CN101900672A - Method for quickly identifying class and viscosity grade of lubricating oil - Google Patents
Method for quickly identifying class and viscosity grade of lubricating oil Download PDFInfo
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Abstract
The invention discloses a method for quickly identifying the class and viscosity grade of lubricating oil. The method comprises the following steps of: (1) performing infrared spectrum measurement on to-be-identified lubricating oil, predicting a class value of the to-be-identified lubricating oil by using the absorbance of the infrared spectrum, and determining a large class of the to-be-measured lubricating oil according to the class value, or predicting a total base number of the to-be-identified lubricating oil according to the absorbance of the infrared spectrum, and determining the large class of the to-be-measured lubricating oil according to the total base number; and (2) measuring the kinematic viscosity of the to-be-identified lubricating oil at 40 DEG C, or measuring the kinematic viscosity at 40 DEG C and the kinematic viscosity at 100 DEG C of the to-be-identified lubricating oil, and determining the viscosity grade of the to-be-identified lubricating oil according to the measured kinematic viscosity or the kinematic viscosity and the viscosity index. The method can quickly identify the class and viscosity grade of an unknown lubricating oil sample.
Description
Technical field
The present invention is a kind of method for quickly identifying of lubricating oil kind, specifically, is the method for quickly identifying of a kind of lubricating oil types and viscosity grade.
Background technology
Lubricating oil of a great variety, as gasoline engine oil, diesel engine oil, gear oil and hydraulic pressure wet goods, every type lubricating oil has multiple viscosity grade and quality grade again.Therefore, in actual application, for various reasons, usually can exist, as damaging engine or miscellaneous part etc. because of lubricating oil types or the unclear serial problem of bringing of grade.
Lubricating oil normally OIL IN LUBRICATING OIL PRODUCTION merchant is evaluated lubricating oil according to bench test and various physico-chemical property analysis, is labeled on the packing material with the product labelling form of standard.These methods need be used a large amount of instrument and equipments, analytical cycle is long, obviously are not suitable for the demand of on-the-spot quick identification.At present, still do not find any quick identification lubricating oil types and viscosity grade method for distinguishing thereof.
J.Braz.chem.S., Vol.15, No.4,570-576,2004 disclose a kind of " using fourier infrared spectrum lubricating oil to be carried out the method for multivariate quality control " (Multivariate Quality Control ofLubricating Oils Using Fourier Transform Infrared Spectroscopy), this method is by measuring the infrared spectrum of lubricating oil, in conjunction with principal component analysis (PCA), lubricating oil sample is divided into mineral oil, synthetic oil and semi-synthetic oil, and then derives T by the most effective major component
2Figure derives Q figure by remaining major component, by T
2Figure and Q figure think from 33 and pick out 7 samples out of control the lubricating oil of having degraded.
Guan Liang etc. are in " petroleum journal " (the 24th the 3rd phase of volume of June in 2008, P350～355) article of " dielectric spectra technology quick identification different formulations system IC engine lubricating oil " is disclosed, this article is by the dielectric spectra of lubricating oil, by principal component analysis (PCA) rejecting abnormalities point, re-use partial least square method and carry out hierarchical classification, by assignment, divide two-layer product zone to separate with Mobil, Esso and Shell company.
Summary of the invention
The method for quickly identifying that the purpose of this invention is to provide a kind of lubricating oil types and viscosity grade, but whether infrared spectrum and the kinematic viscosity quick identification tested oil product of this method by sample is a kind of in I. C. engine oil, hydraulic oil and the gear oil, and, further identify the viscosity grade of oil product according to the kind of discerning.
Other method for quickly identifying of lubricating oil viscosity level provided by the invention comprises the steps:
(1) lubricating oil to be identified is carried out infrared spectrum measurement, predict the classification value of lubricating oil to be identified, determine the kind of lubricating oil to be measured again by its classification value by the absorbance of infrared spectrum; Perhaps predict the total base number of lubricating oil to be identified, determine the kind of lubricating oil to be measured by total base number by the absorbance of infrared spectrum,
(2) measure 40 ℃ of kinematic viscosity of lubricating oil to be identified, the perhaps kinematic viscosity of 40 ℃ of kinematic viscosity and 100 ℃ is determined its viscosity grade by the kinematic viscosity of measuring or kinematic viscosity and viscosity index.
The present invention is by the infrared spectrum of tested oil product, measurable total base number or the classification value that goes out sample, and judge the kind of oil product thus, further discern the viscosity grades of various types of oil products again by 40 ℃ of kinematic viscosity, 100 ℃ of conditions that kinematic viscosity, viscosity index satisfied.This method can be judged the kind and the viscosity grade of unknown oil product rapidly in 15 minutes, for departments such as quality of lubrication oil supervision, engine overhaul provide on-the-spot reference frame.
Description of drawings
The lubricating oil classification model of cognition that Fig. 1 obtains with the leaving-one method cross-verification for the present invention.
Fig. 2 is the infrared original spectrogram of 103 lubricating oil sample.
Total base number predicted value and the prediction between the standard method result-reality figure that Fig. 3 obtains with the leaving-one method cross-verification for the present invention.
Total base number predicted value that Fig. 4 obtains with the leaving-one method cross-verification for the present invention and the residual error distribution plan between the standard method result.
Embodiment
The inventive method is discerned lubricating oil sample in two steps, the first step is measured the infrared spectrum that is identified lubricating oil, by ir data, the predicted value that draws by the forecast model of setting up in advance, judge the kind of oil sample, and then the viscosity number of mensuration lubricating oil sample, judge the viscosity grade of oil sample by the kinematic viscosity under the different temperatures, viscosity index value, thereby only can know fast by the infrared spectrum and the viscosity data of sample which kind oil tested oil product belongs to, and which viscosity grade tested oil product belongs under this kind.
The kind of the described lubricating oil of the inventive method is divided into I. C. engine oil, hydraulic oil and gear oil.The method that the present invention is used to discern lubricating oil types has two kinds: the one, try to achieve the classification value of tested oil product by infrared spectrum, and determine the oil product kind by the classification value; Another kind is the total base number of being tried to achieve tested oil product by infrared spectrum, determines the oil product kind by total base number.
Classification value or total base number by the tested oil product of infrared spectrum measurement, need to be associated with its infrared spectrum by the normal data of known lubricating oil sample or the classification value of giving in advance in advance, set up forecast model, then classification value or the total base number that obtains predicting by the ir data of forecast model and sample.
Specifically, the method of being classified by the classification value of infrared spectrum prediction lubricating oil is: selected some various types of known lubricating oil sample that comprise, measure its infrared spectrum, and carry out the single order differential and handle, be divided into three kinds by I. C. engine oil, hydraulic oil and gear oil then, difference assignment-1,0 ,+1, choose 1800～600cm
-1The absorbance of scope is set up the classification model of cognition with partial least square method, measures the infrared spectrum of lubricating oil to be identified under the condition identical with modeling, chooses the 1800～600cm that handles through the single order differential
-1The absorbance of scope, substitution classification model of cognition, obtain classification discre value by the infrared spectrum prediction, the classification discre value of prediction is-1.5～-0.5 o'clock, lubricating oil to be identified is I. C. engine oil, and the classification discre value of prediction is greater than-0.5 and less than 0.5 o'clock, and lubricating oil to be identified is hydraulic oil, the classification discre value of prediction is 0.5～1.5 o'clock, and lubricating oil to be identified is gear oil.
Method of being classified by the total base number of infrared spectrum prediction lubricating oil of the present invention is: selected some various types of known lubricating oil sample that comprise, be divided into the calibration set and the checking collection that comprise various types of oil product, measure the total base number and the infrared spectrum of each sample of calibration set respectively with standard method, total base number and its 1780～610cm that will measure by standard method through the processing of single order differential
-1The absorbance of infrared spectrum, adopt partial least square method to set up the total base number calibration model, by the accuracy of checking collection sample survey model; Measure the infrared spectrum of lubricating oil to be identified by the condition identical, choose the 1780～610cm that handles through the single order differential with modeling
-1The absorbance of scope, the substitution calibration model obtains the total base number by the infrared spectrum prediction, when total base number was 3.78～15.1mgKOH/g, lubricating oil to be identified was I. C. engine oil, when total base number is 0.84～2.18mgKOH/g, be gear oil, when total base number is 0～0.46mgKOH/g, be hydraulic oil.
The standard method of described working sample total base number is the perchloric acid potentiometric titration, adopts SH/T 0251 method to measure the total base number of known lubricating oil sample, is called the total base number basic value or the reference value of correcting sample.
Above-mentioned partial least square method (PLS) method that is used to set up model of cognition is a kind of homing method based on characteristic variable in essence, if concentration array category assignment with the known class sample, then the PLS method can be used for the discriminance analysis of spectrum class, be commonly referred to pseudo-PLS return (Dummy partial least-squaresregression, DPLS).Similar with quantitative correction, because the PLS method is decomposed spectrum battle array and classification battle array simultaneously, strengthened the effect of classification information when spectral resolution, to extract and the maximally related spectral information of sample class, i.e. the difference between the different classes of spectrum is extracted in maximization.
Setting up prediction residual quadratic sum (PRESS) value that the validation-cross process of the required best main cause subnumber of forecast model by leaving-one method obtain with partial least square method by the known sample determination data chooses.The validation-cross process of leaving-one method is as follows: to a certain main cause subnumber, from correcting sample, choose a sample and be used for prediction, sample with remainder is set up calibration model, predict the classification value or the measured value of this sample, then, this sample is put back to calibration set, from correcting sample, choose another one again, repeat above-mentioned process as prediction.Through modeling and prediction repeatedly, all once predicted and only once predicted until all correcting samples, then obtain the PRESS value of corresponding this factor number:
Y wherein
iBe the practical measurement value or the classification value of i sample,
Be the predicted value that i sample validation-cross process obtains, n is the sample number of calibration set.
Optimum spectral range and preprocessing procedures are selected by the calibration standard deviation SECV in the validation-cross process, and preprocessing procedures comprises average centralization, standardization, level and smooth, single order differential, second-order differential, polynary scatter correction (MSC), standard normal variable conversion (SNV) and orthogonal signal corrections (OSC) etc.
Y wherein
iBe the practical measurement value or the classification value of i sample,
Be the predicted value that i sample validation-cross process obtains, n is the sample number of calibration set.The bright model of building of novel is outstanding more more for SECV.In the actual modeling process, the preferred 1800～600cm of the spectral range of classification model of cognition
-1, the preferred single order differential of preprocessing procedures; Preferred 1780～the 610cm of the spectral range of total base number calibration model
-1, the preferred single order differential of preprocessing procedures.
After setting up the total base number calibration model, need positive model for school building to be verified with checking collection sample.Verification method is: at first adopt and set up the identical method of calibration model the spectrum of checking collection sample is carried out pre-service, and choose identical spectrum range, with the model of setting up the total base number that checking collects sample is predicted then.By verifying the accuracy of the prediction standard deviation S EP evaluation model that collects, require SEP to collect 7% of sample total base number reference value mean value greater than checking.
Y wherein
iBe the reference value of i sample,
Be the predicted value of i sample, m is the sample number of checking collection.
After discerning its kind according to the infrared spectrum that is identified lubricating oil, determine the viscosity grade of sample by following method:
When determining that by (1) step oil product to be identified is I. C. engine oil, measure the kinematic viscosity of its 40 ℃ of kinematic viscosity or 40 ℃ of kinematic viscosity and 100 ℃, according to the form below 1 listed data area is discerned the viscosity grade of oil product to be identified, recognition methods is to discern the oil product rank by 40 ℃ kinematic viscosity value earlier, can not discern, discern in conjunction with 100 ℃ of kinematic viscosity values again, still can not discern, by the kinematic viscosity value and the viscosity index identification oil product rank of 40 ℃ and 100 ℃.
Table 1
The required range of parameter values of identification oil sample is classified on a left side three as in the table 1, and right one classifies the oil product rank of being determined by the left column range of parameter values as.Some oil product rank only needs the single order parameter just can determine, only need can determine with 40 ℃ kinematic viscosity value as 10W; Other oil product ranks need determine with the two-stage parameter value, as 20,20W, 20W/20 oil require determine by kinematic viscosity and 100 ℃ of kinematic viscosity values of 40 ℃; Also have some oil product ranks to determine with three grades of parameters, need be definite as 10W/40 by 40 ℃ kinematic viscosity, 100 ℃ of kinematic viscosity and viscosity index value.
When determining that by (1) step oil product to be identified is gear oil, measure the kinematic viscosity of its 100 ℃ of kinematic viscosity or 40 ℃ and 100 ℃, according to the form below 2 listed data areas are discerned the viscosity grade of oil product to be identified, the method of identification is earlier with 100 ℃ of kinematic viscosity value identification oil product ranks, can not discern, again in conjunction with viscosity index identification oil product rank.
Table 2
The using method of table 2 is identical with table 1, determines the oil product rank by a left side three row parameter value from left to right, as determining 80W level oil, only needs 100 ℃ of kinematic viscosity; Similarly, determine 140 grades of oil, also only need 100 ℃ of kinematic viscosity, but can not with 140 and two kinds of oil of 85W/140 differentiate.Determine 90,85W/90 level oil, need to determine with the 100 ℃ of kinematic viscosity and the viscosity index of sample, but can not with 90 and two kinds of oil of 85W/90 differentiate.
When determining that by (1) step oil product to be identified is hydraulic oil, measure 40 ℃ of kinematic viscosity of oil product to be identified, by following table 3 identification oil product ranks.
Table 3
By table 3, only need to determine 40 ℃ of kinematic viscosity of tested oil sample, can know the ISO3448 viscosity grade of oil product.
This method can be used for quality of lubrication oil detection, engine overhaul and other needs and differentiates the occasion of lubricating oil types with rapid use fast, can carry out quick identification, screen and select for use unknown lubricating oil.
Further describe the present invention below by example, but the present invention is not limited to this.
Example 1
Set up oil product classification model of cognition.
1) collects new oil samples
Collection has 126 lubricating oil infrared spectrums of clear and definite kind sign, and wherein I. C. engine oil is 86,26 in hydraulic oil, and 14 of gear oils, its manufacturer comprises shell, ESSO, Mobil, MOBIL, Great Wall and the Kunlun etc.
2) infrared spectrum of mensuration lubricating oil
Adopt the infrared oil quality analyzer of Varian that above-mentioned each lubricating oil oil product is carried out infrared spectrum measurement, spectral range: 547～4100cm
-1The transmission sample pond, the 0.1mm light path.
3) data processing
Adopt " the Chemical Measurement spectral analysis software 3.0 " of Research Institute of Petro-Chemical Engineering's establishment to set up PLS classification calibration model.
Before the modeling, the infrared spectrum of measuring oil sample is carried out the single order differential handles, the kind of press I. C. engine oil, hydraulic oil and gear oil distinguish assignment-1,0 and+1, choose 1800～600cm then
-1The spectroscopic data of spectrum range, the best main cause subnumber of spectrum adopts the prediction residual quadratic sum (PRESS) of cross verification gained to determine.
4) set up the classification model of cognition
With the 1800～600cm of 126 lubricating oil infrared spectrums through the processing of single order differential
-1The spectroscopic data of spectrum range and its assignment are set up the classification model of cognition with PLS, and best main cause subnumber is chosen for 11 by PRESS figure.126 lubricating oil are seen Fig. 1 with the classification model of cognition that the leaving-one method cross-verification obtains.Fig. 1 shows that 126 lubricating oil are separated fully according to the order of I. C. engine oil (1～No. 86), hydraulic oil (87～No. 112) and gear oil (113～No. 126).The discre value scope of I. C. engine oil is-1.5～-0.5, the discre value scope of hydraulic oil greater than-0.5 and less than 0.5 between, the discre value scope of gear oil is 0.5～1.0.
Example 2
Set up the total base number calibration model of oil product.
1) collects lubricating oil sample
Collect 103 of lubricated oil samples, comprise gasoline engine oil, diesel engine oil, general automobile oil, hydraulic oil and a plurality of kinds of gear wet goods, viscosity grade and quality scale are also varied.The key property distribution range of oil sample sees Table 4.The property distribution scope of collecting oil sample as can be seen from Table 4 is very wide, has represented the situation of each class lubricating oil substantially.
Table 4
2) infrared spectrum and the total base number of mensuration lubricating oil
The infrared spectrum of oil sample adopts the infrared oil quality analyzer of Varian to measure spectral range: 547～4100cm
-1The transmission sample pond, the 0.1mm light path.The infrared original spectrogram of 103 lubricating oil sample is seen Fig. 2.
The total base number of measuring each lubricating oil sample with SH/T 0251 (perchloric acid potentiometric titration) method is used for modeling.
3) set up calibration model
Adopt " the Chemical Measurement spectral analysis software 3.0 " of Research Institute of Petro-Chemical Engineering's establishment to set up calibration model.Before the modelling, at first 103 lubricating oil samples are divided into calibration set (63 samples) and checking collection (36 samples), table 5 is the sample distribution situation of calibration set and checking collection.Infrared spectrum is handled through the single order differential earlier, to eliminate the influence of factors such as color sample and baseline wander, then infrared spectrum is associated with the total base number of being measured by SH/T 0251 method, the best main cause subnumber of spectrum adopts the prediction residual quadratic sum (PRESS) of cross verification gained to determine.
Table 5
The used parameter of total base number and modeling and the results are shown in Table 6.According to the correlativity of the infrared spectrum and the total base number of calibration set sample, choose 1780～610cm
-1The single order differential smoothing of scope participates in modelling.Adopt the PLS method to set up total base number quantitative correction model, best main cause subnumber is chosen for 12 by PRESS figure.Total base number is seen Fig. 3, Fig. 4 respectively with the prediction between predicting the outcome of obtaining of leaving-one method cross-verification and the standard method result-reality figure and residual error distribution plan.The calibration standard deviation (SECV) that the total base number cross-verification obtains is 0.28mgKOH/g.
Table 6
4) modelling verification
With 36 checking collection samples institute's established model is verified.With the method same with setting up calibration model the spectrum that checking collects sample is carried out pre-service, and choose the identical spectra scope, the total base number measurement result of checking collection sample sees Table 7.As shown in Table 7, the total base number result of infrared spectrum measurement is consistent with the measurement result of standard method, and the prediction standard deviation (SEP) of its total base number is 0.36mgKO g, and is suitable with the SECV of calibration model.The residual error major part of predicted value and actual value all is positioned within the repeatability scope of standard method.The SECV of total base number is less than 7% of its reference value mean value.The The above results explanation, in the coverage of institute's established model, the calibration model that this method is set up is accurately to the total base number of measuring the lubricating oil fresh oil as if testing sample.
Table 7
5) repeatability is investigated
With the calibration model of being set up the repeatability of measuring the lubricating oil total base number is tested.Choose a sample arbitrarily from verifying to concentrate, 6 near infrared spectrums of duplicate measurements calculate the total base number of this sample by the calibration model of setting up, and the results are shown in table 8.Test findings shows, compares with the repeatability of standard method, and method of infrared spectrophotometry has better repeatability.
Table 8
Example 3
Choose a Great Wall lubricant corporation that sign arranged clearly produce viscosity grade be the diesel engine oil of 15W/40 as Target Recognition lubricating oil, adopt the inventive method to discern checking, identification step is as follows:
(1) measures the infrared spectrum of lubricating oil to be identified by the method for example 1, carry out choosing 1800～600cm after the single order differential handles
-1The spectroscopic data of spectrum range, the model of cognition that substitution example 1 is set up, the classification value that calculates sample is-1.05.Because this classification value in-0.5～-1.5 scope, judges that this oil is I. C. engine oil.
(2) 40 ℃ of kinematic viscosity and 100 ℃ of kinematic viscosity of measuring lubricating oil to be identified by standard method GB/T 265 are respectively 113.5mm
2/ s and 15.22mm
2/ s, calculating this oil body index according to standard method GB/T 2541 again is 140.Because of 40 ℃ of viscosity of lubricating oil to be identified at 87～144mm
2In/s the scope, 100 ℃ of kinematic viscosity are at 12.5～16.3mm
2In/s the scope, viscosity index is in 131～145 scopes, and the condition of the identification I. C. engine oil viscosity grade that provides according to table 1 judges that this oil viscosity grade is 15W/40.
Recognition result is: this oil product is an I. C. engine oil, and viscosity grade is 15W/40.
Example 4
With the infrared spectrum that example 3 tested lubricating oil are handled through the single order differential, choose 1780～610cm
-1The spectroscopic data of spectrum range, the calibration model that substitution example 2 is set up, the total base number that calculates oil product is 11.51mgKOH/g, judges that this oil is I. C. engine oil.Method according to 3 (2) steps of example determines that the viscosity grade of this oil product is 15W/40.
Example 5
Choosing the viscosity grade that a Mobil Corp. that sign arranged clearly produces is the gear oil of 80W/90, adopts the inventive method to discern checking, and identification step is as follows:
(1) measures the infrared spectrum of lubricating oil to be identified by the method for example 1, carry out choosing 1800～600cm after the single order differential handles
-1The spectroscopic data of spectrum range, the classification model of cognition that substitution example 1 is set up, calculating its classification value is 0.72.Because this classification value in 0.5～1.5 scope, judges that this oil is gear oil.
(2) 40 ℃ of kinematic viscosity and 100 ℃ of kinematic viscosity of measuring lubricating oil to be identified by standard method GB/T 265 are respectively 130.2mm
2/ s and 14.23mm
2/ s, calculating this oil body index according to standard method GB/T 2541 again is 108.Because of its 100 ℃ of kinematic viscosity at 13.5～24.0mm
2In/s the scope, viscosity index according to the gear oil viscosity grade condition for identification that table 2 provides, judges that this oil viscosity grade is 80W/90 in 100～130 scopes.
Recognition result is: lubricating oil to be identified is that viscosity grade is the gear oil of 80W/90.
Example 6
With the infrared spectrum that example 5 tested lubricating oil are handled through the single order differential, choose 1780～610cm
-1The spectroscopic data of spectrum range, the calibration model that substitution example 2 is set up, the total base number that calculates oil product is 1.56mgKOH/g.Because its total base number is in 0.84～2.18mgKOH/g scope, judging this oil is gear oil.Method according to 5 (2) steps of example determines that the viscosity grade of this oil product is 80W/90.
Example 7
Choose viscosity grade that a Shell Co. Ltd that sign arranged clearly produces and be 68 hydraulic oil, adopt the inventive method to discern checking, identification step is as follows:
(1) measures the infrared spectrum of lubricating oil to be identified by the method for example 1, carry out choosing 1800～600cm after the single order differential handles
-1The spectroscopic data of spectrum range, the classification model of cognition that substitution example 1 is set up, calculating its classification value is 0.18.Because this classification value greater than-0.5 with less than in 0.5 the scope, judges that this oil is hydraulic oil.
(2) 40 ℃ of kinematic viscosity measuring lubricating oil to be identified by standard method GB/T265 are 68.48mm
2/ s.According to the ISO viscosity grade condition that table 3 provides, judge that this oil viscosity grade is 68.
Example 8
With the infrared spectrum that example 9 tested lubricating oil are handled through the single order differential, choose 1780～610cm
-1The spectroscopic data of spectrum range, the calibration model that substitution example 2 is set up, the total base number that calculates oil product is 0.34mgKOH/g.Because its total base number in 0～0.46mgKOH/g scope, judges that this oil is hydraulic oil.Method according to 9 (2) steps of example determines that the viscosity grade of this oil product is 68.
Claims (7)
1. other method for quickly identifying of lubricating oil viscosity level comprises the steps:
(1) lubricating oil to be identified is carried out infrared spectrum measurement, predict the classification value of lubricating oil to be identified, determine the kind of lubricating oil to be measured again by its classification value by the absorbance of infrared spectrum; Perhaps predict the total base number of lubricating oil to be identified, determine the kind of lubricating oil to be measured by total base number by the absorbance of infrared spectrum,
(2) measure 40 ℃ of kinematic viscosity of lubricating oil to be identified, the perhaps kinematic viscosity of 40 ℃ of kinematic viscosity and 100 ℃ is determined its viscosity grade by the kinematic viscosity of measuring or kinematic viscosity and viscosity index.
2. in accordance with the method for claim 1, the kind that it is characterized in that described lubricating oil is divided into I. C. engine oil, hydraulic oil and gear oil.
3. in accordance with the method for claim 1, it is characterized in that described method of being classified by the classification value of infrared spectrum prediction lubricating oil of (1) step is: selected some various types of known lubricating oil sample that comprise, measure its infrared spectrum, and carry out the single order differential and handle, be divided into three kinds by I. C. engine oil, hydraulic oil and gear oil then, difference assignment-1,0 ,+1, choose 1800～600cm
-1The absorbance of scope is set up the classification model of cognition with partial least square method, measures the infrared spectrum of lubricating oil to be identified under the condition identical with modeling, chooses the 1800～600cm that handles through the single order differential
-1The absorbance of scope, substitution classification model of cognition, obtain classification discre value by the infrared spectrum prediction, the classification discre value of prediction is-1.5～-0.5 o'clock, lubricating oil to be identified is I. C. engine oil, and the classification discre value of prediction is greater than-0.5 and less than 0.5 o'clock, and lubricating oil to be identified is hydraulic oil, the classification discre value of prediction is 0.5～1.5 o'clock, and lubricating oil to be identified is gear oil.
4. in accordance with the method for claim 1, it is characterized in that described method of being classified by the total base number of infrared spectrum prediction lubricating oil of (1) step is: selected some samples that comprise various types of known lubricating oil, be divided into the calibration set and the checking collection that comprise various types of oil product, measure the total base number and the infrared spectrum of each sample of calibration set respectively with standard method, total base number and its 1780～610cm that will measure by standard method through the processing of single order differential
-1The absorbance of the infrared spectrum of scope adopts partial least square method to set up the total base number calibration model, by the accuracy of checking collection sample survey model; Measure the infrared spectrum of lubricating oil to be identified by the condition identical, choose the 1780～610cm that handles through the single order differential with modeling
-1The absorbance of scope, the substitution calibration model, obtain total base number by the infrared spectrum prediction, when total base number is 3.78～15.1mgKOH/g, lubricating oil to be identified is I. C. engine oil, and when total base number was 0.84～2.18mgKOH/g, lubricating oil to be identified was gear oil, when total base number was 0～0.46mgKOH/g, lubricating oil to be identified was hydraulic oil.
5. in accordance with the method for claim 1, it is characterized in that when determining that by (1) step oil product to be identified is I. C. engine oil, measure its 40 ℃ of kinematic viscosity or and the kinematic viscosity of 40 ℃ and 100 ℃, the listed data area of according to the form below is discerned the viscosity grade of oil product to be identified, recognition methods is to discern the oil product rank by 40 ℃ kinematic viscosity value earlier, can not discern, discern in conjunction with 100 ℃ of kinematic viscosity values again, still can not discern, by 40 ℃, 100 ℃ kinematic viscosity value and viscosity index identification oil product rank, described viscosity index is pressed the method for GB/T1995-88 and is calculated.
6. in accordance with the method for claim 1, it is characterized in that when determining that by (1) step oil product to be identified is gear oil, measure the kinematic viscosity of its kinematic viscosity of 100 ℃ or 40 ℃ and 100 ℃, the listed data area of according to the form below is discerned the viscosity grade of oil product to be identified, the method of identification is earlier with 100 ℃ of kinematic viscosity value identification oil product ranks, can not discern, again in conjunction with viscosity index identification oil product rank.
7. in accordance with the method for claim 1, it is characterized in that when determining that by (1) step oil product to be identified is hydraulic oil, measuring 40 ℃ of kinematic viscosity of oil product to be identified, by following table identification oil product rank.
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Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0706050A1 (en) * | 1994-10-07 | 1996-04-10 | Bp Chemicals S.N.C. | Lubricant property determination |
| US20030164451A1 (en) * | 2002-02-26 | 2003-09-04 | Reischman Paul Thomas | Method for the analysis of lubricant basicity |
| DE10235612B4 (en) * | 2002-08-02 | 2012-06-21 | Siemens Aktiengesellschaft | Method and device for monitoring the quality of lubricating oil |
| DE102007018392A1 (en) * | 2007-04-17 | 2008-10-23 | Bayer Technology Services Gmbh | Lubricant i.e. lubricating oil, quality determining method for e.g. gas turbine, involves recording current/potential gradient to determine residual content at antioxidants, and determining number and size of solid particles |
-
2009
- 2009-05-27 CN CN 200910143621 patent/CN101900672B/en active Active
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