CN116910419A - Evaluation method of multi-index fusion lubricating oil based on cloud picture - Google Patents
Evaluation method of multi-index fusion lubricating oil based on cloud picture Download PDFInfo
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Abstract
The application provides an evaluation method of multi-index fusion lubricating oil based on cloud pictures, which is characterized in that relevant basic data are obtained through analysis of lubricating oil components of equipment in use, corresponding training parameters are constructed according to the acquired component data, corresponding radar cloud pictures are constructed by utilizing Python based on the training data, and eight important lubricating oil monitoring properties are displayed and analyzed more comprehensively; according to the radar cloud chart, the oil change index of the diesel engine oil and the component indexes of the actual monitoring lubricating oil are synthesized, corresponding evaluation analysis is carried out on abrasion and blockage of mechanical equipment such as the diesel engine and the failure abnormal state of the lubricating oil, corresponding early warning is carried out, diagnosis and feedback are carried out on mechanical faults, reasonable judgment can be effectively carried out on the state of the lubricating oil and the oil change period, the lubricating oil is used to the maximum extent, cost is saved, and efficiency is improved.
Description
Technical Field
The application relates to the field of visual cloud image data analysis and processing, in particular to a cloud image-based multi-index fusion lubricating oil evaluation method.
Background
As is well known, with the continuous development of industrialization, the effects of intellectualization and mechanization on human beings are increasing, but in order to make the mechanical equipment work more efficiently and stably, it is very necessary to maintain the mechanical equipment in daily life. The corresponding cost increases due to excessive machine maintenance. And the equipment is not damaged for a long time because the equipment is not required to be maintained, which is unacceptable to a plurality of enterprises. So how to simultaneously have a more scientific equipment maintenance scheme at the lowest cost under the continuous and simplified personnel management is a problem which needs to be solved by future modeling enterprises.
Taking a diesel engine as an example, the stable operation of the diesel engine is not only related to the load of the diesel engine, but also the lubrication of the diesel engine is an important link unavoidable. The relevant papers indicate that lubricating oils are indispensable to modern diesel engines as blood is to the heart. The lubricating oil has the functions of lubrication, antifriction, cooling, leakage prevention, sealing, shock absorption, rust prevention and the like in the diesel engine, and meanwhile, the oil change time of the lubricating oil and the abrasion state of the diesel engine can be judged according to the component monitoring of the lubricating oil, so that the basis can be provided for maintaining the stable and reliable operation of the diesel engine. Therefore, in order to improve the use efficiency of the lubricating oil, the oil change period, the abrasion state and the abrasion position of the diesel engine can be more efficiently and conveniently judged accurately, and then the components of the lubricating oil are required to be analyzed efficiently, clearly, accurately and comprehensively. According to related researches, the traditional lubricating oil monitoring technology generally adopts a certain principal component analysis to the static lubricating oil, only a part of component states of the lubricating oil can be judged, meanwhile, the single principal component monitoring is easy to cause errors in judgment, the oil change period judgment of the lubricating oil and the fault diagnosis of a diesel engine are not ideal, the result display form is single, the efficiency is low, and the efficient and convenient diagnosis on the oil change and the machine wear cannot be carried out.
Disclosure of Invention
Aiming at the problems, the application provides a method for evaluating multi-index fusion lubricating oil based on cloud pictures, which adopts a visual cloud picture data analysis processing technology to carry out fusion analysis on the octave physical and chemical properties of the lubricating oil in use.
Embodiments of the present application are implemented as follows:
the embodiment of the application provides a cloud picture-based multi-index fusion lubricating oil evaluation method, which is characterized by comprising the following steps of:
step a, respectively taking out a plurality of oil samples which can represent all characteristics of the actual diesel engine in use oil at different ships and different sampling points, analyzing the lubricating oil components of the in-use equipment, and obtaining the relevant basic data of the lubricating oil components;
step b, constructing corresponding training parameters according to the acquired component data, wherein the training parameters comprise operation parameters and component index parameters for establishing an evaluation model, and then constructing corresponding radar cloud image analysis based on the training parameters by using a computer programming language;
step c, according to the radar cloud chart, synthesizing oil change indexes of mechanical equipment and various component indexes of actual monitoring lubricating oil, and carrying out corresponding evaluation analysis on wear and blockage of the mechanical equipment and failure abnormal states of the lubricating oil;
and d, according to the failure state of the lubricating oil and the abrasion state evaluation of the mechanical equipment, making corresponding early warning, and making diagnosis and feedback on mechanical faults.
In some alternative embodiments, the lubricating oil composition index parameters described in step a include viscosity, moisture, closed-cell flash point, base number reduction rate, insolubles, iron spectrum analysis, wear element analysis, infrared spectrum analysis.
In some alternative embodiments, the construction of the radar cloud comprises the steps of:
establishing a performance evaluation index set:
u= { viscosity, moisture, flash point, base number reduction rate, insoluble, iron spectrum analysis, abrasion element analysis, infrared spectrum analysis };
establishing a performance degradation degree evaluation set:
v= { excellent, good, general, poor, very poor };
set V ij ＝U R (U i ,V j ) Representing the factor U i The degree of deterioration of the oil performance was rated as V j Wherein 0.ltoreq.V ij ≤1；
The fuzzy subset of factors for domain U is:
wherein a is i Is u i Membership to A, (0.ltoreq.a) i ≤1)；
Similarly, a rank fuzzy subset of the universe of available domains:
wherein b j V is i Membership to B, (0.ltoreq.b) j ≤1)；
Therefore, the following relationship holds:
namely: and B=A.R, obtaining a related physical and chemical performance index function of the lubricating oil, and obtaining the radar cloud picture of the oil change component after finishing the data.
In some alternative embodiments, the above-mentioned ferrographic analysis further performs accurate analysis, and uses WPC and IS to calculate baseline values, warning lines, and danger lines of the ferrographic sample by using a method based on mathematical statistics, and the specific calculation steps are as follows:
setting a baseline value by adopting a mathematical statistics method, selecting WPC as a quantitative parameter of baseline setting, and calculating as follows:
baseline value:
calculation of control lines:
warning value: WPC (WPC) A ＝WPC B +2S
Risk value: WPC (WPC) C ＝WPC B +3S；
Wherein WPC is the abrasive particle concentration, and represents the total abrasion value and the total abrasion amount to reflect the change of the abrasion state; IS the abrasive particle intensity index; d (D) L Is the maximum number of abrasive particles; d (D) S Is the minimum number of abrasive particles; v is the volume of the oil sample; n is WPC of the same type of monitoring object and is taken as a sample space; WPC (WPC) STDi Is a quantitative parameter based on sample space and oil sample volume; s is the standard deviation of test data, S 2 Is delta 2 Delta is the correction value of the measured data;
comparing the WPC value of the abrasive particle concentration of the sample oil with the established baseline value to obtain a discrimination result, and storing the discrimination result in a result database, wherein the discrimination method comprises the following steps:
in some alternative embodiments, the wear elements include copper, aluminum, silicon, the copper elements being from engine wear, the aluminum elements being from piston and cylinder wall wear, the silicon elements being from ambient dust and/or sand.
In some alternative embodiments, the oil change index of each component index parameter of the lubricating oil is respectively: the kinematic viscosity of the lubricating oil exceeds +/-20%; the mass fraction of the water exceeds 0.2% of the lubricating oil; the flash point has a value below 130 ℃; a base number reduction of greater than 50 b The%; the mass fraction of insoluble matters is more than 2% of the lubricating oil; iron content greater than 150ug/g; abrasion element content: cu content is greater than 55ug/g and/or Al content is greater than 35ug/g and/or Si content is greater than 35ug/g; infrared absorption spectroscopy analysis: the mass fraction of the elements Cu, al and Si is more than 30% of the lubricating oil.
The beneficial effects of the application are as follows: the evaluation method of the multi-index fusion lubricating oil based on the cloud picture can simultaneously realize one-to-one or many-to-one high-efficiency comprehensive judgment of the component analysis of the lubricating oil and the wearing state of parts of the diesel engine, and achieves the purposes of timely and high-efficiency oil replacement and timely discovery of the wearing part and the wearing degree of the diesel engine; by establishing a cloud chart to integrate single components of lubricating oil, a high-efficiency positive feedback model is established. Meanwhile, the fault state of the diesel engine corresponding to the abnormal component content or the fault transition state corresponding to the abnormal component content or the oil change period of the lubricating oil judging by the components is realized. By the method, the abnormality of the lubricating oil can be found timely, the fault of the diesel engine can be reflected, early warning and timely diagnosis can be achieved. The service efficiency of lubricating oil and the reliability of a diesel engine are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an embodiment of the present application;
FIG. 2 is a cloud chart of eight ingredient index duty cycle of lubricating oil according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The features and capabilities of the present application are described in further detail below in connection with the examples.
As shown in fig. 1, the application provides a method for evaluating multi-index fusion lubricating oil based on cloud pictures, which comprises the following steps:
1. and taking out a plurality of oil samples which can represent all characteristics of the actual diesel engine in use oil at different ships and different sampling points, and analyzing the lubricating oil components of the in-use equipment to obtain the relevant basic data of the lubricating oil components.
2. Constructing corresponding training parameters according to the acquired component data, wherein the training parameters comprise operation parameters and component index parameters for establishing an evaluation model, and then constructing corresponding radar cloud image analysis based on the training parameters by using a computer programming language;
3. according to the radar cloud chart, the oil change index of the mechanical equipment and various component indexes of the actual monitoring lubricating oil are synthesized, and the abrasion, blockage and failure abnormal state of the lubricating oil of the mechanical equipment are correspondingly evaluated and analyzed;
4. and (5) according to the failure state of the lubricating oil and the abrasion state evaluation of mechanical equipment, making corresponding early warning, and making diagnosis and feedback on mechanical faults.
The index parameters of the lubricating oil comprise viscosity, moisture, closed flash point, alkali number reduction rate, insoluble matters, iron spectrum analysis, abrasion element analysis and infrared spectrum analysis, and the ratio of each index parameter of the components and the index of oil change are shown in the table I. In addition, other factors have some influence, but are negligible considering that they have little influence.
List one
Component data acquisition is based on verification criteria for the different components. The viscosity of the used lubricating oil is obtained by a capillary vessel viscometer, a certain amount of used lubricating oil is taken from a diesel engine, the relative national standard is measured according to the viscosity of the lubricating oil, and the kinematic viscosity of the lubricating oil under the action of gravity only is measured at the temperature of 100 ℃ to obtain initial data. At the moment, the oil change index of the lubricating oil is that the kinematic viscosity is not more than +/-20%, and certain early warning analysis can be performed according to the comparison of the kinematic viscosity and the kinematic viscosity.
The oil used in the process can carry water due to aging and permeation of a cylinder sleeve, water vapor generated in a combustion chamber and the like, so that the oil film strength can be damaged and the hydrolysis of additives can be caused. The monitoring of the water mainly adopts the current national standard, and the water is carried out by the reagent through heating and refluxing a certain volume of lubricating oil and a solvent which is insoluble in water, and the lubricating oil and the solvent can be separated after condensation for a certain time to measure the mass fraction of the water. At the moment, the oil change standard of the lubricating oil is not more than 0.2% of the lubricating oil, so that the comparison data of the lubricating oil and the lubricating oil are arranged into a cloud picture for later analysis.
If the phenomenon of fuel dilution occurs in the diesel engine oil, the monitored value of the flash point of the diesel engine oil can be obviously reduced, the bearing capacity of an oil film can be greatly weakened, the abrasion of the diesel engine can be further increased, the dilution condition of the lubricating oil is detected by adopting a closed cup method at the moment, the flash point value is judged, and the lubricating oil can be timely replaced. The flash point of the method is monitored by the current standard, and the standard can measure samples with the flash point of 10-250 ℃. A certain volume of lubricating oil is measured by the method to obtain the flash point value. At the moment, the oil change standard of the lubricating oil is lower than 130 ℃, and the oil change can be judged. And the test data of the two are arranged in the cloud picture so as to facilitate later analysis.
The lubricating oil of the diesel engine has a certain base number, and the change of the base number has a certain relation with the S content of the fuel oil used by the diesel engine and the oxidative deterioration in the use process. The decrease of the base number can cause the increase of acidic substances in the lubricating oil, thereby causing the increase of oil sludge, and the corrosion and abrasion of parts of the diesel engine. The method mainly adopts the current standard, and a certain volume of lubricating oil is subjected to the method to obtain the alkali value reduction rate. At this time, the oil change standard of the lubricating oil is that the base number reduction rate is more than 50b percent, and the oil change can be judged. And the data of the two are arranged in the cloud picture so as to facilitate later analysis and comparison.
The lubricating oil insoluble matter of diesel engine mainly includes n-pentane insoluble matter and toluene insoluble matter. N-pentane mainly includes some oil insolubles and oil insolubles resulting from the decomposition of fuel oils or additives, while toluene insolubles are mainly from external pollution and carbon and highly carbonized materials resulting from the decomposition of fuel oils and additives, and wear and corrosion of diesel engines. The increase in contaminants reflects the degree of aging and contamination of the lubricating oil. The method mainly adopts the current national standard to measure the insoluble substances, and comprises the specific operation steps of mixing a certain volume of lubricating oil to be measured with n-pentane, carrying out certain centrifugal separation, washing and drying to obtain n-pentane insoluble substances and toluene insoluble substances, and finally obtaining the mass fraction of the insoluble substances. At the moment, the diesel engine oil change standard is that the mass fraction of n-pentane insoluble matters is more than 2 percent, and then the diesel engine oil change can be judged. And the measured data and the oil change standard threshold are arranged on the cloud picture so as to facilitate later analysis.
The iron spectrum analysis is also called abrasive grain analysis. The method mainly adopts the current standard, and uses an analytical iron spectrum analyzer to detect the lubricating oil with a certain volume, so as to obtain the coverage area percentage, the total abrasion value and the abrasion intensity index value of large particles and small particles of each milliliter of lubricating oil. At the moment, the diesel engine oil change standard is that the oil change can be judged if the iron content is more than 150 ug/g. And the measured data and the oil change standard threshold are arranged on the cloud picture so as to facilitate later analysis.
When the infrared spectrum monitoring of lubricating oil irradiates the lubricating oil sample with infrared radiation of the same wavelength, the radiation of certain wavelength is selectively absorbed by the sample to form an infrared absorption spectrum, and the pollution degree of the lubricating oil is judged from the molecular level, so that a certain reference is made for oil change. And (3) analyzing the lubricating oil with a certain volume by adopting a Fourier fast-transformation mid-infrared spectrum analyzer to obtain the data of the peak position of the absorption peak of the lubricating oil, and arranging the measured data and an empirical threshold value into a cloud picture so as to facilitate later analysis.
The elemental analysis of the lubricating oil mainly comprises Fe, cu, AI, si, pb and the like, wherein the Cu content reflects the abrasion and corrosion of an engine bearing, the AI content mainly comes from the abrasion of a piston and a cylinder wall, the Si content mainly comes from external dust, sand and other pollutants, and the trend analysis is further carried out on elements by establishing an element content prediction and regression model. The method mainly adopts the current line standard to analyze. The standard of oil change of the lubricating oil is that Cu is larger than 55ug/g, al is larger than 35ug/g, and Si is larger than 35 ug/g. The wear state of different parts of the diesel engine can be monitored for different elements (the following table II), and the wear state of the diesel engine can be comprehensively analyzed by utilizing multiple elements. And (5) sorting the measured data and a specified threshold value into a cloud picture for analysis and diagnosis.
In the condition monitoring process of the diesel engine, iron element in the lubricating oil is often analyzed independently, and different abrasion conditions of various kinematic pair parts (such as abrasion of a cylinder sleeve and a piston ring) of the diesel engine are judged according to important information such as the size and the shape of abrasive particles (for example, small abrasive particles generated by peeling a cutting and mixing layer formed on the inner surface of a fatigue crack in the bearing of the diesel engine and other small particles entering the crack of the lubricating oil are repeatedly rubbed into spherical abrasive particles, and the diameter is almost no more than 5 mu m). However, since the metal components of different parts are not the same (for example, aluminum element generally constitutes a piston, copper element generally exists in a bearing), the resolution of analysis of ferrospectrum analysis of these nonferrous metals is weak, and the sensitivity is inferior to that of spectrum analysis. Although spectroscopic elemental analysis can accurately determine the content of wear elements, it is not possible to determine the size and shape of wear particles. Therefore, the iron spectrum analysis and the spectrum analysis are combined for comprehensive judgment, and the iron spectrum analysis and the spectrum analysis complement each other, so that the effects of supplementing the advantages and supplementing the disadvantages can be achieved.
The infrared absorption spectrum analysis is based on the spectral measurement of marine diesel engine lubricating oil, mainly by direct excitation of energy such as emitted light, spark, etc., and is based on the direct excitation of lubricating oil elements by energy such as electric arc, spark, etc., and each element has its inherent wavelength, i.e. characteristic spectral line, wherein the qualitative basis of the emission spectrum analysis and the intensity of light are quantitative basis. The lubricant oil change standard at this time is that the mass fraction of Cu, al and Si is more than 30% of the lubricant oil.
Through the collected monitoring data, python is utilized to input, a uniform step length generation sequence and an array splicing function are adopted to integrate the monitoring data into a visual radar chart, the influence degree of different components is different from the oil change state of lubricating oil, the monitoring data are converted into different visual areas of the radar chart according to the importance degree, and the occupation ratio of two adjacent components is represented by different line lengths. Therefore, the visual comparison analysis of the monitoring data can be achieved, the oil change period of the lubricating oil can be found more clearly and accurately, and the abrasion state of the diesel engine can be judged.
Conventional analysis of lubricating oil composition is to evaluate the states of lubricating oil and diesel engine based on only a single component of the main components. The method can be used for comprehensively analyzing the monitoring data and the oil change standard, converting the judging value of one component into the percentage of the total judging value of the oil change of the lubricating oil to evaluate, analyzing the lubricating oil by utilizing a single component, namely judging whether the oil change standard is met or the abrasion part and degree of a diesel engine can be found by utilizing the single component area of a radar cloud picture, and comprehensively analyzing a plurality of components to obtain the cloud picture of the more localized oil change standard and the monitoring data.
Watch II
Taking iron spectrum elemental analysis as an example:
by establishing a cloud chart to integrate single components of lubricating oil, a high-efficiency positive feedback model is established. Meanwhile, the fault state of the diesel engine corresponding to the abnormal component content or the fault transition state corresponding to the abnormal component content or the oil change period of the lubricating oil judging by the components is realized. By the method, the abnormality of the lubricating oil can be found timely, the fault of the diesel engine can be reflected, early warning and timely diagnosis can be achieved. The service efficiency of lubricating oil and the reliability of a diesel engine are improved.
Example 1
The multi-index evaluation method of the lubricating oil specifically comprises the following steps:
1. taking oil samples, firstly taking out a plurality of oil samples which can represent all the characteristics of the actual diesel engine in oil at different ships and different sampling points.
2. Specifically, the experimental verification can be performed by randomly extracting oil samples from the main engine of the number (1), the number (2), the number (3), the number (4), the number (5), the main engine lubricating oil storage bin of the number (6) ship and the auxiliary engine lubricating oil storage bin of the number (7) ship.
3. The running time of the main engine of the ship (1) is 26093 hours, and the service time of the lubricating oil is 1879 hours; (2) the running time of the main engine of the ship is 26093 hours, and the service time of the lubricating oil is 863 hours; (3) the running time of the main engine of the ship is 25062 hours, and the using time of the lubricating oil is 1294 hours; (4) the running time of the main engine of the ship is 2548 hours, and the using time of the lubricating oil is 0 hour; (5) the running time of the main engine of the number ship is 6381 hours, and the using time of the lubricating oil is 863 hours; (6) the samples of the ship No. 7 were random samples.
4. When the lubricating oil sample is extracted, a sampling tube is required to be sampled in an oil tank, and the sampling tube is inserted into a position with a general lower oil level for sampling; so as to avoid the sedimentation of oil at the bottom of the tank. If sampling is performed on the oil return pipeline, a part of oil needs to be discharged firstly to clean the oil valve, meanwhile, sampling from the bottom of the oil pipe needs to be avoided as much as possible, and meanwhile, dynamic oil samples need to be obtained by sampling in sequence in a flowing state.
5. The diesel engine of boats and ships samples the time also need to pay attention to several points: (1) avoid sampling at dead angle. The oil remained in the dead angle of the part does not interact with surrounding oil, and the oil particles are unevenly distributed, so the oil particles are not representative. (2) to avoid oil-sample overfill as much as possible. The oil sample bottle cannot be filled up by the cutting mark during sampling, otherwise, the oil sample cannot be uniformly shaken, secondary sediment is formed in the oil sample bottle, and the representativeness of the oil sample bottle is lost. (3) The sampling device is kept clean, so that the pollution to the oil sample is avoided, meanwhile, the sampling device is required to be stored in a clean and sealed package, and different oil samples cannot be taken by using the same sampling tube. And (4) timely performing detailed labeling after sampling. The details of the name of the marine diesel engine, the working time of equipment, the sampling position, the sampling time, the type of lubricating oil and the like are recorded on the oil sample bottle in time during sampling, so that the follow-up sampling is prevented from making wrong marks, and the phenomenon of oil mixing occurs.
6. After successful sampling, the sample is subjected to physical and chemical property analysis, iron spectrum analysis, pollution degree analysis and the like.
7. The viscosity is determined based on the viscosity grade classification of national standard SAE engine oil, and the viscosity of oil sample liquid obtained by capillary viscometer is measured at 100deg.C to obtain initial data of 12.5mm 2 /s～16.3mm 2 And between/s, the measured parameter is recorded and is conveniently compared with the standard threshold value of oil change. The oil change threshold is that the viscosity of normal lubricating oil cannot be exceeded by +/-20%, and the measured data of seven different marine diesel engines are collated into a cloud picture.
8. Secondly, monitoring the water mainly adopts the current national standard, and the water is carried out by a reagent through heating and refluxing a certain volume of in-use lubricating oil sample liquid and a solvent which is insoluble in water, and the in-use lubricating oil sample liquid and the solvent can be separated through condensation for a certain time to measure the mass fraction of the water. The oil change standard of the lubricating oil is not more than 0.2% of the lubricating oil, and the measured data of seven different marine diesel engines are arranged in a cloud picture.
9. The flash point is monitored using current standards that measure samples having flash points ranging from 10 ℃ to 250 ℃. The flash point value will be determined after this method with a lubricating oil sample. At the moment, the oil change standard of the lubricating oil is lower than 130 ℃, and the oil change can be judged. And the test data are arranged in the cloud picture so as to facilitate later analysis and comparison.
10. The alkali value is mainly determined by the current standard, and the obtained lubricating oil is subjected to the method to obtain the alkali value reduction rate. At this time, the oil change standard of the lubricating oil is that the alkali value reduction rate is more than 50 b % can determine that the oil change can be performed. The measured data of seven different marine diesel engines are collated into a cloud image.
11. And monitoring insoluble matters, wherein the diesel engine oil change standard is that the oil change can be judged by the mass fraction of n-pentane insoluble matters being more than 2%, and the measured data of seven different marine diesel engines are arranged in a cloud picture.
12. The iron filings in the lubricating oil come from the abrasion of parts in the diesel engine, mainly from the abrasion of the cylinder sleeve and the piston ring, and from the table one, the iron filings mainly come from the abrasion of the cylinder sleeve, the piston ring and the crankshaft. The amount of scrap iron can be used as a judging basis for judging the abrasion degree and the position of the diesel engine. The method mainly adopts the current standard, and uses an analytical type iron spectrum analyzer to detect the lubricating oil sample liquid in use, so that the coverage area percentage, the total abrasion value and the abrasion intensity index value of large particles and small particles of each milliliter of lubricating oil can be obtained. At the moment, the diesel engine oil change standard is that the iron content is larger than 150ug/g, so that the oil change can be judged, the measured average value of the iron spectrum content of the sample liquid in use is near the oil change standard, and the measured data of seven different marine diesel engines are arranged in a cloud picture.
13. Monitoring of infrared spectra. When the infrared spectrum monitoring of lubricating oil irradiates the lubricating oil sample with infrared radiation of the same wavelength, the radiation of certain wavelength is selectively absorbed by the sample to form an infrared absorption spectrum, and the pollution degree of the lubricating oil is judged from the molecular level, so that a certain reference is made for oil change. And (3) analyzing the lubricating oil by using a Fourier fast-transformation mid-infrared spectrum analyzer to obtain lubricating oil absorption peak position data, and sorting the measured data of seven different marine diesel engines into a cloud picture.
14. The measured elemental mean was 53.7ug/g for Cu, 28.9ug/g for Al, and 30.7ug/g for Si. However, the standard of oil change of the lubricating oil is that Cu is more than 50ug/g, al is more than 30ug/g and Si is more than 30 ug/g. Different elements can be used for monitoring the wear states of different parts of the diesel engine, and the wear states of the diesel engine can be comprehensively analyzed by utilizing multiple elements. And (5) sorting the measured data and a specified threshold value into a cloud picture for analysis and diagnosis.
15. In order to better obtain comprehensive data of lubricating oil components, the conventional judgment of the degradation degree of oil by a single index is avoided, and the judgment of multi-index fusion analysis is adopted to make a decision of whether to change oil. According to the actual conditions of the samples in the using process of the oil, aiming at fuzzy processing and influences of different equipment, running environments and the like, a fuzzy subset A of each performance index influence level and a single factor evaluation standard are determined. The single-factor blurring processing is based on an evaluation standard, and a single-factor evaluation matrix R is listed in each section according to linear processing. The specific process is as follows:
establishing a performance evaluation index set:
u= { viscosity, moisture, flash point, base number reduction rate,
insoluble, iron spectrum analysis, abrasion element analysis, infrared spectrum analysis };
establishing a performance degradation degree evaluation set:
v= { excellent, good, general, poor, very poor };
set V ij ＝U R (U i ,V j ) Representing the factor U i The degree of deterioration of the oil performance was rated as V j Wherein 0.ltoreq.V ij ≤1；
The fuzzy subset of factors for domain U is:
wherein a is i Is u i Membership to A, (0.ltoreq.a) i ≤1)；
Similarly, a rank fuzzy subset of the universe of available domains:
wherein b j V is i Membership to B, (0.ltoreq.b) j ≤1)；
Therefore, the following relationship holds:
namely: and B=A.R, obtaining a related physical and chemical performance index function of the lubricating oil, and obtaining the radar cloud picture of the oil change component after finishing the data.
The previous analysis method is not accurate enough for analysis of the iron spectrum, and in order to obtain the oil change duty ratio and the state evaluation of the iron spectrum more fully and accurately, a direct-reading iron spectrum analysis method is adopted to analyze the iron spectrum data. The index provided by the direct-reading type ferrograph analysis is the maximum abrasive particle quantity D L And a minimum abrasive particle number D S . In practical monitoring analysis, WPC and IS are often used to calculate baseline values, warning lines and danger lines of a spectrum sample by using a method based on mathematical statistics. The specific calculation steps are as follows:
calculation of baseline values:
setting a baseline value by adopting a mathematical statistics method, and selecting WPC as a quantitative parameter of baseline setting.
Baseline value:
calculation of control lines:
warning value: WPC (WPC) A ＝WPC B +2S
Risk value: WPC (WPC) C ＝WPC B +3S；
Wherein WPC is the abrasive particle concentration, and represents the total abrasion value and the total abrasion amount to reflect the change of the abrasion state; IS the abrasive particle intensity index; d (D) L Is the maximum number of abrasive particles; d (D) S Is the minimum number of abrasive particles; v is the volume of the oil sample; n is WPC of the same type of monitoring object and is used as a sample space; WPC (WPC) STDi Is a quantitative parameter based on sample space and oil sample volume; s is the standard deviation of test data, S 2 Is delta 2 Delta is the correction value of the measured data;
comparing the WPC value of the abrasive particle concentration of the sample oil with the established baseline value to obtain a judging result of 'normal, attention, warning and danger', and storing the result in a result database, wherein the judging method comprises the following steps:
the detection value of the sample is in the normal range by judging that the average value of the content of the iron spectrum of the seven sample solutions is 119 mug/g and is lower than the oil change threshold value of 150 mug/g calculated by the previous formula. Therefore, the different oil change duty ratios of the cloud pictures can be displayed by adopting the visual analysis of the cloud pictures.
And then adopting a uniform step length generation sequence and a corresponding component array to integrate components monitored by the sample liquid into a visualized multi-index fusion radar graph, wherein the specific graph is shown in fig. 2. The lubricating oil change period and the fault diagnosis of the ship equipment corresponding to the influence degrees of different components are different, the lubricating oil change period and the fault diagnosis of the ship equipment are converted into different visual areas of the radar chart according to the importance degrees, and the duty ratio of two adjacent components is represented by different line lengths. Therefore, the visual comparison analysis of the monitoring data can be achieved, the oil change period of the lubricating oil can be found more clearly and accurately, and the abrasion state of the diesel engine can be judged.
The measurement values of various component index parameters of the lubricating oil for the ship in the above embodiment are shown in the following table three.
Watch III
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Claims (6)
1. The evaluation method of the multi-index fusion lubricating oil based on the cloud picture is characterized by comprising the following steps of:
step a, respectively taking out a plurality of oil samples which can represent all characteristics of the actual diesel engine in use oil at different ships and different sampling points, analyzing the lubricating oil components of the in-use equipment, and obtaining the relevant basic data of the lubricating oil components;
step b, constructing corresponding training parameters according to the acquired component data, wherein the training parameters comprise operation parameters and component index parameters for establishing an evaluation model, and then constructing corresponding radar cloud image analysis based on the training parameters by using a computer programming language;
step c, according to the radar cloud chart, synthesizing oil change indexes of mechanical equipment and various component indexes of actual monitoring lubricating oil, and carrying out corresponding evaluation analysis on wear and blockage of the mechanical equipment and failure abnormal states of the lubricating oil;
and d, according to the failure state of the lubricating oil and the abrasion state evaluation of the mechanical equipment, making corresponding early warning, and making diagnosis and feedback on mechanical faults.
2. The method for evaluating the multi-index fusion lubricating oil based on the cloud picture as claimed in claim 1, wherein the index parameters of the lubricating oil components in the step a comprise viscosity, moisture, closed flash point, base number reduction rate, insoluble substances, iron spectrum analysis, abrasion element analysis and infrared spectrum analysis.
3. The evaluation method of the multi-index fusion lubricating oil based on the cloud picture as claimed in claim 2, wherein the construction of the radar cloud picture comprises the following steps:
establishing a performance evaluation index set:
u= { viscosity, moisture, flash point, base number reduction rate,
insoluble, iron spectrum analysis, abrasion element analysis, infrared spectrum analysis }
Establishing a performance degradation degree evaluation set:
v= { excellent, good, general, poor, very poor };
set V ij ＝U R (U i ,V j ) Representing the factor U i The degree of deterioration of the oil performance was rated as V j Wherein 0.ltoreq.V ij ≤1；
The fuzzy subset of factors for domain U is:
wherein a is i Is u i Membership to A, (0.ltoreq.a) i ≤1)；
Similarly, a rank fuzzy subset of the universe of available domains:
wherein b j V is i Membership to B, (0.ltoreq.b) j ≤1)；
Therefore, the following relationship holds:
namely: and B=A.R, obtaining a related physical and chemical performance index function of the lubricating oil, and obtaining the radar cloud picture of the oil change component after finishing the data.
4. The evaluation method of the multi-index fusion lubricating oil based on the cloud picture as set forth in claim 3, wherein the analysis of the iron spectrum IS further accurate, and the baseline value, the warning line and the dangerous line of the iron spectrum sample are calculated by adopting WPC and IS by using a method based on mathematical statistics, and the specific calculation steps are as follows:
setting a baseline value by adopting a mathematical statistics method, selecting WPC as a quantitative parameter of baseline setting, and calculating as follows:
baseline value:
calculation of control lines:
warning value: WPC (WPC) A ＝WPC B +2S
Risk value: WPC (WPC) C ＝WPC B +3S；
Wherein WPC is the abrasive particle concentration, and represents the total abrasion value and the total abrasion amount to reflect the change of the abrasion state; IS the abrasive particle intensity index; d (D) L Is the maximum number of abrasive particles; d (D) S Is the minimum number of abrasive particles; v is the volume of the oil sample; n is WPC of the same type of monitoring object and is taken as a sample space; WPC (WPC) STDi Is a quantitative parameter based on sample space and oil sample volume;s is the standard deviation of test data, S 2 Is delta 2 Delta is the correction value of the measured data;
comparing the WPC value of the abrasive particle concentration of the sample oil with the established baseline value to obtain a discrimination result, and storing the discrimination result in a result database, wherein the discrimination method comprises the following steps:
5. the evaluation method of the cloud picture-based multi-index fusion lubricating oil is characterized in that the abrasion elements comprise copper, aluminum and silicon, the copper elements are from engine abrasion, the aluminum elements are from abrasion of pistons and cylinder walls, and the silicon elements are from dust and/or sand from the outside.
6. The evaluation method of the multi-index fusion lubricating oil based on the cloud picture according to claim 5 is characterized in that the oil change indexes of each component index parameter of the lubricating oil are respectively as follows: the kinematic viscosity of the lubricating oil exceeds +/-20%; the mass fraction of the water exceeds 0.2% of the lubricating oil; the flash point has a value below 130 ℃; a base number reduction of greater than 50 b The%; the mass fraction of insoluble matters is more than 2% of the lubricating oil; iron content greater than 150ug/g; abrasion element content: cu content is greater than 55ug/g and/or Al content is greater than 35ug/g and/or Si content is greater than 35ug/g; infrared absorption spectroscopy analysis: the mass fraction of the elements Cu, al and Si is more than 30% of the lubricating oil.
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| CN117701329A (en) * | 2024-02-06 | 2024-03-15 | 青岛众屹科锐工程技术有限公司 | Lubricating oil reduction and purification control method and system based on data analysis |
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| CN117109906A (en) * | 2023-10-24 | 2023-11-24 | 卡松科技股份有限公司 | Oil online equipment fault analysis method and system based on visualization |
| CN117109906B (en) * | 2023-10-24 | 2024-01-30 | 卡松科技股份有限公司 | Oil online equipment fault analysis method and system based on visualization |
| CN117701329A (en) * | 2024-02-06 | 2024-03-15 | 青岛众屹科锐工程技术有限公司 | Lubricating oil reduction and purification control method and system based on data analysis |
| CN117701329B (en) * | 2024-02-06 | 2024-04-26 | 青岛众屹科锐工程技术有限公司 | Lubricating oil reduction and purification control method and system based on data analysis |
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