CN118128714B

CN118128714B - Intelligent wind power gear box self-adaptive lubrication system and method based on temperature adaptability

Info

Publication number: CN118128714B
Application number: CN202410537794.9A
Authority: CN
Inventors: 牛永春; 周国贞; 杨芝刚; 周国龙
Original assignee: Nanjing Xunlian Hydraulic Technology Co ltd
Current assignee: Nanjing Xunlian Hydraulic Technology Co ltd
Priority date: 2024-04-30
Filing date: 2024-04-30
Publication date: 2024-07-05
Anticipated expiration: 2044-04-30
Also published as: CN118128714A

Abstract

The invention discloses an intelligent wind power gear box self-adaptive lubrication system and method based on temperature adaptability, and particularly relates to the technical field of wind power gear boxes, wherein the system comprises the steps of acquiring a first operation data set of a wind power gear box, and training a lubricating oil training model for acquiring initial oil temperature and initial oil quantity of lubricating oil based on the first operation data set; the first operation data set comprises operation characteristic data of the wind power gear box and first lubricating oil data corresponding to the operation characteristic data, and the first lubricating oil data comprises initial oil temperature and initial oil quantity; according to the invention, details of a starting stage are divided, and the oil quantity and the supply rate of lubricating oil are changed along with continuous operation of the wind power gear box, so that effective lubrication and cooling of the lubricating oil in the gear box are ensured, and therefore, the self-adaptive lubrication of the lubricating oil is accurately regulated to achieve the optimal lubrication effect of the wind power gear box, the working efficiency of the wind power gear box is improved, and the service life of equipment is prolonged.

Description

Intelligent wind power gear box self-adaptive lubrication system and method based on temperature adaptability

Technical Field

The invention relates to the technical field of wind power gearboxes, in particular to an intelligent wind power gearbox self-adaptive lubrication system and method based on temperature adaptability.

Background

The wind power gear box is an important mechanical component in a wind generating set, and mainly transmits power generated by a wind wheel under the action of wind power to a generator to obtain corresponding rotating speed; in order to ensure that the wind power gear box can be normally started in a low-temperature environment, the operation and lubrication state of the wind power gear box need to be monitored and maintained; the traditional wind power gear box lubrication system generally adopts lubricating oil with fixed parameters, and the parameters of the lubricating oil cannot be dynamically adjusted according to actual running temperature changes, so that in the starting process of the wind power gear box, more starting force is needed to overcome the resistance of oil lubrication during starting due to the increase of the viscosity of the lubricating oil, and the surfaces of gears and bearings in the wind power gear box are not easy to cover, so that friction and abrasion between the gears and the bearings are increased; the viscosity of the lubricating oil is reduced by heating the lubricating oil in the prior art, so that the lubricating effect of the wind power gear box is realized;

For example, chinese patent publication No. CN108087534a discloses a temperature-adaptive wind power gear box lubrication system, and although the above method can improve the lubrication effect of the wind power gear box to a certain extent, researches and applications of the inventor find that the above method and the prior art lack of detailed division of the starting stage, and cannot obtain the self-adaptive lubrication of the lubricating oil from the starting stage to the normal operation stage, so that the best lubrication effect is not achieved.

Therefore, the invention provides an intelligent wind power gear box self-adaptive lubrication system and method based on temperature adaptability.

Disclosure of Invention

In order to overcome the defects in the prior art, the invention provides an intelligent wind power gear box self-adaptive lubrication system and method based on temperature adaptability, so as to solve the problems in the background art.

In order to achieve the above purpose, the present invention provides the following technical solutions: an intelligent wind power gear box self-adaptive lubrication method based on temperature adaptability comprises the following steps:

Step 1: acquiring a first operation data set of the wind power gear box, and training a lubricating oil training model for acquiring initial oil temperature and initial oil quantity of lubricating oil based on the first operation data set; the first operation data set comprises operation characteristic data of the wind power gear box and first lubricating oil data corresponding to the operation characteristic data, the operation characteristic data of the wind power gear box comprise operation temperature of the wind power gear box, rotating speed of the wind power gear box and damping evaluation coefficients, and the first lubricating oil data comprise initial oil temperature and initial oil quantity;

Step 2: acquiring a second operation data set of the wind power gear box, and training a lubricating oil adjusting model for acquiring lubricating adjusting data based on the second operation data set; the second operation data set comprises lubricating oil characteristic data and lubricating adjustment data corresponding to the lubricating oil characteristic data; the lubrication oil characteristic data includes an initial oil amount and an initial supply rate of the lubrication oil; the lubrication regulation data comprises a top grade flow regulation quantity and a top grade supply rate regulation quantity of the lubricating oil;

Step 3: acquiring operation characteristic data of the wind power gear box, analyzing the operation characteristic data by utilizing a lubricating oil training model, and determining initial oil temperature and initial oil quantity of lubricating oil of the wind power gear box in a starting stage; regulating the lubricating oil to a first standard viscosity according to the initial oil temperature and the initial oil quantity;

Step 4: when the lubricating oil reaches the first standard viscosity, obtaining actual measurement characteristic data of the lubricating oil, and analyzing the actual measurement characteristic data by utilizing a lubricating oil adjusting model to obtain lubricating adjusting data of the lubricating oil; the measured characteristic data includes an initial oil amount and an initial supply rate;

Step 5: and controlling an oil pump connected with the wind power gear box according to the lubrication adjustment data, and adjusting the lubrication oil in the wind power gear box to a second standard viscosity so that the wind power gear box smoothly enters a normal operation stage.

Further, the generation logic of the damping evaluation coefficient is as follows:

Extracting torque, vibration acceleration and vibration frequency of the wind power gear box, and respectively marking the torque, the vibration acceleration and the vibration frequency as 、And；

Dimensionless calculation is carried out on the torque, the vibration acceleration and the vibration frequency of the wind power gear box so as to determine a damping evaluation coefficient; the calculation formula is as follows: ; wherein: represents the damping evaluation coefficient of the wind power gear box, A torque correction factor representing a wind power gear box,Representing the vibration acceleration correction factor of the wind power gearbox,Represents the vibration frequency correction factor of the wind power gear box, and、AndAre all greater than zero.

Further, the logic for determining the initial oil temperature and the initial oil amount is as follows:

a1: the test lubricating oil with the r capacity is adjusted to the w oil temperature and then is injected into the wind power gear box; r and w are integers greater than zero;

a2: in the starting stage, the rotating speed of the wind power gear box under the w-th oil temperature is obtained, the difference value between the rotating speed and the standard rotating speed is calculated, and whether the difference value between the rotating speed and the standard rotating speed is in a preset difference value interval or not is judged; if the difference value is within the preset difference value interval, jumping to the step a3; if not, let w=w+n and jump back to step a1, n being a constant greater than zero;

a3: acquiring an oil pressure value of the test lubricating oil at the w-th oil temperature, calculating a difference value between the oil pressure value and a preset oil pressure value, and judging whether the difference value between the oil pressure value and the preset oil pressure value is zero or not; if not, let w=w+n and jump back to step a2; if the oil quantity is zero, taking the w oil temperature as an initial oil temperature, taking oil quantity data at the w oil temperature as an initial oil quantity, and returning r=r+1 to the step a1;

a4: repeating the steps a1 to a3 until r=R, obtaining first lubricating oil data of all the test lubricating oils, ending the circulation, wherein R is the preset total capacity of the test lubricating oils in the wind power gear box.

Further, the generation logic of the lubricating oil training model is as follows:

Acquiring a first operation data set, dividing the first operation data set into a first training set and a first test set, constructing a machine learning model, taking the operation temperature, the rotation speed and the damping evaluation coefficient of a wind power gear box in the first training set as input data of the machine learning model, taking the initial oil temperature and the initial oil quantity in the first training set as output data of the machine learning model, training the machine learning model to obtain a first machine learning model, verifying the test accuracy of the first machine learning model by using the first test set, and outputting a first machine learning model with the test accuracy greater than the preset test accuracy as a lubricating oil training model; the first machine learning model is specifically one of a decision tree model, a random forest model, a support vector machine model, a linear model or a neural network model.

Further, the lubrication adjustment data determination logic is as follows:

When the test lubricating oil is in the first standard viscosity, obtaining lubricating oil characteristic data of the test lubricating oil, and obtaining a lubricating evaluation coefficient of the test lubricating oil corresponding to the lubricating oil characteristic data; the lubrication evaluation coefficients of the test lubricating oil corresponding to the lubricating oil characteristic data comprise lubrication evaluation coefficients of the test lubricating oil corresponding to the initial oil quantity and lubrication evaluation coefficients of the test lubricating oil corresponding to the initial supply rate;

performing first data analysis based on the initial oil quantity and the lubrication evaluation coefficient to obtain a priority flow adjustment quantity;

and performing second parameter analysis based on the initial supply rate, the lubrication evaluation coefficient and the optimal flow adjustment amount of the lubricating oil to obtain the optimal supply rate adjustment amount of the lubricating oil.

Further, the method for performing a first data analysis based on the initial oil amount and the lubrication evaluation coefficient includes:

b1: acquiring a lubrication evaluation coefficient of the corresponding test lubricating oil under the capacity e, and acquiring a lubrication evaluation coefficient of the corresponding test lubricating oil under the capacity e+f; e is an integer greater than zero, the initial value of e is the initial oil quantity, and f is a constant integer greater than zero;

b2: taking the lubrication evaluation coefficient of the corresponding test lubricating oil under the capacity e as a first lubrication evaluation coefficient, and taking the lubrication evaluation coefficient of the corresponding test lubricating oil under the capacity e+f as a second lubrication evaluation coefficient;

b3: comparing the first lubrication evaluation coefficient with the second lubrication evaluation coefficient, and judging whether the first lubrication evaluation coefficient is larger than or equal to the second lubrication evaluation coefficient; if less, let e=e+f and return to step b1; if the first lubrication evaluation coefficient is larger than or equal to the first lubrication evaluation coefficient, acquiring a capacity e corresponding to the first lubrication evaluation coefficient;

b4: calculating the difference between the capacity e and the initial oil quantity to obtain an oil quantity regulation difference, and taking the oil quantity regulation difference as the optimal flow regulation quantity of the lubricating oil;

b5: repeating the steps b 1-b 3 until the first lubrication evaluation coefficient is greater than or equal to the second lubrication evaluation coefficient, and ending the cycle.

Further, the method of performing the second parameter analysis based on the initial supply rate, the lubrication evaluation coefficient, and the priority flow adjustment amount of the lubricating oil includes:

c1: acquiring a lubrication evaluation coefficient of the test lubricating oil corresponding to the high-grade flow regulating quantity and the h speed of the lubricating oil; obtaining a lubrication evaluation coefficient of the corresponding test lubricating oil at the h+u speed; h is an integer greater than zero, the initial value of h is the initial feed rate, and u is a constant integer greater than zero;

c2: taking the lubrication evaluation coefficient of the test lubricating oil corresponding to the high-grade flow regulating quantity and the h speed of the lubricating oil as a third lubrication evaluation coefficient; taking the lubrication evaluation coefficient of the corresponding test lubricating oil at the h+u speed as a fourth lubrication evaluation coefficient;

c3: comparing the third lubrication evaluation coefficient with the fourth lubrication evaluation coefficient, and judging whether the third lubrication evaluation coefficient is larger than or equal to the fourth lubrication evaluation coefficient; if less, let h=h+u and return to step c1; if the speed h is greater than or equal to the speed h corresponding to the third lubrication evaluation coefficient is obtained;

c4: calculating the difference between the rate h and the initial supply rate to obtain a supply rate difference, and taking the supply rate difference as a high-grade supply rate adjustment quantity of the lubricating oil;

c5: repeating the steps c1 to c3 until the third lubrication evaluation coefficient is greater than or equal to the fourth lubrication evaluation coefficient, and ending the cycle.

Further, the method for calculating the lubrication evaluation coefficient includes:

；

In the method, in the process of the invention, For the purpose of lubrication evaluation of the coefficients,For the initial oil temperature of the lubricating oil,Is the oil pressure value of the lubricating oil,Is the operation state value of the wind power gear box,AndIs a preset weight;

The running state values of the wind power gear boxes are preset, different values are set according to the opening state or the closing state of the wind power gear boxes, the running state value corresponding to the wind power gear boxes in the opening state is set to be 1, and the running state value corresponding to the wind power gear boxes in the closing state is set to be 0.

Further, the method for generating the lubricating oil adjusting model comprises the following steps: acquiring a second operation data set, dividing the second operation data set into a second training set and a second testing set, constructing a machine learning model, taking initial oil quantity and initial supply rate in the second-stage training set as input data of the machine learning model, taking the optimal flow regulating quantity and the optimal supply rate regulating quantity of lubricating oil in the second-stage training set as output data of the machine learning model, training the machine learning model to obtain a second machine learning model, testing accuracy verification on the second machine learning model by utilizing the second-stage testing set, and outputting a second machine learning model with the accuracy greater than preset testing accuracy as a lubricating oil regulating model; the second machine learning model is specifically one of a decision tree model, a random forest model, a support vector machine model, a linear model or a neural network model.

In a second aspect, the invention provides an intelligent wind power gear box self-adaptive lubrication system based on temperature adaptability, which is used for implementing the intelligent wind power gear box self-adaptive lubrication method based on temperature adaptability, and comprises the following steps:

the first model training module is used for acquiring a first operation data set of the wind power gear box, and training a lubricating oil training model for acquiring initial oil temperature and initial oil quantity of lubricating oil based on the first operation data set; the first operation data set comprises operation characteristic data of the wind power gear box and first lubricating oil data corresponding to the operation characteristic data, the operation characteristic data of the wind power gear box comprise operation temperature of the wind power gear box, rotating speed of the wind power gear box and damping evaluation coefficients, and the first lubricating oil data comprise initial oil temperature and initial oil quantity;

the second model training module is used for acquiring a second operation data set of the wind power gear box and training a lubricating oil adjusting model for acquiring lubricating adjusting data based on the second operation data set; the second operation data set comprises lubricating oil characteristic data and lubricating adjustment data corresponding to the lubricating oil characteristic data; the lubrication oil characteristic data includes an initial oil amount and an initial supply rate of the lubrication oil; the lubrication regulation data comprises a top grade flow regulation quantity and a top grade supply rate regulation quantity of the lubricating oil;

the data analysis module is used for acquiring operation characteristic data of the wind power gear box, analyzing the operation characteristic data by utilizing the lubricating oil training model and determining the initial oil temperature and the initial oil quantity of lubricating oil of the wind power gear box in a starting stage; regulating the lubricating oil to a first standard viscosity according to the initial oil temperature and the initial oil quantity;

The depth analysis module is used for acquiring actual measurement characteristic data of the lubricating oil when the lubricating oil reaches the first standard viscosity, and analyzing the actual measurement characteristic data by utilizing the lubricating oil adjusting model to acquire lubricating adjusting data of the lubricating oil; the measured characteristic data includes an initial oil amount and an initial supply rate;

and the lubrication optimization module is used for controlling an oil pump connected with the wind power gear box according to the lubrication adjustment data, and adjusting the lubrication oil in the wind power gear box to a second standard viscosity so that the wind power gear box smoothly enters a normal operation stage.

In a third aspect, the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;

and the processor executes the intelligent wind power gear box self-adaptive lubrication method based on temperature adaptability by calling the computer program stored in the memory.

In a fourth aspect, the present invention provides a computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described temperature-adaptive-based intelligent wind power gearbox adaptive lubrication method.

The invention has the technical effects and advantages that:

1. According to the method, a first operation data set of the wind power gear box is obtained, and a lubricating oil training model for obtaining initial oil temperature and initial oil quantity of lubricating oil is trained based on the first operation data set; acquiring a second operation data set of the wind power gear box, and training a lubricating oil adjusting model for acquiring lubricating adjusting data based on the second operation data set; acquiring operation characteristic data of the wind power gear box, analyzing the operation characteristic data by utilizing a lubricating oil training model, and determining initial oil temperature and initial oil quantity of lubricating oil of the wind power gear box in a starting stage; regulating the lubricating oil to a first standard viscosity according to the initial oil temperature and the initial oil quantity; when the lubricating oil reaches the first standard viscosity, obtaining actual measurement characteristic data of the lubricating oil, and analyzing the actual measurement characteristic data by utilizing a lubricating oil adjusting model to obtain lubricating adjusting data of the lubricating oil; the measured characteristic data includes an initial oil amount and an initial supply rate; controlling an oil pump connected with the wind power gear box according to the lubrication adjustment data, and adjusting the lubrication oil in the wind power gear box to a second standard viscosity so that the wind power gear box smoothly enters a normal operation stage; according to the application, through continuous experiments and analysis of the steps, proper viscosity of the lubricating oil can be maintained under different lubricating oil capacities and oil temperatures, so that good lubrication between gears and bearing components in the wind power gear box is ensured.

2. According to the invention, the starting stage is divided in detail, and the oil quantity and the supply rate of the lubricating oil are changed along with the continuous operation of the wind power gear box, so that the effective lubrication and cooling of the lubricating oil in the gear box are ensured, the self-adaptive lubrication of the lubricating oil is accurately regulated to achieve the optimal lubrication effect of the wind power gear box, the working efficiency of the wind power gear box is improved, and the service life of equipment is prolonged.

Drawings

FIG. 1 is a flow chart of the method of example 1;

FIG. 2 is a schematic diagram of the system of example 2;

FIG. 3 is a schematic diagram of an electronic device according to embodiment 3;

Fig. 4 is a schematic diagram of a computer readable storage medium according to embodiment 4.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and a similar second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

Example 1

Referring to fig. 1, the embodiment discloses that an intelligent wind power gear box self-adaptive lubrication method based on temperature adaptability is provided:

Step 1: acquiring a first operation data set of the wind power gear box, and training a lubricating oil training model for acquiring initial oil temperature and initial oil quantity of lubricating oil based on the first operation data set;

it should be appreciated that: the invention is applied to a wind power gear box, which comprises three operation phases, namely a starting phase, a normal operation phase and a high load phase; in the starting stage, as the gears and bearings in the wind power gear box do not reach the normal running temperature yet, the wind power gear box is lubricated by presetting initial oil temperature and initial oil quantity so as to ensure that a good lubricating effect is realized in a low-temperature environment; in the normal operation stage, the operation of the wind power gear box reaches a stable state, and meanwhile, the viscosity and the temperature of the lubricating oil reach an equilibrium state; in the high load phase, the lubricating oil needs to have high compression and wear resistance to cope with the high pressure and high friction force generated by the gear box under the high load condition. The self-adaptive lubrication optimization problem of the wind power gear box in the low-temperature environment in the starting stage is mainly solved, so that the self-adaptive lubrication problem in the normal operation stage and the high-load stage is not important, and the self-adaptive lubrication optimization method is not repeated;

Specifically, the first operation data set comprises operation characteristic data of the wind power gear box and first lubricating oil data corresponding to the operation characteristic data, the operation characteristic data of the wind power gear box comprise operation temperature of the wind power gear box, rotating speed of the wind power gear box and damping evaluation coefficients, and the first lubricating oil data comprise initial oil temperature and initial oil quantity;

Wherein, the generation logic of the damping evaluation coefficient is as follows:

Dimensionless calculation is carried out on the torque, the vibration acceleration and the vibration frequency of the wind power gear box so as to determine a damping evaluation coefficient; the calculation formula is as follows: ; wherein: represents the damping evaluation coefficient of the wind power gear box, A torque correction factor representing a wind power gear box,Representing the vibration acceleration correction factor of the wind power gearbox,Represents the vibration frequency correction factor of the wind power gear box, and、AndAre all greater than zero;

It should be appreciated that: the torque of the wind power gear box is the torque transmitted by the wind power gear box and is obtained through a torque sensor arranged on the wind power gear box; the vibration acceleration is obtained through an acceleration sensor, and the vibration frequency is obtained through a vibration sensor; the torque, the vibration acceleration and the vibration frequency of the wind power gear box are all stored in a system database; the larger the value of the damping evaluation coefficient is, the larger the running resistance of the corresponding wind power gear box is, and further, the higher the lubricating data requirement of the corresponding lubricating oil is;

specifically, the logic for determining the initial oil temperature and the initial oil amount is as follows:

It should be noted that: the rotating speed is acquired based on a rotating speed sensor of a pre-installed wind power gear box; the constant integer constant can be determined manually;

it should be noted that: the oil pressure value is calculated based on any prior art, for example, the oil pressure is measured by using an oil pressure sensor or an on-line oil monitoring device;

a4: repeating the steps a1 to a3 until r=R, obtaining first lubricating oil data of all the test lubricating oils, ending the circulation, wherein R is the preset total capacity of the test lubricating oils in the wind power gear box;

It should be appreciated that: the preset oil pressure threshold value is obtained by collecting oil pressure values for a plurality of times by a worker in the historical normal working stage of the two oil pressure sensors respectively, namely, collecting a plurality of groups of oil pressure values, wherein one group of oil pressure values comprises two oil pressure values which are collected by the two oil pressure sensors respectively; calculating an average value of oil pressure values among a plurality of groups of oil pressure values, and taking the average value of the oil pressure values as a preset oil pressure threshold value;

In an implementation, the generation logic of the lubricating oil training model is: acquiring a first operation data set, dividing the first operation data set into a first training set and a first test set, constructing a machine learning model, taking the operation temperature, the rotation speed and the damping evaluation coefficient of a wind power gear box in the first training set as input data of the machine learning model, taking the initial oil temperature and the initial oil quantity in the first training set as output data of the machine learning model, training the machine learning model to obtain a first machine learning model, verifying the test accuracy of the first machine learning model by using the first test set, and outputting a first machine learning model with the test accuracy greater than the preset test accuracy as a lubricating oil training model;

the first machine learning model is specifically one of a decision tree model, a random forest model, a support vector machine model, a linear model or a neural network model;

it should be noted that: according to the method, through continuous testing and adjustment, a lubricating oil training model for acquiring the initial oil temperature and the initial oil quantity of lubricating oil is trained in the starting stage of the wind power gear box, so that good lubricating effect of the wind power gear box under different running oil temperatures and oil quantities is ensured.

it should be noted that: the initial supply rate is a constant value, is obtained by manual experiment simulation and is pre-stored in a system database, and the invention is not repeated;

it should be appreciated that: the high-grade flow regulating quantity of the lubricating oil is the oil level in the lubricating oil tank, and the proper quantity of the lubricating oil can ensure that the lubricating oil can fully cover a lubricating area in the wind power gear box in the starting stage and form a sufficient lubricating film to protect parts such as gears, bearings and the like;

The optimal supply rate adjustment amount mainly affects the fluidity of the lubricating oil, and an appropriate supply rate can ensure that the lubricating oil can flow to a portion to be lubricated at an appropriate speed in a start-up stage, and form a good lubricating film to reduce friction and wear.

Wherein the lubrication adjustment data has a determination logic as follows:

when the test lubricating oil is in the first standard viscosity, obtaining lubricating oil characteristic data of the test lubricating oil, and obtaining a lubricating evaluation coefficient of the test lubricating oil corresponding to the lubricating oil characteristic data;

specifically, the lubrication evaluation coefficients of the test lubricating oil corresponding to the lubricating oil characteristic data comprise the lubrication evaluation coefficients of the test lubricating oil corresponding to the initial oil quantity and the lubrication evaluation coefficients of the test lubricating oil corresponding to the initial supply rate;

specifically, the method for performing the first data analysis based on the initial oil amount and the lubrication evaluation coefficient includes:

the method for calculating the lubrication evaluation coefficient comprises the following steps:

；

Wherein the preset weight is obtained by the person skilled in the art, a plurality of groups of comprehensive parameters are collected, corresponding weights are set for each group of comprehensive parameters, the preset weight and the collected comprehensive parameters are substituted into a formula, any two formulas form a binary one-time equation set, the calculated weights are filtered and averaged to obtain AndIs a value of (2);

It should be appreciated that the lubrication evaluation coefficient is only used for judging whether the lubrication condition of the wind power gear box reaches the optimal state, so the calculation of the lubrication evaluation coefficient is a dimensionality removal calculation;

The method comprises the steps that the running state value of a wind power gear box is preset, different values are set according to the opening state or the closing state of the wind power gear box, the running state value corresponding to the wind power gear box in the opening state is set to be 1, and the running state value corresponding to the wind power gear box in the closing state is set to be 0;

b5: repeating the steps b 1-b 3 until the first lubrication evaluation coefficient is greater than or equal to the second lubrication evaluation coefficient, and ending the cycle;

performing second parameter analysis based on the initial supply rate, the lubrication evaluation coefficient and the optimal flow adjustment amount of the lubricating oil to obtain the optimal supply rate adjustment amount of the lubricating oil;

specifically, the method for performing the second parameter analysis based on the initial supply rate, the lubrication evaluation coefficient, and the priority flow adjustment amount of the lubricating oil includes:

c5: repeating the steps c1 to c3 until the third lubrication evaluation coefficient is greater than or equal to the fourth lubrication evaluation coefficient, and ending the cycle;

In an implementation, a method for generating a lubrication oil conditioning model includes: acquiring a second operation data set, dividing the second operation data set into a second training set and a second testing set, constructing a machine learning model, taking initial oil quantity and initial supply rate in the second-stage training set as input data of the machine learning model, taking the optimal flow regulating quantity and the optimal supply rate regulating quantity of lubricating oil in the second-stage training set as output data of the machine learning model, training the machine learning model to obtain a second machine learning model, testing accuracy verification on the second machine learning model by utilizing the second-stage testing set, and outputting a second machine learning model with the accuracy greater than preset testing accuracy as a lubricating oil regulating model;

the second machine learning model is specifically one of a decision tree model, a random forest model, a support vector machine model, a linear model or a neural network model;

It should be appreciated that: in the initial stage of the starting stage of the wind power gear box, because the damping evaluation coefficient of the wind power gear box is maximum, if more oil quantity of lubricating oil is injected or the oil temperature is too low, the viscosity of the lubricating oil in the wind power gear box is increased and the lubricating oil is unevenly distributed, and the increase and the unevenly distributed lubricating oil can cause abrasion to gears and bearings in the wind power gear box;

In this regard, the invention divides the starting phase to the normal operation phase into three phases according to the first standard viscosity and the second standard viscosity, which are respectively a first starting phase, a second starting phase and a third starting phase; the first starting period is a period when the lubricating oil is from an un-started state to the first standard viscosity, and the second starting period is a period when the lubricating oil is from the first standard viscosity to the second standard viscosity; the third starting period is when the lubricating oil reaches the optimal lubrication state;

the method comprises the steps of determining initial oil temperature and initial oil quantity in a starting stage by using a lubricating oil training model in a first starting stage, and adjusting the lubricating oil to a first standard viscosity according to the initial oil temperature and the initial oil quantity; therefore, the smooth operation of the wind power gear box in the starting stage can be ensured, and the abrasion impact between the gears and the bearing components in the wind power gear box is reduced to the maximum extent;

it should be appreciated that: the oil mass and the feed rate of the lubricating oil are the preconditions for determining the normal operation of the wind power gearbox;

It should be noted that: with reference to the above description, the initial oil amount is obtained by analysis of a lubricating oil training model, the supply rate is a constant value, and the initial oil amount is obtained by system inquiry or manually adjusting a flowmeter of the oil pump;

Also to be described is: analyzing the measured characteristic data by using a lubricating oil adjusting model to obtain lubricating adjusting data of the wind power gear box, wherein the step is to self-adaptively adjust the lubricating oil in the second starting period, and further explain that after the wind power gear box enters the second starting period from the first starting period, the lubricating evaluation coefficient of the wind power gear box is in a preset threshold value, and meanwhile, the wind power gear box needs to be prevented from entering a normal operation stage too late so as to prevent the lubricating oil from not reaching an optimal lubricating state in the starting period and affecting the operation efficiency of the subsequent stages (a normal operation stage and a high load stage);

Step 5: controlling an oil pump connected with the wind power gear box according to the lubrication adjustment data, and adjusting the lubrication oil in the wind power gear box to a second standard viscosity so that the wind power gear box smoothly enters a normal operation stage;

Specifically, the first standard viscosity is smaller than the second standard viscosity; the method is further explained that in the first starting period, the damping evaluation coefficient of gears and bearings in the wind power gear box is larger, so that the wind power gear box runs smoothly due to the first standard viscosity of lower lubricating oil, when the wind power gear box enters the second starting period, the running temperature of the wind power gear box in the second starting period gradually rises, in order to avoid damage to an oil film of the lubricating oil due to overhigh running temperature, the second standard viscosity is higher than the first standard viscosity, and in the starting period, the general viscosity trend of the lubricating oil in the period tends to rise (the viscosity value is from small to large), so that the first standard viscosity cannot be higher than the second standard viscosity, and when the wind power gear box enters the normal running period, the oil quantity and the supply rate start to be stable at the moment along with the running requirement of the wind power gear box, but the lubrication optimization problem in the normal running period is not solved by the method, and the method is not repeated;

It should be noted that: the first standard viscosity and the second standard viscosity are used for obtaining a standard viscosity data set and calculating quartiles of the lubricating oil according to experimental data of different wind power gear boxes, and the second quartiles and the third quartiles in the standard viscosity data set are used as the first standard viscosity and the second standard viscosity respectively; or threshold calculation setting is carried out on the standard viscosity data set, the calculation process is the prior art, and the threshold calculation setting is not limited excessively.

The first standard viscosity and the second standard viscosity are disclosed:

The first standard viscosity and the second standard viscosity are set as follows:

In the method, in the process of the invention, Is the first quartile of the standard viscosity,The third quartile of the standard viscosity,Is a quartile-space of the four-dimensional space,Is the standard viscosity average value of the mixture,Is the maximum value of the standard viscosity,Standard viscosity minimum.

According to the embodiment, a first operation data set of the wind power gear box is obtained, and a lubricating oil training model for obtaining initial oil temperature and initial oil quantity of lubricating oil is trained based on the first operation data set; acquiring a second operation data set of the wind power gear box, and training a lubricating oil adjusting model for acquiring lubricating adjusting data based on the second operation data set; acquiring operation characteristic data of the wind power gear box, analyzing the operation characteristic data by utilizing a lubricating oil training model, and determining initial oil temperature and initial oil quantity of lubricating oil of the wind power gear box in a starting stage; regulating the lubricating oil to a first standard viscosity according to the initial oil temperature and the initial oil quantity; when the lubricating oil reaches the first standard viscosity, obtaining actual measurement characteristic data of the lubricating oil, and analyzing the actual measurement characteristic data by utilizing a lubricating oil adjusting model to obtain lubricating adjusting data of the lubricating oil; the measured characteristic data includes an initial oil amount and an initial supply rate; controlling an oil pump connected with the wind power gear box according to the lubrication adjustment data, and adjusting the lubrication oil in the wind power gear box to a second standard viscosity so that the wind power gear box smoothly enters a normal operation stage; according to the application, through continuous experiments and analysis of the steps, proper viscosity of the lubricating oil can be maintained under different lubricating oil capacities and oil temperatures, so that good lubrication between gears and bearing components in the wind power gear box is ensured.

According to the embodiment, details of the starting stage are divided, the oil quantity and the supply rate of the lubricating oil are changed along with continuous operation of the wind power gear box, and effective lubrication and cooling of the lubricating oil in the gear box are further ensured, so that the self-adaptive lubrication of the lubricating oil is accurately regulated to achieve the optimal lubrication effect of the wind power gear box, the working efficiency of the wind power gear box is improved, and the service life of equipment is prolonged.

Example 2

Referring to fig. 2, the embodiment provides an intelligent wind power gear box self-adaptive lubrication system based on temperature adaptability, which includes:

Example 3

Referring to fig. 3, the present embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program for the processor to call;

The processor executes the intelligent wind power gear box self-adaptive lubrication method based on temperature adaptability of the embodiment 1 by calling the computer program stored in the memory.

Example 4

Referring to fig. 4, the present embodiment provides a computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the intelligent wind power gearbox adaptive lubrication method based on temperature adaptability of embodiment 1.

The formulas related in the above are all formulas with dimensions removed and numerical values calculated, and are a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and weight factors in the formulas and various preset thresholds in the analysis process are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data; the size of the weight factor is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the weight factor depends on the number of sample data and the corresponding processing coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected.

The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, from one website site, computer, server, or data center over a wired network. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims

1. The intelligent wind power gear box self-adaptive lubrication method based on temperature adaptability is characterized by comprising the following steps of:

the first operation data set comprises operation characteristic data of the wind power gear box and first lubricating oil data corresponding to the operation characteristic data, the operation characteristic data of the wind power gear box comprise operation temperature of the wind power gear box, rotating speed of the wind power gear box and damping evaluation coefficients, and the first lubricating oil data comprise initial oil temperature and initial oil quantity;

The generation logic of the damping evaluation coefficient is as follows:

Step 2: acquiring a second operation data set of the wind power gear box, and training a lubricating oil adjusting model for acquiring lubricating adjusting data based on the second operation data set;

the second operation data set comprises lubricating oil characteristic data and lubricating adjustment data corresponding to the lubricating oil characteristic data; the lubrication oil characteristic data includes an initial oil amount and an initial supply rate of the lubrication oil; the lubrication regulation data comprises a top grade flow regulation quantity and a top grade supply rate regulation quantity of the lubricating oil;

2. The intelligent wind power gear box self-adaptive lubrication method based on temperature adaptability according to claim 1, wherein the determination logic of the initial oil temperature and the initial oil quantity is as follows:

3. The intelligent wind power gear box self-adaptive lubrication method based on temperature adaptability according to claim 2, wherein the generation logic of the lubricating oil training model is as follows:

4. The intelligent wind power gear box self-adaptive lubrication method based on temperature adaptability according to claim 3, wherein the determination logic of the lubrication adjustment data is as follows:

；

5. The intelligent wind power gear box self-adaptive lubrication method based on temperature adaptability according to claim 4, wherein the method for performing the first data analysis based on the initial oil quantity and the lubrication evaluation coefficient comprises the following steps:

6. The temperature-adaptive intelligent wind power gear box self-adaptive lubrication method according to claim 5, wherein the method for performing the second parameter analysis based on the initial supply rate, the lubrication evaluation coefficient and the optimal flow adjustment amount of the lubricating oil comprises the following steps:

7. The intelligent wind power gear box self-adaptive lubrication method based on temperature adaptability according to claim 6, wherein the generation method of the lubricating oil adjustment model comprises the following steps: acquiring a second operation data set, dividing the second operation data set into a second training set and a second testing set, constructing a machine learning model, taking initial oil quantity and initial supply rate in the second-stage training set as input data of the machine learning model, taking the optimal flow regulating quantity and the optimal supply rate regulating quantity of lubricating oil in the second-stage training set as output data of the machine learning model, training the machine learning model to obtain a second machine learning model, testing accuracy verification on the second machine learning model by utilizing the second-stage testing set, and outputting a second machine learning model with the accuracy greater than preset testing accuracy as a lubricating oil regulating model; the second machine learning model is specifically one of a decision tree model, a random forest model, a support vector machine model, a linear model or a neural network model.

8. Temperature-adaptive-based intelligent wind power gear box self-adaptive lubrication system for implementing the temperature-adaptive-based intelligent wind power gear box self-adaptive lubrication method as claimed in any one of claims 1 to 7, comprising:

9. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;

the processor performs the intelligent wind power gearbox adaptive lubrication method based on temperature adaptation of any one of claims 1-7 by invoking a computer program stored in the memory.

10. A computer readable storage medium, characterized in that instructions are stored which, when run on a computer, cause the computer to perform the temperature-adaptive intelligent wind power gearbox adaptive lubrication method according to any of claims 1-7.