A fan large component fault diagnosis method based on multi-source heterogeneous data

Abstract

The application discloses to the fan fault diagnosis technical field, specifically to a kind of fan major component fault diagnosis method based on multi-source heterogeneous data, including specific steps as follows: the operating parameter of fan is detected by parameter detection module, after detection, then, parameter data detected by parameter detection module is transported to first database by central processing unit, to store the parameter data detected by parameter detection module, the data stored in first database is compared with the data stored in first storage module by first comparison module, the application can realize the diagnosis of fan fault when fan is running, and can solve the problem that fan needs to be stopped for repair when fault is detected, so that the running efficiency of fan can be avoided to a certain extent.

Description

Technical Field

[0001] This invention relates to the field of wind turbine fault diagnosis technology, specifically a method for diagnosing faults in major wind turbine components based on multi-source heterogeneous data. Background Technology

[0002] A fan is a machine that uses input mechanical energy to increase gas pressure and discharge gas; it is a type of driven fluid machinery. In China, "fan" is a common abbreviation for gas compression and gas transportation machinery, and generally includes ventilators, blowers, and wind turbines. Fans are widely used in factories, mines, tunnels, cooling towers, vehicles, ships, and buildings for ventilation, dust removal, and cooling; in boilers and industrial furnaces for ventilation and induced draft; in air conditioning equipment and household appliances for cooling and ventilation; in grain drying and conveying; as a wind source for wind tunnels; and for inflating and propelling hovercraft.

[0003] Currently, troubleshooting wind turbines typically requires stopping the turbines, which can affect their operating efficiency. Therefore, this paper proposes a fault diagnosis method for major wind turbine components based on multi-source heterogeneous data. Summary of the Invention

[0004] In view of the problems existing in the above and / or the existing method for fault diagnosis of large wind turbine components based on multi-source heterogeneous data, the present invention is proposed.

[0005] Therefore, the purpose of this invention is to provide a fault diagnosis method for large wind turbine components based on multi-source heterogeneous data, which can solve the aforementioned existing problems.

[0006] To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution: A method for fault diagnosis of major wind turbine components based on multi-source heterogeneous data includes the following specific steps: Step 1: The operating parameters of the wind turbine are detected by the parameter detection module. After detection, the parameter data detected by the parameter detection module is sent to the first database through the central processing unit for storage. Step 2: Compare the data stored in the first database with the data stored in the first storage module using the first comparison module; Step 3: If the difference between the compared parameters exceeds the set value, it will be directly sent to the second comparison module; Step 4: If the difference between the compared parameters does not exceed the set value, the data compared by the first comparison module will be recorded by the recording module. After recording, all the data recorded by the recording module will be compared by the comparison module to determine whether the current parameter is different from the previous parameter. If they are different, they will be sent to the second comparison module. Step 5: The second comparison module compares the parameter difference compared by the comparison module and the parameter difference compared by the first comparison module with the data stored in the second storage module to determine what kind of fault the wind turbine will experience when a certain parameter of the wind turbine changes. Step Six: The analysis module merges the differences between various parameters. After merging, the third comparison module compares the merged data with the data stored in the third storage module to determine what kind of fault the wind turbine will experience when its multiple parameters change. Step 7: The statistics module performs statistics on the data compared by the second comparison module and the data compared by the third comparison module. After the statistics are performed, the arrangement module arranges the data from the statistics module for easy viewing. Finally, the sending module sends the arranged data to the staff.

[0007] As a preferred embodiment of the wind turbine major component fault diagnosis method based on multi-source heterogeneous data described in this invention, it further includes a fault diagnosis system, the fault diagnosis system comprising: The parameter detection module is used to detect the operating parameters of the fan; The first database is used to store the parameter data detected by the parameter detection module; The central processing unit is used to transmit the parameter data detected by the parameter detection module to the first database.

[0008] In a preferred embodiment of the wind turbine major component fault diagnosis method based on multi-source heterogeneous data described in this invention, the parameter detection module is connected to the central processing unit, and the central processing unit is connected to the first database.

[0009] As a preferred embodiment of the wind turbine major component fault diagnosis method based on multi-source heterogeneous data described in this invention, the fault diagnosis system further includes: The first storage module stores the standard operating parameters of the fan. The first comparison module is used to compare the data stored in the first database with the data stored in the first storage module; The recording module is used to record the data compared by the first comparison module. The comparison module is used to compare all the data recorded by the recording module.

[0010] In a preferred embodiment of the wind turbine major component fault diagnosis method based on multi-source heterogeneous data described in this invention, the first database is connected to the first comparison module, the first comparison module is connected to the first storage module, the first comparison module is connected to the recording module, and the recording module is connected to the comparison module.

[0011] As a preferred embodiment of the wind turbine major component fault diagnosis method based on multi-source heterogeneous data described in this invention, the fault diagnosis system further includes: The second storage module is used to store fault conditions caused by various parameter differences; The second comparison module is used to compare the parameter difference compared by the comparison module and the parameter difference compared by the first comparison module with the data stored in the second storage module. The analysis module is used to merge multiple existing parameter differences; The third storage module is used to store various fault conditions of the wind turbine; The third comparison module is used to compare the data merged by the analysis module with the data stored in the third storage module.

[0012] As a preferred embodiment of the wind turbine major component fault diagnosis method based on multi-source heterogeneous data described in this invention, the comparison module is connected to the second comparison module, the second comparison module is connected to the second storage module, the second comparison module is connected to the analysis module, the analysis module is connected to the third comparison module, and the third comparison module is connected to the third storage module.

[0013] As a preferred embodiment of the wind turbine major component fault diagnosis method based on multi-source heterogeneous data described in this invention, the fault diagnosis system further includes: The statistics module is used to perform statistical analysis on the data compared by the second comparison module and the data compared by the third comparison module. The sorting module is used to sort the data collected by the statistics module; The sending module is used to send the data arranged by the arranging module to the staff.

[0014] As a preferred embodiment of the wind turbine major component fault diagnosis method based on multi-source heterogeneous data described in this invention, the third comparison module is connected to the statistics module, and the statistics module is connected to the arrangement module.

[0015] In a preferred embodiment of the wind turbine major component fault diagnosis method based on multi-source heterogeneous data described in this invention, the arrangement module is connected to the sending module, and the sending module is set as a terminal.

[0016] Compared with existing technologies: This invention enables the diagnosis of wind turbine faults during operation, thereby solving the problem that currently, wind turbines usually need to be stopped for maintenance, thus avoiding impact on wind turbine operating efficiency to a certain extent. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the process of the present invention.

[0018] In the diagram: parameter detection module 10, central processing unit 20, first database 30, first comparison module 40, recording module 50, comparison module 60, second comparison module 70, analysis module 80, third comparison module 90, statistics module 100, arrangement module 110, sending module 120, first storage module 130, second storage module 140, and third storage module 150. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0020] This invention provides a method for fault diagnosis of major wind turbine components based on multi-source heterogeneous data. Please refer to [link / reference]. Figure 1 The specific steps are as follows: Step 1: The operating parameters of the fan are detected by the parameter detection module 10. After detection, the parameter data detected by the parameter detection module 10 is sent to the first database 30 by the central processing unit 20 for storage. Step 2: The first comparison module 40 compares the data stored in the first database 30 with the data stored in the first storage module 130; Step 3: If the difference between the compared parameters exceeds the set value, it will be directly sent to the second comparison module 70; Step 4: If the difference between the compared parameters does not exceed the set value, the data compared by the first comparison module 40 will be recorded by the recording module 50. After recording, all the data recorded by the recording module 50 will be compared by the comparison module 60 to determine whether the current parameter is different from the previous parameter. If they are different, they will be sent to the second comparison module 70. Step 5: The second comparison module 70 compares the parameter difference compared by the comparison module 60 and the parameter difference compared by the first comparison module 40 with the data stored in the second storage module 140 to determine what kind of fault the wind turbine will experience when a certain parameter of the wind turbine changes. Step Six: The analysis module 80 merges the differences between the various parameters. After merging, the third comparison module 90 compares the merged data with the data stored in the third storage module 150 to determine what kind of fault the wind turbine will experience when its multiple parameters change. Step 7: The statistics module 100 performs statistics on the data compared by the second comparison module 70 and the data compared by the third comparison module 90. After the statistics are performed, the data compiled by the statistics module 100 is arranged by the arrangement module 110 for personnel to view. Finally, the data arranged by the arrangement module 110 is sent to the staff by the sending module 120.

[0021] It also includes a fault diagnosis system, comprising: a parameter detection module 10 for detecting the operating parameters of the wind turbine; a first database 30 for storing the parameter data detected by the parameter detection module 10; a central processing unit 20 for transmitting the parameter data detected by the parameter detection module 10 to the first database 30; a first storage module 130 for storing the standard operating parameters of the wind turbine; a first comparison module 40 for comparing the data stored in the first database 30 with the data stored in the first storage module 130; a recording module 50 for recording the data compared by the first comparison module 40; a comparison module 60 for comparing all the data recorded by the recording module 50; and a second storage module 140 for storing fault conditions caused by various parameter differences. The second comparison module 70 is used to compare the parameter differences compared by the comparison module 60 and the parameter differences compared by the first comparison module 40 with the data stored in the second storage module 140; the analysis module 80 is used to merge multiple existing parameter differences; the third storage module 150 is used to store various fault conditions of the fan; the third comparison module 90 is used to compare the data merged by the analysis module 80 with the data stored in the third storage module 150; the statistics module 100 is used to perform statistics on the data compared by the second comparison module 70 and the data compared by the third comparison module 90; the arrangement module 110 is used to arrange the data statistically analyzed by the statistics module 100; and the sending module 120 is used to send the data arranged by the arrangement module 110 to the staff. The parameter detection module 10 is connected to the central processing unit 20. The central processing unit 20 is connected to the first database 30. The first database 30 is connected to the first comparison module 40. The first comparison module 40 is connected to the first storage module 130. The first comparison module 40 is connected to the recording module 50. The recording module 50 is connected to the comparison module 60. The comparison module 60 is connected to the second comparison module 70. The second comparison module 70 is connected to the second storage module 140. The second comparison module 70 is connected to the analysis module 80. The analysis module 80 is connected to the third comparison module 90. The third comparison module 90 is connected to the third storage module 150. The third comparison module 90 is connected to the statistics module 100. The statistics module 100 is connected to the arrangement module 110. The arrangement module 110 is connected to the sending module 120, and the sending module 120 is set as a terminal.

[0022] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for fault diagnosis of major wind turbine components based on multi-source heterogeneous data, characterized in that, The specific steps are as follows: Step 1: The operating parameters of the fan are detected by the parameter detection module (10). After detection, the parameter data detected by the parameter detection module (10) is sent to the first database (30) through the central processing unit (20) to store the parameter data detected by the parameter detection module (10). Step 2: The data stored in the first database (30) is compared with the data stored in the first storage module (130) through the first comparison module (40); Step 3: If the difference between the compared parameters exceeds the set value, it will be directly sent to the second comparison module (70); Step 4: If the difference between the compared parameters does not exceed the set value, the data compared by the first comparison module (40) will be recorded by the recording module (50). After recording, all the data recorded by the recording module (50) will be compared by the comparison module (60) to determine whether the current parameter is different from the previous parameter. If they are different, they will be sent to the second comparison module (70). Step 5: The second comparison module (70) compares the parameter difference compared by the comparison module (60) and the parameter difference compared by the first comparison module (40) with the data stored in the second storage module (140) to determine what kind of fault the fan will have when a certain parameter of the fan changes. Step 6: The analysis module (80) merges the differences between the various parameters. After merging, the third comparison module (90) compares the data merged by the analysis module (80) with the data stored in the third storage module (150) to determine what kind of fault the wind turbine will have when the multiple parameters of the wind turbine change. Step 7: The statistical module (100) performs statistics on the data compared by the second comparison module (70) and the data compared by the third comparison module (90). After the statistics are performed, the data statistically performed by the statistical module (100) is arranged by the arrangement module (110) for personnel to view. Finally, the data arranged by the arrangement module (110) is sent to the staff by the sending module (120). It also includes a fault diagnosis system, the fault diagnosis system comprising: The parameter detection module (10) is used to detect the operating parameters of the fan; The first database (30) is used to store the parameter data detected by the parameter detection module (10); The central processing unit (20) is used to transmit the parameter data detected by the parameter detection module (10) to the first database (30); The first storage module (130) stores the standard operating parameters of the fan; The first comparison module (40) is used to compare the data stored in the first database (30) with the data stored in the first storage module (130); The recording module (50) is used to record the data compared by the first comparison module (40); The comparison module (60) is used to compare all the data recorded by the recording module (50); The second storage module (140) is used to store fault conditions caused by various parameter differences; The second comparison module (70) is used to compare the parameter difference compared by the comparison module (60) and the parameter difference compared by the first comparison module (40) with the data stored in the second storage module (140); Analysis module (80) is used to merge multiple existing parameter differences; The third storage module (150) is used to store various fault conditions of the fan; The third comparison module (90) is used to compare the data merged by the analysis module (80) with the data stored in the third storage module (150); The statistics module (100) is used to perform statistics on the data compared by the second comparison module (70) and the data compared by the third comparison module (90); The arrangement module (110) is used to arrange the data collected by the statistics module (100); The sending module (120) is used to send the data arranged by the arranging module (110) to the staff; The parameter detection module (10) is connected to the central processing unit (20), the central processing unit (20) is connected to the first database (30), the first database (30) is connected to the first comparison module (40), the first comparison module (40) is connected to the first storage module (130), the first comparison module (40) is connected to the recording module (50), the recording module (50) is connected to the comparison module (60), the comparison module (60) is connected to the second comparison module (70), and the second comparison module (70) is connected to the first comparison module (70). 70) is connected to the second storage module (140), the second comparison module (70) is connected to the analysis module (80), the analysis module (80) is connected to the third comparison module (90), the third comparison module (90) is connected to the third storage module (150), the third comparison module (90) is connected to the statistics module (100), the statistics module (100) is connected to the arrangement module (110), the arrangement module (110) is connected to the sending module (120), and the sending module (120) is set as a terminal.

Images