CN114508499A - Fan health degree early warning system based on big data of unit operation - Google Patents
Fan health degree early warning system based on big data of unit operation Download PDFInfo
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- CN114508499A CN114508499A CN202111580029.8A CN202111580029A CN114508499A CN 114508499 A CN114508499 A CN 114508499A CN 202111580029 A CN202111580029 A CN 202111580029A CN 114508499 A CN114508499 A CN 114508499A
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- 230000036541 health Effects 0.000 title claims abstract description 66
- 238000010977 unit operation Methods 0.000 title claims abstract description 23
- 238000004364 calculation method Methods 0.000 claims abstract description 28
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- 238000001228 spectrum Methods 0.000 claims description 15
- 230000009471 action Effects 0.000 claims description 6
- 230000005611 electricity Effects 0.000 claims description 5
- 238000000034 method Methods 0.000 claims description 4
- 238000010205 computational analysis Methods 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000001816 cooling Methods 0.000 description 3
- 238000009423 ventilation Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 125000004122 cyclic group Chemical group 0.000 description 2
- 238000004378 air conditioning Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
- F04D27/001—Testing thereof; Determination or simulation of flow characteristics; Stall or surge detection, e.g. condition monitoring
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Abstract
The invention discloses a fan health degree early warning system based on big data of unit operation, which comprises an early warning establishing system, a data preprocessing system, a calculation analysis system, a data determining system and a data comparison system, and relates to the technical field of fan detection, wherein a fatigue degree health value is set in the system in advance according to the working time of a fan gear, an early warning system is generated, when the early warning establishing system is input, the circulating load force of the fan gear can be calculated, so that the fatigue degree of the fan gear can be determined, when the fatigue degree of the fan gear is calculated, the final data can be compared with a preset health value through a data calculating module, when the calculated value exceeds the preset health value, a PCU control module performs early warning and early warning through an alarming module, and a display panel can display the value exceeding the health value, the accuracy of the whole data is improved, and meanwhile the operation efficiency can be improved.
Description
Technical Field
The invention relates to the technical field of fan detection, in particular to a fan health degree early warning system based on big unit operation data.
Background
The fan is a driven fluid machine which increases the pressure of gas and discharges the gas by means of input mechanical energy. The fan is a Chinese habit short for gas compression and gas conveying machinery, and generally comprises a ventilator, a blower and a wind driven generator, and is widely used for ventilation, dust exhaust and cooling of factories, mines, tunnels, cooling towers, vehicles, ships and buildings, and ventilation and induced air of boilers and industrial furnaces; cooling and ventilation in air conditioning equipment and household appliances; drying and selecting grain, wind tunnel wind source and air cushion boat inflating and propelling.
The operation inside the fan cannot be separated from the transmission of the gear, however, after the gear reaches a certain service life, the health value of the gear is affected and is easy to damage or fatigue, the failure of the gear transmission is mainly the failure of gear teeth, the proportion of fatigue broken teeth is the largest in all failure modes of the gear teeth, and surface contact fatigue is secondly generated, so the fatigue damage is one of the most main modes of the gear failure, the life of the health value of the fan gear needs to be predicted, and once the health value exceeds the normally set health value, early warning can be timely sent out to process the fan gear.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The invention is provided in view of the above and/or the existing problems in the fan health degree early warning system based on the big data of unit operation.
Therefore, the invention aims to provide a fan health degree early warning system based on big data of unit operation.
In order to solve the technical problems, the invention provides the following technical scheme: the utility model provides a fan health degree early warning system based on big data of unit operation which characterized in that: the method comprises the following steps:
the early warning establishing system comprises: and setting a fatigue health value in the system in advance according to the working duration of the fan gear to generate an early warning system.
A data preprocessing system: and obtaining the damage of all loads to the fan gear under the action of variable loads by using a Miner rule.
A computational analysis system: an accurate load spectrum can be established, the load spectrum can be obtained by circularly counting the actually measured random load time history of the wind field, and the actually measured load spectrum is provided by a professional load processing company for testing.
A data determination system: and calculating the final accumulated damage amount and fatigue degree of the fan gear through the amplitude-variable load circulation of the fan gear.
Data comparison system: and finally, predicting the overall health degree according to the fatigue life of the fan gear, and if the health degree exceeds a set health value, giving out an early warning alarm.
The invention relates to a preferable scheme of a fan health degree early warning system based on big data of unit operation, wherein: when the early warning establishing system is input, calculation is needed according to the working life of the fan gear, then the health value of the stress cycle times of the gear is input and stored, and the working life of the fan gear is calculated by using a calculation formula;
calculated as equation (1) is:
N＝60njLh(1)
the invention relates to a preferable scheme of a fan health degree early warning system based on big data of unit operation, wherein: after the early warning establishment system is input, the circulating load force of the fan gear can be calculated, so that the fatigue degree of the fan gear can be determined; and the damage fatigue degree depends on the health degree of the fan gear, and a calculation formula is used for calculation.
The invention relates to a preferable scheme of a fan health degree early warning system based on big data of unit operation, wherein: the calculation is that formula (2) is:
the invention relates to a preferable scheme of a fan health degree early warning system based on big data of unit operation, wherein: ni in the formula (2) is the cycle number of the ith load, Ni is the cycle number corresponding to the fatigue failure of the fan gear under the action of the ith load, and Ni can be obtained from a load spectrum, when the damage rate D of the fan gear is larger than or equal to 1, the condition that the health of the fan gear is influenced and the fatigue failure occurs in the time of the load spectrum is shown.
As a preferred scheme of the fan health degree early warning system based on big data of unit operation, the invention comprises the following steps: after the fatigue of fan gear calculates, can pass through data calculation module with the healthy numerical value of setting for in advance with last data and compare, healthy numerical value passes through the numerical value and predetermines the module storage in storage module, and storage module passes through the data contrast module with the numerical value after predetermineeing healthy numerical value and calculation and compares, recycle judge module afterwards and judge whether the numerical value after the calculation exceeds predetermined healthy numerical value, when the numerical value after the calculation exceeds predetermined healthy numerical value, PCU control module will carry out the early warning alarm through alarm module.
As a preferred scheme of the fan health degree early warning system based on big data of unit operation, the invention comprises the following steps: the early warning system comprises an early warning machine with an integrated appearance, an operating panel is arranged on the outer wall of the early warning machine and electrically connected with a data calculation module, a display panel is arranged at one end of the operating panel, a preset panel is connected to one side of the early warning machine and electrically connected with a numerical value preset module, an alarm is mounted at the upper end of one side, away from the preset panel, of the early warning machine, the alarm is electrically connected with an alarm module, and an early warning indicator lamp and a loudspeaker are further arranged at the upper end of the alarm.
The invention relates to a preferable scheme of a fan health degree early warning system based on big data of unit operation, wherein: the alarm module sends out early warning through early warning pilot lamp and speaker, and display panel can show the numerical value that surpasss the health value.
The invention has the beneficial effects that: according to the working duration of the fan gear, a fatigue degree health value is set in the system in advance, an early warning system is generated, after the early warning system is input, the cyclic loading capacity of the fan gear can be calculated, the fatigue degree of the fan gear can be determined, after the fatigue degree of the fan gear is calculated, the final data can be compared with the health value set in advance through the data calculation module, when the calculated value exceeds the preset health value, the PCU control module performs early warning and warning through the warning module, meanwhile, the display panel can display the value exceeding the health value, the accuracy of the whole data is improved, and meanwhile, the working efficiency can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is a block diagram of a fan gear health detection system of the present invention.
Fig. 2 is a flow chart of the early warning calculation of the present invention.
Fig. 3 is a schematic structural diagram of the warning device of the present invention.
FIG. 4 is a block diagram of a numerical detection module of a fan gear according to the present invention.
FIG. 5 is a block diagram of a module for determining a detection value according to the present invention.
FIG. 6 is a block diagram of a fan gear health warning system according to the present invention.
In the drawings, the components represented by the respective reference numerals are listed below:
100. an early warning machine; 200. an operation panel; 300. a display panel; 400. presetting a panel; 500. an alarm; 600. an early warning indicator light; 700. a loudspeaker.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Referring to fig. 1 and 2, a fan health degree early warning system based on big data of unit operation, its characterized in that: the method comprises the following steps: the early warning establishing system comprises: according to the working duration of the fan gear, a fatigue degree health value is set in the system in advance, an early warning system is generated, and a data preprocessing system: and (3) obtaining the damage of all loads to the fan gear under the variable load action by utilizing a Miner rule, and calculating and analyzing the system: an accurate load spectrum can be established, the load spectrum can be obtained by circularly counting the actually measured random load time history of the wind field, the actually measured load spectrum is provided by a professional load processing company through testing, and the data determination system comprises: through the variable amplitude load circulation of the fan gear, the final accumulated damage amount and fatigue degree of the fan gear are calculated, and a data comparison system is adopted: finally, predicting the overall health degree according to the fatigue life of the fan gear, if the health degree exceeds a set health value, giving out an early warning alarm, calculating according to the working life of the fan gear when an early warning establishing system is input, then inputting and storing the health value of the stress cycle times of the gear, and calculating the working life of the fan gear by using a calculation formula; calculated as equation (1) is: when the early warning establishment system is input, the cyclic load force of the fan gear can be calculated, so that the fatigue degree of the fan gear can be determined; the damage fatigue degree depends on the health degree of the fan gear, and is calculated by using a calculation formula, wherein the calculation formula (2) is as follows:
ni in the formula (2) is the cycle number of the ith load, Ni is the cycle number corresponding to the fatigue failure of the fan gear under the action of the ith load, and Ni can be obtained from a load spectrum, when the damage rate D of the fan gear is larger than or equal to 1, the condition that the health of the fan gear is influenced and the fatigue failure occurs in the time of the load spectrum is shown.
Referring to fig. 3, 4, 5 and 6, after the fatigue of the fan gear is calculated, the last data can be compared with a health value set in advance through a data calculation module, the health value is stored in a storage module through a value presetting module, the storage module compares the preset health value with the calculated value through a data comparison module, then a judgment module is used for judging whether the calculated value exceeds the preset health value, when the calculated value exceeds the preset health value, a PCU control module performs early warning alarm through an alarm module, the early warning system comprises an early warning machine 100 with an integrated appearance, an operation panel 200 is arranged on the outer wall of the early warning machine 100, the operation panel 200 is electrically connected with the data calculation module, a display panel 300 is arranged at one end of the operation panel 200, and a preset panel 400 is connected to one side of the early warning machine 100, it predetermines the module electricity and is connected to predetermine panel 400 and numerical value, alarm 500 is installed to one side upper end that panel 400 was predetermine to early warning machine 100 keeping away from, alarm 500 is connected with the alarm module electricity, alarm 500's upper end still sets up early warning pilot lamp 600 and speaker 700 respectively, alarm module sends the early warning through early warning pilot lamp 600 and speaker 700, and display panel 300 can show the numerical value that surpasss the health value
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (8)
1. The utility model provides a fan health degree early warning system based on big data of unit operation which characterized in that: the method comprises the following steps:
the early warning establishing system comprises: and setting a fatigue health value in the system in advance according to the working duration of the fan gear to generate an early warning system.
A data preprocessing system: and obtaining the damage of all loads to the fan gear under the action of variable loads by using a Miner rule.
A computational analysis system: an accurate load spectrum can be established, the load spectrum can be obtained by circularly counting the actually measured random load time history of the wind field, and the actually measured load spectrum is provided by a professional load processing company for testing.
A data determination system: and calculating the final accumulated damage amount and fatigue degree of the fan gear through the amplitude-variable load circulation of the fan gear.
Data comparison system: and finally, predicting the overall health degree according to the fatigue life of the fan gear, and if the health degree exceeds a set health value, giving out an early warning alarm.
2. The wind turbine health early warning system based on big data of unit operation according to claim 1, characterized in that: when the early warning establishing system is input, calculation is needed according to the working life of the fan gear, then the health value of the stress cycle times of the gear is input and stored, and the working life of the fan gear is calculated by using a calculation formula;
calculated as equation (1) is:
N＝60njLh (1)。
3. the wind turbine health early warning system based on big data of unit operation according to claim 1, characterized in that: after the early warning establishing system is input, the circulating load force of the fan gear can be calculated, so that the fatigue degree of the fan gear can be determined; and the damage fatigue degree depends on the health degree of the fan gear, and a calculation formula is used for calculation.
4. The wind turbine health early warning system based on big data of unit operation according to claim 3, characterized in that: the calculation is that formula (2) is:
5. the wind turbine health early warning system based on big data of unit operation according to claim 4, characterized in that: ni in the formula (2) is the cycle number of the ith load, Ni is the cycle number corresponding to the fatigue failure of the fan gear under the action of the ith load, and Ni can be obtained from a load spectrum, when the damage rate D of the fan gear is larger than or equal to 1, the condition that the health of the fan gear is influenced and the fatigue failure occurs in the time of the load spectrum is shown.
6. The wind turbine health early warning system based on big data of unit operation according to claim 1, characterized in that: after the fatigue of fan gear calculates, can pass through data calculation module with the healthy numerical value of setting for in advance with last data and compare, healthy numerical value passes through the numerical value and predetermines the module storage in storage module, and storage module passes through the data contrast module with the numerical value after predetermineeing healthy numerical value and calculation and compares, recycle judge module afterwards and judge whether the numerical value after the calculation exceeds predetermined healthy numerical value, when the numerical value after the calculation exceeds predetermined healthy numerical value, PCU control module will carry out the early warning alarm through alarm module.
7. The wind turbine health early warning system based on big data of unit operation according to claim 1, characterized in that: early warning system includes early warning machine (100) of outward appearance formula as an organic whole, be provided with operating panel (200) on the outer wall of early warning machine (100), operating panel (200) are connected with data calculation module electricity, the one end of operating panel (200) is provided with display panel (300), one side of early warning machine (100) is connected with and presets panel (400), it presets the module electricity with numerical value and is connected to predetermine panel (400), one side upper end of presetting panel (400) is kept away from in early warning machine (100) installs alarm (500), alarm (500) are connected with the alarm module electricity, the upper end of alarm (500) still sets up early warning pilot lamp (600) and speaker (700) respectively.
8. The wind turbine health early warning system based on big data of unit operation according to claim 6, characterized in that: the alarm module sends out early warning through the early warning indicator lamp (600) and the loudspeaker (700), and the display panel (300) can show the numerical value that surpasss the health value.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106600066A (en) * | 2016-12-19 | 2017-04-26 | 南京理工大学 | SCADA data-based wind driven generator gearbox fatigue life estimation method |
| CN108345762A (en) * | 2018-03-20 | 2018-07-31 | 中石化石油机械股份有限公司 | A kind of large module gear rack Prediction method for fatigue life for gear rack drilling machine |
| CN209856065U (en) * | 2019-02-11 | 2019-12-27 | 台山市科信特电机有限公司 | Intelligent brushless fan driving system |
| CN112287554A (en) * | 2020-10-31 | 2021-01-29 | 重庆望江工业有限公司 | Method for predicting contact fatigue damage of gear surface |
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- 2021-12-22 CN CN202111580029.8A patent/CN114508499B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106600066A (en) * | 2016-12-19 | 2017-04-26 | 南京理工大学 | SCADA data-based wind driven generator gearbox fatigue life estimation method |
| CN108345762A (en) * | 2018-03-20 | 2018-07-31 | 中石化石油机械股份有限公司 | A kind of large module gear rack Prediction method for fatigue life for gear rack drilling machine |
| CN209856065U (en) * | 2019-02-11 | 2019-12-27 | 台山市科信特电机有限公司 | Intelligent brushless fan driving system |
| CN112287554A (en) * | 2020-10-31 | 2021-01-29 | 重庆望江工业有限公司 | Method for predicting contact fatigue damage of gear surface |
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