Fretting wear and damage accumulation-based wind-loading key part service life prediction method

Abstract

The invention discloses a wind loading key part service life prediction method based on fretting wear and damage accumulation. The wind loading key part service life prediction method comprises the steps of establishing a multi-rigid-body system dynamic model of wind power equipment; constructing a multi-flexible body system dynamics finite element model of the key parts of the wind power equipment; constructing a multi-field coupling model of the key parts; carrying out fretting wear analysis by adopting a multi-field coupling model of the key part; constructing a life prediction model and a damage accumulation model; and inputting stress-strain data of each node of the key part into the life prediction model and the damage accumulation model to obtain a life prediction result and an accumulated damage value of the key part of the wind power equipment, and the like. The method has the remarkable effects that the service life prediction and the fatigue damage calculation can be accurately carried out on the key parts of the wind power equipment, and the method has important significance for improving the service reliability of the wind power equipment.

Description

Technical Field

[0001] The invention relates to the technical field of mechanical engineering life prediction, and in particular to a method for predicting the life of key wind turbine components based on micro-motion wear and damage accumulation. Background Art

[0002] Wear damage is one of the main failure forms of most equipment components. Fretting wear, as a form of wear, refers to the local micro-slipping of the contact area of ​​mechanical tight-fit structures (such as bearing inner ring and shaft, bolt connection, spline, etc.) under vibration and alternating reciprocating conditions, which leads to wear or fatigue. In actual engineering, the fretting phenomenon can cause hidden and complex damage to the connection structure of wind power equipment, leading to the failure of the connection structure of wind power equipment, which seriously affects the service life and reliability of wind power equipment.

[0003] At the same time, because the key components of energy and power equipment such as wind power equipment are in a harsh working environment for a long time and are subjected to complex alternating impact loads, the tight-fitting contact interfaces of wind power equipment squeeze each other and have relatively reciprocating tiny sliding, which will cause wear, corrosion and fatigue fracture of components as the running time increases. In addition, micro-motion wear is highly hidden and cannot be discovered and treated in time, gradually evolving into serious failures and even leading to major accidents.

[0004] Therefore, if the reliability and service life of key components of wind power equipment can be accurately evaluated and predicted, it will effectively improve the service reliability of wind power equipment, reduce the probability of failure, and be of great significance to enterprises in reducing operation and maintenance costs and improving economic benefits. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a life prediction method for key wind turbine components based on micro-wear and damage accumulation, which can accurately evaluate and predict the reliability and service life of key wind turbine components.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A method for predicting the life of key wind turbine components based on fretting wear and damage accumulation includes the following steps:

[0008] Step 1: Establish a multi-rigid body system dynamics model of wind power equipment based on kinematics, vibration and multi-body system dynamics;

[0009] Step 2: Based on the multi-rigid body system dynamics model of wind power equipment, analyze the dynamics of key components under the influence of internal excitation, external excitation and damping, and construct a multi-flexible body system dynamics finite element model of key components of wind power equipment;

[0010] Step 3: Construct a multi-field coupling model of key components based on the kinematic and dynamic parameters output by the multi-rigid body system dynamics model and the multi-flexible body system dynamics finite element model of the wind power equipment;

[0011] Step 4: Use the multi-field coupling model of key components to perform fretting wear analysis to obtain the mutual influence between contact pressure, transient temperature field, stress field and fretting friction wear of key components at different friction stages under different working conditions, as well as the mutual influence between contact pressure, transient temperature field, stress field and wear amount;

[0012] Step 5: Based on the mutual influence between contact pressure, transient temperature field, stress field and micro-friction wear of key components at different friction stages under different working conditions, as well as the mutual influence between contact pressure and wear amount, a life prediction model and a damage accumulation model are constructed;

[0013] Step 6: Input the stress-strain data of each node of the key components into the life prediction model and the damage accumulation model to obtain the life prediction results and cumulative damage values ​​of the key components of the wind power equipment.

[0014] Furthermore, in step 2, a dynamic substructure method is used to construct a multi-flexible body system dynamics finite element model of key components of wind power equipment.

[0015] Furthermore, when analyzing the dynamics of key components under the influence of internal excitation, external excitation and damping in step 2, the following steps are also included:

[0016] The nonlinear contact state of each contact interface of the dual parts under the action of impact and vibration loads is analyzed, and the influence of the dual parts on the working state at different wear stages is obtained.

[0017] Furthermore, the specific steps of constructing the multi-field coupling model of key components in step 3 are as follows:

[0018] Step 3.1, based on the Archard wear model, calculate the wear state of key components between adjacent increments;

[0019] Step 3.2, using the arbitrary Lagrangian-Euler method to change only the displacement of the contact node without changing other variables in the finite element analysis results;

[0020] Step 3.3, obtain accurate wear information of contact surfaces of key components at different friction stages under different working conditions through post-processing method.

[0021] Step 3.4: Construct a multi-field coupling model of key components based on accurate wear information.

[0022] Furthermore, the specific steps of using the multi-field coupling model of key components to perform fretting wear analysis in step 4 are as follows:

[0023] Step A1, establishing a finite element model of key components based on a multi-field coupling model of key components;

[0024] Step A2, set the value of the number of loops M, and initialize loop steps j and m to 1;

[0025] Step A3, performing the mth cycle step;

[0026] Step A4: the main shaft of the wind power equipment does not rotate;

[0027] Step A5, obtaining the contact pressure and sliding distance of each node of the key components;

[0028] Step A6, using the loop jump technology to discretize the loop process into N incremental steps, and determine whether j=N is established. If so, go to step A7, otherwise set j=j+1, and control the spindle to rotate a certain angle and then return to step A5;

[0029] Step A7, obtaining the contact pressure, temperature field, and stress field of each node of the contact interface;

[0030] Step A8, calculating the wear amount of each node and determining the wear direction;

[0031] Step A9, judging whether the set number of cycles is reached, if so, ending the process and obtaining the mutual influence law between the contact pressure, transient temperature field, stress field and fretting friction and wear at different friction stages under different working conditions, and the mutual influence law between the contact pressure, transient temperature field, stress field and wear amount, otherwise, entering step A10;

[0032] Step A10, determining whether the current node is an edge node, if so, correcting the wear direction by a trigonometric function relationship and then proceeding to step A11, otherwise directly proceeding to step A11;

[0033] Step A11, using adaptive grid technology to update the node position, and setting m=m+1, then returning to step A3.

[0034] Furthermore, the wear amount in step A8 includes axial eccentric wear amount and tangential eccentric wear amount.

[0035] Furthermore, the life prediction model uses the SWT parameter life equation and the Brown-Miller critical plane method to predict the life of key components of wind power equipment.

[0036] Furthermore, the damage accumulation model uses the MP linear accumulation rule to obtain the cumulative damage values ​​of key components of wind power equipment.

[0037] Furthermore, the stress-strain data in step 6 are normal stress-strain and shear stress-strain components of each node of the key components obtained based on the finite element model of the key components.

[0038] The remarkable effects of the present invention are:

[0039] 1. The prediction method described in the present invention can accurately predict the life span and calculate fatigue damage of key components of wind power equipment, which is of great significance for improving the service reliability of wind power equipment, reducing the probability of failure, reducing the operation and maintenance costs of enterprises, and improving economic benefits.

[0040] 2. Based on the prediction method described in the present invention, the structure of key components of wind power equipment can be optimized and improved, and the purpose of reducing wear can be achieved by coordinating and matching the contact interface structural characteristics and dynamic parameters of key components of wind power equipment, thereby improving the efficiency and service life of key components of wind power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flow chart of the method of the present invention;

[0042] Figure 2 It is a flow chart of fretting wear analysis. DETAILED DESCRIPTION

[0043] The specific implementation manner and working principle of the present invention are further described in detail below with reference to the accompanying drawings.

[0044] like Figure 1 As shown, this embodiment proposes a life prediction method for key wind equipment components based on fretting wear and damage accumulation, and the specific steps are as follows:

[0045] Step 1: Based on basic theories such as kinematics, vibration and multi-body system dynamics, establish a multi-rigid body system dynamics model of wind power equipment;

[0046] Step 2: Based on the multi-rigid body system dynamics model of wind power equipment and supported by the finite element method, a multi-flexible body system dynamics finite element model of key components of wind power equipment is constructed using the dynamic substructure method to analyze the dynamic laws of key components under the influence of internal excitation, external excitation and damping;

[0047] In the embodiment of the present invention, when analyzing the dynamic laws of key components under the influence of internal excitation, external excitation and damping in step 2, the following steps are also included:

[0048] The nonlinear contact state of each contact interface of the dual parts under the action of impact and vibration loads is analyzed, and the influence of the dual parts on the working state at different wear stages is obtained, providing the interactive mechanical boundary for the micro-friction and wear analysis of the dual parts.

[0049] Through steps 1 and 2, a multi-body system dynamics model of key components of wind power equipment (planetary carrier, gear ring, bearing, drive shaft, spline, etc.) was established. By analyzing the dynamic laws considering the influence of internal excitation, external excitation and damping, the mechanical causes and dynamic characteristics of friction and wear at the contact interface under different working conditions can be studied.

[0050] Step 3: Based on friction thermodynamics, thermoelasticity theory and Archard wear theory, the microscopic micro-motion wear mechanism of the contact interface under different working conditions is studied, and the heat transfer control equation and wear model are established; the influence of interface characteristics on the heat conduction and micro-motion friction wear of the contact parts is studied, and on the basis of the friction heat-stress coupling model, the kinematic and dynamic parameters output by the multi-rigid body system dynamics model and the multi-flexible body system dynamics finite element model of the wind power equipment are used to construct a multi-field coupling model based on friction heat-stress-wear-vibration for key components;

[0051] In the embodiment of the present invention, the specific steps of constructing the multi-field coupling model of key components in step 3 are as follows:

[0052] Step 3.1, based on the Archard wear model, the wear state of key components is calculated between adjacent incremental steps to simulate the wear accumulation and dynamic evolution process;

[0053] Step 3.2, using the arbitrary Lagrangian-Euler method to change only the displacement of the contact node without changing other variables in the finite element analysis results;

[0054] Step 3.3, obtain accurate wear information of contact surfaces of key components at different friction stages under different working conditions through post-processing method.

[0055] Step 3.4: Use the full coupling algorithm or the segmented sequential coupling algorithm according to different working conditions, and build a multi-field coupling model of key components based on the precise wear information obtained, so as to study the mutual influence of contact pressure, transient temperature field, stress field and micro-friction and wear in different friction stages, and reveal the micro-friction and wear mechanism.

[0056] The multi-field coupling model of key components constructed in step 3 can analyze the intrinsic relationship between interface contact behavior, friction heat and wear behavior under the action of multi-field coupling, and reveal the mechanism and influence law of micro-wear between interfaces.

[0057] Step 4: Use the multi-field coupling model of key components to perform fretting wear analysis to obtain the mutual influence between contact pressure, transient temperature field, stress field and fretting friction wear of key components at different friction stages under different working conditions, as well as the mutual influence between contact pressure, transient temperature field, stress field and wear amount;

[0058] In the embodiment of the present invention, the step of using the multi-field coupling model of key components to perform fretting wear analysis in step 4 is as follows: Figure 2 As shown, the details are as follows:

[0059] Step A1, establishing a finite element model of key components based on a multi-field coupling model of key components;

[0060] Step A2, set the value of the number of loops M, and initialize loop steps j and m to 1;

[0061] Step A3, performing the mth cycle step;

[0062] Step A4: the main shaft of the wind power equipment does not rotate;

[0063] Step A5, obtaining the contact pressure and sliding distance of each node of the key components;

[0064] Step A6, using the loop jump technology to discretize the loop process into N incremental steps, and determine whether j=N is established. If so, go to step A7, otherwise set j=j+1, and control the spindle to rotate a certain angle and then return to step A5;

[0065] Step A7, obtaining the contact pressure, temperature field, and stress field of each node of the contact interface;

[0066] Step A8, calculating the wear amount of each node and determining the wear direction, wherein the wear amount in this example includes the axial eccentric wear amount and the tangential eccentric wear amount;

[0067] The influence of axial eccentric wear and tangential eccentric wear on the temperature and contact pressure of key components can be known, so as to obtain the shape structure, quantity, arrangement mode and contact interface contact pressure, stress distribution, friction heat and other tribological behaviors of key components and the influence of interface wear morphology after analysis.

[0068] Step A9, judging whether the set number of cycles, i.e. m=M, is reached. If so, the process is terminated and the mutual influence law between the contact pressure, transient temperature field, stress field and fretting friction and wear at different friction stages under different working conditions and the mutual influence law between the contact pressure, transient temperature field, stress field and wear amount are obtained. Otherwise, step A10 is entered;

[0069] Step A10, determining whether the current node is an edge node, if so, correcting the wear direction by a trigonometric function relationship and then proceeding to step A11, otherwise directly proceeding to step A11;

[0070] Step A11, using adaptive grid technology to update the node position, and setting m=m+1, then returning to step A3.

[0071] In the above steps, the finite element software ABAQUS was used to establish a three-dimensional contact finite element model of the key components of wind power equipment. The wear depth and wear direction of irregular nodes of the UMESHMOTION program were corrected based on the finite element simulation results and the Archard wear model. The adaptive mesh technology (ALE) was used to realize the dynamic adjustment of the mesh after contact interface wear, and the fatigue life distribution under the surface wear state was calculated based on the critical plane method; thereby analyzing the interaction between micro-motion wear and fatigue fracture between components, and then building an experimental test platform for simulating micro-motion wear of key components based on the analysis results.

[0072] Step 5: Based on the mutual influence between contact pressure, transient temperature field, stress field and micro-friction wear of key components at different friction stages under different working conditions, as well as the mutual influence between contact pressure and wear amount, a life prediction model and a damage accumulation model are constructed;

[0073] Through the life prediction model, the life prediction of key components (planetary carriers, gear rings, bearings, drive shafts, splines, etc.) can be realized; through the damage accumulation model, the cumulative damage value of key components of wind power equipment can be calculated, so as to obtain the service reliability of key components of wind power equipment and reduce the probability of failure.

[0074] In the embodiment of the present invention, the life prediction model described in step 5 uses the SWT parameter life equation and the Brown-Miller critical plane method to predict the life of key components of wind power equipment.

[0075] Among them, the expression of the SWT parameter life equation is:

[0076]

[0077] In the formula, σ max and is the maximum normal stress and normal strain amplitude on a certain plane; N f is the fatigue life. f and b are fatigue strength coefficient and strength index, ε′ f and c are the fatigue ductility coefficient and ductility factor, E is the elastic modulus, It is called the SWT parameter, and the plane with the largest SWT parameter is the critical plane.

[0078] The Brown-Miller critical plane method is expressed as:

[0079]

[0080] In the formula, Δγ max / 2 and σ m are the maximum shear strain amplitude and the average normal stress on a plane, respectively.

[0081] The plane where the maximum shear strain amplitude is located is the critical plane, and the positive strain on this plane affects the fatigue life. Parts are subjected to cyclic loads of different amplitudes at different times. The ratio of the number of cycles experienced in each period to the predicted life of that period is the damage factor. The damage factor is combined in different ways to form a cumulative damage factor. When the cumulative damage reaches 1, the part fails.

[0082] In the embodiment of the present invention, the damage accumulation model described in step 5 uses the MP linear accumulation rule to obtain the cumulative damage value of the key components of the wind power equipment.

[0083] The expression of Miner-Palmgren (MP) linear accumulation law is:

[0084]

[0085] Where N is the preset number of cycles. After every ΔN cycles, the wear value is calculated and the grid is updated. The stress-strain history will change. i is the life under the current stress-strain state, and D is the accumulated damage value.

[0086] In this embodiment, the established life prediction model and damage accumulation model are further trained through the following training process, which is described by taking the life prediction model as an example:

[0087] Step B1, construct multiple groups of random parameter combinations based on the structural parameters, material property parameters, temperature, contact pressure and stress parameters of key components, the distribution of which satisfies the probability distribution of the structural parameters, material property parameters, temperature, contact pressure and stress parameters corresponding to the key components, and simultaneously obtain the life standard value determined according to the mutual influence law of micro-friction and wear corresponding to the random parameter combination;

[0088] Step B2, inputting multiple groups of random parameter combinations into the constructed life prediction model to obtain life prediction value combination data;

[0089] Step B3, determining a prediction dishonesty decision factor for the life prediction value combination data, and determining a prediction data dishonesty domain of the prediction dishonesty decision factor according to the prediction dishonesty decision factor;

[0090] In specific implementation, the prediction default decision factor reflects the accuracy of the life prediction value in the corresponding life prediction value combination data, thereby excluding obviously unreasonable prediction values, thereby accelerating the convergence speed of the life prediction model.

[0091] Step B4, determining multiple prediction data untrustworthy subdomains of the prediction data untrustworthy domain, determining a total amount of data trustworthy features according to all prediction data untrustworthy subdomains, and determining trustworthy data in the life prediction value combination data according to the total amount of data trustworthy features;

[0092] The predicted data untrustworthy subdomain may be implemented in the following manner, namely: firstly, the data hierarchy structure of the predicted data untrustworthy domain is analyzed; then, the predicted data untrustworthy domain is divided according to the data hierarchy structure to obtain a plurality of predicted data untrustworthy subdomains. Other methods may also be used in other embodiments, which will not be described in detail here.

[0093] The process of determining the total amount of data trustworthy features is as follows:

[0094] Select a predicted data untrustworthy subdomain, and determine the untrustworthy data balance amount and untrustworthy data fluctuation amount of the predicted data untrustworthy subdomain;

[0095] Determine the data likelihood credibility value of the predicted data dishonest subdomain according to the dishonest data balance amount and the dishonest data fluctuation amount;

[0096] Repeat the above steps to determine the data likelihood credibility values ​​of the remaining prediction data untrustworthy subdomains;

[0097] The total amount of data credible features is determined based on all data likelihood credible values.

[0098] The fluctuation amount of the untrustworthy data reflects the degree of data fluctuation of the life prediction untrustworthy value in the untrustworthy subdomain of the predicted data. As a preferred embodiment, the standard deviation of all life prediction untrustworthy values ​​in the untrustworthy subdomain of the predicted data can be used to set it. In other embodiments, other methods can also be used to set it, which will not be repeated here.

[0099] Among them, in some embodiments, determining the data likelihood credibility value of the predicted data dishonest sub-domain according to the dishonest data balance amount and the dishonest data fluctuation amount can be implemented in the following manner.

[0100] It should be noted that the total amount of data credibility features in the present application reflects the credibility of the data extracted from each predicted data untrustworthy subdomain of the predicted data untrustworthy domain. The larger the total amount of data credibility features, the more credible the data extracted from each predicted data untrustworthy subdomain of the predicted data untrustworthy domain. Conversely, the less credible the data extracted from each predicted data untrustworthy subdomain of the predicted data untrustworthy domain.

[0101] In some embodiments, determining the life prediction credible data in the life prediction value combination data by the total amount of the data credible features can be implemented by the following steps:

[0102] The total amount of the data credible features is compared with a preset data credible feature threshold. When the total amount of the data credible features is less than or equal to the preset data credible feature threshold, data is extracted from the prediction data untrustworthy subdomain corresponding to each data likelihood credible value to obtain the prediction data credible subdomain of each prediction data untrustworthy subdomain, thereby obtaining the life prediction credible data in the life prediction value combination data;

[0103] When the total amount of the data trustworthy features is greater than the preset data trustworthy feature threshold, multiple predicted data untrustworthy subdomains of the predicted data untrustworthy domain are re-determined, and the above steps are repeated until the total amount of the data trustworthy features is less than or equal to the preset data trustworthy feature threshold.

[0104] In specific implementation, when the total amount of the data credible features is less than or equal to the preset data credible feature threshold, all the life prediction untrustworthy values ​​greater than or equal to (corresponding data likelihood credible value - corresponding untrustworthy data fluctuation amount) in each predicted data untrustworthy subdomain are combined into a predicted data credible subdomain of the corresponding predicted data untrustworthy subdomain, and all the predicted data credible subdomains and other data in the life prediction value combination data except the predicted data untrustworthy domain are combined into the life prediction credible data of the life prediction value combination data; when the total amount of the data credible features is greater than the preset data credible feature threshold, the data hierarchy of the predicted data untrustworthy domain is re-analyzed, and corresponding calculations are performed until the total amount of the data credible features is less than or equal to the preset data credible feature threshold.

[0105] Step B5, determining the matching cost between the reliable data in the life prediction value combination data and the life standard value corresponding to the random parameter combination;

[0106] The matching cost can be achieved by the following steps:

[0107] Determine a prediction deviation factor by using the credible data in the life prediction value combination data and the life standard value, where the prediction deviation factor may be a difference between the credible data in the life prediction value combination data and the life standard value;

[0108] Determine the deviation value between the prediction deviation factors of two adjacent moments;

[0109] The deviation value is compared with a preset deviation threshold value, and when the deviation value is greater than or equal to the preset deviation threshold value, the matching cost between the credible data in the life prediction value combination data and the life standard value is set to 0;

[0110] When the deviation value is less than a preset deviation threshold, the sum of the prediction deviation factors at two adjacent moments in the group of disparity factors is used as the matching cost between the credible data in the life prediction value combination data and the life standard value.

[0111] In this application, the matching cost is a parameter used to evaluate the degree of match between the credible data in the life prediction value combination data and the life standard value corresponding to the random parameter combination. The closer the matching cost is to 0, the less match there is between the credible data in the life prediction value combination data and the life standard value, that is, the less credible the life prediction value is. When the life prediction value is unreliable, that is, when the credible data in the life prediction value combination data and the life standard value differ greatly, the deviation value between the prediction deviation factors at two adjacent moments is used to measure whether the prediction result is credible. When the deviation value is large, that is, the prediction result is unreliable, the matching cost is set to 0. When the deviation value is small, the sum of the two is taken as the matching cost between the prediction result and the life standard value.

[0112] Step B6: adjust the life prediction model according to the matching cost until the matching cost meets the threshold requirement to obtain the required life prediction model.

[0113] Step 6: Input the stress-strain data of each node of key components into the life prediction model and damage accumulation model to obtain the life prediction results and cumulative damage values ​​of key components of wind power equipment (planet carrier, gear ring, bearing, drive shaft, spline, etc.).

[0114] In an embodiment of the present invention, the stress-strain data in step 6 are normal stress-strain and shear stress-strain components of each node of the key component obtained based on the finite element model of the key component.

[0115] In summary, the prediction method described in the present invention can accurately predict the life span and calculate fatigue damage of key components of wind power equipment, which is of great significance for improving the service reliability of wind power equipment, reducing the probability of failure, reducing the operation and maintenance costs of enterprises, and improving economic benefits. Based on the prediction method described in the present invention, the structure of key components of wind power equipment can be optimized and improved, and the purpose of reducing wear can be achieved by coordinating and matching the contact interface structural characteristics and dynamic parameters of key components of wind power equipment, thereby improving the efficiency and service life of key components of wind power equipment.

[0116] The technical solution provided by the present invention is described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, the present invention can also be improved and modified in a number of ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting the life of key wind turbine components based on fretting wear and damage accumulation, characterized in that: The steps include: Step 1: Establish a multi-rigid body system dynamics model of wind power equipment based on kinematics, vibration and multi-body system dynamics; Step 2: Based on the multi-rigid body system dynamics model of wind power equipment, analyze the dynamics of key components under the influence of internal excitation, external excitation and damping, and construct a multi-flexible body system dynamics finite element model of key components of wind power equipment; Step 3: Construct a multi-field coupling model of key components based on the kinematic and dynamic parameters output by the multi-rigid body system dynamics model and the multi-flexible body system dynamics finite element model of the wind power equipment; Step 4: Use the multi-field coupling model of key components to perform fretting wear analysis to obtain the mutual influence between contact pressure, transient temperature field, stress field and fretting friction wear of key components at different friction stages under different working conditions, as well as the mutual influence between contact pressure, transient temperature field, stress field and wear amount; Step 5: Based on the mutual influence between contact pressure, transient temperature field, stress field and micro-friction wear of key components at different friction stages under different working conditions, as well as the mutual influence between contact pressure and wear amount, a life prediction model and a damage accumulation model are constructed; Step 6: Input the stress-strain data of each node of the key components into the life prediction model and the damage accumulation model to obtain the life prediction results and cumulative damage values ​​of the key components of the wind power equipment.

2. The method for predicting the life of key wind turbine components based on fretting wear and damage accumulation according to claim 1 is characterized in that: In step 2, the dynamic substructure method is used to construct the multi-flexible body system dynamics finite element model of key components of wind power equipment.

3. The method for predicting the life of key wind turbine components based on fretting wear and damage accumulation according to claim 1 is characterized in that: In step 2, when analyzing the dynamic laws of key components under the influence of internal excitation, external excitation and damping, the following steps are also included: The nonlinear contact state of each contact interface of the dual parts under the action of impact and vibration loads is analyzed, and the influence of the dual parts on the working state at different wear stages is obtained.

4. The method for predicting the life of key wind turbine components based on fretting wear and damage accumulation according to claim 1 is characterized in that: The specific steps for building the multi-field coupling model of key components in step 3 are as follows: Step 3.1, based on the Archard wear model, calculate the wear state of key components between adjacent increments; Step 3.2, using the arbitrary Lagrangian-Euler method to change only the displacement of the contact node without changing other variables in the finite element analysis results; Step 3.3, obtain accurate wear information of contact surfaces of key components at different friction stages under different working conditions through post-processing method. Step 3.4: Construct a multi-field coupling model of key components based on accurate wear information.

5. The method for predicting the life of key wind turbine components based on fretting wear and damage accumulation according to claim 1 is characterized in that: The specific steps of using the multi-field coupling model of key components for fretting wear analysis in step 4 are as follows: Step A1, establishing a finite element model of key components based on a multi-field coupling model of key components; Step A2, set the value of the number of loops M, and initialize loop steps j and m to 1; Step A3, performing the mth cycle step; Step A4: the main shaft of the wind power equipment does not rotate; Step A5, obtaining the contact pressure and sliding distance of each node of the key components; Step A6, using the loop jump technology to discretize the loop process into N incremental steps, and determine whether j=N is established. If so, go to step A7, otherwise set j=j+1, and control the spindle to rotate a certain angle and then return to step A5; Step A7, obtaining the contact pressure, temperature field, and stress field of each node of the contact interface; Step A8, calculating the wear amount of each node and determining the wear direction; Step A9, judging whether the set number of cycles is reached, if so, ending the process and obtaining the mutual influence law between the contact pressure, transient temperature field, stress field and fretting friction and wear at different friction stages under different working conditions, and the mutual influence law between the contact pressure, transient temperature field, stress field and wear amount, otherwise, entering step A10; Step A10, determining whether the current node is an edge node, if so, correcting the wear direction by trigonometric function relationship and then proceeding to step A11, otherwise directly proceeding to step A11; Step A11, using adaptive grid technology to update the node position, and setting m=m+1 and then returning to step A3.

6. The method for predicting the life of key wind turbine components based on fretting wear and damage accumulation according to claim 5 is characterized in that: The wear amount in step A8 includes axial eccentric wear amount and tangential eccentric wear amount.

7. The method for predicting the life of key wind turbine components based on fretting wear and damage accumulation according to claim 1 is characterized in that: The life prediction model adopts the SWT parameter life equation and the Brown-Miller critical plane method to predict the life of key components of wind power equipment.

8. The method for predicting the life of key wind turbine components based on fretting wear and damage accumulation according to claim 1 is characterized in that: The damage accumulation model adopts the MP linear accumulation rule to obtain the cumulative damage values ​​of key components of wind power equipment.

9. The method for predicting the life of key wind turbine components based on fretting wear and damage accumulation according to claim 1 is characterized in that: The stress-strain data described in step 6 are the normal stress-strain and shear stress-strain components of each node of the key components obtained based on the finite element model of the key components.

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