A wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method

Abstract

The application specifically provides a wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method. The method comprises the following steps: obtaining evidence metric combination parameters; converting the evidence metric combination parameters into probability metric combination parameters, and determining the mean value data of the probability metric combination parameters; 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; 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 to a maximum possible failure focus; determining the reliability data and the likelihood data of the wind power sliding bearing thermal elastohydrodynamic lubrication based on the maximum possible failure focus; changing the oil film safety threshold, and cyclically executing the above steps to obtain the wind power sliding bearing thermal elastohydrodynamic lubrication reliability analysis result. The accuracy of the 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 to be abnormally increased, and even the gluing failure. The thickness is coupled with 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 are difficult to 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 are difficult to 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:

[0007] Step S101: acquiring evidence metric combination parameters, wherein the parameter types in the evidence metric combination parameters at least include load and dynamic viscosity;

[0008] 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;

[0009] 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;

[0010] Step S104: Construct a quadratic polynomial response surface function based on the current oil film safety threshold, and selectively update the sample acquisition center based on the quadratic polynomial response surface function, so that the sample acquisition center approaches the maximum possible failure focal element.

[0011] Step S105: Based on the maximum possible failure coke element, determine the reliability data and similarity data of the thermo-elastohydrodynamic lubrication of the wind turbine sliding bearing;

[0012] Step S106: Change the oil film safety threshold and repeat steps S103-S105 to obtain the reliability analysis results of the thermo-elastohydrodynamic lubrication of the wind turbine sliding bearing.

[0013] In one technical solution of the above-mentioned reliability assessment method for thermo-elasto-fluidic lubrication of wind turbine sliding bearings, step S102 includes:

[0014] The evidence metric combination parameters are converted into probability metric combination parameters using the following formula, and the thermo-elasto-hydrodynamic lubrication reliability index of the wind turbine sliding bearing is obtained:

[0015] ;

[0016] in, Representative probability metric combination parameters, Representative probability metric combination parameters The step-like probability density function, Represents the total number of Jiao Yuan. and Representing jiao yuan respectively The upper and lower bounds, The basic confidence assignment function represents each focal element. Represents an indicator function. Representative probability metric combination parameters norm, reliability index For probability metric combination parameters From the origin of the coordinate system in space to the limit state surface of the approximate minimum oil film thickness The shortest distance, Represents constraints; and, when hour, Otherwise, it is 0.

[0017] In one technical solution of the above-mentioned reliability assessment method for thermo-elasto-hydrodynamic lubrication of wind turbine sliding bearings, step S103, "constructing a sample acquisition center based on probability metric combination parameters," includes:

[0018] A sample collection center is constructed based on a combination of probability metrics and parameters;

[0019] Initialize the sample collection center point;

[0020] A plurality of sample points are arranged based on the sample collection center to obtain a sample point set.

[0021] In one of the technical solutions of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method, the "substituting the sample collection center into the thermal elastohydrodynamic oil film iterative analysis model to output the minimum oil film thickness data" in the step S103 comprises:

[0022] The sample point set is screened to obtain new samples and old samples.

[0023] Based on the stored sample database, the minimum oil film thickness corresponding to the old sample is obtained.

[0024] The new sample is substituted into the thermal elastohydrodynamic oil film iterative analysis model to obtain the minimum oil film thickness corresponding to the new sample.

[0025] In one of the technical solutions of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method, the "substituting the new sample into the thermal elastohydrodynamic oil film iterative analysis model to obtain the minimum oil film thickness corresponding to the new sample" in the step S103 comprises:

[0026] The new sample is divided into a grid.

[0027] The elastic deformation data of each node on the divided grid is obtained.

[0028] The temperature data is obtained.

[0029] Based on the elastic deformation data, the minimum oil film thickness data corresponding to the new sample is obtained.

[0030] Based on the temperature data, the pressure data of the new sample is obtained.

[0031] Based on the pressure data, the convergence of the pressure is judged.

[0032] Based on the convergence of the pressure, the "obtaining the elastic deformation data of each node on the divided grid" and the subsequent steps are selectively re-executed, or the minimum oil film thickness data corresponding to the new sample is output.

[0033] In one of the technical solutions of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method, the method comprises:

[0034] The temperature data and the minimum oil film thickness data corresponding to the new sample are obtained by the following formula:

[0035] ;

[0036] Wherein, represents the reference value of the entire film thickness distribution. Represents the radius of curvature. This represents the change in film thickness, specifically describing the change in film thickness as the substrate has a certain curvature, depending on the location. As the capacitance increases, the film thickness will change according to a quadratic function. Represents the elastic deformation data of the nodes. Represents the current pressure and density The temperature below, Represents ambient temperature. This represents the initial density when the temperature remains unchanged. ,in Represents the unit of thermodynamic temperature. This represents the dimension of the parameter's relationship to temperature changes.

[0037] In one technical solution of the above-mentioned reliability assessment method for thermo-elasto-hydrodynamic lubrication of wind turbine sliding bearings, step S104, "constructing a quadratic polynomial response surface function based on the current oil film safety threshold," includes:

[0038] Obtain the current oil film safety threshold;

[0039] Based on the sample point set and the minimum oil film thickness corresponding to the sample point set, construct the true minimum oil film thickness limit state function data under the current oil film safety threshold.

[0040] In one technical solution of the above-mentioned reliability assessment method for thermo-elasto-fluidic lubrication of wind turbine sliding bearings, step S104, "selectively updating the sample acquisition center based on the quadratic polynomial response surface function, so that the sample acquisition center approximates the maximum possible failure focal element," includes:

[0041] By judging constants Determine whether the sample collection center needs to be updated;

[0042] The sample collection center is selectively updated using the following formula:

[0043] ;

[0044] in, Represents the sample collection center. Representative probability metric combination parameters The mean, Representative probability metric combination parameters mean The corresponding true limit state function for minimum oil film thickness. represent The corresponding true limit state function for minimum oil film thickness. Representing the a sample collection center, representing the first a sample collection center, for the sample collection center a judgment constant of convergence of the updating process;

[0045] wherein, each time sequence iteration obtains an approximate reliability index and an approximate design point, obtains a design checking point for transition, so that each iteration step realizes the update of the sample collection center through the design checking point obtained by the last step, and further makes the sample collection center approximate to the maximum possible failure focus element of the minimum oil film thickness true limit state surface;

[0046] set the reliability index the corresponding probability metric combination parameter the design checking point map the design checking point back to the evidence metric combination parameter;

[0047] and take the focus element corresponding to the evidence metric combination parameter as the maximum possible failure focus element .

[0048] In one of the technical solutions of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method, the step S105 comprises:

[0049] The reliability data and the quasi-truth data of the wind power sliding bearing thermal elastohydrodynamic lubrication are obtained by the following formula:

[0050] ;

[0051] wherein, represents that the focus element is completely within the reliable domain, represents that the focus element is completely or partially within the reliable domain, represents the basic reliability distribution 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;

[0052] And the maximum value and the minimum value of the minimum oil film thickness true limit state function are obtained.

[0053] In one of the technical solutions of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method, the step S106 comprises:

[0054] Change the oil film safety threshold, and cyclically execute the above steps S103-S105 to obtain multiple sets of reliability data and quasi-truth data of the wind power sliding bearing thermal elastohydrodynamic lubrication;

[0055] Based on the credibility data and the plausibility data of the thermal elastohydrodynamic lubrication of the multiple sets of wind power sliding bearings, a plurality of credibility data analysis combination coordinates and a plurality of plausibility data analysis combination coordinates are obtained;

[0056] The plurality of credibility data analysis combination coordinates are connected to obtain a cumulative credibility function curve, and the plurality of plausibility data analysis combination coordinates are connected to obtain a cumulative plausibility function curve;

[0057] 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 a reliability degree of the wind power sliding bearing thermal elastohydrodynamic lubrication.

[0058] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects:

[0059] (1) The evidence metric is converted into a probability metric through homogenization processing, sample collection center updating is carried out based on the sequence iteration mechanism of the response surface and the probability reliability analysis, and the maximum possible failure focus region is approximated. At this time, 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 iterative analysis model is effectively reduced.

[0060] (2) The sample genetic management technology is used to screen new and old samples for the sample point set, so as to avoid repeated calculation of the minimum oil film thickness in the iteration process of approximating the maximum possible failure focus region, and further reduce the calculation cost of the wind power sliding bearing elastohydrodynamic lubrication reliability analysis. BRIEF DESCRIPTION OF DRAWINGS

[0061] The disclosure of the present application will become more readily understood by referring to the accompanying drawings. It will be readily understood to those skilled in the art that the drawings are only for the purpose of illustration and are not intended to limit the scope of protection of the present application. In addition, similar numbers in the figures are used to represent similar components, wherein:

[0062] Figure 1 is a main step flow diagram of a wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method according to an embodiment of the present application;

[0063] Figure 2 is a reliability analysis flow diagram of a wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method according to an embodiment of the present application;

[0064] Figure 3 is a schematic diagram of step S102 of a wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method according to an embodiment of the present application;

[0065] Figure 4 is a schematic diagram of the position relationship between the focal element and the minimum oil film thickness real limit state function of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method according to an embodiment of the present application;

[0066] Figure 5 is a schematic diagram of the comparison results of the present embodiment and the comparative embodiment in the embodiment of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method according to an embodiment of the present application;

[0067] Figure 6 is a schematic diagram of the sample collection center update and the mpp point moving path in the embodiment of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method according to an embodiment of the present application;

[0068] Figure 7 is the lubrication reliability analysis results of the present embodiment and the traditional method under different BPA structures according to an embodiment of the present application. DETAILED DESCRIPTION

[0069] Some embodiments of the present application will be 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 application, and are not intended to limit the protection scope of the present application.

[0070] In the description of the present application, "module" and "processor" can include hardware, software or a combination of both. A module can include hardware circuit, various suitable sensors, communication port, memory, and can also include software part such as program code, and can be a combination of software and hardware. The processor can be a central processor, microprocessor, image processor, 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 magnetic disk, hard disk, optical disk, flash memory, read-only memory, 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 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 plural forms.

[0071] Some terms related to the present application will be explained first.

[0072] BPA, Basic Probability Assignment, basic probability assignment function;

[0073] Dempster, Dempster's Combination Rule, rule of evidence combination;

[0074] mpp point, Maximum Power Point, maximum possible failure focus element.

[0075] Referring to the drawings Figure 1 , Figure 1 is the main step flowchart of the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method according to an embodiment of the application. As shown in Figures 1-2 , the wind power sliding bearing thermal elastohydrodynamic lubrication reliability evaluation method in the embodiment of the application mainly includes the following steps S101-S106.

[0076] Step S101: obtaining evidence metric combination parameters, wherein the parameter types in the evidence metric combination parameters at least include load, dynamic viscosity;

[0077] Specifically, the step S101 includes:

[0078] obtaining combination parameters measured by evidence variables, wherein the types of the combination parameters at least include load, dynamic viscosity;

[0079] Based on the combination parameters, an identification framework, a focus element and a basic belief assignment function are constructed.

[0080] Specifically, the basic belief assignment function BPA is used to describe the uncertainty of the parameters of the load, dynamic viscosity and the like in the combination parameters of the wind power sliding bearing, and the belief function and the plausibility function are used to describe the reliability of the wind power sliding bearing thermal elastohydrodynamic lubrication together as the upper and lower probability boundaries of the focus element. For the combination parameters measured by the evidence variables, the identification framework and the basic belief assignment function of any single type of parameter in the combination 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 combination parameters, the identification framework, the focus element and the basic belief assignment function are constructed using the Dempster and other evidence combination rules, wherein the basic belief assignment function is based on the basic belief assignment function of each focus element .

[0081] Specifically, the basic belief assignment data of each focus element is obtained by the following formula:

[0082] ;

[0083] wherein, represents the total number of focus elements, represents the th parameter in its focus element ​basic credibility allocation of the upper limit.

[0084] Step S102: converting the evidence metric combination parameter into a probability metric combination parameter, and determining mean value data of the probability metric combination parameter;

[0085] Specifically, as shown in the figure, Figure 3 the step S102 includes:

[0086] 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:

[0087] ;

[0088] wherein, represents the probability metric combination parameter, represents the step probability density function of the probability metric combination parameter (i.e. the random parameter) , represents the total number of focal elements, represents the upper limit and the lower limit of the focal element , respectively, represents the basic credibility 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 , represents the constraint condition, i.e. the constraint condition is ; and when , , otherwise 0.

[0089] Step S103: constructing a sample collection center based on the probability metric combination parameter, and substituting the sample collection center into the thermal elastohydrodynamic oil film iterative analytical model to output minimum oil film thickness data;

[0090] Specifically, the “constructing a sample collection center based on the probability metric combination parameter” in the step S103 includes:

[0091] constructing a sample collection center based on the probability metric combination parameter;

[0092] initializing a sample collection center point;

[0093] arranging a plurality of sample points based on the sample collection center to obtain a sample point set.

[0094] ​​Specifically, the sample collection center point is initialized, that is, the minimum film thickness data is set wherein, representing the sample collection center, representing the probability metric combination parameter the mean value.

[0095] Specifically, the "substituting the sample collection center into the thermal elastohydrodynamic lubrication oil film iterative analysis model to output the minimum film thickness data" in the step S103 includes:

[0096] The sample point set is filtered to obtain new samples and old samples;

[0097] Based on the stored sample database, the minimum film thickness corresponding to the old sample is obtained;

[0098] The new sample is substituted into the thermal elastohydrodynamic lubrication oil film iterative analysis model to obtain the minimum film thickness corresponding to the new sample.

[0099] Specifically, the old sample is a sample point already stored in the stored sample database, and the new sample is a sample point not searched in the stored sample database.

[0100] Specifically, the "substituting the new sample into the thermal elastohydrodynamic lubrication oil film iterative analysis model to obtain the minimum film thickness corresponding to the new sample" in the step S103 includes:

[0101] The new sample is divided into a grid;

[0102] The elastic deformation data of each node on the divided grid is obtained;

[0103] The temperature data is obtained;

[0104] Based on the elastic deformation data, the minimum film thickness data corresponding to the new sample is obtained;

[0105] Based on the temperature data, the pressure data of the new sample is obtained;

[0106] Based on the pressure data, the convergence of the pressure is judged;

[0107] Based on the convergence of the pressure, the "obtaining the elastic deformation data of each node on the divided grid" and the subsequent steps are selectively re-executed, or the minimum film thickness data corresponding to the new sample is output.

[0108] Specifically, by dividing the grid for the new sample, effective convergence of the sample is realized.

[0109] Specifically, the elastic deformation data of each node on the divided grid is obtained by the following formula:

[0110] ;

[0111] wherein, the elastic deformation data of the nodes, represent the elastic modulus of the two contact surfaces; represent the position coordinates of the concentrated force, the lower limit and upper limit functions of the integral and represent the inlet zone coordinates and the outlet zone coordinates of the calculation domain, respectively; represent the distributed force on the microelement , which is converted into a concentrated force, equivalent to the load transmitted by the gear box to the journal , the in the film thickness equation is due to this concentrated force.

[0112] Specifically, the method comprises:

[0113] 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 through the following formula:

[0114] ;

[0115] wherein, represent the reference value of the entire film thickness distribution; represent the radius of curvature, represent the film thickness variation, that is, it is described that when the substrate has a certain curvature, with the increase of the position , the film thickness will change in the form of a quadratic function, the elastic deformation data of the nodes, represent the temperature under the current pressure and density , represent the ambient temperature, represent the initial density when the temperature does not change, wherein represent the thermodynamic temperature unit, represent the dimension of the parameter related to the change of temperature;

[0116] If it is judged that the pressure does not converge, the elastic deformation data of each node on the divided grid and the subsequent steps are re-executed.

[0117] Specifically, the pressure data of the new sample is obtained through the following formula: :

[0118] ;

[0119] wherein, is the ambient temperature The dynamic viscosity at that point.

[0120] Specifically, the grid division of the new sample is performed using a multi-grid method.

[0121] Step S104: Construct a quadratic polynomial response surface function based on the current oil film safety threshold, and selectively update the sample acquisition center based on the quadratic polynomial response surface function, so that the sample acquisition center approaches the maximum possible failure focal element.

[0122] Specifically, the step S104 of "constructing a quadratic polynomial response surface function based on the current oil film safety threshold" includes:

[0123] Obtain the current oil film safety threshold;

[0124] Based on the sample point set and the minimum oil film thickness corresponding to the sample point set, construct the true minimum oil film thickness limit state function data under the current oil film safety threshold.

[0125] Specifically, the true limit state function for minimum oil film thickness is obtained by approximating the response surface without cross terms using a quadratic response surface, through the following formula:

[0126] ;

[0127] in, The dimension representing the variables of uncertainty in the evidence. , , Represents the response surface coefficient. This represents the combined parameters measured by the evidence variables. This represents the true limiting state function for approximate minimum oil film thickness, and the... Let be a quadratic polynomial response surface function, and . The true limit state function for minimum oil film thickness The response surface, in which Representing the oil film safety threshold, the specific steps include collecting samples... Substituting the samples into the thermo-elastic fluid oil film iterative analytical model, we obtain the samples. Minimum oil film thickness Calculate the current oil film safety threshold. Limit state function value Combine sample X with the current oil film safety threshold. Limit state function value Substitute into the above formula to calculate the response surface coefficients. , , Thus, the true limit state function for minimum oil film thickness is obtained. Quadratic response surface without cross terms The function expression of the critical oil film thickness is greater than 3 times the surface roughness to avoid boundary lubrication, and when is determined is determined as lubrication failure.

[0128] Specifically, 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:

[0129] whether the sample collection center needs to be updated by judging the constant

[0130] The sample collection center is selectively updated by the following formula:

[0131]

[0132] wherein, represents the sample collection center, represents the mean of the probability metric combination parameter represents the mean of the probability metric combination parameter corresponding to the minimum oil film thickness true limit state function, represents the minimum oil film thickness true limit state function corresponding to represents the minimum oil film thickness true limit state function corresponding to represents the sample collection center of the first version, represents the sample collection center of the first version, is the sample collection center update process convergence judgment constant; wherein, the approximate reliability index and the approximate design point are obtained each time the sequence iteration is performed, the design checking point for transition is obtained, so that each iteration step realizes the update of the sample collection center through the design checking point obtained in the last step, and the sample collection center approximates the maximum possible failure focus of the minimum oil film thickness true limit state surface;

[0133] wherein, the approximate reliability index and the approximate design point are obtained each time the sequence iteration is performed, the design checking point for transition is obtained, so that each iteration step realizes the update of the sample collection center through the design checking point obtained in the last step, and the sample collection center approximates the maximum possible failure focus of the minimum oil film thickness true limit state surface;

[0134] The probability metric combination parameter corresponding to the reliability index is the design checking point , and the design checking point is mapped back to the evidence metric combination parameter;

[0135] and the focus corresponding to the evidence metric combination parameter is taken as the maximum possible failure focus . ​​​​​

[0136] Specifically, the response surface approximation minimum oil film thickness limit state function is solved in each iteration process and the mapping transformation method iteration conversion formula of step S102 is solved, and the approximate reliability index is obtained through each sequence 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 , so that 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.

[0137] Step S105: Based on the maximum possible failure focus element, determine the reliability data and the quasi-truth data of the wind power sliding bearing thermal elastohydrodynamic lubrication;

[0138] Specifically, the step S105 includes:

[0139] The reliability data and the quasi-truth data of the wind power sliding bearing thermal elastohydrodynamic lubrication are obtained by the following formula:

[0140] ;

[0141] Wherein, represents that the focus element is completely within the reliability domain, represents that the focus element is completely or partially located within the reliability 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;

[0142] 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 reliability domain .

[0143] Specifically, as shown in Figure 4 , obtaining the maximum value and the minimum value of the minimum oil film thickness true limit state function includes:

[0144] Performing extreme value analysis on the minimum oil film thickness true limit state function at each focus element to obtain the maximum value and the minimum value of the minimum oil film thickness true limit state function.

[0145] Specifically, when the focal element is on the lubrication reliable region , , the focal element is completely in the lubrication reliable region, and the basic credibility distribution of the focal element is , which takes into account and ; when the focal element is on the lubrication failure region , , the focal element is completely in the lubrication failure region, and the basic credibility distribution of the focal element is , which does not take into account or ; when the focal element is on the lubrication uncertain region , , the focal element is partially in the lubrication reliable region, and the basic credibility distribution of the focal element is , which only takes into account .

[0146] Step S106: changing the oil film safety threshold, and repeatedly performing the above steps S103-S105 to obtain the wind power sliding bearing thermal elastohydrodynamic lubrication reliability analysis result.

[0147] Specifically, the step S106 comprises:

[0148] changing the oil film safety threshold, and repeatedly performing the above steps S103-S105 to obtain a plurality of groups of credibility data and likelihood data of wind power sliding bearing thermal elastohydrodynamic lubrication;

[0149] based on the plurality of groups of credibility data and likelihood data of wind power sliding bearing thermal elastohydrodynamic lubrication, a plurality of groups of credibility data analysis combination coordinates and a plurality of groups of likelihood data analysis combination coordinates are obtained;

[0150] Specifically, the analysis combination coordinates are in the form of (current oil film safety threshold, credibility data of wind power sliding bearing thermal elastohydrodynamic lubrication), or (current oil film safety threshold, likelihood data of wind power sliding bearing thermal elastohydrodynamic lubrication);

[0151] 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 likelihood data analysis combination coordinates to obtain a cumulative likelihood function curve;

[0152] based on the cumulative credibility function curve and the cumulative likelihood 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.

[0153] ​Specifically, the uncertainty degree of the elastohydrodynamic lubrication reliability is determined by the size of the interval between the cumulative belief function curve and the cumulative plausibility function curve, if the interval is less than a preset distance, it is proved that the reliability degree is higher, otherwise, it is proved that the reliability degree is lower.

[0154] Based on the above steps S101-S106, the evidence measure combination parameter is converted into a probability measure combination parameter through a homogenization technique, so that the non-probabilistic characteristics of evidence theory are retained, while mature tools of probability theory are used for reliability analysis, thereby improving the accuracy of the reliability. 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. According to the current oil film safety threshold, a quadratic polynomial response surface function is constructed, 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 belief data and plausibility 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. Change the oil film safety threshold, and cyclically execute the above steps S103-S105 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.

[0155] Embodiment: In this embodiment, a sliding bearing in a 15MW wind power device selected from literature is taken as an example;

[0156] From Figure 5 It can be seen that T1 is the present embodiment, and T2 is the comparative embodiment. The temperature variation curves with rotational speed of the two embodiments show a highly consistent distribution trend, i.e., the temperature gradually increases with the input rotational 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 the present embodiment can accurately capture the dynamic evolution law of the temperature with rotational speed, and can effectively calculate the minimum oil film thickness.

[0157] Based on the performance of the thermal elastohydrodynamic lubrication reliability analysis of the wind turbine sliding bearing, the load [100, 450] KN and the dynamic viscosity [0.005, 0.3] N·s / m2 are set as the evidence measurement 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 with 6 and 8 subintervals.

[0158] Table 1 BPA structure of load and dynamic viscosity parameters with 6 and 8 subintervals

[0159]

[0160] Taking the BPA structure with 6 subintervals as an example, Table 2 shows the calculation process of the sample collection center update and the reliability index under the oil film safety threshold of 0.023 um based on the response surface and the reliability analysis of the sequence iteration mechanism. The iteration convergence error ε of the sample collection center is 1e-3. The load and dynamic viscosity parameters shown in Table 1 are converted into probability measurements, and the mean values are 275 KN and 1.52e-1 N·s / m2, respectively. The minimum oil film thickness is obtained by calling the thermal elastohydrodynamic oil film iterative analysis model, which is 5.19e-1 um. 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 approximate minimum oil film thickness limit state function quadratic polynomial response surface is constructed based on the 5 sample points. Based on the constructed response surface, the reliability index is solved by iterative mapping transformation method for 5 times .

[0161] As shown in the first iteration step in 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. 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), and the error is 3.64e-1, which is greater than the iteration convergence error 1e-3. The response surface is continuously updated.

[0162] As shown in Table 2, the error of the sample collection center after the third iteration is 3.67e-4, which is less than the iteration convergence error 1e-3, and meets the convergence requirement. Therefore, the mpp point obtained (load 272.60 KN, dynamic viscosity 5.27e-3 N·s / m2) is taken as the maximum possible failure focus area of the wind turbine sliding bearing in this embodiment.

[0163] Therefore, the approximate response surface has good approximation accuracy to the real minimum oil film thickness limit state surface at this time, and the reliability index can be obtained by a small number of sequence iteration times in the embodiment. As shown in Table 2, the entire iteration process only needs to call the thermal elastohydrodynamic oil film iteration analysis model 18 times, and therefore, the method has a faster convergence speed.

[0164] Table 2 Sample collection center update and reliability index of the sample under the oil film safety threshold of 0.023 um

[0165]

[0166] As Figure 6 shown, the sample collection center update and mpp point movement path of the BPA structure of the 6 subintervals and 8 subintervals 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 element (mpp point). Whether the BPA structure is 6 subintervals or 8 subintervals, the sample collection center always falls in the focus element with the maximum BPA and intersects with the limit state surface (i.e. the maximum possible failure focus element). Therefore, the method has self-adaptability to the BPA structure change of the parameters such as load and dynamic viscosity.

[0167] As Figure 7 shown in the left graph, a series of minimum oil film thickness limit state functions are obtained by changing the oil film safety threshold , and the confidence and plausibility of the thermal elastohydrodynamic lubrication of the wind power sliding bearing 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 thermal elastohydrodynamic lubrication of the wind power sliding bearing. Among them, by comparing the embodiment with the traditional evidence theory reliability analysis method, it can be seen that the error of the BPA structure of 8 subintervals is smaller than that of the BPA structure of 6 subintervals. Further, it is shown that 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 and small interval.

[0168] As Figure 7As shown in the right graph, the method of the embodiment and the traditional evidence theory reliability analysis method are compared under the structures of 6 and 8 sub-intervals BPA. Under the structure of 6 sub-intervals BPA at the first threshold value 0.023 um, the traditional evidence theory reliability analysis method needs to calculate 4*6*6=144 times of minimum oil film thickness (4 times of minimum oil film thickness calculation is required for one focus element in the extreme value analysis by interval analysis method), and the method of the embodiment only needs to calculate 18 times. Under the structure of 8 sub-intervals BPA, the traditional evidence theory reliability analysis method needs to calculate 4*8*8=256 times of minimum oil film thickness, and the method of the embodiment only needs to calculate 84 times. Therefore, the calculation efficiency of the method of the embodiment is generally higher than that of the traditional evidence theory reliability analysis method, especially when the number of sub-intervals contained is large.

[0169] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art can understand that, in order to achieve the effect of the present application, the different steps do not have to be executed in such an order, and they can be executed simultaneously (in parallel) or in other orders, and these changes are within the protection scope of the present application.

[0170] Those skilled in the art can understand that all or part of the processes in the method of the above embodiment can also be completed by a computer program instructing related hardware, 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.

[0171] 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 related technical features without departing from the principles of the present application, and the technical solutions after these 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: ； wherein represents a probability metric combination parameter, represents a probability metric combination parameter a stepwise probability density function, represents the total number of focal elements, and represent the upper and lower bounds, respectively, of a focal element , represents a basic confidence allocation function for each focal element, represents an indicator function, represents a norm of the probability metric combination parameter a reliability index is the shortest distance from the coordinate origin in the space of the probability metric combination parameter to the minimum oil film thickness limit state surface , represents a constraint condition; and, when , , 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 real limit state function corresponding to, representing a minimum oil film thickness real limit state function corresponding to, representing a sample collection center of the version, representing a sample collection center of the version, a sample collection center a judgment constant for the convergence of the update process; Wherein, the reliability index and the 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; and taking the focus element corresponding to the evidence metric combination parameter as the most likely failed focus element .

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 the 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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