Method for generating estimation model of oil physical properties, method for diagnosing oil physical properties, and system for diagnosing oil physical properties

Abstract

An object of the present invention is to provide a technique for optically quantifying physical properties of oil.SOLUTION: According to an aspect of the present invention, there is provided a method for generating an estimation model of an oil physical property, the method including using an information processing system including an input device, an output device, a processing device, and a storage device, the information processing system including an estimation model generation unit configured to generate a machine-learning-capable estimation model, the estimation model generation unit being configured to input a light absorption spectrum obtained by transmitting light having a wavelength of at least a part of wavelengths from 800 nm to 2,500 nm to an oil including a base oil and an additive, a value reflecting a physical property value of the oil, and a value reflecting a physical property value of the oil as explanatory variables, and generate the estimation model by machine learning based on the light absorption spectrum.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] The present invention relates to an oil diagnostic technology, particularly to the maintenance of large machinery that uses industrial oils such as lubricating oil, insulating oil, and processing oil, and relates to a technology suitable for monitoring machinery by measuring changes in the physical properties of lubricating oil and other oils that occur with use, and diagnosing the remaining life of the oil and predicting the condition of the machinery. [Background technology]

[0002] Diagnosing the physical properties of lubricants used in rotating parts such as bearings and gears is an important technique for maintaining large rotating machinery. Examples of large rotating machinery include wind turbine gearboxes, air compressors, ships, power generation turbines, construction machinery, agricultural machinery, cutting machines, pumps, and reduction gears for railway vehicles.

[0003] In addition to lubricating oils, insulating oils are used in transformers and other devices for electrical insulation, and diagnosing the physical properties of insulating oils is also important. Processing oils are also used during machining. There are processing oils for various purposes, such as cutting oil, press oil, heat treatment oil, rust prevention oil, and cleaning oil. In this specification, industrial oils such as lubricating oil, insulating oil, and processing oil are sometimes collectively referred to as oil.

[0004] Lubricating oils are classified into various types depending on their intended use, such as engine oil, turbine oil, hydraulic oil, bearing oil, sliding surface oil, gear oil, compressor oil, and cutting oil. Various additives are blended into the base oil (oil used as the base material) to ensure that each type of lubricating oil meets the required performance. Additives are also blended into other oils to achieve the properties required for each type.

[0005] In recent years, machine condition monitoring has often adopted a strategy to minimize the lifecycle costs of the machine. Large machines such as power generation turbines use large amounts of lubricating oil, and changing the lubricating oil requires stopping the machine, which has negative aspects such as power generation losses and production stoppages. In addition, it requires costs for purchasing and shipping new oil, oil change labor, and waste oil disposal, so it is desirable to use the lubricating oil for as long as possible. Refrigerant fluids in electric vehicles and data centers also undergo oil diagnosis, and replacement or hardware repairs are carried out. Similarly, the color and other aspects of insulating oil in transformers are monitored.

[0006] Recently, from the perspective of carbon neutrality, automobiles and other vehicles that use large amounts of petroleum-derived fuels are being electrified, and fuel demand will decrease in the future. However, there are often no alternatives for industrial oil, so there is a demand to minimize usage by extending oil change intervals, etc. This is because reducing oil consumption reduces carbon dioxide emissions. However, overlooking oil deterioration and contamination can lead to machine breakdowns.

[0007] Regarding the diagnosis of lubricating oil properties, we define and distinguish between "deterioration" and "contamination." Broadly speaking, it is necessary to diagnose two types of deterioration: (1) oxidative deterioration of lubricating oil over time, and (2) contamination by external contaminants such as water, dust, and wear particles.

[0008] (1) Oxidative degradation of lubricating oils includes degradation due to oxidation of the base oil and degradation due to the consumption of additives. Oxidative degradation of lubricating oils leads to a decrease in wear resistance, changes in viscosity and viscosity index, a decrease in rust prevention properties, and a decrease in corrosion prevention properties. As a result, it can accelerate wear and material fatigue in gearboxes. While it is desirable to use oil for as long as possible, any abnormal deterioration or contamination must be promptly replaced and the equipment inspected.

[0009] As lubricating oil is used for a long time, additives are consumed, and its performance as a lubricating oil declines. Specifically, when antioxidants are consumed, oxidation of the base oil can progress rapidly. When the base oil oxidizes, the viscosity of the lubricating oil increases, the thickness of the lubricating film on the sliding surface changes, and appropriate lubricating performance cannot be achieved. Furthermore, additive consumption is an oxidation reaction, and as additive consumption and base oil oxidation progress, the acid number and base number, which are indicators of the acidity of the lubricating oil, change. When the acidity of a lubricating oil increases, corrosion and rust become more likely to occur in parts such as bearings and gears. Viscosity is sometimes called kinematic viscosity. Changes in density are also observed as the viscosity and acid number of a lubricating oil change over time.

[0010] Against this background, efforts have been made to prevent machinery breakdowns and avoid excessive oil changes by measuring the viscosity, acid number, base number, and density of lubricating oils.

[0011] Conventional methods for evaluating the physical properties of lubricating oils include definitions for viscosity, acid number, base number, and density. Lubricating oil density represents the mass per unit volume, usually expressed as grams per cubic centimeter. Viscosity (kinematic viscosity) is an index of the fluidity of a lubricating oil. Viscosity is measured by measuring the flow time of a liquid under calibrated gravity. Acid number is the amount of potassium hydroxide required to neutralize the acidic components contained in 1 g of lubricating oil, expressed in mg.

[0012] Base number is an index that indicates the amount of base components contained in engine oil. It is expressed as the number of milligrams of potassium hydroxide, equivalent to the amount of acid, required to neutralize the base components contained in 1 g of lubricating oil. Generally, engine oil contains sulfur, and combustion in the engine produces sulfuric acid, which corrodes cylinders and piston rings, so basic components are added to engine oil, and therefore the base number of engine oil is defined. Both acid number and base number are measured using neutralization titration.

[0013] All of the conventional methods for evaluating the physical properties of lubricating oils mentioned above use expensive, precise analytical equipment, and sometimes require the use of reagents or pretreatment during analysis, making them highly popular in laboratories and oil analysis companies. However, installing a sensor in a machine that uses lubricating oil to learn the oil's physical properties allows for early detection of abnormalities in the oil and the machine, so oil sensors are sometimes used. Unlike analytical equipment, oil sensors cannot measure all of the oil's physical properties, and methods known include MEMS-type sensors that measure low-viscosity oils, and sensors that measure the permittivity and conductivity to predict ongoing changes in the oil's physical properties. Because these sensors require contact with the oil for measurement, the oil sensors are installed by modifying the pipes or tanks through which the oil flows.

[0014] Patent Document 1 describes a technique in which lubricating oil that has deteriorated due to use is filtered through a filter, and the infrared absorption spectrum of the filtered filter is measured by Fourier transform infrared spectroscopy, and the oxidation rate is estimated based on the absorption spectrum. -1 From 4000cm -1 That is, mid-infrared spectroscopy, which has a wavelength range of 2.5 μm to 20 μm, is used.

[0015] Patent Document 2 describes a method for extracting organic molecules using a supercritical fluid and measuring the vibrations associated with the C-H bonds of the extracted organic molecules by infrared absorption. ―1 That is, spectroscopy using mid-infrared light in the wavelength range of 3.125 μm to 6.25 μm is used. [Prior art documents] [Patent documents]

[0016] [Patent Document 1] JP 2016-035400 A [Patent Document 2] Japanese Patent Application Laid-Open No. 2011-102703 Summary of the Invention [Problem to be solved by the invention]

[0017] Conventional chemical analysis techniques are used to measure the viscosity, acid number, base number, and density of lubricating oils. Recently, there has been an increasing demand for measuring various physical properties of lubricating oils by installing oil sensors on machines that use lubricating oils. However, conventional methods require the application of force to the lubricating oil using equipment or the use of reagents for analysis, making them unsuitable for measurement with an oil sensor.

[0018] There is no way to measure the acid value or base value with an oil sensor, and although there are methods to predict them from the dielectric constant and conductivity, it is difficult to predict many phenomena that occur in oil, such as oxidation of the base oil, consumption of additives, the inclusion of foreign matter such as moisture, fine particles, and wear powder, and the generation of sludge, from the dielectric constant and conductivity, and the accuracy is poor.

[0019] There are MEMS type oil sensors that can measure viscosity, but they can only measure low viscosity oils (50 cP or less) such as engine oil, and cannot measure high viscosity oils such as gear oil.

[0020] Installing oil sensors is costly, requires modifying the machine's piping and oil tank to prevent oil leaks, and poses the challenge of ensuring the reliability of the machine after modification.

[0021] An object of the present invention is to provide a technique for optically quantifying the physical properties of oil. [Means for solving the problem]

[0022] One aspect of the present invention is a method for generating an estimation model of oil physical properties, which uses an information processing system having an input device, an output device, a processing device, and a storage device, and the information processing system has an estimation model generation unit that generates an estimation model that can be subjected to machine learning, and the estimation model generation unit receives as input an absorption spectrum obtained by transmitting light with at least a portion of wavelengths between 800 nm and 2500 nm through an oil containing a base oil and additives, and values ​​that reflect the physical property values ​​of the oil, and generates an estimation model by machine learning using values ​​based on the absorption spectrum as explanatory variables and values ​​that reflect the physical property values ​​of the oil as target variables.

[0023] Another aspect of the present invention is a method for diagnosing oil properties using a diagnostic system consisting of an information processing device that implements the above-mentioned estimation model, in which an absorption spectrum obtained by transmitting light with at least a portion of wavelengths between 800 nm and 2500 nm through an oil to be diagnosed of the same type as the oil containing the additive is input to the diagnostic system as the absorption spectrum of the oil to be diagnosed, a value based on the absorption spectrum of the oil to be diagnosed is input to the estimation model, and a value reflecting the physical property value of the oil to be diagnosed is obtained from the estimation model.

[0024] Another aspect of the present invention is an oil property diagnostic system including an information processing device that implements the above estimation model. [Effects of the Invention]

[0025] According to the present invention, it is possible to provide a technique for optically quantifying the physical properties of oil. Problems, configurations, effects, etc. other than those described above will become clear from the following description of the embodiments. [Brief explanation of the drawings]

[0026] [Figure 1] Schematic diagram of an optical sensor that measures near-infrared absorption spectra. [Figure 2] Schematic diagram of another optical sensor that measures near-infrared absorption spectra. [Figure 3] FIG. 3 is a graph showing the relationship between the kinematic viscosity and temperature of the oil of the example. [Figure 4] FIG. 4 is a graph showing the relationship between the density and temperature of the oil in the example. [Figure 5] FIG. 2 is a graph showing the relationship between the operating time of the machine in the example and the viscosity, acid value, base value, and insoluble content of the oil. [Figure 6] FIG. 1 is a block diagram of an oil diagnosis system according to an embodiment. [Figure 7] 3 is a flowchart of an oil diagnosis method according to an embodiment. [Figure 8] 10 is a flowchart of a measurement data collection process according to an embodiment. [Figure 9] FIG. 2 is a graph showing an example of a near-infrared spectrum of engine oil. [Figure 10] 10 is a flowchart of an object state estimation model generation process according to an embodiment. [Figure 11] A graph showing an example of a near-infrared spectrum, differentiated twice. [Figure 12] 3 is a flowchart of an oil property estimation process according to an embodiment. [Figure 13] FIG. 10 is a graph showing the consistency between predicted values ​​and actual measured values ​​using the estimation model of the embodiment. [Figure 14] FIG. 4 is a flow chart showing an example of engine oil viscosity estimation taking into account the temperature characteristics of viscosity according to the embodiment. [Figure 15] 1 is a schematic diagram of a lubricant monitoring system for a wind turbine generator according to an embodiment; [Figure 16] Conceptual diagram of a rotating part equipped with a lubricant sensor. [Figure 17] FIG. 4 is a flow chart showing a lubricant diagnostic process according to an embodiment. [Figure 18] Graph showing an example of displaying results. DETAILED DESCRIPTION OF THE INVENTION

[0027] The embodiments will be described in detail with reference to the drawings. However, the present invention should not be interpreted as being limited to the description of the embodiments shown below. Those skilled in the art will easily understand that the specific configuration can be changed within the scope of the idea or purpose of the present invention.

[0028] In the configurations of the embodiments described below, the same parts or parts having similar functions are denoted by the same reference numerals in different drawings, and duplicated explanations may be omitted.

[0029] When there are multiple elements with the same or similar functions, they may be described using the same reference numeral with different subscripts. However, when there is no need to distinguish between multiple elements, the subscripts may be omitted.

[0030] The designations "first," "second," "third," etc. in this specification are used to identify components and do not necessarily limit the number, order, or content of the components. Furthermore, numbers used to identify components are used in different contexts, and a number used in one context does not necessarily indicate the same configuration in another context. Furthermore, this does not prevent a component identified by a certain number from also serving the function of a component identified by another number.

[0031] In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings, etc. may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings, etc.

[0032] All publications, patents, and patent applications cited herein are hereby incorporated by reference in their entirety.

[0033] In this specification, elements appearing in the singular include the plural unless the context clearly indicates otherwise.

[0034] In this specification, the wavelength range from 800 nm to 2500 nm is referred to as the near-infrared region.

[0035] In the embodiment, a machine monitoring method using changes in optical absorption intensity in the near-infrared region of oil containing base oil and additives, such as lubricating oil, is used to diagnose oil deterioration and contamination using an optical sensor in the near-infrared region. In principle, if transmittance = 1 - absorbance, then in the embodiment, transmittance and absorbance have the same meaning. Transmittance and absorbance can also be expressed as percentages. Absorbance, also known as optical density, is the negative value of the common logarithm of transmittance.

[0036] Lubricating oils include engine oil, turbine oil, hydraulic oil, bearing oil, sliding surface oil, gear oil, compressor oil, and cutting oil. Lubricating oils are composed of base oil and additives. Additives include antioxidants, rust inhibitors, antifoaming agents, viscosity index improvers, oiliness improvers, extreme pressure additives, detergent dispersants, pour point depressants, and emulsifiers. Oils used for purposes other than lubrication include transformer oil, cleaning oil, and cutting oil. For many oils, the degree of additive consumption through use can be used to determine when to change the oil or to indicate any abnormalities in the machine.

[0037] One example described in the working examples is a diagnostic method for oil containing base oil and additives, in which the intensity of light transmitted through the oil is measured using an optical sensor having a detector sensitive to wavelengths in the range of 800 nm to 2500 nm, i.e., the near-infrared region, and the viscosity, acid number, and base number of the oil are determined in advance using methods such as chemical analysis, and the viscosity, acid number, and base number of the oil are then determined using machine learning.

[0038] According to this configuration, when detecting changes in the physical properties of oil by measuring changes in the light absorption intensity of the oil (measurement object) in the wavelength range of 2500 nm to 800 nm using an optical sensor, the state of the oil can be determined accurately.

[0039] One example of an embodiment is a method for generating an estimation model of oil physical properties, which uses an information processing system having an input device, an output device, a processing device, and a storage device, and the information processing system has an estimation model generation unit that generates an estimation model that can be learned by machine learning, and inputs an absorption spectrum obtained by transmitting light of at least a portion of wavelengths between 800 nm and 2500 nm through an oil containing a base oil and additives, and a value that reflects the physical properties of the oil, into the estimation model generation unit, and generates an estimation model using values ​​based on the absorption spectrum as explanatory variables and values ​​that reflect the physical properties of the oil as target variables.

[0040] Another aspect of the present invention is a method for diagnosing oil properties using a diagnostic system consisting of an information processing device that implements the above-mentioned estimation model, in which an absorption spectrum obtained by transmitting light with at least a portion of wavelengths between 800 nm and 3000 nm through an oil to be diagnosed of the same type as the oil containing the base oil and the additives is input to the diagnostic system, a value based on the absorption spectrum is input to the estimation model, and a value reflecting the physical properties of the oil to be diagnosed is obtained from the estimation model.

[0041] Another example is an oil property diagnostic system that includes an information processing device that implements the above estimation model.

[0042] Another example is a method for diagnosing oil properties, which involves using an optical sensor having a light source and a detector that detects light emitted from the light source to obtain an absorption spectrum in the wavelength range of 800 nm to 2500 nm, and inputting data based on the absorption spectrum into an estimation model implemented in an information processing device to predict the concentration of the additive.

[0043] <Method of measurement by sensor> 1 is a schematic diagram showing the configuration of an example of an optical sensor. Measurement of the near-infrared absorption spectrum of a lubricant is performed by optical sensor 700, which has a light source that generates light with a wavelength in the near-infrared region and a detector that detects wavelengths in the near-infrared region. The lubricant to be measured is measured by detector 103, which detects the light intensity of light emitted from light source 101 (indicated by the arrow in the figure) after it passes through lubricant 102.

[0044] Figure 1 shown above shows one form of sensor, but it is also possible for the light emitted from the light source 101 to pass through the lubricating oil 102 and enter the detector 103 using means such as reflection by a mirror, focusing by a lens, or passing through a prism.

[0045] 2 is a diagram showing another embodiment of the optical sensor. Optical sensor 700A has light source 101 and detector 103 arranged on the same plane, in contact with lubricating oil 102, with reflector 104 installed on the opposite side, and light emitted from light source 101 (indicated by the arrow in the figure) reflected by reflector 104 and received by detector 103. Even in this embodiment, a lens, mirror, or prism may be installed along the optical path.

[0046] By using such a measurement, absorption spectrum data can be obtained without bringing the optical sensor into contact with the oil.

[0047] <Scope of application> Types of oils containing base oil and additives include engine oil, turbine oil, hydraulic oil, bearing oil, slideway oil, gear oil, compressor oil, cutting oil, insulating oil, cutting processing oil, press processing oil, heat treatment oil, rust preventative oil, cleaning oil, etc. Types of additives include antioxidants, rust inhibitors, antifoaming agents, viscosity index improvers, oiliness improvers, extreme pressure additives, detergents, dispersants, pour point depressants, emulsifiers, etc.

[0048] <Near infrared spectroscopy> Near-infrared spectroscopy is an analytical method using light in the wavelength range from 800 nm to 2500 nm. Compared to ultraviolet-visible spectroscopy, which has shorter wavelengths than near-infrared, and mid-infrared spectroscopy, which has longer wavelengths than near-infrared, ultraviolet-visible wavelengths are a region where electronic transitions occur. Optical absorption occurs primarily due to functional groups such as aromatic benzene rings and naphthalene rings, making it difficult to obtain detailed information about molecular structure. Mid-infrared wavelengths are absorbed by intramolecular electronic transitions, i.e., intramolecular atomic vibrations (stretching vibrations, bending vibrations, etc.) such as CH and OH. While a lot of information about molecular structure can be obtained, the absorption is strong and easily saturated due to direct transitions, making it difficult to predict physical properties from mid-infrared absorption spectra. In the near-infrared region, which lies between ultraviolet-visible and mid-infrared wavelengths, absorption due to electronic transitions is almost nonexistent. Instead, absorption occurs at wavelengths equivalent to the sum of the absorption energy due to multiple intramolecular transitions, known as overtones or combination tones, which are two or three times higher than absorption in the near-infrared region.

[0049] Quantitative analysis using optical absorption spectroscopy generally uses the Beer-Lambert law, which states that absorbance is proportional to the concentration and the optical path length. The relationship between transmittance and absorbance is determined by taking the common logarithm of the transmittance, and the negative value is taken as the absorbance.

[0050] Since absorption in the near-infrared region is a forbidden transition, it is very weak compared to absorption in the mid-infrared region, and the Beer-Lambert law can be applied very well to absorbance measurements using transparent cells with optical path lengths of approximately 3 mm to 20 mm.

[0051] <Physical property prediction using near-infrared absorption spectrum data> Almost all of the base oils and additives that make up lubricating oils are organic compounds. Absorption spectra in the near-infrared region contain a lot of information about the molecular structure of the organic compounds that make up the lubricating oil. On the other hand, the inventors have discovered that since the physical properties of lubricating oils, such as viscosity, acid number, and base number, are determined by the molecular structure that makes up the lubricating oil, it is possible to predict the physical properties of lubricating oils, such as viscosity, acid number, and base number, from near-infrared absorption spectrum data, which contains a wealth of information about the molecular structure that makes up the lubricating oil. The physical properties of lubricating oils can be predicted by the following procedure.

[0052] When there are multiple lubricant samples with different usage times for a single lubricant, the viscosity, acid number, and base number of the new oil and the multiple lubricant samples with different usage times are measured using an analytical device, etc. Near-infrared absorption spectrum data is obtained for the new oil and the multiple lubricant samples with different usage times using a spectrophotometer or optical sensor that can measure absorbance in the near-infrared range.

[0053] Spectral preprocessing is an effective step in spectral analysis. In the first step, smoothing is performed to remove spectral noise. For example, a method is used in which the centered moving average method is applied to a selected interval of approximately 3 to 20 adjacent points. In the second step, a second-order derivative filter is applied to the spectrum, which makes it possible to extract wavelengths with large changes in the amount of spectral change, and to extract peaks from spectra that are complexly overlapping and have unclear peak positions, which is typical of near-infrared absorption spectra. Either of these two steps can be performed first. For the derivative filter, a first-order derivative can be used, or it is possible to proceed with the analysis without a derivative filter.

[0054] Next, a model for predicting viscosity, acid number, and base number from near-infrared absorption spectrum data can be created by applying a multivariate analysis method such as partial least squares regression (PLS), with viscosity, acid number, and base number as the response variables and the preprocessed near-infrared absorption spectrum data as the explanatory variables. Validation, which takes place after the prediction calculation, is the process of verifying the input data and model parameters to ensure that the model or system outputs accurate and reliable results, and can be selected from the general methods listed below.

[0055] Cross-validation can be used when the number of samples is not large, as in big data. k-fold cross-validation divides a dataset evenly into k folds, and in each evaluation, one fold is used as the test set and the remaining k-1 folds are used as the training set. Leave-One-Out Cross-Validation (LOOCV) uses each sample in the dataset as a test set and evaluates the remaining samples as the training set, which is useful when the amount of data is small. Stratified k-fold cross-validation divides the data into k folds, just like k-fold cross-validation, but ensures that the class ratio within each fold is the same as in the original dataset. Either method can be used, but when comparing results, the same validation method should be used.

[0056] By creating such a model, for example, by measuring the near-infrared absorption spectrum of a lubricant sample with unknown viscosity, the viscosity can be predicted using the model. However, such a model is only applicable between the same lubricant; for lubricants with different compositions, different lubricant products even if made by the same manufacturer, or lubricants from different manufacturers, a separate model must be created.

[0057] <Viscosity prediction using near-infrared absorption spectrum data> Viscosity has temperature dependency, i.e., temperature characteristics. Viscosity μ can be expressed as a function of temperature T. μ = f(T)

[0058] Figure 3 is an example graph showing the relationship between the kinematic viscosity of oil and temperature. As shown in Figure 3, the higher the temperature, the easier it is for the molecules in the lubricating oil to move, and therefore the viscosity decreases. Since the near-infrared absorption spectrum contains a lot of information about the molecules that make up the lubricating oil, it is possible to predict the viscosity from the near-infrared absorption spectrum of the lubricating oil.

[0059] Lubricants are sometimes evaluated using a physical property called kinematic viscosity ν. Viscosity μ indicates the difficulty of an object moving within a fluid. On the other hand, kinetic viscosity ν indicates the difficulty of the fluid itself to move. The density of an object affects this difficulty of movement. Even if the viscosity μ is the same, if the density is different, the difficulty of movement (kinetic viscosity ν) will be different. Kinematic viscosity ν is obtained by dividing viscosity μ by density.

[0060] The procedure for predicting viscosity μ from near-infrared absorption spectra is as follows. As mentioned above, viscosity μ is known to have temperature characteristics. Furthermore, the temperature of lubricating oils used in machinery changes from moment to moment depending on the outside air temperature and operating conditions. If viscosity μ has temperature characteristics, this means that the near-infrared absorption spectrum of the lubricating oil also has temperature characteristics. When continuously measuring viscosity μ to track changes in the lubricating oil over time, it is necessary to determine in advance that viscosity μ will be obtained at a certain temperature, otherwise changes in the lubricating oil cannot be tracked.

[0061] Therefore, since the temperature characteristics of the viscosity μ of a lubricating oil are determined depending on the type of lubricating oil, and the viscosity μ can be expressed as a function of temperature, it is possible to obtain the near-infrared absorption spectrum at a certain time and at the same time obtain the measured temperature of the lubricating oil, and then use the temperature characteristic formula for viscosity μ, which has been experimentally obtained in advance, to obtain the viscosity μ at a reference temperature, relative to the viscosity μ predicted from the obtained near-infrared absorption spectrum.

[0062] To achieve high accuracy, it is advisable to determine the temperature characteristic equation of viscosity μ (temperature characteristic information of viscosity) for each type of lubricant (type of base oil and type of additive). It may also be determined for each usage time of the lubricant. However, in order to sacrifice accuracy for efficiency, one or more generalized temperature characteristic information may be used for multiple types of lubricant.

[0063] The "kinematic viscosity ν" is sometimes used to evaluate the performance of lubricating oils. As mentioned above, kinematic viscosity ν is the value obtained by dividing viscosity μ by density, and density also has temperature characteristics. Therefore, by creating a temperature correction formula for the density of the lubricating oil to be measured in advance, it is possible to determine the density of the lubricating oil from the temperature at which the near-infrared absorption spectrum is measured, and then divide the viscosity μ obtained from the near-infrared absorption spectrum by the density to determine the kinematic viscosity ν.

[0064] To achieve high accuracy, it is advisable to determine the density-temperature characteristic formula (density-temperature characteristic information) for each type of lubricant (type of base oil and type of additive). It may also be determined for each usage time of the lubricant. However, to sacrifice accuracy for efficiency, one or more generalized temperature characteristic information may be used for multiple types of lubricant.

[0065] Figure 4 shows an example of the relationship between temperature and density of lubricating oil products. There are various grades of lubricating oil products with different viscosities, but in general, the density of lubricating oil tends to decrease as the temperature increases.

[0066] Since the acid value and base value have no temperature characteristics, there is no need to perform temperature correction on the near-infrared absorption spectrum.

[0067] <Relationship between machine operating time and viscosity, acid number, and base number> Figure 5 is a graph showing the relationship between the operating time of a gasoline engine and the viscosity, acid number, base number, and insoluble matter properties of engine oil. Operating time can be evaluated by converting it to mileage. The viscosity of engine oil decreases for a while after the start of use and then gradually increases. The initial decrease in viscosity is due to molecules being broken down by shear force. The acid number of engine oil gradually increases after the start of use, and the base number continues to decrease from the beginning.

[0068] In Figure 5, after 11,000 hours, oil oxidation progresses, increasing the amount of insoluble matter that causes sludge and varnish. It's best to decide to change the oil before the insoluble matter begins to increase. Viscosity, acid number, and base number can be predicted from near-infrared absorption spectroscopy. Continuously measuring any one of these parameters is effective for predicting oil life. Continuously measuring multiple parameters allows for highly accurate predictions. Because viscosity measurements may vary depending on the temperature, it's necessary to compare the viscosity at the same temperature—for example, at 40°C. Other temperatures within the oil's operating range are also acceptable. Alternatively, kinematic viscosity, which takes into account the oil's density, can be used instead of viscosity. [Example]

[0069] Figure 6 shows an example of a system used to diagnose engine oil in the embodiment. The diagnostic system 1200 in the embodiment is configured using a normal computer such as a server. The diagnostic system 1200 includes an input device 1210, an output device 1220, and a processing device 1230, and stores programs and data for processing in a storage device 1240. The storage device 1240 may be configured by combining known storage devices such as semiconductor memories and magnetic disk devices.

[0070] The storage device 1240 includes an estimation model generation unit 1250 and an oil property estimation unit 1260. In this embodiment, the estimation model generation unit 1250 and the oil property estimation unit 1260 are included in the same device, but they may also be configured as separate devices.

[0071] The estimation model generation unit 1250 includes a measurement database 1251, a preprocessing unit 1252A, a learning database 1253, and a multivariate analysis unit 1254. The oil property estimation unit 1260 includes a preprocessing unit 1252B, an estimation model 1261, and a temperature compensation unit 1262.

[0072] 7 shows an example of engine oil diagnosis using the diagnosis system 1200. First, measurement data is acquired from actual engine oil, and measurement database 1251 is generated. This is done by collecting engine oil samples and analyzing the engine oil using known methods (S1310). An example of measurement data is shown in FIG. 5, for example.

[0073] Using a preprocessing unit 1252A of the estimation model generation unit 1250, explanatory variables and response variables for model generation are prepared from the actual measurement data and stored in a learning database 1253. A multivariate analysis unit 1254 uses the explanatory variables and response variables to generate an estimation model 1261 of the oil's physical properties (S1320).

[0074] The oil physical properties are predicted by the oil physical properties estimation unit 1260 using the obtained estimation model 1261 (S1330). Using the prediction results, for example, if the viscosity of new engine oil is 20 cP, it is recommended to change the oil when the viscosity reaches 25 cP through use.

[0075] Figure 8 is a flow diagram of the measurement data collection process S1310. The above engine oil was applied to an automobile engine and operated continuously. Every 10 hours (S1311), 10 ml of engine oil was sampled (S1312), and the near-infrared absorption spectrum was measured (S1313) using the optical sensor shown in Figure 1 or 2. The wavelength resolution of the near-infrared absorption spectrum was 2 nm. The viscosity, acid number, and base number of the engine oil were measured using any known method (S1314). Although not required, color information of the engine oil may also be measured if necessary (S1315). The measured near-infrared absorption spectrum, antioxidant concentration, viscosity, acid number, and base number are linked to time information and recorded in the measurement database 1251 (S1316). Note that the sample volume and sample acquisition time interval are merely examples; the oil properties and analytical methods for these properties may also be selected from known methods.

[0076] Figure 9 shows an example of the near-infrared absorption spectrum of engine oil. In this example, the absorbance is shown for wavelengths from 1550 nm to 1950 nm. Multiple curves show the spectra after different periods of use.

[0077] Next, explanatory variables are generated using the measured near-infrared absorption spectrum, and a model for estimating the physical properties of the oil is generated using the separately measured viscosity as the objective variable (FIG. 7, S1320).

[0078] In generating a model, it is possible to use a known machine learning method in which explanatory variables are input and a target variable is output. In this example, a PLS regression analysis was performed.

[0079] 10 shows a flow diagram of the oil property estimation model generation process S1320. This process is performed by the estimation model generation unit 1250 using a general computer, with the processing unit 1230 executing software.

[0080] First, the preprocessing unit 1252A reads out data of the near-infrared absorption spectrum at a certain point in time (S1321) from the actual measurement database 1251. The preprocessing of the near-infrared absorption spectrum in the preprocessing unit 1252A involves smoothing (S1322) by the Savitzky-Golay method (SG method) using data from nine adjacent points, and second-order differentiation of the spectrum (S1323).

[0081] Figure 11 shows a graph of the results of differentiating the spectrum twice. The curves show the derivatives of the spectrum at different times after use. Differentiation can emphasize the differences in the near-infrared absorption spectrum over time. Differentiation may not be necessary in some cases. Differentiation may also be performed only once. Alternatively, differentiation may be performed three or more times.

[0082] The twice-differentiated near-infrared absorption spectrum is stored in the learning database 1253 as an explanatory variable x, and the antioxidant concentration at the same time as the objective variable y.

[0083] The multivariate analysis unit 1254 performed PLS regression analysis using the explanatory variable x and the response variable y (S1324) to generate an oil property estimation model 1261. After the PLS regression analysis, cross-validation was performed (S1325). The generated estimation model 1261 is implemented in the oil property estimation unit 1260.

[0084] 12 is a flow diagram of the oil property estimation process S1330 using the generated estimation model 1261. This process is performed by a general computer-based oil property estimation unit 1260 through software processing.

[0085] First, the near-infrared absorption spectrum of the engine oil whose physical properties are to be determined is acquired and input from the input device 1210 of the diagnostic system 1200 (S1331). The near-infrared absorption spectrum is acquired by the optical sensor shown in Fig. 1 or 2. One of the features of the embodiment is that the near-infrared absorption spectrum can be collected contactlessly and remotely.

[0086] The near-infrared absorption spectrum is subjected to preprocessing such as smoothing and second differentiation by preprocessing unit 1252B (S1332) in the same manner as when the estimation model was generated in Fig. 10. The preprocessed near-infrared absorption spectrum is input to the oil property estimation model to estimate the viscosity (S1333).

[0087] The near-infrared absorption spectrum of the engine oil was measured once a day, and when the predicted viscosity reached 25 cP, a notification was sent to prompt an oil change.

[0088] Figure 13 is a graph showing the consistency between predicted and measured viscosity values ​​using estimation model 1261. The reference values ​​plotted as triangles are the measured values ​​of the samples, and the straight line obtained as a result of analysis using the PLS model is the PLS regression equation. The predicted value is the result of prediction from the measured values ​​using this regression equation (estimation model), and if the reference value and predicted value match well, then a good PLS model has been constructed. The PLS regression equation in Figure 13 can be used as a calibration curve to quantify the viscosity of a sample with unknown viscosity.

[0089] In the above example, the viscosity of the engine oil is finally determined and output from the output device 1220, but as will be described later, the acid number, base number, or usage time may also be output. Furthermore, the estimation model was generated using multivariate analysis using software capable of executing PLS without using special hardware, but other well-known machine learning methods using a GPU (Graphic Processor Unit) or the like may also be employed.

[0090] FIG. 14 shows an example of engine oil viscosity estimation that takes into account the temperature characteristics of viscosity for more accurate viscosity prediction.

[0091] To measure the near-infrared absorption spectrum of engine oil, a sensor is attached to the engine, or engine oil is sampled periodically, such as once a week, and the near-infrared absorption spectrum of the engine oil is measured. Suppose the temperature of the lubricating oil when the near-infrared absorption spectrum was acquired at a certain time was 70°C. The acquired spectral data is input to preprocessing unit 1252B (S2001), and second-order differentiation and smoothing are performed under appropriate conditions as preprocessing (S2002).

[0092] The estimation model 1261 performs PLS regression analysis on the preprocessed data to obtain a predicted viscosity value (S2003). The predicted viscosity value output by the estimation model 1261 is a predicted viscosity value at 70°C. Thereafter, the temperature compensation unit 1262 performs temperature compensation.

[0093] The temperature at which the near-infrared absorption spectrum is measured is obtained (S2004), and this temperature value and the temperature characteristic equation for the viscosity of the lubricating oil (S2005) are used to estimate the viscosity at 40°C from the predicted viscosity value at 70°C (S2006).

[0094] Furthermore, using the separately obtained temperature characteristic equation for density (S2007), the predicted viscosity value at 40°C is divided by the density value at 40°C to estimate the kinematic viscosity at 40°C. Here, an example of predicting viscosity and kinematic viscosity at 40°C is shown, but it is also possible to predict kinematic viscosity at 60°C or 100°C, for example, as long as the temperature characteristic equation for viscosity and density is within the range that can be covered.

[0095] The temperature characteristic equation showing the temperature dependency of viscosity (or kinematic viscosity) and the temperature characteristic equation (or table) showing the temperature dependency of density as explained in FIG. 3 are obtained in advance by actual measurement, simulation, or as theoretical values, and stored. [Example]

[0096] An example of diagnosing hydraulic oil (hereafter referred to as hydraulic oil) on a large ship is shown below using the system in Figure 6. The acid number of new hydraulic oil was 1.6, and it was supposed to be changed when the acid number reached 2. From past experience, it was known that the oil would need to be changed after an average of 3,000 hours of use.

[0097] The hydraulic oil was used continuously under the same conditions as those used on large ships, with 15 ml sampled every 10 hours. The near-infrared absorption spectrum of the sampled oil was obtained using the optical sensor shown in Figure 1. The acid value, viscosity, and contamination level (mass method) were also determined using the sampled oil.

[0098] The obtained near-infrared absorption spectrum was used as the explanatory variable in PLS regression analysis, and the acid value was used as the target variable. The near-infrared absorption spectrum was smoothed at 11 points and differentiated twice, after which a PLS regression analysis was performed. Cross-validation was then performed on the results. The results of this analysis demonstrated that it is possible to predict the acid value from the near-infrared absorption spectrum.

[0099] Furthermore, for this hydraulic oil, viscosity was used as the response variable and near-infrared absorption spectrum data as the explanatory variable. After performing nine-point smoothing and two-time differentiation on the near-infrared absorption spectrum, PLS regression analysis was performed. Cross-validation was performed on the results, and it was found that viscosity can be predicted from the near-infrared absorption spectrum of hydraulic oil. [Example]

[0100] An example of gas engine oil diagnosis is shown below using the system in Figure 6. The viscosity of new oil is 10 cP, and it is recommended to change it when the viscosity drops to 13 cP. The base number of the new oil is also 50, and it is recommended to change it when it drops to 20.

[0101] This gas engine was operated continuously, and an optical sensor capable of measuring near-infrared absorption spectra was installed in a sight glass (a part made of a transparent material that is installed so that the color of the oil can be seen) installed in the engine oil piping.

[0102] Near-infrared absorption spectra were measured every hour during engine operation, continuing for 1000 hours. In addition, small amounts of engine oil were sampled every 20 hours to measure viscosity and base number.

[0103] A PLS regression analysis was performed using the acquired near-infrared absorption spectrum and separately measured viscosity data to generate a viscosity estimation model. Using the estimation model, it was confirmed that the viscosity can be predicted from the near-infrared absorption spectrum of gas engine oil. In a similar manner, it was also confirmed that the base number can be predicted from the near-infrared absorption spectrum of gas engine oil. [Example]

[0104] In this embodiment, the configurations of embodiments 1 to 3 are applied to a system and method for monitoring lubricating oil for a wind power generator. This embodiment is a monitoring system for lubricating oil supplied to a mechanical drive unit of a wind power generator. This system includes a diagnostic system 1200 shown in FIG. 6.

[0105] A memory device in the monitoring system stores additive concentration data that stores the concentration of additives in the lubricating oil in chronological order as a reference, and the diagnostic system 1200 estimates the time when the viscosity or acid value of the lubricating oil, determined from the near-infrared absorption spectrum of the lubricating oil, will reach a predetermined threshold value.

[0106] (1. Overall system configuration) Figure 15 shows a schematic diagram of a lubricant monitoring system for a wind power generator with a lubricant supply system. Inside the nacelle 3 of the wind power generator 1, there are a main shaft 31, a gearbox 33, a generator 34, and bearings such as yaw and pitch bearings (not shown), all of which are supplied with lubricant from an oil tank 37. The wind power generator also has the general components of a wind power generator, such as a hub 4, a nacelle bulkhead 30, a shrink disk 32, a main frame 35, a radiator 36, and a coupling 38.

[0107] 15, multiple wind turbines 1 are usually installed on the same site, and these are collectively referred to as a farm 200a, etc. Each wind turbine 1 is equipped with various sensors (not shown) in its lubricating oil supply system, and sensor signals reflecting the state of the lubricating oil are collected in a server 210 in the nacelle 3.

[0108] Furthermore, sensor signals obtained from the server 210 of each wind turbine generator 1 are sent to an aggregation server 220 arranged for each farm. Data from the aggregation server 220 is sent to a central server 240 via a network 230. Data from other farms 200b and 200c is also sent to the central server 240. Furthermore, the central server 240 can send instructions to each wind turbine generator 1 via the aggregation server 220 and server 210. The central server 240 basically has the functions of the diagnostic system 1200 shown in Fig. 6, and the system of this embodiment is capable of remotely monitoring oil.

[0109] (2. Sensor placement) 16 is a conceptual diagram of a rotating part equipped with a lubricant sensor. Lubricant is supplied to a rotating part 302 from a lubricant supply device 301 such as a pump. The lubricant supply device 301 is connected to an oil tank 37 and receives the supply of lubricant. The rotating part 302 is, for example, a gearbox 33 or other general part where mechanical contact occurs, and is not particularly limited.

[0110] The optical sensor 304 is disposed in the lubricating oil flow path etc. in order to detect the state of the lubricating oil. A specific example of the optical sensor 304 is shown in FIG.

[0111] In this embodiment, a transparent measuring unit 303 is provided in a flow path (near the end of the lubricant path) branching off from the lubricant flow path connected to the lubricant drain port of the rotating part 302, and a portion of the lubricant is introduced into this measuring unit 303. An optical sensor 304 is then installed in the measuring unit 303. The measuring unit 303 is not provided in the main lubricant flow path in order to adjust the flow rate of the lubricant in the measuring unit 303 to a flow rate suitable for detecting the state of the lubricant. The lubricant discharged from the rotating part 302 returns to the oil tank 37 via a filter 305. Note that the filter 305 is not essential. The optical sensor 304 measures the near-infrared absorption spectrum of the oil. The state of the lubricant can be evaluated based on the temporal change in the near-infrared absorption spectrum of the lubricant.

[0112] In this embodiment, the optical sensor 304 includes an optical sensor equipped with a near-infrared light source and a light receiving element. The optical sensor acquires the near-infrared absorption spectrum of the lubricating oil.

[0113] The optical sensor 304 transmits near-infrared light from a near-infrared light source through the oil and detects the near-infrared light that has passed through the oil with a light-receiving element with sensitivity according to the wavelength, thereby acquiring a near-infrared absorption spectrum. The acquired near-infrared absorption spectrum reflects the oil's absorptivity or transmittance for light of each wavelength. The acquired near-infrared absorption spectrum and estimation model are used to determine the viscosity and acid value of the lubricating oil, and to diagnose the degree of deterioration and remaining life.

[0114] The quality of lubricating oil deteriorates with use and it no longer performs its original function. For this reason, maintenance such as replacement is required depending on the degree of deterioration in quality. In order to know the timing of such maintenance, it is useful for the efficiency of maintenance management to be able to monitor data collected by optical sensors 304 installed on-site from a remote location. The data collected by the optical sensors 304 is collected, for example, in a server 210 in the nacelle 3, and then sent via an aggregation server 220 that aggregates data within the farm 200 to a central server 240 that aggregates data from multiple farms.

[0115] However, for analyses that require equipment for measurement, such as viscosity measurements and acid number measurements specified in ASTM standards, it is necessary to collect lubricating oil samples as appropriate and analyze them using separately provided equipment.The results of these viscosity measurements and acid number measurements are also stored as data in the central server 240, and it is desirable to aggregate the data and understand the physical properties of the lubricating oil taking this data into consideration.

[0116] Furthermore, the data to be aggregated may include not only data related to lubricants but also data indicating the operating status of the wind turbine generator. For example, the wind turbine output value (the higher the value, the faster the rate of lubricant deterioration), the actual operating time (the longer the value, the faster the rate of lubricant deterioration), the machine temperature (the higher the value, the faster the rate of lubricant deterioration), the shaft rotation speed (the faster the value, the faster the rate of lubricant deterioration), etc. These can be collected from sensors of known configurations installed at various locations on the wind turbine generator, or from control signals from the device.

[0117] (3. Lubricant diagnosis flow) Figure 17 is a flow diagram showing the lubricant oil diagnosis process according to this embodiment. The process shown in Figure 17 is performed under the control of either the server 210, the aggregation server 220, or the central server 240 in Figure 15. In the following example, it is assumed that the process is performed by the central server 240. Functions such as calculation and control are realized by software stored in the server's storage device being executed by a processor, and the specified processes are realized in cooperation with other hardware. Note that functions equivalent to those configured by software can also be realized by hardware such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0118] When the central server 240 performs control, it has multiple wind power generators 1 under its control, so the following processing is performed for each wind power generator. This processing is basically a repetitive process, and the start timing is set by a timer or the like; for example, the processing starts at midnight every day (S601). The central server 240 can also perform the processing at any timing in response to instructions from an operator.

[0119] In step S602, the central server 240 checks when the lubricant needs to be replaced. The initial value of the replacement time can be calculated based on the physical properties, for example, by using the Arrhenius reaction rate, assuming that the lubricant is operating at its design temperature, and the remaining life can be set as an initial value. This replacement time can be updated later in step S610 based on actual measurement data.

[0120] If it is time to change the lubricant, the lubricant is changed in step S603. Since changing the lubricant is usually done by a worker, the central server 240 displays and notifies the worker when and what to change.

[0121] If it is not time to change the lubricant, in step S604, the central server 240 performs a diagnosis using sensor data. As sensor data, in addition to the near-infrared absorption spectrum of the lubricant obtained by the optical sensor, the oil temperature, oil pressure, and particle concentration contained in the lubricant, which can be measured using conventional technology, can be used. The data collected by the optical sensor 304 is sent to the central server 240, and the central server evaluates the physical properties of the lubricant, for example, by comparing the oil temperature, oil pressure, and particle concentration contained in the lubricant obtained from the sensor with predetermined thresholds.

[0122] If the diagnosis result in step S605 is abnormal, the lubricating oil is changed in step S603. If no abnormality is found, step S606 is performed.

[0123] In S605, machine learning using correlations of near-infrared absorption spectrum data is used to determine that there is an abnormality in viscosity when the viscosity estimated from the near-infrared absorption spectrum measured by the optical sensor exceeds a predetermined threshold.

[0124] In process S606, the near-infrared absorption spectrum and the like are input to the central server 240, and the data is stored in chronological order.

[0125] From the perspective of preventive and planned maintenance of wind turbines, it is desirable to perform predictive diagnosis of lubricant deterioration based on changes in the viscosity or acid value of the lubricant before it is determined that there is an abnormality.

[0126] The replacement timing estimation result obtained in step S607 can be displayed as a lubricant diagnosis result (step S608).

[0127] Figure 18 shows an example of the display of the results of process S610. It is recommended that this lubricant be replaced when its viscosity exceeds 200 cP. The near-infrared absorption spectrum data acquired by the optical sensor predicts that the viscosity will reach 200 in 50 months, so the new replacement time can be set to a time before that (for example, half a month before). Process S610 completes one cycle of processing, and in process S602 of the next cycle, a determination process is performed in accordance with the new replacement time.

[0128] According to the above embodiment, by installing an optical sensor in a machine that uses lubricating oil, the physical properties of the lubricating oil, such as viscosity, acid number, and base number, can be quantified by the sensor without having to come into contact with the lubricating oil.

[0129] According to the above embodiment, efficient oil maintenance management can be realized, which reduces energy consumption, reduces carbon emissions, prevents global warming, and contributes to the realization of a sustainable society. [Explanation of symbols]

[0130] Diagnostic system 1200, estimation model generation unit 1250, oil property estimation unit 1260, actual measurement data collection process S1310, property estimation model generation process S1320, oil property estimation process S1330

Claims

1. An information processing system including an input device, an output device, a processing device, and a storage device is used, the information processing system includes an estimation model generation unit that generates an estimation model capable of machine learning, An absorption spectrum obtained by transmitting light with at least a part of wavelengths between 800 nm and 2500 nm through an oil containing a base oil and an additive, and a value reflecting the physical property value of the oil are input to the estimation model generation unit, generating an estimation model by machine learning using values ​​based on the absorption spectrum as explanatory variables and values ​​reflecting the physical property values ​​of the oil as objective variables; A method for generating an estimation model of oil properties.

2. The estimation model generation unit generates an estimation model using multivariate analysis, which is a type of machine learning. The method for generating an estimation model of oil properties according to claim 1 .

3. The estimation model generation unit performs multivariate analysis using PLS regression analysis. The method for generating an estimation model of oil properties according to claim 2 .

4. the wavelength of the light is in the range of 1550 nm to 1950 nm; The method for generating an estimation model of oil properties according to claim 1 .

5. The physical property value of the oil is one or more physical property values ​​selected from viscosity, acid number, and base number. The method for generating an estimation model of oil properties according to claim 1 .

6. The explanatory variable is a value obtained by differentiating the absorption spectrum one or more times. The method for generating an estimation model of oil properties according to claim 1 .

7. The estimation model generation unit verifies the estimation model by cross-validation. The method for generating an estimation model of oil properties according to claim 1 .

8. A method for diagnosing oil properties using a diagnostic system comprising an information processing device that implements the estimation model according to claim 1, comprising: an absorption spectrum obtained by transmitting light having at least a part of wavelengths between 800 nm and 2500 nm through an oil to be diagnosed that is the same type as the oil containing the additive, is input to the diagnostic system as the diagnostic object absorption spectrum; inputting a value based on the diagnostic object absorption spectrum into the estimation model; Obtaining values ​​that reflect the physical property values ​​of the oil to be diagnosed from the estimation model; Diagnostic methods for oil properties.

9. a value obtained by differentiating the diagnostic object absorption spectrum one or more times is input into the estimation model; The method for diagnosing oil properties according to claim 8.

10. The physical property value of the oil is one or more physical property values ​​selected from viscosity, acid number, and base number. The method for diagnosing oil properties according to claim 8.

11. If the physical property value of the oil is viscosity, The value reflecting the physical property value of the diagnostic object oil obtained from the estimation model is corrected using the temperature of the diagnostic object oil at the time when the diagnostic object absorption spectrum is measured. The method for diagnosing oil properties according to claim 10.

12. If the physical property value of the oil is viscosity, The viscosity at a reference temperature is calculated using the temperature of the oil to be diagnosed at the time when the absorption spectrum of the oil to be diagnosed is measured, the viscosity obtained from the estimation model, and the temperature characteristic information of the viscosity calculated in advance. The method for diagnosing oil properties according to claim 11.

13. If the physical property value of the oil is viscosity, The kinematic viscosity at the reference temperature is calculated using the temperature of the oil to be diagnosed at the time when the absorption spectrum of the oil to be diagnosed is measured, the viscosity at the reference temperature, and the temperature characteristic information of the density calculated in advance. The method for diagnosing oil properties according to claim 12.

14. An oil property diagnostic system comprising an information processing device in which the estimation model according to claim 1 is implemented.

15. The physical property value of the oil is one or more physical property values ​​selected from viscosity, acid number, and base number. The oil property diagnostic system according to claim 14.

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