Wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method

Abstract

The invention particularly provides a thermal elastohydrodynamic lubrication reliability evaluation method for a wind power sliding bearing. The method comprises the following steps: acquiring evidence measurement combination parameters; converting the evidence measurement combination parameters into probability measurement combination parameters, and determining mean value data of the probability measurement combination parameters; constructing a sample collection center based on the probability measurement combination parameters, substituting the sample collection center into the thermal elastohydrodynamic oil film iteration analysis model, and outputting minimum oil film thickness data; constructing a quadratic polynomial response surface function based on the current oil film safety threshold value, and selectively updating the sample collection center based on the quadratic polynomial response surface function to enable the sample collection center to approach the maximum possible failure focal element; determining credibility data and likelihood data of thermal elastohydrodynamic lubrication of the wind power sliding bearing based on the maximum possible failure focal element; and changing an oil film safety threshold value, and circularly executing the steps to obtain a thermal elastohydrodynamic lubrication reliability analysis result of the wind power sliding bearing. The accuracy of reliability is ensured, and the calculation cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil film lubrication reliability diagnosis, and particularly provides a wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method. BACKGROUND

[0002] When the minimum oil film thickness is lower than the critical value, the risk of direct contact of the metal surface microconvex body of the bearing bush increases sharply, which causes the friction and wear to be aggravated, the temperature rise to be abnormal, and even the gluing failure. The thickness is affected by the coupling of multiple evidence uncertainty parameters such as the viscosity of lubricating oil, the shaft neck rotating speed, the load amplitude, the gap ratio and the material thermal expansion coefficient. The mapping relationship between these parameters and the minimum oil film thickness is highly nonlinear and unclear.

[0003] The prior art is an analytical method for analyzing the thermal elastohydrodynamic lubrication state through the Reynolds equation. There are a large number of time-consuming thermal elastohydrodynamic oil film analyses. The accuracy problems such as the results being too conservative or having large deviations cannot meet the lubrication reliability requirements under the strong mutation heavy load working conditions of wind power, and restrict the accuracy of the reliability evaluation of the sliding bearing.

[0004] Correspondingly, there is a need for a new wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation scheme to solve the above problems. SUMMARY

[0005] In order to overcome the above defects, the present application is proposed to provide a wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method to solve or at least partially solve the technical problems in the prior art, such as the analytical method for analyzing the thermal elastohydrodynamic lubrication state through the Reynolds equation. There are a large number of time-consuming thermal elastohydrodynamic oil film analyses. The accuracy problems such as the results being too conservative or having large deviations cannot meet the lubrication reliability requirements under the strong mutation heavy load working conditions of wind power, and restrict the accuracy of the reliability evaluation of the sliding bearing.

[0006] In a first aspect, the present application provides a wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method, comprising the following steps: Step S101: obtaining evidence metric combination parameters, wherein the parameter types in the evidence metric combination parameters at least include load and dynamic viscosity; Step S102: converting the evidence metric combination parameters into probability metric combination parameters, and determining the mean value data of the probability metric combination parameters; Step S103: constructing a sample collection center based on the probability metric combination parameters, and substituting the sample collection center into a thermal elastohydrodynamic oil film iterative analysis model to output minimum oil film thickness data; Step S104: constructing a quadratic polynomial response surface function based on the current oil film safety threshold, and selectively updating the sample collection center based on the quadratic polynomial response surface function, so that the sample collection center approximates the maximum possible failure focal element; Step S105: determining the reliability data and the quasi-truth data of the thermal elastohydrodynamic lubrication of the wind power sliding bearing based on the maximum possible failure focal element; Step S106: changing the oil film safety threshold, and cyclically executing the above steps S103-S105 to obtain the reliability analysis result of the thermal elastohydrodynamic lubrication of the wind power sliding bearing.

[0007] In one of the above technical solutions of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method, the step S102 comprises: The evidence metric combination parameter is converted into a probability metric combination parameter by the following formula, and the wind power sliding bearing thermal elastohydrodynamic lubrication reliability index is obtained: ; Wherein, represents the probability metric combination parameter, represents the probability metric combination parameter of the step function, represents the total number of focal elements, and respectively represent the upper limit and the lower limit of the focal element , and represents the basic reliability allocation function of each focal element, represents the indicator function, represents the norm of the probability metric combination parameter , and the reliability index is the shortest distance from the coordinate origin in the space of the probability metric combination parameter to the approximate minimum oil film thickness limit state surface , and represents the constraint condition; and when , , otherwise 0.

[0008] In one of the above technical solutions of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method, the "constructing a sample collection center based on a probability metric combination parameter" in the step S103 comprises: Constructing a sample collection center based on a probability metric combination parameter; Initializing the sample collection center point; Arranging a plurality of sample points based on the sample collection center to obtain a sample point set.

[0009] In one of the technical solutions of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method, the step S103 includes: Filtering the sample point set to obtain new samples and old samples; Based on the stored sample database, the minimum oil film thickness corresponding to the old sample is obtained; Substitute the new sample into the thermal elastohydrodynamic oil film iterative analysis model to obtain the minimum oil film thickness corresponding to the new sample.

[0010] In one of the technical solutions of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method, the step S103 includes: Divide the grid for the new sample; Obtain the elastic deformation data of each node on the divided grid; Obtain the temperature data; Based on the elastic deformation data, obtain the minimum oil film thickness data corresponding to the new sample; Based on the temperature data, obtain the pressure data of the new sample; Based on the pressure data, judge the convergence of the pressure; Based on the convergence of the pressure, selectively re-execute the "obtain the elastic deformation data of each node on the divided grid" and the subsequent steps, or output the minimum oil film thickness data corresponding to the new sample.

[0011] In one of the technical solutions of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method, the method includes: Obtain the temperature data and the minimum oil film thickness data corresponding to the new sample by the following formula: ; Wherein, represents the reference value of the entire film thickness distribution; represents the radius of curvature, represents the film thickness change, that is, when the substrate has a certain curvature, with the increase of the position , the film thickness will change in the form of quadratic function, represents the elastic deformation data of the node, represents the temperature under the current pressure and density , represents the ambient temperature, represents the initial density when the temperature does not change, , wherein represents the thermodynamic temperature unit, Represents the dimension of the parameter related to temperature change.

[0012] In one technical solution of the above-mentioned method for evaluating the reliability of thermal elastohydrodynamic lubrication of wind turbine sliding bearings, the step S104 of "constructing a quadratic polynomial response surface function based on the current oil film safety threshold" includes: Get the current oil film safety threshold; Based on the sample point set and the minimum oil film thickness corresponding to the sample point set, real minimum oil film thickness limit state function data under the current oil film safety threshold is constructed.

[0013] In one technical solution of the above-mentioned method for evaluating the reliability of thermal elastohydrodynamic lubrication of wind turbine sliding bearings, the step S104 of "selectively updating the sample collection center based on the quadratic polynomial response surface function so that the sample collection center approaches the most likely failure focal element" includes: By judging the constant Determine whether the sample collection center needs to be updated; The sample collection center is optionally updated by the following formula: ; in, On behalf of the sample collection center, Represents the probability metric combination parameter The mean of Represents the probability metric combination parameter The mean The corresponding minimum oil film thickness true limit state function, represent The corresponding minimum oil film thickness true limit state function, Representative The sample collection center of the Representative The sample collection center of the For sample collection center The judgment constant of the convergence of the update process; In each sequence iteration, an approximate reliability index and an approximate design point are obtained, and a design verification point for transition is obtained, so that each iteration step updates the sample collection center through the design verification point obtained in the previous step, thereby making the sample collection center approach the maximum possible failure focal element of the true limit state surface of the minimum oil film thickness; Setting reliability indicators The corresponding probability metric combination parameter Design verification point , the design verification point mapping back the evidence metric combination parameter; and taking the asperity corresponding to the evidence metric combination parameter as the maximum possible failure asperity .

[0014] In one of the technical solutions of the method for evaluating the thermal elastohydrodynamic lubrication reliability of the wind power sliding bearing, the step S105 comprises: The reliability data and the plausibility data of the thermal elastohydrodynamic lubrication of the wind power sliding bearing are obtained through the following formula: ; wherein, represents that the asperity is completely within the reliable domain, represents that the asperity is completely or partially within the reliable domain, represents the basic reliability distribution function of each asperity, represents the reliability data of the thermal elastohydrodynamic lubrication of the wind power sliding bearing, represents the plausibility data of the thermal elastohydrodynamic lubrication of the wind power sliding bearing; and the maximum value and the minimum value of the minimum oil film thickness true limit state function are obtained.

[0015] In one of the technical solutions of the method for evaluating the thermal elastohydrodynamic lubrication reliability of the wind power sliding bearing, the step S106 comprises: The oil film safety threshold is changed, and the steps S103-S105 are cyclically executed to obtain multiple sets of reliability data and plausibility data of the thermal elastohydrodynamic lubrication of the wind power sliding bearing; Based on the multiple sets of reliability data and plausibility data of the thermal elastohydrodynamic lubrication of the wind power sliding bearing, multiple sets of reliability data analysis combination coordinates and multiple sets of plausibility data analysis combination coordinates are obtained; The multiple sets of reliability data analysis combination coordinates are connected to obtain a cumulative reliability function curve, and the multiple sets of plausibility data analysis combination coordinates are connected to obtain a cumulative plausibility function curve; Based on the cumulative reliability function curve and the cumulative plausibility function curve, a thermal elastohydrodynamic lubrication reliability analysis result of the wind power sliding bearing is obtained, wherein the thermal elastohydrodynamic lubrication reliability analysis result of the wind power sliding bearing at least comprises a reliability degree of the thermal elastohydrodynamic lubrication of the wind power sliding bearing.

[0016] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects: (1) The evidence metric is converted into a probability metric through homogenization treatment, sample collection center updating is performed based on the sequence iteration mechanism of the response surface and the probability reliability analysis, and the maximum possible failure asperity region is approximated. The approximate response surface has good approximation accuracy for the true minimum oil film thickness limit state surface, and the number of calls of the thermal elastohydrodynamic oil film iteration analysis model is effectively reduced.

[0017] (2) The sample genetic management technology is used to screen the new and old samples in the sample point set to avoid repeated calculation of the minimum oil film thickness in the iterative process of approaching the maximum possible failure focal element area, further reducing the computational cost of the elastohydrodynamic lubrication reliability analysis of wind turbine sliding bearings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The disclosure of the present invention will be more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. Furthermore, similar numbers in the drawings represent similar components, wherein: Figure 1 This is a flow chart of the main steps of a method for evaluating the reliability of thermal elastohydrodynamic lubrication of a wind turbine sliding bearing according to one embodiment of the present invention; Figure 2 1. It is a schematic diagram of a reliability analysis process of a method for evaluating the reliability of thermal elastohydrodynamic lubrication of a wind turbine sliding bearing according to an embodiment of the present invention; Figure 3 2 is a schematic diagram of step S102 of a method for evaluating the reliability of thermal elastohydrodynamic lubrication of a wind turbine sliding bearing according to an embodiment of the present invention; Figure 4 Schematic diagram of the positional relationship between the focal element and the true limit state function of the minimum oil film thickness in a method for evaluating the reliability of thermal elastohydrodynamic lubrication of a wind turbine sliding bearing according to one embodiment of the present invention; Figure 5 1 is a schematic diagram showing comparison results between this embodiment and a comparative embodiment of a method for evaluating the reliability of thermal elastohydrodynamic lubrication of a wind power sliding bearing according to an embodiment of the present invention; Figure 6 1 is a schematic diagram of sample collection center updates and MPP point movement paths for multiple subintervals in an embodiment of a method for evaluating the reliability of thermal elastohydrodynamic lubrication of a wind turbine sliding bearing according to an embodiment of the present invention; Figure 7 1 is a lubrication reliability analysis result of this embodiment and a traditional method under different BPA structures according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Some embodiments of the present invention are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0020] In the description of the present application, "module" and "processor" can include hardware, software or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and can also include a software portion such as program code, and can be a combination of software and hardware. The processor can be a central processing unit, a microprocessor, a graphics processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor can be implemented in software, hardware or a combination of both. The non-transitory computer readable storage medium includes any suitable medium that can store program code, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B or both A and B. The term "at least one of A or B" or "at least one of A and B" has a similar meaning as "A and / or B" and can include only A, only B or both A and B. The singular form of the term "one", "this" can also include the plural form.

[0021] Some terms related to the present application are explained here.

[0022] BPA, Basic Probability Assignment, basic probability assignment function; Dempster, Dempster's Combination Rule, evidence combination rule; mpp point, Maximum Power Point, maximum possible failure focus.

[0023] Referring to the accompanying Figure 1 , Figure 1 is a schematic diagram of the main steps of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method according to an embodiment of the present application. As shown in Figures 1-2 , the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method in the embodiment of the present application mainly includes the following steps S101-S106.

[0024] Step S101: obtaining evidence metric combination parameters, wherein the parameter types in the evidence metric combination parameters include at least load and dynamic viscosity; Specifically, the step S101 includes: Obtaining combination parameters measured by evidence variables, wherein the types of the combination parameters include at least load and dynamic viscosity; Based on the combination parameters, a recognition framework, a focus and a basic probability assignment function are constructed.

[0025] Specifically, the uncertainty of the load, dynamic viscosity and other types of parameters in the combined parameters of the wind power sliding bearing is described using a basic belief assignment function BPA, the belief function and the plausibility function are used to describe the reliability of the thermal elastohydrodynamic lubrication of the wind power sliding bearing together with the upper and lower probability boundaries of the focal elements, for the combined parameters measured by the evidence variable, the identification framework and the basic belief assignment function of any single type of parameter in the combined parameters can be constructed first, and then based on the identification framework and the basic belief assignment function of any single type of parameter in the combined parameters, the identification framework, the focal elements and the basic belief assignment function are constructed using the Dempster combination rule, wherein the basic belief assignment function is based on the basic belief assignment function of each focal element .

[0026] Specifically, the basic belief assignment data of each focal element is obtained by the following formula : ; wherein, represents the total number of focal elements, represents the basic belief assignment of the i-th parameter on the focal element .

[0027] Step S102: converting the evidence measurement combined parameter into a probability measurement combined parameter, and determining the mean value data of the probability measurement combined parameter; Specifically, as shown in the figure, the step S102 includes: Figure 3 converting the evidence measurement combined parameter into a probability measurement combined parameter by the following formula, and obtaining the wind power sliding bearing thermal elastohydrodynamic lubrication reliability index: ; wherein, represents the probability measurement combined parameter, represents the stepwise probability density function of the probability measurement combined parameter (i.e. the random parameter) , represents the total number of focal elements, and represent the upper limit and the lower limit of the focal element , respectively, represents the basic belief assignment function of each focal element, represents the indicator function, represents the norm of the probability measurement combined parameter , and the reliability index is the inner coordinate origin of the probability measurement combined parameter to the approximate minimum oil film thickness limit state surface ​the shortest distance of the two points, representing a constraint condition, i.e. ; and, when , , otherwise 0.

[0028] Step S103: constructing a sample collection center based on the probability metric combination parameter, and substituting the sample collection center into a thermal elastic flow oil film iterative analysis model to output minimum oil film thickness data; Specifically, the "constructing a sample collection center based on the probability metric combination parameter" in the step S103 includes: constructing a sample collection center based on the probability metric combination parameter; initializing a sample collection center point; arranging a plurality of sample points based on the sample collection center to obtain a sample point set.

[0029] Specifically, initializing the sample collection center point, i.e. , wherein represents the sample collection center, represents the mean value of the probability metric combination parameter .

[0030] Specifically, the "substituting the sample collection center into a thermal elastic flow oil film iterative analysis model to output minimum oil film thickness data" in the step S103 includes: screening the sample point set to obtain new samples and old samples; obtaining the minimum oil film thickness corresponding to the old samples based on a storage sample database; substituting the new samples into the thermal elastic flow oil film iterative analysis model to obtain the minimum oil film thickness corresponding to the new samples.

[0031] Specifically, the old samples are sample points already stored in the storage sample database, and the new samples are sample points not searched in the storage sample database.

[0032] Specifically, the "substituting the new samples into the thermal elastic flow oil film iterative analysis model to obtain the minimum oil film thickness corresponding to the new samples" in the step S103 includes: performing grid division on the new samples; obtaining elastic deformation data of each node on the divided grid; obtaining temperature data; obtaining the minimum oil film thickness data corresponding to the new samples based on the elastic deformation data; obtaining pressure data of the new samples based on the temperature data; judging the convergence of the pressure based on the pressure data; Based on the convergence of the pressure, selectively re-perform "obtaining the elastic deformation data of each node on the division grid" and subsequent steps, or output the minimum oil film thickness data corresponding to the new sample.

[0033] Specifically, by performing the division grid on the new sample, effective convergence of the sample is achieved.

[0034] Specifically, the elastic deformation data of each node on the division grid is obtained by the following formula: ; Wherein, represents the elastic deformation data of the node, represents the elastic modulus of the two contact surfaces; represents the position coordinates where the concentrated force occurs, and the lower and upper limit functions of the integral and represent the inlet and outlet coordinates of the calculation domain, respectively; represents the distributed force acting on the microelement ; is converted into a concentrated force, which is equivalent to the load transmitted by the gear box to the journal , and is the change in film thickness caused by this concentrated force.

[0035] Specifically, the method comprises: If it is judged that the pressure converges, the temperature data and the minimum oil film thickness data corresponding to the new sample are obtained by the following formula: ; Wherein, represents the reference value of the entire film thickness distribution; represents the radius of curvature, represents the film thickness change, that is, when the substrate has a certain curvature, the film thickness will change in the form of a quadratic function as the position increases, represents the elastic deformation data of the node, represents the temperature under the current pressure and density , represents the ambient temperature, represents the initial density when the temperature does not change, wherein represents the thermodynamic temperature unit, represents the dimension of the parameter related to the change in temperature; If it is judged that the pressure does not converge, re-perform "obtaining the elastic deformation data of each node on the division grid" and subsequent steps.

[0036] Specifically, the pressure data of the new sample is described by the following formula : ; wherein, is the dynamic viscosity under the ambient temperature.

[0037] Specifically, the grid division performed on the new sample adopts a multi-grid method.

[0038] Step S104: constructing a quadratic polynomial response surface function based on the current oil film safety threshold, and selectively updating the sample collection center based on the quadratic polynomial response surface function, so that the sample collection center approximates the maximum possible failure focus; Specifically, the step S104 of "constructing a quadratic polynomial response surface function based on the current oil film safety threshold" includes: obtaining the current oil film safety threshold; based on the sample point set and the minimum oil film thickness corresponding to the sample point set, constructing the real minimum oil film thickness limit state function data under the current oil film safety threshold.

[0039] Specifically, the quadratic non-crossing term response surface is used to approximate the minimum oil film thickness real limit state function, which is obtained by the following formula: ; wherein, represents the dimension of the evidence uncertain variable, 、 、 represents the response surface coefficient, represents the combination parameter measured in the evidence variable, represents the approximate minimum oil film thickness real limit state function, and the is a quadratic polynomial response surface function, and is the response surface of the minimum oil film thickness real limit state function , wherein represents the oil film safety threshold, and the specific steps include substituting the collected sample into the thermal elastohydrodynamic oil film iterative analytical model to obtain the minimum oil film thickness under the sample , calculating the limit state function value under the current oil film safety threshold . Substitute the sample X and the limit state function value under the current oil film safety threshold into the above formula to calculate the response surface coefficient 、 ,​ , thereby obtaining the true limit state function of the minimum oil film thickness The quadratic response surface without cross terms To avoid boundary lubrication, the critical oil film thickness is required to be greater than 3 times the surface roughness. When , it is determined to be lubrication failure.

[0040] Specifically, the step S104 of "selectively updating the sample collection center based on the quadratic polynomial response surface function so that the sample collection center approaches the most likely failure focal element" includes: By judging the constant Determine whether the sample collection center needs to be updated; The sample collection center is optionally updated by the following formula: ; in, On behalf of the sample collection center, Represents the probability metric combination parameter The mean of Represents the probability metric combination parameter The mean The corresponding minimum oil film thickness true limit state function, represent The corresponding minimum oil film thickness true limit state function, Representative The sample collection center of the Representative The sample collection center of the For sample collection center The judgment constant of the convergence of the update process; In each sequence iteration, an approximate reliability index and an approximate design point are obtained, and a design verification point for transition is obtained, so that each iteration step updates the sample collection center through the design verification point obtained in the previous step, thereby making the sample collection center approach the maximum possible failure focal element of the true limit state surface of the minimum oil film thickness; Setting reliability indicators The corresponding probability metric combination parameter Design verification point , the design verification point Mapping back to the evidence metric combination parameters; The focal element corresponding to the evidence metric combination parameter is taken as the focal element with the greatest possible failure .

[0041] Specifically, the response surface approximation minimum oil film thickness limit state function is solved in each iteration process and the mapping transformation method iteration and the approximate design point , and the iteration meets the preset convergence criterion to obtain the transition design checking point The sample collection center is updated in each iteration process The sample collection area is always kept near the maximum failure focus element of the minimum oil film thickness true limit state surface, thereby effectively improving the accuracy of the minimum oil film thickness limit state response surface construction and the accuracy of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability analysis.

[0042] Step S105: Based on the maximum possible failure focus element, the reliability data and the quasi-truth data of the wind power sliding bearing thermal elastohydrodynamic lubrication are determined. Specifically, the step S105 includes: The reliability data and the quasi-truth data of the wind power sliding bearing thermal elastohydrodynamic lubrication are obtained by the following formula: ; Wherein, represents that the focus element is completely within the reliable domain, represents that the focus element is completely or partially located within the reliable domain, represents the basic reliability allocation function of each focus element, represents the reliability data of the wind power sliding bearing thermal elastohydrodynamic lubrication, represents the quasi-truth data of the wind power sliding bearing thermal elastohydrodynamic lubrication. And the maximum value and the minimum value of the minimum oil film thickness true limit state function are obtained, which are used to accurately determine the relationship between the focus element and the reliable domain .

[0043] Specifically, as shown in Figure 4 , the maximum value and the minimum value of the minimum oil film thickness true limit state function include: The extreme value analysis is performed on the minimum oil film thickness true limit state function on each focus element to obtain the maximum value and the minimum value of the minimum oil film thickness true limit state function.

[0044] Specifically, when the focus element is , , the focus element is completely in the lubrication reliable domain, that is the basic credibility assignment of the focal element corresponding to the focal element simultaneously and ; when the focal element is on the lubrication failure domain , then the focal element is completely in the lubrication failure domain, and the basic credibility assignment of the focal element is not counted in or ; when the focal element is on the lubrication reliability domain , then the focal element is partially in the lubrication reliability domain, and the basic credibility assignment of the focal element is only counted in .

[0045] Step S106: change the oil film safety threshold, and repeatedly execute the above steps S103-S105 to obtain the wind power sliding bearing thermal elastohydrodynamic lubrication reliability analysis result.

[0046] Specifically, the step S106 comprises: changing the oil film safety threshold, and repeatedly executing the above steps S103-S105 to obtain a plurality of groups of wind power sliding bearing thermal elastohydrodynamic lubrication credibility data and plausibility data; based on a plurality of groups of wind power sliding bearing thermal elastohydrodynamic lubrication credibility data and plausibility data, a plurality of groups of credibility data analysis combination coordinates and a plurality of groups of plausibility data analysis combination coordinates are obtained; Specifically, the analysis combination coordinates are in the form of (current oil film safety threshold, wind power sliding bearing thermal elastohydrodynamic lubrication credibility data), or (current oil film safety threshold, wind power sliding bearing thermal elastohydrodynamic lubrication plausibility data); connecting the plurality of groups of credibility data analysis combination coordinates to obtain a cumulative credibility function curve; and connecting the plurality of groups of plausibility data analysis combination coordinates to obtain a cumulative plausibility function curve; based on the cumulative credibility function curve and the cumulative plausibility function curve, a wind power sliding bearing thermal elastohydrodynamic lubrication reliability analysis result is obtained, wherein the wind power sliding bearing thermal elastohydrodynamic lubrication reliability analysis result at least includes the reliability degree of wind power sliding bearing thermal elastohydrodynamic lubrication.

[0047] Specifically, the uncertainty degree of elastohydrodynamic lubrication reliability is determined by the distance between the cumulative credibility function curve and the cumulative plausibility function curve. If the distance is less than a preset distance, it is proved that the reliability degree is high, otherwise, it is proved that the reliability degree is low.

[0048] Based on the above steps S101-S106, the evidence metric combination parameter is converted into a probability metric combination parameter through a homogenization technique, so that the non-probabilistic characteristics of evidence theory are retained while the mature tools of probability theory are used for reliability analysis, thereby improving the accuracy of reliability, and the sample collection center is screened through the sample genetic management method, avoiding repeated calculation of the minimum oil film thickness in the iteration process of approaching the maximum possible failure focus region, further reducing the calculation cost of the wind power sliding bearing elastohydrodynamic lubrication reliability analysis, and a quadratic polynomial response surface function is constructed according to the current oil film safety threshold, and the sample collection center is selectively updated, so that the sample collection center approaches the maximum possible failure focus, and through the maximum possible failure focus, the reliability data and the quasi-truth data of the wind power sliding bearing thermal elastohydrodynamic lubrication are determined, thereby realizing the collection and construction of parameter samples such as load and dynamic viscosity of the approximate minimum oil film thickness limit state function in the maximum failure focus region which contributes most to the calculation of the reliability index, improving the accuracy of the calculation of the reliability index and reducing the calculation cost of the high-precision reliability evaluation method, the oil film safety threshold is changed, and the above steps S103-S105 are cyclically executed to obtain the wind power sliding bearing thermal elastohydrodynamic lubrication reliability analysis result, which ensures the accuracy of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability analysis result while effectively reducing the number of calls of the thermal elastohydrodynamic oil film iterative analysis model.

[0049] Embodiment: In this embodiment, a sliding bearing in a 15MW wind power device selected from the literature is taken as an example; From Figure 5 It can be seen that T1 is the embodiment, T2 is the comparative embodiment, and the temperature variation curves with speed of the two present a highly consistent distribution trend, that is, the temperature gradually increases with the input speed increasing from 0 to 35r / min, and the change slopes in the low speed (0~10r / min) and high speed (25~35r / min) intervals are basically consistent, therefore, the thermal elastohydrodynamic oil film iterative analysis model constructed in this embodiment can accurately capture the dynamic evolution law of the temperature with speed, and can effectively calculate the minimum oil film thickness.

[0050] Based on the performance of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability analysis of the sequence iterative response surface, the load [100, 450] KN and the dynamic viscosity [0.005, 0.3] N·s / ㎡ are set as the evidence metric combination parameters, and the BPA (basic probability assignment function) of each variable in the corresponding interval is obtained by using the basic credibility distribution function of the evidence theory. Table 1 shows the BPA structure of each variable taking 6 and 8 subintervals.

[0051] Table 1 BPA structure of load and dynamic viscosity parameters taking 6 and 8 subintervals

[0052] Table 2 shows the calculation process of sample collection center updating and reliability index of oil film safety threshold 0.023 um based on the sequence iteration mechanism of response surface and reliability analysis, taking the BPA structure of 6 subintervals as an example. The iteration convergence error ε of the sample collection center is 1e-3. The load and dynamic viscosity parameters of the evidence measure shown in Table 1 are converted into probability measures, and the mean value is 275 KN and 1.52e-1 N·s / m2. The minimum oil film thickness is 5.19e-1 um obtained by calling the thermal elastohydrodynamic oil film iterative analysis model. Taking it as the sample collection center, 4 sample points are collected along the axial direction, and the minimum oil film thickness is calculated, as shown in the second column of the first iteration step in Table 2. The quadratic polynomial response surface of the approximate minimum oil film thickness limit state function is constructed from the 5 sample points. Based on the constructed response surface, the reliability index is solved by iterative 5 times according to the mapping transformation method .

[0053] As shown in the first iteration step of Table 2, the mpp point is: load 100 KN, dynamic viscosity 3.17e-2 N·s / m2, and the minimum oil film thickness is 1.96e-1, and the sample collection center of the second iteration step is obtained by updating the sample collection center (load 146.92 KN, dynamic viscosity 2.4e-2 N·s / m2), the error is 3.64e-1, which is greater than the iteration convergence error 1e-3. Continue to update the response surface.

[0054] As shown in Table 2, the sample collection center error is 3.67e-4 after the third iteration, which is less than the iteration convergence error 1e-3, meeting the convergence requirement, so the obtained mpp point (load 272.60 KN, dynamic viscosity 5.27e-3 N·s / m2) is taken as the maximum possible failure focus area of the wind power sliding bearing in this embodiment.

[0055] It can be seen that the approximate response surface at this time has good approximation accuracy to the true minimum oil film thickness limit state surface, and the reliability index can be obtained by this embodiment through a small number of sequence iteration times. As shown in Table 2, the entire iteration process only needs to call the thermal elastohydrodynamic oil film iterative analysis model 18 times, so the method has a faster convergence speed.

[0056] Table 2 Sample collection center updating and reliability index under oil film safety threshold 0.023 um

[0057] As Figure 6As shown, the sample collection center updates and mpp point moves path of BPA structure of 6 sub-intervals and 8 sub-intervals are compared, and the uniformization process brings the BPA information of the evidence variable into the search process of the sample collection center (i.e. the design checking point of the approximately equivalent probability reliability problem). When the BPA structure changes, the sample collection center point position of the embodiment also changes, but always moves towards the maximum possible failure focus (mpp point). Whether the BPA structure is 6 sub-intervals or 8 sub-intervals, the sample collection center always falls within the focus element with the largest BPA and intersects with the limit state surface (i.e. the maximum possible failure focus element). Therefore, the method has self-adaptability to the changes of the BPA structure of parameters such as load and dynamic viscosity.

[0058] As shown in the left figure, the oil film safety threshold is changed Figure 7 As shown in the left figure, the oil film safety threshold is changed A series of minimum oil film thickness limit state functions are obtained, and the reliability and plausibility of the wind power sliding bearing thermal elastohydrodynamic lubrication are solved by using the method and the traditional evidence theory reliability analysis method. As can be seen, under the two BPA structures, the cumulative confidence function curve (CCBF) and the cumulative plausibility function curve (CCPF) results of the method are similar to the reference results in most cases. Therefore, the method has high calculation accuracy for the reliability analysis of the wind power sliding bearing thermal elastohydrodynamic lubrication. Among them, by comparing the method with the traditional evidence theory reliability analysis method, it can be seen that the error of the BPA structure with 8 sub-intervals is smaller than that of the BPA structure with 6 sub-intervals. Further, the uniformization technology approximately equivalently converts the thermal elastohydrodynamic lubrication reliability analysis model based on the evidence theory into a probability reliability analysis model, which not only improves the calculation efficiency, but also better guarantees the accuracy when the identification framework of the evidence variable is a relatively narrow interval.

[0059] As shown in the left figure, the oil film safety threshold is changed Figure 7 As shown in the left figure, the oil film safety threshold is changed As shown in the left figure, the oil film safety threshold is changed

[0060] It should be noted that although the above embodiments describe the steps in a specific order, those skilled in the art can understand that in order to achieve the effect of the present application, the steps between different steps do not necessarily have to be executed in such an order, they can be executed simultaneously (in parallel) or in other order, and these changes are within the protection scope of the present application.

[0061] Those skilled in the art can understand that all or part of the processes in the method of the above embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable storage medium can include any entity or device, medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the content included in the computer readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable storage medium does not include electrical carrier signals and telecommunication signals.

[0062] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will fall within the protection scope of the present application.

Claims

1. A wind turbine sliding bearing thermal elastohydrodynamic lubrication reliability assessment method, characterized in that, The method comprises the following steps: Step S101: acquiring evidence metric combination parameters, wherein the parameter types in the evidence metric combination parameters at least include load and dynamic viscosity; Step S102: converting the evidence metric combination parameters into probability metric combination parameters, and determining mean value data of the probability metric combination parameters; Step S103: constructing a sample collection center based on the probability metric combination parameters, and substituting the sample collection center into a thermal elastohydrodynamic lubrication (TEHL) film iterative analysis model to output minimum film thickness data; Step S104: constructing a quadratic polynomial response surface function based on a current oil film safety threshold, and selectively updating the sample collection center based on the quadratic polynomial response surface function, so that the sample collection center approximates a maximum possible failure focus; Step S105: determining reliability data and likelihood data of TEHL of a wind power sliding bearing based on the maximum possible failure focus; Step S106: changing the oil film safety threshold, and cyclically executing the steps S103-S105 to obtain a TEHL reliability analysis result of the wind power sliding bearing.

2. The wind turbine sliding bearing thermo-hydrodynamic lubrication reliability assessment method of claim 1, wherein, The step S102 comprises: The evidence metric combination parameters are converted into the probability metric combination parameters by the following formula, and the TEHL reliability index of the wind power sliding bearing is acquired: ； in, represents the probability metric combination parameter, Represents the probability metric combination parameter The step-like probability density function of represents the total number of focal elements, and Representing Jiao Yuan The upper and lower bounds of represents the basic credibility distribution function of each focal element, represents the indicator function, Represents the probability metric combination parameter Norm, reliability index Combination parameters for probability metrics The coordinate origin in space to the approximate minimum oil film thickness limit state surface The shortest distance, represents the constraint condition; and when hour, , otherwise 0.

3. The wind turbine sliding bearing thermo-hydrodynamic lubrication reliability assessment method of claim 2, wherein, The "constructing a sample collection center based on the probability metric combination parameters" in the step S103 comprises: constructing the sample collection center based on the probability metric combination parameters; initializing a sample collection center point; arranging a plurality of sample points based on the sample collection center to obtain a sample point set.

4. The wind turbine sliding bearing thermo-hydrodynamic lubrication reliability assessment method of claim 3, wherein, The "substituting the sample collection center into the TEHL film iterative analysis model to output the minimum film thickness data" in the step S103 comprises: screening the sample point set to obtain new samples and old samples; acquiring minimum film thickness corresponding to the old samples based on a stored sample database; substituting the new samples into the TEHL film iterative analysis model to obtain minimum film thickness corresponding to the new samples.

5. The wind turbine sliding bearing thermo-hydrodynamic lubrication reliability assessment method of claim 4, wherein, The "substituting the new samples into the TEHL film iterative analysis model to obtain minimum film thickness corresponding to the new samples" in the step S103 comprises: performing grid division on the new samples; acquiring elastic deformation data of each node on the divided grid; acquiring temperature data; acquiring minimum film thickness data corresponding to the new samples based on the elastic deformation data; acquiring pressure data of the new samples based on the temperature data; judging convergence of the pressure based on the pressure data; based on the convergence of the pressure, selectively re-executing the "acquiring elastic deformation data of each node on the divided grid" and subsequent steps, or outputting the minimum film thickness data corresponding to the new samples.

6. The wind turbine sliding bearing thermo-hydrodynamic lubrication reliability assessment method of claim 5, wherein, The method comprises: acquiring the temperature data and the minimum film thickness data corresponding to the new samples by the following formula: ； wherein, a reference value representative of the whole film thickness distribution; a reference value representative of the radius of curvature, a reference value representative of the film thickness variation, a reference value representative of the elastic deformation data of the node, a reference value representative of the temperature at the current pressure and density at the current pressure, a reference value representative of the ambient temperature, a reference value representative of the initial density when the temperature has not changed, wherein a reference value representative of the thermodynamic temperature unit, a reference value representative of the dimension of the parameter related to the temperature variation.

7. The wind turbine sliding bearing thermo-hydrodynamic lubrication reliability assessment method of claim 6, wherein, The "constructing a quadratic polynomial response surface function based on the current oil film safety threshold" in the step S104 comprises: acquiring the current oil film safety threshold; Based on the sample point set and the minimum oil film thickness corresponding to the sample point set, a real minimum oil film thickness limit state function data under a current oil film safety threshold is constructed.

8. The wind turbine sliding bearing thermo-hydrodynamic lubrication reliability assessment method of claim 7, wherein, The "selectively updating the sample collection center based on the quadratic polynomial response surface function so that the sample collection center approximates the maximum possible failure focus" in the step S104 includes: by judging a constant determining whether the sample collection center needs to be updated; The sample collection center is selectively updated by the following formula: ； wherein, representing a sample collection center, representing a probability metric combination parameter a mean value of, representing a probability metric combination parameter a mean value of a minimum oil film thickness true limit state function corresponding to, representing a minimum oil film thickness true limit state function corresponding to, representing a sample collection center of version representing a sample collection center of version representing a sample collection center of version representing a sample collection center of version a sample collection center a judgment constant for the convergence of the updating process; Wherein, the approximate reliability index and the approximate design point are obtained in each sequence iteration, the design checking point for transition is obtained, so that the update of the sample collection center is realized by the design checking point obtained in the last step in each iteration step, and then the sample collection center approximates the maximum possible failure focus of the real limit state surface of the minimum oil film thickness. Setting reliability indicators Corresponding probability metric combination parameters For designing a design check point Mapping back the design check point To evidence metric combination parameters; The focal element corresponding to the evidence metric combination parameter is taken as the focal element with the greatest possible failure .

9. The wind turbine sliding bearing thermo-hydrodynamic lubrication reliability assessment method of claim 8, wherein, The step S105 includes: The reliability data and the pseudo-true data of the wind power sliding bearing thermal elastohydrodynamic lubrication are obtained by the following formula: ； wherein, represents that the focal element is completely within the reliable region, represents that the focal element is completely or partially within the reliable region, represents a basic trustworthiness assignment function for each focal element, represents trustworthiness data for thermal elastohydrodynamic lubrication of wind turbine sliding bearings, represents plausibility data for thermal elastohydrodynamic lubrication of wind turbine sliding bearings. And the maximum and minimum values of the real limit state function of the minimum oil film thickness are obtained.

10. The wind turbine sliding bearing thermo-hydrodynamic lubrication reliability assessment method of claim 9, wherein, The step S106 includes: The oil film safety threshold is changed, and the above steps S103-S105 are cyclically executed to obtain multiple groups of reliability data and pseudo-true data of the wind power sliding bearing thermal elastohydrodynamic lubrication; Based on multiple groups of reliability data and pseudo-true data of the wind power sliding bearing thermal elastohydrodynamic lubrication, multiple groups of reliability data analysis combination coordinates and multiple groups of pseudo-true data analysis combination coordinates are obtained; The multiple groups of reliability data analysis combination coordinates are connected to obtain a cumulative reliability function curve, and the multiple groups of pseudo-true data analysis combination coordinates are connected to obtain a cumulative pseudo-true function curve; Based on the cumulative reliability function curve and the cumulative pseudo-true function curve, a wind power sliding bearing thermal elastohydrodynamic lubrication reliability analysis result is obtained, wherein the wind power sliding bearing thermal elastohydrodynamic lubrication reliability analysis result at least includes the reliability degree of the wind power sliding bearing thermal elastohydrodynamic lubrication.

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