CN113435705A - Bearing monitoring method and system and computer readable storage medium - Google Patents
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Abstract
The embodiment of the invention provides a bearing monitoring method, a bearing monitoring system and a computer-readable storage medium. The method is applied to a wind generating set and comprises the following steps: acquiring a plurality of data sources which can be used for preliminarily reflecting the health state of a bearing of the wind generating set; distinguishing physical characteristics of the plurality of data sources in a plurality of different aspects, and generating a first index of the plurality of data sources based on a result of the distinguishing; generating a second index for the plurality of data sources based on the priorities of the plurality of data sources; fusing the first index and the second index to form a fused index of the bearing of the wind generating set; and finally determining the health state of the wind generating set bearing based on the fusion index so as to monitor the bearing. The bearing monitoring method, the bearing monitoring system and the computer readable storage medium can comprehensively evaluate the bearing of the wind generating set, and realize more accurate monitoring of the bearing.
Description
Technical Field
The embodiment of the invention relates to the technical field of wind power generation, in particular to a bearing 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 detect potential deterioration of the wind turbine generator system in advance and ensure normal operation of the wind turbine generator system, a data source which can reflect the operation state of the wind turbine generator system, such as a data source related to a bearing of the wind turbine generator system, needs to be collected, and the bearing of the wind turbine generator system, for example, needs to be monitored according to the data source of the bearing of the wind turbine generator system. However, the conventional monitoring of the bearing of the wind turbine generator system is usually performed from a single data source, so that the bearing monitoring of the unilateral evaluation cannot be well considered in the aspects of accuracy, timeliness and the like, and further, the situations of misjudgment, missed judgment, untimely judgment and the like may occur.
Disclosure of Invention
The embodiment of the invention aims to provide a bearing monitoring method, a bearing monitoring system and a computer readable storage medium, which are applied to a wind generating set and can comprehensively evaluate a bearing of the wind generating set, so that the bearing can be monitored more accurately.
One aspect of the embodiment of the invention provides a bearing monitoring method, which is applied to a wind generating set. The method comprises the following steps: acquiring a plurality of data sources which can be used for preliminarily reflecting the health state of a bearing of the wind generating set; differentiating physical characteristics of the plurality of data sources in a plurality of different aspects, and generating a first index of the plurality of data sources based on a result of the differentiation; generating a second indicator for the plurality of data sources based on the priorities of the plurality of data sources; fusing the first index and the second index to form a fused index of the wind turbine generator set bearing; and finally determining the health state of the wind generating set bearing based on the fusion index so as to monitor the bearing.
Another aspect of an embodiment of the present invention also provides a bearing monitoring system. The bearing monitoring system includes one or more processors for implementing the bearing 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 monitoring method as described above.
The bearing monitoring method, the system and the computer readable storage medium of the embodiment of the invention can combine a plurality of data sources which can be used for preliminarily reflecting the health state of the bearing of the wind generating set, comprehensively evaluate the data sources and generate a new monitoring variable, namely a fusion index of the bearing, thereby being capable of creating an alarm index with higher latitude, taking the physical characteristics such as lead, accuracy and the like into consideration, more effectively monitoring the bearing, improving the accuracy and timeliness of judging the running condition of the wind generating set and reducing the misjudgment rate of the wind generating set.
Drawings
FIG. 1 is a schematic view of a wind turbine generator system;
FIG. 2 is a flow chart of a bearing monitoring method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of corresponding lead amounts of different component monitoring systems of a wind generating set;
FIG. 4 is a detailed step of generating a first index for a plurality of data sources based on the result of the distinguishing according to an embodiment of the present invention;
FIG. 5 is a schematic block diagram of a bearing monitoring system of 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 monitoring method according to an embodiment of the invention. As shown in fig. 2, the bearing 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, a plurality of data sources are obtained that may be used to preliminarily reflect the health status of the wind turbine generator set bearings.
In some embodiments, the plurality of Data sources may include, for example, but not limited to, SCADA bearing temperature Data from a SCADA (Supervisory Control And Data Acquisition) System, CMS (Condition Monitoring System) bearing vibration Data from a vibration Monitoring System, And vibration And oil reports from manual operations, among others.
Alternatively, the CMS bearing vibration data may comprise CMS bearing HI (Health Index) obtained from a vibration monitoring system strongly coupled to the wind park.
In step S12, physical characteristics of the plurality of data sources in a plurality of different aspects are discriminated, and a first index K1 of the plurality of data sources is generated based on the result of the discrimination.
In some embodiments, the physical characteristics of the various aspects may include, for example, but are not limited to, advance, correct rate, time interval, and the like.
For data sources such as SCADA bearing temperature data, CMS bearing vibration data, vibration reports and oil reports, the data sources are different in the aspects of lead, accuracy and the like.
Fig. 3 discloses a schematic diagram of the corresponding lead of the monitoring system of different components of the wind generating set. As can be seen from FIG. 3, the advance is vibration, oil and temperature in sequence from high to low, wherein the vibration can be month ahead, the oil can be week ahead, and the temperature can be day ahead. However, the accuracy is artificially higher than the data-driven system. In addition, the time interval between the vibration report and the oil report from the manual work may vary from month to quarter.
Therefore, the physical characteristics of the data sources, such as SCADA bearing temperature data, CMS bearing vibration data, vibration reports, and oil reports, in terms of advance, accuracy, time intervals, etc., are distinguished based on the differences in the physical characteristics of the data sources in these different respects. FIG. 4 discloses specific steps of generating a first index K1 of a plurality of data sources based on the result of the distinguishing according to an embodiment of the invention. As shown in fig. 4, the generation of the first index K1 of the plurality of data sources based on the result of the distinction in step S12 may include steps S121 to S123.
In step S121, a characteristic matrix of a plurality of data sources is formed.
In one embodiment, the division is performed according to the dimension of a plurality of physical characteristics in the horizontal direction, for example, the horizontal direction may be divided according to three physical characteristics of advance, accuracy and time interval; the vertical direction is differentiated according to the dimension of each degree (such as low, medium and high) to form a characteristic matrix of a plurality of data sources, for example, as shown in table one:
watch 1
| Data source | Advance amount | Rate of accuracy | Time interval |
| CMS bearing vibration data | Height of | In | Short length |
| Vibration report | In | Height of | Long and long |
| Oil report | In | Height of | Long and long |
| SCADA shaftTemperature data | Is low in | In | Short length |
In step S122, the property matrix of table one is converted into a datamation matrix.
Each of the four data sources in table one, SCADA bearing temperature data, CMS bearing vibration data, vibration report and oil report, may be used to characterize the degree of deterioration of a wind turbine generator system bearing to some extent, and the characterized degree of deterioration may be assigned a different value, such as 0,1 or 2, respectively. Wherein 0 represents that the bearing of the wind generating set is normal and is not degraded, and a green code is output, such as a bright green light; 1 represents that the bearing of the wind generating set is likely to be degraded, and a yellow alarm is output, such as a yellow lamp; and 2, outputting a red alarm, such as a red light, when the bearing of the wind generating set fails.
In one embodiment, different weight values may be added to the degree corresponding to each physical characteristic in the longitudinal direction of the characteristic matrix formed in table one to form a datamation matrix, wherein the higher the advance, the higher the weight value is; the higher the accuracy is, the higher the weight value is; the shorter the time interval, the higher the weight value. For example, as shown in Table II:
watch two
| Data source | Advance amount | Rate of accuracy | Time interval |
| CMS bearing vibration data (0,1,2) | High (3) | Middle (1) | Short (2) |
| Vibration report (0,1,2) | Middle (2) | High (2) | Long (1) |
| Oil report (0,1,2) | Middle (2) | High (2) | Long (1) |
| SCADA bearing temperature data (0,1,2) | Low (1) | Middle (1) | Short (2) |
In step S123, the first index K1 of the plurality of data sources is generated based on the datamation matrix of table two.
In other embodiments, in order to enable the four data sources in table two to be weighted without shifting, the bearing monitoring method according to the embodiment of the present invention may further include: respectively normalizing the weights corresponding to the plurality of physical characteristics of each data source in the horizontal direction of the datamation matrix in the second table to form a normalized datamation matrix of the plurality of data sources, for example, as shown in the third table:
watch III
In this case, the generating of the first index K1 of the plurality of data sources based on the datamation matrix in step S123 may include: a first index K1 for the plurality of data sources is generated based on the normalized datamation matrix of table three.
In one embodiment, generating the first index K1 for the plurality of data sources based on the normalized datamation matrix of table three may include: the degradation degree level represented by each data source in the third table is respectively assigned with different values, for example, 0,1 or 2 is used for respectively indicating that the bearing is normal, possibly degraded or failed; for each data source, multiplying a numerical value corresponding to the represented degradation degree grade of the data source by a weight value of each physical characteristic after normalization respectively to obtain the weight value of each data source; and generating a first index K1 of the plurality of data sources based on a sum of the weighted values of at least two of the plurality of data sources, for example, as shown in the following formula:
K1＝O1*A1*B1*C1+O2*A2*B2*C2+O3*A3*B3*C3+O4*A4*B4*C4 (1)
wherein, O1 represents the degradation degree grade (0,1 or 2) represented by CMS bearing vibration data, O2 represents the degradation degree grade (0,1 or 2) represented by a vibration report, O3 represents the degradation degree grade (0,1 or 2) represented by an oil report, and O4 represents the degradation degree grade (0,1 or 2) represented by SCADA bearing temperature data.
It should be noted that, since the vibration report and the oil report are manually generated, the time interval may vary according to actual situations, such as monthly or quarterly acquisition. Thus, during the current month of bearing monitoring, there may be instances where there is no vibration report and/or oil report. In this case, there will be no corresponding vibration report and/or oil report in equation (1) above for calculating the first indicator K1. That is, in the absence of vibration reports and/or oil reports, there is no corresponding O2 a 2B 2C 2 and/or O3 A3B 3C 3 in equation (1) above.
The first indicator K1 of the plurality of data sources may be a value in the range of 0 to 1. Wherein a larger value indicates a higher degree of deterioration of the bearing. For example, 0 means that the bearing is normal and no degradation occurs; 1 indicates that the bearing is malfunctioning; 0.5 indicates that deterioration of the bearing or the like may occur.
In order to secure the first index K1 in the state of 0-1, the two limit states of the above equation (1) are further calculated.
The maximum limit state (i.e. the fault state, and the corresponding values represented by O1, O2, O3 and O4 are all 2) of the first index K1 is shown in the following formula:
K1_2＝2*(3/6)*(1/6)*(2/6)+2*(2/5)*(2/5)*(1/5)+2*(2/5)*(2/5)*(1/5)
+2*(1/4)*(1/4)*(2/4)＝0.25
the minimum limit state (i.e., the normal state, and the corresponding values represented by O1, O2, O3, and O4 are all 0) of the first index K1 is represented by the following formula:
K1_0＝0
the minimum and maximum two limit state values are 0 and 0.25, respectively, and therefore, in order to ensure that the first index K1 can be in a state of 0-1, the minimum and maximum two limit state values are converted to 0 and 1, and four times the sum of the weight values calculated in the above formula (1) may be used as the first index K1 of the plurality of data sources.
Referring back to fig. 2, in step S13, a second index K2 of the plurality of data sources is generated based on the priorities of the plurality of data sources.
In some embodiments, generating the second index K2 for the plurality of data sources based on the priorities of the plurality of data sources may include: sequentially judging whether the bearing represented by the corresponding data source is degraded or not according to the priority sequence of the data sources; if the data source of the prior priority represents that the bearing is degraded, outputting a numerical value corresponding to the degradation degree level represented by the data source of the prior priority, otherwise, judging the next priority; if the plurality of data sources represent that the bearing is not degraded, outputting a numerical value corresponding to the degradation; and generating a second index K2 based on the output value.
The second indicator K2 of the plurality of data sources may be a value in the range of 0 to 1. Wherein a larger value indicates a higher degree of deterioration of the bearing. For example, 0 means that the bearing is normal and no degradation occurs; 1 indicates that the bearing is malfunctioning; 0.5 indicates that deterioration of the bearing or the like may occur.
For example, for four data sources of SCADA bearing temperature data, oil report, vibration report and CMS bearing vibration data, the priority of the four data sources is from high to low: SCADA bearing temperature data, oil reports, vibration reports, and CMS bearing vibration data. Therefore, the flow of the determination is as follows:
if the temperature data of the SCADA bearing is red alarm or yellow alarm, then red alarm (1) or yellow alarm (0.5) is output
Else
If the oil liquid in the current month reports that a red alarm or a yellow alarm exists, a red alarm (1) or a yellow alarm (0.5) is output
Else
If the If vibration report in the current month has a vibration report red alarm or a yellow alarm, a red alarm (1) or a yellow alarm (0.5) is output
Else
If the vibration data of the CMS bearing of the If is red alarm or yellow alarm, then red alarm (1) or yellow alarm (0.5) is output
Else
If none, the output is green (0)
Continuing to refer to fig. 2, in step S14, the first index K1 and the second index K2 are fused to form a fused index K of the wind turbine generator set bearing.
The fusion index K is also a value in the range of 0 to 1. Wherein a larger value indicates a higher degree of deterioration of the bearing. For example, 0 means that the bearing is normal and no degradation occurs; 1 indicates that the bearing is malfunctioning; 0.5 indicates that deterioration of the bearing or the like may occur.
In some embodiments, fusing the first index K1 and the second index K2 to form a fused index K of the wind park bearing may include: if at least one of the vibration report and the oil report does not exist in the current month, the fusion index K is equal to a second index K2; and if the vibration report and the oil report exist in the current month, the fusion index K is equal to the first index K1.
In step S15, the health status of the wind turbine generator system bearing is finally determined based on the fusion index K to monitor the bearing.
In other embodiments, a bearing monitoring method of an embodiment of the present invention may further include: and generating a corresponding alarm strategy based on the fusion index K of the bearing of the wind generating set. The bearing can be correspondingly controlled according to the generated alarm strategy.
In one embodiment, generating the corresponding alarm policy based on the fusion index K may include: and when the fusion index K of the bearing of the wind generating set is larger than a first numerical value, such as 0.5, generating a first alarm, such as a yellow alarm, and lighting a yellow lamp. And when the fusion index K of the bearing of the wind generating set is larger than a second numerical value, generating a second alarm different from the first alarm, wherein the second numerical value is larger than the first numerical value. The second value may be, for example, 0.7 and the second alarm may be set to, for example, a red alarm, which is illuminated to produce a more urgent alarm than the first alarm.
Because the first index K1 and the second index K2 are two totally different attributes in logic, only the current month can be judged, a time sequence chart between the months cannot be carried out, but the degradation degree of the bearing of the wind generating set can be conveniently checked in time through visual red and yellow alarm display.
The bearing monitoring method provided by the embodiment of the invention can combine a plurality of data sources which can be used for preliminarily reflecting the health state of the bearing of the wind generating set, comprehensively evaluate the plurality of data sources, and generate a new monitoring variable, namely the fusion index K of the bearing, so that an alarm index with a higher latitude can be created, the physical characteristics such as the lead and the accuracy are considered, the bearing can be more effectively and accurately monitored, the accuracy and the timeliness of judging the running condition of the wind generating set 100 are improved, and the misjudgment rate of the wind generating set 100 is reduced.
The embodiment of the invention also provides a bearing monitoring system 200, which is applied to the wind generating set 100. FIG. 5 discloses a schematic block diagram of a bearing monitoring system 200 according to an embodiment of the present invention. As shown in fig. 5, the bearing monitoring system 200 may include one or more processors 201 for implementing the bearing monitoring method described in any of the above embodiments. In some embodiments, the bearing monitoring system 200 may include a computer-readable storage medium 202, and the computer-readable storage medium 202 may store a program that may be invoked by the processor 201, and may include a non-volatile storage medium. In some embodiments, the bearing monitoring system 200 may include a memory 203 and an interface 204. In some embodiments, the bearing monitoring system 200 of embodiments of the present invention may also include other hardware depending on the application.
The bearing monitoring system 200 of the embodiment of the present invention has similar beneficial technical effects to the bearing monitoring method described above, and therefore, the description thereof is omitted.
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 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 monitoring method, the system and the computer readable storage medium provided by the embodiment of the invention are described in detail above. The bearing 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 used to help understanding the core idea 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 (16)
1. A bearing monitoring method is applied to a wind generating set, and is characterized in that: it includes:
acquiring a plurality of data sources which can be used for preliminarily reflecting the health state of a bearing of the wind generating set;
differentiating physical characteristics of the plurality of data sources in a plurality of different aspects, and generating a first index of the plurality of data sources based on a result of the differentiation;
generating a second indicator for the plurality of data sources based on the priorities of the plurality of data sources;
fusing the first index and the second index to form a fused index of the wind turbine generator set bearing; and
finally determining the health state of the wind generating set bearing based on the fusion index so as to monitor the bearing.
2. The method of claim 1, wherein: the generating a first indicator of the plurality of data sources based on the differentiated result includes:
forming a property matrix for the plurality of data sources;
converting the characteristic matrix into a datamation matrix; and
generating the first indicator for the plurality of data sources based on the datamation matrix.
3. The method of claim 2, wherein: the forming the characteristic matrix of the plurality of data sources comprises:
and dividing the data sources according to the dimensions of the plurality of physical characteristics in the horizontal direction, and dividing the data sources according to the dimensions of respective degrees in the vertical direction to form the characteristic matrix of the plurality of data sources.
4. The method of claim 3, wherein: the converting the property matrix into a datamation matrix comprises:
and adding different weight values to the degree corresponding to each physical characteristic in the longitudinal direction of the characteristic matrix to form the datamation matrix.
5. The method of claim 4, wherein: further comprising:
respectively normalizing the weights corresponding to the plurality of physical characteristics of each of the data sources in the transverse direction of the datamation matrix to form a normalized datamation matrix of the plurality of data sources,
wherein the generating the first indicator for the plurality of data sources based on the datamation matrix comprises generating the first indicator for the plurality of data sources based on the normalized datamation matrix.
6. The method of claim 5, wherein: the generating the first indicator of the plurality of data sources based on the normalized datamation matrix comprises:
respectively assigning different values to the degradation degree level represented by each data source;
for each data source, multiplying a numerical value corresponding to the degradation degree level represented by the data source by a weight value of each normalized physical characteristic of the data source to obtain the weight value of each data source; and
generating the first indicator for a plurality of the data sources based on a sum of weighted values for at least two of the data sources.
7. The method of claim 1, wherein: the generating second indicia of the plurality of data sources based on the priorities of the plurality of data sources comprises:
sequentially judging whether the bearing represented by the corresponding data source is degraded or not according to the priority order of the data sources;
if the data source with the prior priority represents that the bearing is degraded, outputting a numerical value corresponding to the degradation degree level represented by the data source with the prior priority, otherwise, judging the next priority;
if the data sources represent that the bearing is not degraded, outputting a numerical value corresponding to the degradation; and
generating the second index based on the output numerical value.
8. The method of any of claims 1 to 7, wherein: the plurality of data sources includes SCADA bearing temperature data from a SCADA system, CMS bearing vibration data from a vibration monitoring system, and vibration and oil reports from manual labor.
9. The method of claim 8, wherein: the plurality of physical characteristics includes an advance, a correct rate, and a time interval.
10. The method of claim 8, wherein: the priority of the data sources is from high to low: the SCADA bearing temperature data, the oil report, the vibration report, and the CMS bearing vibration data.
11. The method of claim 10, wherein: the fusing the first index and the second index to form a fused index of the wind turbine generator set bearing comprises:
if at least one of the vibration report and the oil report does not exist in the current month, the fusion index is equal to the second index; and
the fusion indicator equals the first indicator if the vibration report and the oil report are both present in the month.
12. The method of claim 1, wherein: the first index, the second index, and the fusion index are all numerical values in the range of 0 to 1.
13. The method of claim 12, wherein: the larger the numerical value among the first index, the second index, and the fusion index is, the higher the degree of deterioration of the bearing is.
14. The method of claim 1, wherein: further comprising:
and generating a corresponding alarm strategy based on the fusion index.
15. A bearing monitoring system, its characterized in that: comprising one or more processors for implementing the bearing monitoring method according to any of claims 1-14.
16. A computer-readable storage medium, having stored thereon a program which, when executed by a processor, carries out a bearing monitoring method according to any one of claims 1-14.
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| Publication number | Priority date | Publication date | Assignee | Title |
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