind generating set 100 is reduced.
With continued reference to fig. 2, the bearing vibration monitoring method of the embodiment of the present invention may further include step S16 and step S17.
In step S16, the bearing rotation speed is acquired by a rotation speed encoder (not shown) of the wind turbine generator system 100.
In step S17, a bearing failure frequency is calculated based on the bearing rotation speed.
Therefore, in step S14, a corresponding bearing fault frequency energy status indicator at multiple scales can be obtained based on the multi-scale frequency domain data and according to the calculated bearing fault frequency.
In the embodiment of the present invention, the bearing failure frequency calculated in step S17 may include at least one of an outer ring failure frequency, an inner ring failure frequency, a single rolling element failure frequency, and a cage outer ring failure frequency of the bearing, and then the health indicator of the bearing obtained in step S15 includes a health indicator of at least one corresponding one of the outer ring failure frequency, the inner ring failure frequency, the single rolling element failure frequency, and the cage outer ring failure frequency of the bearing.
The outer ring fault frequency of the bearing is shown as the following formula:
fouter cover＝r/60*1/2*n(1-d/D*cosα) (1)
The failure frequency of the inner ring of the bearing is shown as the following formula:
finner part＝r/60*1/2*n(1+d/D*cosα) (2)
The single fault frequency of the rolling body of the bearing is shown as the following formula:
froller＝r/60*1/2*D/d*[1-(d/D)^2*cos^2(α)] (3)
The fault frequency of the outer ring of the retainer of the bearing is shown as the following formula:
fhealth-care product＝r/60*1/2*(1-d/D*cosα) (4)
Wherein r represents the bearing rotation speed in revolutions per minute; n represents the number of the balls; d represents the diameter of the rolling body; d represents the pitch diameter of the bearing; and alpha represents the contact angle of the rolling body.
How to obtain the corresponding bearing fault frequency energy state index at the multi-scale based on the multi-scale frequency domain data and according to the calculated bearing fault frequency will be described in detail below with reference to fig. 3 and 4.
Fig. 3 discloses detailed steps of obtaining a multi-scale corresponding bearing fault frequency energy status indicator according to the calculated bearing fault frequency based on multi-scale frequency domain data according to an embodiment of the present invention. As shown in fig. 3, in an embodiment of the present invention, obtaining the corresponding bearing fault frequency energy status indicator at multiple scales based on the multi-scale frequency domain data and according to the calculated bearing fault frequency may include step 21 and step S22.
In step S21, energy amplitude data corresponding to positions of multiple multiples of the bearing fault frequency at multiple scales may be obtained according to the calculated bearing fault frequency based on the multi-scale frequency domain data.
In some embodiments, the multiple multiples of the bearing failure frequency may include one to six times the bearing failure frequency.
For example, the inner ring failure frequency of the bearing is taken as an example. Assuming that the inner ring failure frequency of the bearing is calculated as 11 (x-axis) according to the above equation (2), one to six times the inner ring failure frequency 11, i.e., y-axis data (i.e., energy amplitude data) corresponding to the x-axis 11, 22, 33, 44, 55, 66 is sought.
In step S22, energy amplitude data corresponding to positions at which multiple multiples of the bearing fault frequency are located at multiple scales may be accumulated to obtain an energy state index of the bearing fault frequency at multiple scales.
Fig. 4 discloses detailed steps of obtaining a multi-scale corresponding bearing fault frequency energy status indicator according to the calculated bearing fault frequency based on the multi-scale frequency domain data according to another embodiment of the present invention. As shown in fig. 4, in another embodiment of the present invention, obtaining the corresponding bearing fault frequency energy status indicator at the multi-scale based on the multi-scale frequency domain data and according to the calculated bearing fault frequency may include steps S31 and S32, in consideration of energy leakage of the corresponding FFT due to the window function.
In step S31, energy amplitude data within a range in which multiple multiplied frequency bands of the bearing fault frequency are located at multiple scales may be obtained based on the multi-scale frequency domain data and according to the calculated bearing fault frequency.
In some embodiments, the frequency bands of multiple multiples of the bearing fault frequency may include: the frequency ranges of the predetermined frequency are added/subtracted centering on a plurality of multiples of the bearing fault frequency, respectively.
For example, the x-axis data obtained is a range of +/-1Hz for the calculated corresponding inner loop fault frequency, taking into account the corresponding FFT's energy leakage due to the window function. The inner ring failure frequency of the bearing is still taken as an example. Assuming that the inner ring failure frequency of the bearing is calculated as 11 (x-axis) according to the above formula (2), the frequency band of one to six times the inner ring failure frequency 11, i.e., the y-axis data (i.e., energy amplitude data) corresponding to the x-axis of [10,12], [21,23], [32,34], [43,45], [54,56], [65,67] is sought.
In step S32, energy amplitude data in a range where multiple frequency doubling frequency bands of the bearing fault frequency are located at multiple scales may be accumulated to obtain an energy state index of the bearing fault frequency at multiple scales.
In some embodiments, the processing the bearing fault frequency energy status indicator at multiple scales in step S15, and obtaining the health indicator of the bearing may include: accumulating and normalizing the bearing fault frequency energy state indexes (CI) under multiple scales to obtain the health indexes of the bearing. The Health Indicator (HI) of the bearing may be, for example, a value in the range of 0 to 1.
The bearing fault frequency energy CI under multiple scales is accumulated and normalized, so that the health index of the bearing is solidified in a 0-1 interval, and the interpretation and visualization of the health index of the bearing are facilitated.
After resampling X ═ 4,16,32,64,128 on the bearing vibration frequency data after FFT, 5 pieces of frequency domain data are obtained, and calculation is performed in the manner shown in fig. 4, for example, to obtain 5 pieces of CI, and an energy accumulation value of energy leakage is considered, and the accumulated value is accumulated into one piece of energy data, and normalization processing is performed along with the data accumulation, as shown below:
(max-min)xnorm＝(x-xmin)/(xmax-xmin) (5)
thus, a HI in the range of 0-1 is formed, and the bearing can be monitored more accurately. Wherein, the larger the numerical value of the Health Index (HI) of the bearing is, the higher the risk of the bearing failure is.
In some embodiments, the bearing vibration monitoring method of embodiments of the present invention may further include: and generating a corresponding alarm strategy based on the health index of the bearing. The bearing can be correspondingly controlled according to the generated alarm strategy.
In one embodiment, generating the respective alarm strategy based on the health indicator of the bearing may include: when the health indicator of the bearing is greater than a first value, e.g. 0.5, a first alarm record, e.g. a yellow alarm line, is generated.
In another embodiment, generating the respective alarm strategy based on the health indicator of the bearing may further include: and when the health index of the bearing is greater than a second value, generating a second alarm record different from the first alarm record, wherein the second value is greater than the first value. The second value may be, for example, 0.7 and the second alarm log may be set, for example, to a red alarm line, thereby generating a more urgent alarm than the first alarm log.
The bearing vibration monitoring method provided by the embodiment of the invention can generate more accurate health indexes of the bearing and build a higher-dimensional alarm strategy, so that the bearing can be more effectively and accurately monitored, the accuracy of judging the running condition of the wind generating set 100 is improved, and the misjudgment rate of the wind generating set 100 is reduced.
The embodiment of the invention also provides a bearing vibration monitoring system 200, which is applied to the wind generating set 100. FIG. 5 discloses a schematic block diagram of a bearing vibration monitoring system 200 according to an embodiment of the present invention. As shown in fig. 5, the bearing vibration monitoring system 200 may include one or more processors 201 for implementing the bearing vibration monitoring method described in any of the above embodiments. In some embodiments, bearing vibration monitoring system 200 may include a computer-readable storage medium 202, and computer-readable storage medium 202 may store a program that may be invoked by processor 201, and may include a non-volatile storage medium. In some embodiments, the bearing vibration monitoring system 200 may include a memory 203 and an interface 204. In some embodiments, the bearing vibration monitoring system 200 of embodiments of the present invention may also include other hardware depending on the application.
The bearing vibration monitoring system 200 of the embodiment of the present invention has similar beneficial technical effects to the bearing vibration monitoring method described above, and therefore, the details are not repeated herein.
The embodiment of the invention also provides a computer readable storage medium. The computer-readable storage medium has stored thereon a program which, when executed by a processor, implements the bearing vibration monitoring method described in any of the above embodiments.
Embodiments of the invention may take the form of a computer program product embodied on one or more storage media including, but not limited to, disk storage, CD-ROM, optical storage, and the like, in which program code is embodied. Computer-readable storage media include permanent and non-permanent, removable and non-removable media and may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer readable storage media include, but are not limited to: phase change memory/resistive random access memory/magnetic memory/ferroelectric memory (PRAM/RRAM/MRAM/FeRAM) and like new memories, Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, may be used to store information that may be accessed by a computing device.
The bearing vibration monitoring method, the system and the computer readable storage medium provided by the embodiment of the invention are described in detail above. The bearing vibration monitoring method, the system and the computer-readable storage medium according to the embodiments of the present invention are described herein by using specific embodiments, and the above description of the embodiments is only for assisting understanding of the core ideas of the present invention and is not intended to limit the present invention. It should be noted that, for those skilled in the art, various improvements and modifications can be made without departing from the spirit and principle of the present invention, and these improvements and modifications should fall within the scope of the appended claims.
Claims (18)
1. A bearing vibration monitoring method is applied to a wind generating set and is characterized in that: it includes:
obtaining vibration data of the wind generating set;
converting the vibration data into frequency domain data;
resampling the frequency domain data to obtain multi-scale frequency domain data;
acquiring a bearing fault frequency energy state index corresponding to the multi-scale based on multi-scale frequency domain data; and
and processing the bearing fault frequency energy state index under the multi-scale condition to obtain a health index of the bearing so as to monitor the bearing.
2. The method of claim 1, wherein: the converting the vibration data into frequency domain data comprises:
and converting the vibration data into the frequency domain data through fast Fourier transform.
3. The method of claim 1, wherein: the acquiring of the vibration data of the wind generating set comprises the following steps:
acquiring vibration data of a bearing of the wind turbine generator set through a CMS vibration sensor, wherein the CMS vibration sensor comprises an acceleration sensor, and the vibration data comprises vibration amplitude of acceleration.
4. The method of claim 3, wherein: the vibration data of the bearing of the wind generating set comprises vibration data of the bearing of the power section provided by the main controller of the wind generating set.
5. The method of claim 3, wherein: further comprising:
acquiring the rotating speed of a bearing through a rotating speed encoder of the wind generating set;
calculating a bearing failure frequency based on the bearing rotational speed,
and acquiring a corresponding bearing fault frequency energy state index under the multi-scale according to the calculated bearing fault frequency based on the multi-scale frequency domain data.
6. The method of claim 5, wherein: the obtaining the corresponding bearing fault frequency energy state index under the multi-scale according to the calculated bearing fault frequency based on the multi-scale frequency domain data comprises:
acquiring energy amplitude data corresponding to positions of multiple frequency doubling of the bearing fault frequency under the multi-scale based on the multi-scale frequency domain data and according to the calculated bearing fault frequency; and
and accumulating energy amplitude data corresponding to positions where multiple frequency multiples of the bearing fault frequency are located under the multiple scales to obtain the bearing fault frequency energy state index under the multiple scales.
7. The method of claim 5, wherein: the obtaining the corresponding bearing fault frequency energy state index under the multi-scale according to the calculated bearing fault frequency based on the multi-scale frequency domain data comprises:
acquiring energy amplitude data in a range where a plurality of frequency doubling frequency bands of the bearing fault frequency are located under the multi-scale condition based on the multi-scale frequency domain data and according to the calculated bearing fault frequency; and
and accumulating the energy amplitude data within the range of the frequency bands of the multiple frequency multiples of the bearing fault frequency under the multiple scales to obtain the energy state index of the bearing fault frequency under the multiple scales.
8. The method of claim 7, wherein: the frequency bands of multiple multiples of the bearing fault frequency include: and respectively adding/subtracting a frequency range of a preset frequency by taking a plurality of multiplied frequencies of the bearing fault frequency as a center.
9. The method of claim 6 or 7, wherein: the multiple multiples of the bearing fault frequency include: the bearing failure frequency is one to six times the frequency.
10. The method of any one of claims 1 to 8, wherein: the bearing failure frequency comprises: the bearing comprises at least one of outer ring fault frequency, inner ring fault frequency, single rolling body fault frequency and retainer outer ring fault frequency of the bearing, wherein the health indexes of the bearing comprise health indexes of at least one corresponding one of the outer ring fault frequency, the inner ring fault frequency, the single rolling body fault frequency and the retainer outer ring fault frequency of the bearing.
11. The method of claim 1, wherein: the processing of the bearing fault frequency energy state index under the multi-scale condition to obtain the health index of the bearing comprises the following steps:
and accumulating the bearing fault frequency energy state indexes under the multiple scales and carrying out normalization processing to obtain the health indexes of the bearing.
12. The method of claim 11, wherein: the health index of the bearing is a value in the range of 0 to 1.
13. The method of claim 12, wherein: the greater the value of the health indicator of the bearing, the higher the risk of the bearing failing.
14. The method of claim 1, 12 or 13, wherein: further comprising:
and generating a corresponding alarm strategy based on the health index of the bearing.
15. The method of claim 14, wherein: the generating a corresponding alarm strategy based on the health indicator of the bearing comprises:
and when the health index of the bearing is larger than a first numerical value, generating a first alarm record.
16. The method of claim 15, wherein: the generating a corresponding alarm strategy based on the health indicator of the bearing further comprises:
and when the health index of the bearing is greater than a second value, generating a second alarm record different from the first alarm record, wherein the second value is greater than the first value.
17. The utility model provides a bearing vibration monitored control system which characterized in that: comprising one or more processors configured to implement the method of any one of claims 1-16.
18. A computer-readable storage medium, having stored thereon a program which, when executed by a processor, carries out the method of any one of claims 1-16.
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Cited By (2)
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| CN113435705A (en) * | 2021-06-02 | 2021-09-24 | 上海电气风电集团股份有限公司 | Bearing monitoring method and system and computer readable storage medium |
| CN113775481A (en) * | 2021-09-24 | 2021-12-10 | 上海电气风电集团股份有限公司 | CMS vibration protection method and device and computer readable storage medium |
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