Online fault diagnosis method, system and equipment for wind turbine generator gearbox and medium
By constructing a fault feature matrix and using numerical simulation or digital twin technology to obtain a database, the problem of traditional methods relying on actual fault data is solved, high-precision fault diagnosis of wind turbine gearboxes is achieved, and the operating process is simplified.
Patent Information
- Application Number
- CN202510155633.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-12
- Publication Date
- 2025-09-23
AI Technical Summary
Traditional gearbox fault diagnosis methods rely on a large amount of actual fault data, are difficult to apply directly in actual operation, have insufficient diagnostic accuracy, and rely on expert experience.
By collecting vibration data from the wind turbine gearbox, a fault feature matrix of the measured vibration data is constructed, and a fault feature database is obtained using numerical simulation or digital twin technology. The similarity is calculated to determine the fault, thus achieving diagnosis without the need for a large amount of actual fault data.
It achieves high accuracy and simple operation of wind turbine gearbox fault diagnosis, reduces dependence on actual fault data, and improves the practicality of diagnosis.
Smart Images
Figure CN120687864A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of power equipment fault diagnosis, and relates to a method, system, equipment and medium for online fault diagnosis of a wind turbine gearbox. Background Art
[0002] The wind turbine gearbox is one of the most important mechanical components in a wind turbine. Its primary function is to transmit the mechanical energy generated by the rotor to the generator under the influence of wind. For large wind turbines, the gearbox is a major source of failure, making it extremely important to ensure proper operation of the wind turbine gearbox.
[0003] Traditional gearbox fault diagnosis methods, such as empirical methods and mechanism modeling, have problems such as insufficient diagnostic accuracy and reliance on expert experience. In recent years, with the development of sensor technology and data analysis technology, fault diagnosis methods based on vibration data analysis have gradually become a research hotspot. However, this method relies on a large amount of actual fault data and is difficult to directly apply in actual operation. Summary of the Invention
[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method, system, device and medium for online fault diagnosis of wind turbine gearboxes. This method, system, device and medium can perform fault diagnosis of wind turbine gearboxes without relying on a large amount of actual fault data.
[0005] To achieve the above object, the present invention discloses an online fault diagnosis method for a wind turbine gearbox, comprising:
[0006] Collect vibration data of wind turbine gearbox;
[0007] Constructing a fault feature matrix of measured vibration data according to the vibration data of the wind turbine gearbox;
[0008] Obtain a fault feature database through numerical simulation or digital twin technology;
[0009] Calculate the similarity between the fault feature matrix of the measured vibration data and the feature matrix of known faults in the fault feature database;
[0010] Determine whether the wind turbine gearbox is faulty based on the calculated similarity.
[0011] Furthermore, vibration data of the wind turbine gearbox is collected through a vibration sensor.
[0012] Furthermore, the process of constructing a fault feature matrix of measured vibration data based on the vibration data of the wind turbine gearbox is as follows:
[0013] performing noise reduction on vibration data of the wind turbine gearbox;
[0014] Feature extraction is performed on the noise-reduced vibration data to obtain the fault feature matrix of the measured vibration data.
[0015] Furthermore, the process of determining whether the wind turbine gearbox is faulty based on the calculated similarity is as follows:
[0016] When the calculated similarity is greater than or equal to 90%, the fault of the current wind turbine gearbox is determined to be the fault corresponding to the characteristic matrix of the known fault, and the preset fault characteristic matrix is replaced by the fault characteristic matrix of the measured vibration data to update the fault characteristic database.
[0017] Furthermore, the fault feature matrix is:
[0018]
[0019] Furthermore, the similarity is characterized by a correlation coefficient, which is r(X, Y):
[0020]
[0021] Where X represents the element in the fault feature matrix of the measured vibration data; Y represents the element in the feature matrix of the known fault; Cov(X,Y) is the covariance of X and Y; Var[X] is the variance of X; and Var[Y] is the variance of Y.
[0022] The present invention discloses an online fault diagnosis system for a wind turbine gearbox, comprising:
[0023] An acquisition module is used to collect vibration data of the wind turbine gearbox;
[0024] A construction module, configured to construct a fault feature matrix of measured vibration data based on the vibration data of the wind turbine gearbox;
[0025] An acquisition module is used to obtain a fault feature database through numerical simulation or digital twin technology;
[0026] A first calculation module is used to calculate the similarity between the fault feature matrix of the measured vibration data and the feature matrix of known faults in the fault feature database;
[0027] The second calculation module is used to determine whether the wind turbine gearbox is faulty according to the calculated similarity.
[0028] Furthermore, the building blocks include:
[0029] a noise reduction module, configured to reduce noise on vibration data of the wind turbine gearbox;
[0030] The extraction module is used to extract features from the noise-reduced vibration data and obtain a fault feature matrix of the measured vibration data.
[0031] The present invention discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the wind turbine gearbox online fault diagnosis method are implemented.
[0032] The present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the wind turbine gearbox online fault diagnosis method are implemented.
[0033] The present invention has the following beneficial effects:
[0034] During specific operation, the online fault diagnosis method, system, equipment and medium for the wind turbine gearbox described in the present invention obtain a fault feature database through numerical simulation or digital twin technology, calculate the similarity between the fault feature matrix of the measured vibration data and the feature matrix of the known faults in the fault feature database, and determine whether the wind turbine gearbox is faulty based on the calculated similarity. Among them, the fault feature database is obtained by numerical simulation or digital twin technology, thereby not relying on a large amount of actual fault data, and realizing fault diagnosis of the wind turbine gearbox. The operation is simple and the practicability is extremely strong. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:
[0036] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION
[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.
[0038] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.
[0039] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.
[0040] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.
[0041] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.
[0042] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.
[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.
[0044] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.
[0045] Example 1
[0046] refer to Figure 1 The wind turbine gearbox online fault diagnosis method of the present invention comprises the following steps:
[0047] 1) Collect vibration data of the wind turbine gearbox through a vibration sensor;
[0048] 2) Perform singular value decomposition (SVD) noise reduction on the collected vibration data to obtain the noise-reduced vibration data to improve the accuracy and reliability of the data;
[0049] 3) Feature extraction is performed on the noise-reduced vibration data to obtain a fault feature matrix of the measured vibration data with the dimensions of time domain parameters × frequency domain parameters (2 × 10);
[0050] 4) obtaining a fault feature database, wherein a feature matrix of known faults in the fault feature database is obtained through numerical simulation or digital twin technology and is used for comparison with the fault feature matrix of measured data;
[0051] 5) Calculate the similarity between the fault feature matrix of the measured vibration data and the feature matrix of the known faults in the fault feature database;
[0052] 6) When the calculated similarity is greater than or equal to 90%, the fault of the current wind turbine gearbox is determined to be the fault corresponding to the characteristic matrix of the known fault, and the fault characteristic matrix of the measured vibration data is used to replace the preset fault characteristic matrix to update the system's fault characteristic database;
[0053] 7) Repeat steps 1) to 6) to update the fault feature database to improve the fault diagnosis accuracy of the wind turbine gearbox.
[0054] In step 2), the process of singular value decomposition (SVD) noise reduction processing is: converting the vibration data into a matrix form and performing singular value decomposition on the matrix to remove noise components and retain useful signals.
[0055] The feature extraction process in step 3) is achieved by calculating the time domain parameters and frequency domain parameters of the noise-reduced vibration data, and constructing a 2×10 fault feature matrix using the time domain parameters and frequency domain parameters of the noise-reduced vibration data. The fault feature matrix is:
[0056]
[0057] The time domain parameters include mean, mean square value, variance, peak value, peak-to-peak value, root mean square value, peak factor, kurtosis, margin index and pulse factor; the frequency domain parameters include period, peak frequency, average frequency, phase, power spectral density, energy spectral density, pulse index, frequency center of gravity, angular domain center of gravity and frequency standard deviation.
[0058] In step 4), numerical simulation or digital twin technology is used to simulate the vibration response of the wind turbine gearbox under different fault conditions and generate a corresponding fault feature matrix; the wind turbine gearbox faults include but are not limited to loose bearings inside the gearbox, gear cracks, poor gear meshing, gear bonding and gear lubricant deterioration.
[0059] The similarity in step 5) is reflected by the correlation coefficient, which is r(X, Y):
[0060]
[0061] Where X represents the element in the fault feature matrix of the measured vibration data; Y represents the element in the feature matrix of the known fault; Cov(X,Y) is the covariance of X and Y; Var[X] is the variance of X; and Var[Y] is the variance of Y.
[0062] In this embodiment, when a fault occurs, an alarm signal is issued.
[0063] It should be noted that the present invention can be integrated into an online monitoring system of a wind turbine generator system to achieve real-time monitoring and fault diagnosis.
[0064] Example 2
[0065] The wind turbine gearbox online fault diagnosis system of the present invention comprises:
[0066] An acquisition module is used to collect vibration data of the wind turbine gearbox;
[0067] A construction module, configured to construct a fault feature matrix of measured vibration data based on the vibration data of the wind turbine gearbox;
[0068] An acquisition module is used to obtain a fault feature database through numerical simulation or digital twin technology;
[0069] The first calculation module is used to calculate the similarity between the fault feature matrix of the measured vibration data and the feature matrix of the known faults in the fault feature database;
[0070] The second calculation module is used to determine whether the wind turbine gearbox is faulty according to the calculated similarity.
[0071] In this embodiment, the building blocks include:
[0072] a noise reduction module, configured to reduce noise on vibration data of the wind turbine gearbox;
[0073] The extraction module is used to extract features from the noise-reduced vibration data and obtain a fault feature matrix of the measured vibration data.
[0074] In this embodiment, the process of determining whether the wind turbine gearbox is faulty based on the calculated similarity is as follows:
[0075] When the calculated similarity is greater than or equal to 90%, the fault of the current wind turbine gearbox is determined to be the fault corresponding to the characteristic matrix of the known fault, and the preset fault characteristic matrix is replaced by the fault characteristic matrix of the measured vibration data to update the fault characteristic database.
[0076] In this embodiment, the fault feature matrix is:
[0077]
[0078] In this embodiment, the similarity is represented by a correlation coefficient, and the correlation coefficient r(X, Y) is:
[0079]
[0080] Where X represents the element in the fault feature matrix of the measured vibration data; Y represents the element in the feature matrix of the known fault; Cov(X,Y) is the covariance of X and Y; Var[X] is the variance of X; and Var[Y] is the variance of Y.
[0081] The division of modules in the embodiments of the present application is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present application may be integrated into a single processor, or may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.
[0082] Example 3
[0083] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the wind turbine blade surface crack target detection method are implemented, for example, including: collecting vibration data from a wind turbine gearbox; performing noise reduction on the vibration data of the wind turbine gearbox; performing feature extraction on the noise-reduced vibration data to obtain a fault feature matrix of the measured vibration data; obtaining a fault feature database through numerical simulation or digital twin technology; calculating the similarity between the fault feature matrix of the measured vibration data and feature matrices of known faults in the fault feature database; and determining whether the wind turbine gearbox is faulty based on the calculated similarity. The memory may include internal memory, such as a high-speed random access memory (RAM), or may also include non-volatile memory, such as at least one disk drive. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry standard architecture bus, a peripheral component interconnect standard bus, an extended industry standard architecture bus, etc. The bus may be classified as an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the programs may include program code, and the program code includes computer operating instructions. The memory may include internal memory and nonvolatile memory and provides instructions and data to the processor.
[0084] Example 4
[0085] A computer-readable storage medium stores a computer program. When executed by a processor, the computer program implements the steps of the target detection method for cracks on the surface of a wind turbine blade, for example, including: collecting vibration data of a wind turbine gearbox; performing noise reduction on the vibration data of the wind turbine gearbox; performing feature extraction on the noise-reduced vibration data to obtain a fault feature matrix of the measured vibration data; obtaining a fault feature database through numerical simulation or digital twin technology; calculating the similarity between the fault feature matrix of the measured vibration data and the feature matrix of known faults in the fault feature database; and determining whether the wind turbine gearbox is faulty based on the calculated similarity. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.
[0086] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.
[0087] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0088] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.
[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0090] Those skilled in the art will readily identify other embodiments of the present invention after considering the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.
[0091] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.
[0092] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.
Claims
1. A wind turbine gearbox online fault diagnosis method, characterized in that: include: Collect vibration data of wind turbine gearbox; Constructing a fault feature matrix of measured vibration data according to the vibration data of the wind turbine gearbox; Obtain a fault feature database through numerical simulation or digital twin technology; Calculate the similarity between the fault feature matrix of the measured vibration data and the feature matrix of known faults in the fault feature database; Determine whether the wind turbine gearbox is faulty based on the calculated similarity.
2. The wind turbine gearbox online fault diagnosis method according to claim 1, characterized in that: Vibration data of the wind turbine gearbox is collected through vibration sensors.
3. The wind turbine gearbox online fault diagnosis method according to claim 1, characterized in that: The process of constructing a fault feature matrix of measured vibration data according to the vibration data of the wind turbine gearbox is as follows: performing noise reduction on vibration data of the wind turbine gearbox; Feature extraction is performed on the noise-reduced vibration data to obtain the fault feature matrix of the measured vibration data.
4. The wind turbine gearbox online fault diagnosis method according to claim 1, characterized in that: The process of determining whether the wind turbine gearbox is faulty based on the calculated similarity is as follows: When the calculated similarity is greater than or equal to 90%, the fault of the current wind turbine gearbox is determined to be the fault corresponding to the characteristic matrix of the known fault, and the preset fault characteristic matrix is replaced by the fault characteristic matrix of the measured vibration data to update the fault characteristic database.
5. The wind turbine gearbox online fault diagnosis method according to claim 1, characterized in that: The fault feature matrix is:
6. The wind turbine gearbox online fault diagnosis method according to claim 1, characterized in that: The similarity is characterized by the correlation coefficient r(X, Y): Where X represents the element in the fault feature matrix of the measured vibration data; Y represents the element in the feature matrix of the known fault; Cov(X,Y) is the covariance of X and Y; Var[X] is the variance of X; and Var[Y] is the variance of Y.
7. A wind turbine gearbox online fault diagnosis system, characterized in that: include: An acquisition module is used to collect vibration data of the wind turbine gearbox; A construction module, configured to construct a fault feature matrix of measured vibration data based on the vibration data of the wind turbine gearbox; An acquisition module is used to obtain a fault feature database through numerical simulation or digital twin technology; A first calculation module is used to calculate the similarity between the fault feature matrix of the measured vibration data and the feature matrix of known faults in the fault feature database; The second calculation module is used to determine whether the wind turbine gearbox is faulty according to the calculated similarity.
8. The wind turbine gearbox online fault diagnosis system according to claim 7, characterized in that: The building blocks include: a noise reduction module, configured to reduce noise on vibration data of the wind turbine gearbox; The extraction module is used to extract features from the noise-reduced vibration data and obtain a fault feature matrix of the measured vibration data.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the wind turbine gearbox online fault diagnosis method according to any one of claims 1 to 6 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the wind turbine gearbox online fault diagnosis method according to any one of claims 1 to 6 are implemented.