CN111126685B - Method for establishing engine lubricating oil quality prediction model - Google Patents
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Abstract
The invention discloses a method for establishing an engine lubricating oil quality prediction model, which selects the initial oxidation temperature as a lubricating oil quality reference index, selects N motor vehicles running under the conventional urban working condition as test objects, carries out long-time running condition and lubricating oil performance tracking monitoring, periodically samples, and at least records the type, total running mileage, total running time and the initial oxidation temperature of a sampled oil sample; the method comprises the steps of selecting an exponential function to fit an independent variable and a dependent variable, performing linear transformation on the exponential function, introducing an intermediate variable, performing multiple linear regression analysis on the intermediate variable, the running mileage and the running time, and completing the establishment of an engine lubricating oil quality prediction model.
Description
Technical Field
The invention belongs to the technical field of lubricating oil, relates to a technology for judging the quality of engine lubricating oil, and particularly relates to a method for establishing a model for predicting the quality of the engine lubricating oil.
Background
The lubricating oil for vehicles mainly refers to engine lubricating oil, and is the key for keeping the engine running reliably, reducing the friction energy loss of the engine and preventing the early abrasion of engine parts. Oil changes are also the most frequent item in vehicle maintenance. According to the requirements of 4S stores and automobile vendors, the oil change period commonly used by civil cars in China is basically 5000km or 6 months for driving. The method is characterized in that the recommended oil change periods of 278 types of civil cars of different brands in the domestic market at present are counted, about 56.1% of the civil cars recommend 5000km for engine oil change, and among 109 types of civil cars of different brands, 78 types of cars recommend oil change for 6 months, 6 types of cars recommend 3 months, and about 77% of the civil cars require oil change within 6 months. Because of the huge automobile reserve and the massive use and replacement of the automobile lubricating oil, the consumption of the automobile lubricating oil accounts for more than half of the annual total consumption of the lubricating oil in China. The excessive use of the lubricating oil for the vehicle not only causes the waste of petroleum resources, but also has difficult waste oil recovery and easily causes environmental pollution. Therefore, reasonable and scientific replacement of the lubricating oil for the vehicle is paid attention to by related people and broad vehicle owners. Foreign countries have advocated increasing the oil change cycle to over 1 kilometre. The Nippon Ringshi Mitsubishi lubricating oil research institute proposes that the lubricating oil is completely failed after the running speed is up to 15000km by using nitrate as a main degradation index; the U.S. native general brand vehicle patent indicates that engine oil changes can be made every 12000km (up to 16000km depending on operating conditions) or 12 months. The road condition, air quality and other factors of China are different from those of foreign countries, so that although automobile engines and lubricating oil thereof adopted in China are basically the same as those of foreign countries, the extension of the oil change period still has doubt and the progress is slow. A plurality of groups of long-time driving monitoring experiments are carried out on the civil cars running under the typical urban working condition in China, and the results show that when the running time reaches 6 months, the physicochemical indexes of all groups of lubricating oil far do not reach the national current oil change standard (GB/T8028-2010), and the test oil still has good lubricating and dispersing capacity. Therefore, the extension of the current oil change period in China is positive.
At present, methods for evaluating oil change periods are mainly based on lubricating oil tests, such as a lubricating oil spot test method, an infrared spectroscopy method, sensor online monitoring and the like. The american scholars s.jagamathan uses microsensor technology and neural network algorithms to provide a method for predicting the service life of engine oil by monitoring the operating conditions of an engine and lubricating oil; and (4) determining the consumption of the antioxidant content in the lubricating oil along with time by using a cyclic voltammetry method by using R.E.Kauffman to judge the oil change period of the lubricating oil. Chinese scholars also research the service life evaluation of the engine oil, and the physical and chemical properties of the engine oil are represented by measuring the dielectric constant of the engine oil for the vehicle, and the degradation degree of the lubricating oil is represented by the transmission value and the scattering value of infrared light. However, these evaluation methods all require the quality detection of the lubricating oil, but no method for establishing a lubricating oil quality detection model exists at present.
Disclosure of Invention
According to the physical and chemical properties of a large number of obtained lubricating oil samples and corresponding operating condition data thereof, the invention researches the relation between the initial oxidation temperature change, the operating mileage and the operating time of three types of vehicle lubricating oil by a multiple linear regression analysis method, tries to establish a mathematical model of the oil change period of the vehicle lubricating oil based on the initial oxidation temperature, and is convenient for a user to directly refer and use when determining oil change.
In view of the above, the present invention aims to provide a method for establishing an engine lubricating oil quality prediction model, which solves the problem in the prior art that the quality of the lubricating oil product of the engine of the motor vehicle cannot be predicted.
The technical scheme adopted by the invention for realizing the purpose is as follows:
the method for establishing the engine lubricating oil quality prediction model is characterized by comprising the following steps of:
selecting a test object, selecting N motor vehicles running under conventional urban working conditions as the test object, carrying out long-time running condition and lubricating oil performance tracking monitoring, periodically sampling, and at least recording the type, total running mileage, total running time and initial oxidation temperature of a sampled oil sample;
step 3, data sorting, namely grouping the data according to the types of the lubricating oil and independently calculating model parameters for each type of the lubricating oil;
in the above formula, Y is an explained variable;
an estimated value of the dependent variable y when the independent variable is given; μ is a random error, which represents the difference between a specific value and an average value, also called residual error; α is a constant, representing the estimated value of the dependent variable when all independent variables (i.e., explanatory variables) take values of 0; x is the number of i To explain the variables; gamma ray i Is a partial regression coefficient, and represents that when other independent variables take fixed values, the independent variable x i Each time a unit is changed
The amount of change in (c);
firstly, respectively carrying out nonlinear fitting on original variable and initial oxidation temperature data, selecting an exponential function as a fitting model, wherein the model form is shown as a formula
In the above formula y 0 、A、R 0 Are all constants, x is independent variable, y is dependent variable, and in the fitting process, y can be obtained 0 、A、R 0 Taking the value of (a);
step 7, carrying out linear transformation on the formula II to obtain an intermediate variable Y * Respectively associated with the running mileage x 1 Run time x 2 Linear relation of (c), the intermediate variable Y * And x 1 、x 2 Importing the data into data analysis software to perform multiple linear regression analysis, and performing variable replacement on the calculation result to realize regression analysis of the change characteristics of the initial oxidation temperature of the vehicle lubricating oil along with the running mileage and the running time, as follows;
wherein y is the initial oxidation temperature, x 1 The unit is hundred kilometers for the running mileage; x is the number of 2 Is the running time, the unit is month; y is 0 Critical initial oxidation temperature, y, for protecting engine from lubricating oil 0 =200; c is a constant term, B 1 Partial regression coefficients for the operating mileage, B 2 And finishing the establishment of an engine lubricating oil quality prediction model for the partial regression coefficient of the running time.
Preferably, in the step 7, a specific method for performing linear transformation on the formula two is as follows:
after linear transformation of formula two, i.e. ln (y-y) 0 )＝ln A-R 0 x
Setting an intermediate variable Y * ＝ln(y-y 0 ) The above equation is converted into: y is * ＝ln A-R 0 x, the initial oxidation temperature-operating mileage, the initial oxidation temperature-operating time are respectively expressed as:
Y * ＝ln A-R 0 x 1 ，Y * ＝ln A-R 0 x 2 wherein x is 1 Representing the mileage run, x 2 Representing run time.
Preferably, the lubricating oil category includes mineral oils, semi-synthetic oil lubricants and fully synthetic lubricants.
Preferably, in the step 2, the number of the motor vehicles N is 4-10.
The invention has the beneficial effects that:
(1) The invention uses multiple linear regression analysis method to establish regression analysis model of initial oxidation temperature, mileage and operation time of lubricating oil for mineral oil, semisynthetic oil, fully synthetic oil and mixed oil on the basis of test data of 7 sets of real lubricating oil of vehicles 312. The fitting model has high precision and a simple formula, can better reflect the influence of the running mileage and the running time of the vehicle on the initial oxidation temperature, is convenient for a user to refer and refer when determining to replace the lubricating oil for the vehicle, and provides a certain basis for scientifically evaluating the oil change period.
(2) The fitting model of the invention can be used for checking the typical oil change period (5000 km and 6 months) of a civil car, and the obtained predicted values of the initial oxidation temperature are far higher than the critical values when the additive is completely consumed, which indicates that the oil change is a waste; according to the model calculation, mineral oil can run for 6366km in 12 months, semi-synthetic oil and fully-synthetic oil can run for about 8468km and 15030km in 18 months respectively, and the appropriate extension of the oil change period in practical application is experimental basis.
Drawings
FIG. 1 is a graph of initial oxidation temperature of mineral oil versus operating mileage.
FIG. 2 is a graph of the initial oxidation temperature of mineral oil versus run time.
FIG. 3 is the relationship between the initial oxidation temperature of the semi-synthetic oil and the mileage.
FIG. 4 is a graph showing the relationship between the initial oxidation temperature and the operation time of a semi-synthetic oil
FIG. 5 is a graph of synthetic oil initial oxidation temperature versus operating mileage.
FIG. 6 is a graph of synthetic oil initial oxidation temperature versus run time.
Detailed Description
The invention is illustrated in the following with reference to the accompanying drawings.
The invention provides a method for establishing an engine lubricating oil quality prediction model, which comprises the following specific steps:
1. the test object is selected, and 7 civil cars running under the conventional urban working conditions are selected as the test objects to carry out long-time running condition and lubricating oil performance tracking monitoring. The basic information of the test vehicle, the brand and kind of the used oil, the total operating mileage, and the total operating time are shown in table 1. The test vehicle is completely used according to the will of the vehicle owner, and no additional requirement is required. Test oil samples were taken from the test vehicle crankcase periodically (taken about once a month) depending on the particular operating conditions of the vehicle until the test was completed. The initial oxidation temperature (the heating rate is 10 ℃/min, the oxygen flow is 100mL/min, and the oxygen pressure is 3.5 MPa) of a test oil sample is measured by using a NETZSCH HP204 high-Pressure Differential Scanning Calorimeter (PDSC), and the change characteristic of the oxidation stability of the lubricating oil along with the extension of service time is analyzed. The test time span is: the total data samples were 312 groups from 2015 to 2018.
TABLE 1 test vehicle and test lubricating oil types
2. The data analysis method and the prior art show that the initial oxidation temperature change of the lubricating oil can most represent the decline rule of the performance of the vehicle lubricating oil in a plurality of lubricating oil physicochemical indexes, the determination of whether the vehicle lubricating oil needs to be replaced according to the initial oxidation temperature of the lubricating oil is more scientific, and other indexes such as generation of acidic substances, accumulation of insoluble mixtures or reduction of the anti-wear performance are closely related to reduction of the anti-oxidation capability of the lubricating oil. For the vehicle owner, the operating mileage (commonly called kilometers) and the service life of the lubricating oil are the most direct and intuitive parameters for determining the oil change period. Therefore, the initial oxidation temperature is used as an index, and on the basis of a large amount of test data, a relation model of the initial oxidation temperature of the lubricating oil, the operation mileage and the operation time under the typical operation working condition of China is established and is most easily adopted by car owners.
The invention aims at vehicles adopting single mineral oil, semisynthetic oil and fully synthetic oil, respectively, establishes a multiple linear regression model by taking the initial oxidation temperature as an explained variable and taking the running mileage and the running time as explained variables (table 2), obtains a more practical equation through ridge regression analysis, and analyzes the change characteristics of the oxidation stability of the lubricating oil for vehicles along with the running mileage and the running time.
TABLE 2 analysis of model variables
The first step is as follows: selecting an initial oxidation temperature as a dependent variable, an operation mileage and an operation time as independent variables, and selecting a model as a multiple linear regression model as shown in table 1; the general model form of multiple linear regression analysis is:
in the above formula, Y is an explained variable;
an estimated value of the dependent variable y when the independent variable is given; μ is a random error, which represents the difference between a specific value and an average value, also called residual error; α is a constant, representing the estimated value of the dependent variable when all independent variables (i.e., explanatory variables) take values of 0; x is the number of i To explain the variables; gamma ray i Is a partial regression coefficient, which means that when other independent variables take on fixed values, the independent variable x i Each time a unit is changed
The amount of change in (c).
The second step: respectively making scatter diagrams of the initial oxidation temperature of the lubricating oil of the test vehicle along with the running mileage and the running time to observe the distribution trend (taking mineral oil as an example), as shown in figures 1 and 2;
the third step: the exponential function is selected according to the requirement of the multiple linear regression model to perform variable transformation so that the independent variable and the dependent variable satisfy a linear relation (taking mineral oil as an example), and the method for performing variable transformation on the original data is as follows. Firstly, respectively carrying out nonlinear fitting on original variables (operating mileage and time) and initial oxidation temperature data, wherein an exponential function is selected as a fitting model, and the form of the model is shown as formula 1
In the formula y 0 、A、R 0 Are all constants. In the fitting process, y 0 Are all around 200 (approximately equal to the base oil initial oxidation temperature), thus fixing y 0 Is 200.
The fitting results were as follows:
wherein x 1 、x 2 Respectively representing the running mileage/100 km (in hundred kilometers) and the running time/month (in months), y 1 Representing mileage run alone x 1 Dependent variable as independent variable, y 2 Representing the running time x alone 2 Dependent variables as independent variables are obtained by linear transformation (as shown in fig. 3 and 4):
ln(y 1 -200)＝ln43.101-0.034x 1
ln(y 2 -200)＝ln40.233-0.237x 2
let Y * = ln (y-200) and replaces the above formula:
Y * ＝ln43.101–0.034x 1 formula 2
Y * ＝ln40.233–0.237x 2 Formula 3
The fourth step: at this time, the transition variable Y * And x 1 、x 2 Is a linear relationship. The semi-synthetic oil and the fully synthetic oil can be subjected to variable transformation in the same way, and the intermediate variable Y is converted * And x 1 、x 2 Importing the data into data analysis software for multiple linear regression analysis to obtain Y * ＝C+B 1 x 1 +B 2 x 2 The calculation result is processedPerforming variable displacement, namely realizing regression analysis of the change characteristics of the initial oxidation temperature of the lubricating oil for the vehicle along with the running mileage and the running time, wherein the regression analysis is shown in a formula 4 and a table 3;
wherein y is the initial oxidation temperature, x 1 The unit is hundred kilometers for the running mileage; x is a radical of a fluorine atom 2 Is the running time, the unit is month; y is 0 Critical initial oxidation temperature, y, for protecting the engine from lubricating oils 0 =200; c is a constant term, B 1 Partial regression coefficients for the operating mileage, B 2 And finishing the establishment of the engine lubricating oil quality prediction model for the partial regression coefficient of the running time.
TABLE 3 analysis of model parameters
According to the comparison calculation of the established engine lubricating oil quality prediction model and the original data, under the urban working condition, the mineral lubricating oil can run for 6366km in 12 months; semi-synthetic lubricating oils could run 8468km in 18 months while fully synthetic oils could run 15030km in 18 months as shown in table 4.
TABLE 4 model oil change period prediction Table
To summarize:
(1) The invention uses multiple linear regression analysis method to build regression analysis model of initial oxidation temperature, mileage and running time of lubricating oil for mineral oil, semisynthetic oil, fully synthetic oil and mixed oil on the basis of 7 sets of real vehicle lubricating oil test data of vehicle 312. The fitting model has high precision and a simple formula, can better reflect the influence of the running mileage and the running time of the vehicle on the initial oxidation temperature, is convenient for a user to refer and refer when determining to replace the lubricating oil for the vehicle, and provides a certain basis for scientifically evaluating the oil change period.
(2) The engine lubricating oil quality prediction model fitted by the invention is used for checking the typical oil change period (5000 km, 6 months) of the existing civil passenger car, and the obtained predicted values of the initial oxidation temperature are far higher than the critical value when the additive is completely consumed, which indicates that the oil change is a waste; according to the model calculation, mineral oil can run for 6366km in 12 months, semi-synthetic oil and fully-synthetic oil can run for about 8468km and 15030km in 18 months respectively, and the appropriate extension of the oil change period in practical application is experimental basis.
Claims (4)
1. The method for establishing the engine lubricating oil quality prediction model is characterized by comprising the following steps of:
step 1, selecting parameters, namely selecting an initial oxidation temperature as a quality characterization parameter of the engine lubricating oil, and selecting an operation mileage and an operation time independent variable;
selecting a test object, selecting N motor vehicles running under conventional urban working conditions as the test object, carrying out long-time running condition and lubricating oil performance tracking monitoring, periodically sampling, and at least recording the type, total running mileage, total running time and initial oxidation temperature of a sampled oil sample;
step 3, data sorting, namely grouping the data according to the types of the lubricating oil and independently calculating model parameters for each type of the lubricating oil;
step 4, selecting the initial oxidation temperature as a dependent variable, the running mileage and the running time as independent variables, and selecting a multiple linear regression model, wherein the multiple linear regression model is in the form of:
in the above formula, Y is an explained variable;
representing the estimated value of the dependent variable y when the independent variable is given to take a value; μ is a random error, which represents the difference between a specific value and an average value, also called residual error; alpha is a constant and represents the estimated value of the dependent variable when all independent variable values are 0; x is a radical of a fluorine atom i To explain the variables; gamma ray i Is a partial regression coefficient, and represents that when other independent variables take fixed values, the independent variable x i Each time a unit is changed
The amount of change in (c);
step 5, respectively making scatter diagrams of the initial oxidation temperature of the lubricating oil of the test vehicle along with the running mileage and the running time to observe the distribution trend;
step 6, selecting an exponential function according to the requirement of the multiple linear regression model to perform variable transformation so that the independent variable and the dependent variable satisfy the linear relation, wherein the method for performing variable transformation on the original data comprises the following steps:
firstly, respectively carrying out nonlinear fitting on original variable and initial oxidation temperature data, selecting an exponential function as a fitting model, wherein the model form is shown as a formula
In the above formula y 0 、A、R 0 Are all constants, x is independent variable, y is dependent variable, and in the fitting process, y can be obtained 0 、A、R 0 Taking the value of (A);
linearly transforming the formula II to obtain an intermediate variable Y * Respectively associated with the running mileage x 1 Run time x 2 Linear relation of (2), the intermediate variable Y * And x 1 、x 2 Importing data into data analysis software to perform multiple linear regression analysis, and performing variable setting on the calculation resultThe regression analysis of the change characteristics of the initial oxidation temperature of the vehicle lubricating oil along with the operating mileage and the operating time can be realized, as follows;
wherein y is the initial oxidation temperature, x 1 The unit is hundred kilometers for the running mileage; x is the number of 2 Is the running time, the unit is month; y is 0 Critical initial oxidation temperature, y, for protecting the engine from lubricating oils 0 =200; c is a constant term, B 1 Partial regression coefficients for the operating mileage, B 2 And finishing the establishment of the engine lubricating oil quality prediction model for the partial regression coefficient of the running time.
2. The engine oil quality prediction model building method of claim 1, characterized by: in the step 6, a specific method for performing linear transformation on the formula two is as follows:
after the formula two is linearly transformed, namely ln (y-y) 0 )＝lnA-R 0 x
Setting an intermediate variable Y * ＝ln(y-y 0 ) The above equation is converted into: y is * ＝lnA-R0 x The initial oxidation temperature-operating mileage and the initial oxidation temperature-operating time are respectively expressed as follows:
Y * ＝ln A-R 0 x 1 ，Y * ＝ln A-R 0 x 2 wherein x is 1 Representing the mileage run, x 2 Representing run time.
3. The engine oil quality prediction model building method of claim 1, characterized by: the lubricating oil category includes mineral oils, semi-synthetic oil lubricants and fully synthetic lubricants.
4. The engine oil quality prediction model building method of claim 1, characterized by: in the step 2, the number N of the motor vehicles is 4-10.
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| CN112834729A (en) * | 2021-01-08 | 2021-05-25 | 三一汽车起重机械有限公司 | Hydraulic oil quality monitoring method, device and system |
| CN113012763B (en) * | 2021-02-24 | 2022-02-11 | 西南石油大学 | Crude oil oxidation reaction kinetic model building method based on four-group components |
| CN113343411A (en) * | 2021-04-20 | 2021-09-03 | 北京新联铁集团股份有限公司 | Method and device for determining gear box lubricating oil replacement time and electronic equipment |
| CN113987408A (en) * | 2021-10-29 | 2022-01-28 | 中国重汽集团济南动力有限公司 | Gear oil change judgment and prediction method and device for gear box and storage medium |
| CN114675010B (en) * | 2022-05-31 | 2022-09-13 | 卡松科技股份有限公司 | Intelligent analysis method for oxidation resistance of lubricating oil |
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