Wind turbine generator gearbox tooth surface strength optimization method considering multi-working-condition load distribution

Abstract

The invention belongs to the field of data processing, particularly relates to a wind turbine generator gear box tooth surface strength optimization method considering multi-working-condition load distribution, and aims to solve the problem that the optimization effect is affected due to the fact that strength optimization checking is carried out only for a single working condition in the prior art. The method comprises the following steps: acquiring working condition data of the gearbox under various preset working conditions; load spectrum decoupling processing is conducted on the working condition data, a dynamic load distribution matrix synchronous with the tooth surface meshing period is determined, stress field conversion is conducted on the dynamic load distribution matrix, and a tooth root bending stress field and a tooth surface contact stress field are obtained; inputting the tooth root bending stress field and the tooth surface contact stress field into a pre-constructed double-constraint optimization model to obtain a tooth surface strengthening parameter tensor output by the model; and inputting the tooth surface strengthening parameter tensor into a preset process mapping system. Based on the method provided by the invention, the multi-working-condition operation data of the gearbox are synchronously obtained for optimization calculation, and the limitation of traditional single load analysis is broken through.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of data processing, and particularly relates to a wind turbine gearbox gear face strength optimization method considering multi-working condition load distribution. BACKGROUND

[0002] As a core component of the transmission system, the wind turbine gearbox is subjected to alternating loads and complex working condition impacts for a long time, and pitting, peeling and fatigue fracture of the gear face are the main failure modes. In order to ensure the normal operation of the wind turbine gearbox, the traditional gear face strength optimization method mainly relies on the combination of static load assumption and empirical design. The optimization process is based on a simplified load model of the rated working condition, and the tooth root bending stress and the gear face contact stress are calculated by theoretical formula.

[0003] However, the existing technology is usually a strength optimization check only for a single working condition (such as rated power generation), but the wind turbine needs to cope with dynamic load mutations such as grid frequency modulation and emergency braking during actual operation. The time and space distribution of such dynamic loads (such as local overload of the gear face) is difficult to be quantified, which leads to design redundancy or insufficient strength, and further affects the technical effect. SUMMARY

[0004] In order to solve the above problems in the prior art, i.e., the problem of being able to only perform strength optimization check for a single working condition and affecting the optimization effect, in a first aspect, the application provides a wind turbine gearbox gear face strength optimization method considering multi-working condition load distribution, which comprises: obtaining working condition data of the gearbox under multiple preset working conditions; performing load spectrum decoupling processing on the working condition data, determining a dynamic load distribution matrix synchronized with the gear meshing period, and performing stress field conversion on the dynamic load distribution matrix to obtain a tooth root bending stress field and a gear face contact stress field; inputting the tooth root bending stress field and the gear face contact stress field into a pre-constructed double-constraint optimization model to obtain a gear face strengthening parameter tensor output by the model, the double-constraint optimization model comprising: a stress feature extraction module for determining a geometric coordinate set of a high stress danger area according to the tooth root bending stress field, a Hertz contact analysis module for calculating a local contact pressure gradient according to the gear face contact stress field, and a parameter generation module for determining the gear face strengthening parameter tensor according to the geometric coordinate set and the local contact pressure gradient; inputting the gear face strengthening parameter tensor into a preset process mapping system, the process mapping system determining a process control instruction set of a numerical control machining equipment according to the gear face strengthening parameter tensor.

[0005] In some preferred embodiments, the load spectrum decoupling processing comprises: Collecting a rotational speed pulse signal of a gear box input shaft as a time reference signal; According to the time reference signal, determining an analysis time window synchronized with a gear meshing period; Performing a dynamic envelope extraction operation on the working condition data in each analysis time window, and constructing a two-dimensional matrix representing dynamic load intensity at different meshing phases as the dynamic load distribution matrix.

[0006] In some preferred embodiments, the stress field conversion of the dynamic load distribution matrix comprises: Obtaining a three-dimensional geometric model of the gear; According to the three-dimensional geometric model of the gear, constructing a finite element grid model containing the gear topography; According to the dynamic load distribution matrix, determining a dynamic load component and mapping the dynamic load component to the corresponding nodes of the finite element grid model; Based on the mapped nodes, synchronously generating spatial distribution data sets of the root bending stress field and the tooth surface contact stress field through a preset stress field iterative algorithm.

[0007] In some preferred embodiments, the determination of the geometric coordinate set of the high stress risk area according to the root bending stress field comprises: Scanning the gradient distribution characteristics of the root bending stress field; Identifying a continuous stress concentration area exceeding a preset material strength threshold; Extracting the geometric center coordinates of each stress concentration area to obtain the geometric coordinate set.

[0008] In some preferred embodiments, the calculation of the local contact pressure gradient according to the tooth surface contact stress field comprises: Determining the maximum and minimum values of the tooth surface contact pressure in a single meshing period; Calculating the absolute difference between the maximum and minimum contact pressures; Dividing the absolute difference by the actual length of the tooth surface contact line to obtain a quantification factor of the pressure distribution gradient.

[0009] In some preferred embodiments, the determination of the tooth surface reinforcement parameter tensor according to the geometric coordinate set and the local contact pressure gradient comprises: According to the local contact pressure gradient, determining a material strength enhancement coefficient; According to the geometric coordinate set, generating a modification amount distribution map of the tooth surface profile using an interpolation algorithm; Integrating the modification amount distribution map and the material strength enhancement coefficient into the tooth surface reinforcement parameter tensor.

[0010] In some preferred embodiments, the process mapping system determines a set of process control instructions of a numerical control machining device according to the tooth surface strengthening parameter tensor, comprising: According to the tooth surface strengthening parameter tensor, analytically obtain tooth surface modification amount distribution data; According to the tooth surface strengthening parameter tensor and the root strength enhancement coefficient threshold, select a corresponding strengthening process type; According to the tooth surface modification amount distribution data and the strengthening process type, determine a set of machining control parameters; According to the set of machining control parameters, determine a set of process control instructions.

[0011] In some preferred embodiments, the method further comprises: Real-time acquisition of vibration response signals of the gearbox during operation of the wind turbine; According to the vibration response signals, compare the deviation degree of the actual vibration spectrum characteristics and the design expected value; When the deviation exceeds the tolerance threshold, trigger an iterative update process of the tooth surface strengthening parameters.

[0012] In some preferred embodiments, the iterative update process of the tooth surface strengthening parameters comprises: Recalculate the local contact pressure gradient; Update the modification amount distribution atlas in the tooth surface strengthening parameter tensor; According to the updated local contact pressure gradient and the modification amount distribution atlas, update the set of process control instructions and drive the numerical control device to perform compensation machining.

[0013] The beneficial effects of the present application are: Based on the method proposed in the present application, by synchronously acquiring multi-working condition operation data of the gearbox, the limitation of traditional single load analysis is broken. When the gearbox faces complex working conditions such as power grid frequency modulation and wind speed mutation, based on the load spectrum decoupling processing technology, the dynamic load characteristics in the meshing period are accurately captured, the seamless connection from data acquisition to physical field calculation is realized, and the maintenance personnel can accurately locate the high stress danger area which is difficult to find by traditional methods.

[0014] Based on the method proposed in the present application, by using a double-constraint optimization model, through the collaborative mechanism of stress feature extraction, Hertz contact analysis and parameter generation, the accurate collaborative optimization of tooth surface strength and geometric quantity is realized, and finally the output strengthening parameter tensor can directly drive the intelligent manufacturing system, breaking through the experience limitation of traditional gear design method. BRIEF DESCRIPTION OF DRAWINGS

[0015] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings. Figure 1 is a flowchart of a wind turbine gearbox gear surface strength optimization method considering multi-working condition load distribution according to an embodiment of the present application; Figure 2 is a structural schematic diagram of an electronic device according to an embodiment of the present application; Figure 3 is a structural schematic diagram of a computer system according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] The present application will be further described below in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related application, and not to limit the application. In addition, it should be noted that only the parts related to the application are shown in the drawings for ease of description.

[0017] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0018] Please refer to Figure 1 The first embodiment of the present application provides a wind turbine gearbox gear surface strength optimization method considering multi-working condition load distribution, comprising: Step S10, obtaining working condition data of the gearbox under multiple preset working conditions; Wherein, the types of preset working conditions include but are not limited to: Rated power generation condition, i.e. the running state of the wind turbine continuously outputting rated power; Power grid frequency modulation condition, i.e. power regulation process responding to power grid frequency fluctuation; Emergency braking condition, i.e. instantaneous torque impact state when overspeed protection is triggered; Wind speed mutation condition, i.e. transient process of wind speed changing ≥5m / s within 10 seconds, in addition to this, any stable and continuous working condition can be used as the preset working condition of the gearbox.

[0019] Wherein, the working condition data specifically refers to the torque time sequence and the speed pulse signal corresponding to the gearbox under various working condition types.

[0020] It is easy to understand that the torque time sequence can be obtained by installing a strain torque sensor on the input shaft of the gearbox, continuously recording the sensor data at a certain sampling frequency, which is used to represent the dynamic load distribution of the transmission chain and capture the torque fluctuation characteristics during power grid frequency modulation.

[0021] As for the speed pulse signal, it can be obtained by collecting the input shaft speed through a magnetic encoder, which is used to provide the gear meshing phase reference and solve the variable speed working condition synchronization problem.

[0022] Step S20, load spectrum decoupling processing is performed on the working condition data to determine a dynamic load distribution matrix synchronized with the meshing period of the tooth surface, and stress field conversion is performed on the dynamic load distribution matrix to obtain a tooth root bending stress field and a tooth surface contact stress field; In this step, the dynamic load distribution matrix is generated through load spectrum decoupling processing. The magnetic encoder pulse signal of the input shaft of the gearbox mentioned above is used as the time reference signal for load spectrum decoupling processing to provide an accurate phase reference for the meshing period. Specifically, it includes: The rotational speed pulse signal of the input shaft of the gearbox is collected as the time reference signal. According to the time reference signal, an analysis time window synchronized with the meshing period of the tooth surface is determined. Dynamic envelope extraction is performed on the working condition data in each analysis time window to construct a two-dimensional matrix representing the dynamic load intensity at different meshing phases as the dynamic load distribution matrix.

[0023] Wherein, the rotational speed pulse signal is the magnetic encoder pulse signal mentioned above. Based on this, the torque envelope feature is extracted, the alternating load component is separated, and then a two-dimensional dynamic load distribution matrix is generated with meshing phase as row index and load component as column element.

[0024] Specifically, the stress field conversion of the dynamic load distribution matrix includes: A three-dimensional geometric model of the gear is obtained. According to the three-dimensional geometric model of the gear, a finite element grid model containing the gear topography is constructed. According to the dynamic load distribution matrix, the dynamic load component is determined, and the dynamic load component is mapped to the corresponding nodes of the finite element grid model. Based on the mapped nodes, the spatial distribution data set of the tooth root bending stress field and the tooth surface contact stress field is generated synchronously through the preset stress field iteration algorithm.

[0025] Specifically, an accurate three-dimensional model can be constructed based on gear design parameters (module, number of teeth, pressure angle) through NX or SolidWorks. On this basis, the tooth profile error of the actual gear can be compared with the model through a three-coordinate measuring instrument.

[0026] In this embodiment, different grid types are selected for different gear topographies. Specifically, for the tooth surface contact area, hexahedral dominant grid can be used, for the tooth root transition area, pyramid transition unit can be used, and for the spoke structure, tetrahedral simplified grid can be used. The specific parameters of the grid, such as element size, aspect ratio, and Jacobian, can be set according to the specific topographic features, which are not limited in this embodiment.

[0027] As a feasible implementation, the dynamic load mapping mechanism includes: The meshing period is discretized into several phase points, and the load components are mapped to the mesh nodes of the tooth surface corresponding to the phase points according to the phase marks in the dynamic load distribution matrix.

[0028] As a feasible implementation, the spatial distribution data set of the tooth root bending stress field and the tooth surface contact stress field is synchronously generated by a preset stress field iteration algorithm, including: The stress integral path is set at the tooth root fillet, the normal stress component is calculated by the material constitutive relation, the pressure distribution in the long axis direction of the contact ellipse is calculated based on the modified Hertz contact theory (considering material plastic deformation), and the convergence standard of the stress change rate is set to end the iteration process when the stress change rate converges.

[0029] In step S30, the tooth root bending stress field and the tooth surface contact stress field are input into a pre-constructed double-constraint optimization model to obtain a tooth surface reinforcement parameter tensor output by the model. The double-constraint optimization model includes: a stress feature extraction module for determining a geometric coordinate set of a high stress danger area according to the tooth root bending stress field, a Hertz contact analysis module for calculating a local contact pressure gradient according to the tooth surface contact stress field, and a parameter generation module for determining the tooth surface reinforcement parameter tensor according to the geometric coordinate set and the local contact pressure gradient. The tooth surface reinforcement parameter tensor includes spatial coordinates, modification amounts, and material strength coefficients, wherein the spatial coordinates are used to indicate the tooth surface reinforcement position, the modification amounts are used to indicate the tooth profile geometric modification amount, and the material strength coefficients are used to indicate the material reinforcement degree.

[0030] The double-constraint optimization model used in the embodiment refers to a special calculation framework designed to solve the collaborative optimization of tooth root bending stress and tooth surface contact stress. It locates the dangerous area of the tooth root bending stress field through the stress feature extraction module, quantifies the unevenness of the tooth surface contact pressure distribution through the Hertz contact analysis module, and integrates the input parameters to generate the tooth surface reinforcement parameter tensor through the parameter generation module.

[0031] In step S40, the tooth surface reinforcement parameter tensor is input into a preset process mapping system, and the process mapping system determines a process control instruction set of a numerical control machining equipment according to the tooth surface reinforcement parameter tensor.

[0032] In this embodiment, parameter-process direct mapping is realized based on the preset process mapping system. For example, the input tooth surface reinforcement parameter tensor (containing 217 reinforcement points) is automatically generated by the process mapping system through analysis, and the content includes: 1528 grinding path points, 8 laser quenching blocks, 3 shot peening blocks, and total machining time of 3.2 hours. The specific process mapping logic can be set by those skilled in the art, and this embodiment does not improve it.

[0033] In some embodiments, the determining the set of geometric coordinates of the high stress risk area according to the tooth root bending stress field comprises: scanning gradient distribution characteristics of the tooth root bending stress field; identifying continuous stress concentration areas exceeding a preset material strength threshold; extracting geometric center coordinates of each stress concentration area to obtain the set of geometric coordinates.

[0034] In some embodiments, the calculating the local contact pressure gradient according to the tooth surface contact stress field comprises: determining a maximum value and a minimum value of the tooth surface contact pressure in a single meshing period; calculating an absolute difference value of the maximum contact pressure and the minimum contact pressure; dividing the absolute difference value by an actual length of the tooth surface contact line to obtain a quantification factor of the pressure distribution gradient.

[0035] In some embodiments, the determining the tooth surface reinforcement parameter tensor according to the set of geometric coordinates and the local contact pressure gradient comprises: determining a material strength enhancement coefficient according to the local contact pressure gradient; generating a tooth surface profile modification amount distribution map using an interpolation algorithm according to the set of geometric coordinates; integrating the tooth surface profile modification amount distribution map and the material strength enhancement coefficient into the tooth surface reinforcement parameter tensor.

[0036] In some embodiments, the process mapping system determines a set of process control instructions of a numerical control machining device according to the tooth surface reinforcement parameter tensor, comprising: obtaining tooth surface modification amount distribution data by analyzing the tooth surface reinforcement parameter tensor; selecting a corresponding reinforcement process type according to the tooth surface reinforcement parameter tensor and a root strength enhancement coefficient threshold; determining a set of machining control parameters according to the tooth surface modification amount distribution data and the reinforcement process type; determining a set of process control instructions according to the set of machining control parameters.

[0037] In some embodiments, the method further comprises: collecting a vibration response signal of the gearbox in real time during operation of the wind turbine generator; comparing a deviation degree of an actual vibration spectrum characteristic with a design expected value according to the vibration response signal; triggering an iterative update process of the tooth surface reinforcement parameter when the deviation exceeds a preset tolerance threshold.

[0038] The preset tolerance threshold is used to indicate a maximum tolerance range acceptable by the system.

[0039] In some embodiments, the iterative update process of the tooth surface reinforcement parameter comprises: recomputing the local contact pressure gradient; updating the modification amount distribution atlas in the tooth surface reinforcement parameter tensor; updating the process control instruction set of the numerical control equipment according to the updated local contact pressure gradient and the modification amount distribution atlas, and driving the numerical control equipment to perform compensation machining.

[0040] The second embodiment of the present application provides a wind turbine gearbox tooth surface strength optimization system considering multi-working condition load distribution, comprising: a data acquisition module configured to acquire working condition data of the gearbox under multiple preset working conditions; a load decoupling module configured to perform load spectrum decoupling processing on the working condition data, determine a dynamic load distribution matrix synchronized with the tooth surface meshing period, and perform stress field conversion on the dynamic load distribution matrix to obtain a tooth root bending stress field and a tooth surface contact stress field; a data output module configured to input the tooth root bending stress field and the tooth surface contact stress field into a pre-constructed double-constraint optimization model to obtain a tooth surface reinforcement parameter tensor output by the model, the double-constraint optimization model comprising: a stress feature extraction module for determining a geometric coordinate set of a high stress danger area according to the tooth root bending stress field, a Hertz contact analysis module for calculating a local contact pressure gradient according to the tooth surface contact stress field, and a parameter generation module for determining the tooth surface reinforcement parameter tensor according to the geometric coordinate set and the local contact pressure gradient; a process mapping module configured to input the tooth surface reinforcement parameter tensor into a preset process mapping system, the process mapping system determining a process control instruction set of a numerical control machining equipment according to the tooth surface reinforcement parameter tensor.

[0041] Reference Figure 2 The third embodiment of the present application further provides an electronic device, comprising: at least one processor; and a memory communicatively connected with the at least one processor; wherein the memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the method of the first embodiment.

[0042] The fourth embodiment of the present application further provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to be executed by the computer to implement the method of the first embodiment.

[0043] Reference Figure 3 which shows the structural schematic diagram of a computer system of a server suitable for implementing the method, system and device embodiments of the present application. Figure 3 The server shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present application.

[0044] AsFigure 3 As shown, the computer system includes a central processing unit which performs various appropriate actions and processes according to programs stored in a read only memory or loaded from a storage section into a random access memory. In the random access memory, various programs and data required for system operation are also stored. The central processing unit, the read only memory, and the random access memory are connected to each other through a bus. An I / O interface is also connected to the bus.

[0045] Connected to the I / O interface are an input section including a keyboard, a mouse, and the like; an output section including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a speaker and the like; a storage section including a hard disk and the like; and a communication section including a network interface card such as a LAN (Local Area Network) card, a modem, and the like. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as necessary. A removable media such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is attached to the drive as necessary, so that a computer program read out therefrom is installed into the storage section as necessary.

[0046] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present application. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for executing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section, and installed from a removable media. When the computer program is executed by the central processing unit, the above-described functions defined in the methods of the present application are performed. It should be noted that the computer readable medium described above in the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, be but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination of the above.

[0047] More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0048] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0049] The computer program product of the present application can be a computer program embodied on a non-transitory computer readable medium. Such non-transitory computer readable medium can include, but is not limited to, floppy diskettes, CD-ROMs, DVDs, flash memories, memory sticks, and hard drives.

[0050] The terms "first", "second", etc. are used to distinguish similar objects, rather than to describe or indicate a particular order or sequence.

[0051] The term "comprising" or any other similar term is intended to encompass the inclusion of one or more stated elements, steps, integers, or components, but not to the exclusion of any other elements, steps, integers, or components. It is further noted that the claims can be drafted to exclude any elements, steps, integers, or components.

[0052] The technical solutions of the present application have been described above in conjunction with the preferred embodiments shown in the drawings.

[0053] The above description is merely illustrative of the application, and not in limitation of the application. Changes and modifications can be made by those skilled in the art. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims

1. A method for optimizing the tooth surface strength of a wind turbine gearbox considering multi-condition load distribution, characterized in that, The method includes: Acquire operating data of the gearbox under various preset operating conditions; The load spectrum decoupling process is performed on the working condition data to determine the dynamic load distribution matrix that is synchronized with the tooth surface meshing cycle. The stress field transformation is performed on the dynamic load distribution matrix to obtain the tooth root bending stress field and the tooth surface contact stress field. The tooth root bending stress field and the tooth surface contact stress field are input into a pre-constructed dual-constraint optimization model to obtain the tooth surface strengthening parameter tensor output by the model. The dual-constraint optimization model includes: a stress feature extraction module, used to determine the geometric coordinate set of the high stress danger area based on the tooth root bending stress field; a Hertz contact analysis module, used to calculate the local contact pressure gradient based on the tooth surface contact stress field; and a parameter generation module, used to determine the tooth surface strengthening parameter tensor based on the geometric coordinate set and the local contact pressure gradient. The tooth surface strengthening parameter tensor is input into a preset process mapping system, and the process mapping system determines the process control instruction set of the CNC machining equipment based on the tooth surface strengthening parameter tensor.

2. The method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution according to claim 1, characterized in that, The load spectrum decoupling process includes: The rotational speed pulse signal of the gearbox input shaft is collected as a time reference signal; Based on the time reference signal, determine the analysis time window that is synchronized with the tooth surface meshing cycle; Within each analysis time window, a dynamic envelope extraction operation is performed on the operating condition data to construct a two-dimensional matrix characterizing the dynamic load intensity of different meshing phases as the dynamic load distribution matrix.

3. The method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution according to claim 1, characterized in that, The stress field transformation of the dynamic load distribution matrix includes: Obtain the 3D geometric model of the gear; Based on the three-dimensional geometric model of the gear, a finite element mesh model containing the gear morphology is constructed; Based on the dynamic load distribution matrix, determine the dynamic load components and map the dynamic load components to the corresponding nodes of the finite element mesh model; Based on the mapped nodes, a spatial distribution dataset of the tooth root bending stress field and the tooth surface contact stress field is generated synchronously using a preset stress field iterative algorithm.

4. The method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution according to claim 1, characterized in that, The determination of the geometric coordinate set of the high-stress-critical region based on the tooth root bending stress field includes: Gradient distribution characteristics of the bending stress field at the tooth root; Identify continuous stress concentration regions that exceed a preset material strength threshold; Extract the geometric center coordinates of each stress concentration region to obtain the geometric coordinate set.

5. The method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution according to claim 1, characterized in that, The calculation of the local contact pressure gradient based on the tooth surface contact stress field includes: Determine the maximum and minimum values ​​of tooth surface contact pressure within a single meshing cycle; Calculate the absolute difference between the maximum contact pressure and the minimum contact pressure; Divide the absolute difference by the actual length of the tooth surface contact line to obtain the quantization factor of the pressure distribution gradient.

6. The method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution according to claim 1, characterized in that, The step of determining the tooth surface strengthening parameter tensor based on the geometric coordinate set and the local contact pressure gradient includes: The material strength enhancement factor is determined based on the local contact pressure gradient. Based on the geometric coordinate set, an interpolation algorithm is used to generate a profile modification distribution map of the tooth surface. The modified shape distribution map and the material strength enhancement coefficient are integrated into the tooth surface strengthening parameter tensor.

7. The method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution according to claim 1, characterized in that, The process mapping system determines the process control instruction set of the CNC machining equipment based on the tooth surface strengthening parameter tensor, including: Based on the tooth surface strengthening parameter tensor, the tooth surface modification amount distribution data is obtained analytically. Based on the tooth surface strengthening parameter tensor and the root strength enhancement coefficient threshold, select the corresponding strengthening process type; Based on the tooth surface modification amount distribution data and the strengthening process type, a set of machining control parameters is determined; Based on the set of processing control parameters, determine the set of process control instructions.

8. The method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution according to claim 1, characterized in that, The method further includes: The vibration response signal of the gearbox is collected in real time during the operation of the wind turbine. Based on the vibration response signal, compare the degree of deviation between the actual vibration spectrum characteristics and the design expectation value; When the deviation exceeds the tolerance threshold, the iterative update process of the tooth surface strengthening parameters is triggered.

9. The method for optimizing the tooth surface strength of wind turbine gearboxes considering multi-condition load distribution according to claim 8, characterized in that, The iterative update process for the tooth surface strengthening parameters includes: Recalculate the local contact pressure gradient; Update the profile modification distribution map in the tooth surface strengthening parameter tensor; Based on the updated local contact pressure gradient and the shaping amount distribution map, the process control instruction set is updated, and the CNC equipment is driven to perform compensation machining.

Images