Method for generating oil type estimation model, oil type estimation method, and oil type estimation system

Abstract

The purpose of the present invention is to provide a technique for identifying the type of an oil.　One embodiment of the present invention relates to a method for generating an oil type estimation model that utilizes an information processing system provided with an input device, an output device, a processing device, and a storage device. The information processing system is provided with an estimation model generation unit that generates an estimation model. The estimation model generation unit captures absorption spectrum data obtained by transmitting light of at least some wavelengths of 800-2500 nm through oil of a known type, analyzes the absorption spectrum data using a dimension reduction algorithm, and generates an oil type estimation model.

Description

Method for generating an oil type estimation model, method for estimating oil type, and oil type estimation system

[0001] The present invention relates to an oil diagnostic technology, particularly to a technology suitable for the maintenance of large machinery that uses industrial oils such as lubricating oil, insulating oil, and processing oil, by determining whether the correct oil is being used through measurements using sensors and monitoring the machinery based on the results.

[0002] Diagnosis of the properties of lubricating oils used in rotating parts such as bearings and gears is an important technology for the maintenance and repair of large rotating machinery, such as wind power generator 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 properties of insulating oils is also important. Processing oils are also used in machining. There are various types of 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] Each oil product uses different types and concentrations of base oils and additives. In many machines, it is necessary to select the appropriate oil to create the appropriate lubrication for the gears, bearings, and other parts.

[0006] In recent years, machine condition monitoring has often adopted a strategy to minimize the lifecycle cost 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 can result in power generation losses and production stoppages. In addition, costs for purchasing and shipping new oil, oil change work, and waste oil disposal are also required, 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 characteristics of insulating oil in transformers are monitored.

[0007] Recently, from the perspective of carbon neutrality, automobiles and other vehicles that use large amounts of petroleum-based 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 need to minimize its use by extending the oil change interval, etc. This is because reducing oil consumption reduces carbon dioxide emissions. However, overlooking oil deterioration and contamination can lead to machine breakdowns.

[0008] Regarding the diagnosis of lubricating oil properties, it is necessary to 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.

[0009] (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, and a decrease in corrosion prevention. 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 requires a prompt oil change and equipment inspection.

[0010] On the other hand, there is a background to the circulation of counterfeit lubricating oil. Furthermore, when changing lubricating oil, it is possible to unintentionally put in genuine oil or oil that is not the lubricating oil that should be used. When changing oil in a machine, new oil is generally poured in while a small amount of degraded oil remains in the machine. Therefore, if the wrong type of lubricating oil is poured in, additives may precipitate, which can cause machine failure. If lubricating oil with a different viscosity or type or concentration of additives is poured in, the thickness of the lubricating film during operation may change, and wear resistance and corrosion resistance may deteriorate, resulting in failure.

[0011] Counterfeit lubricants are generally manufactured at low cost and sold in branded containers at a higher price to make a profit or at a lower price than genuine products. In this case, they may use inferior base oils, reduce the amount of additives, or use cheaper additives, resulting in inferior lubricant performance and machine failure.

[0012] To combat counterfeit lubricants, oil manufacturers have made it more difficult to copy lubricant containers. When the viscosity or color is clearly different from genuine oil, it can be detected using oil analysis and sensors that measure viscosity and color. When the container is indistinguishable and the viscosity and color are close to genuine oil, it is possible for an oil analysis company to perform composition analysis to identify the counterfeit. However, due to the time and expense involved, composition analysis to determine whether the product is genuine is not commonly performed.

[0013] Service providers may monitor the condition of machines during maintenance services, but they may perform maintenance monitoring services on the assumption that genuine oil will be used.

[0014] As one method of oil diagnosis, Patent Document 1 discloses a method of obtaining a standardized standard value for evaluating oil properties using an optical sensor.

[0015] Patent Document 2 discloses that the concentration of an additive having a diphenylamine skeleton is quantitatively measured with high accuracy using a sensor that measures the optical properties of a lubricating oil.

[0016] JP 2024-072170 A JP 2022-118670 A

[0017] Using counterfeit lubricants or putting in the wrong type of lubricant can cause machinery to break down.

[0018] An object of the present invention is to provide a technique for identifying the type of oil.

[0019] One aspect of the present invention is a method for generating an estimation model of an oil type, 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 that generates an estimation model, the estimation model generation unit taking in absorption spectrum data obtained by transmitting light with at least a portion of wavelengths between 800 nm and 2500 nm through oil of a known type, analyzing the absorption spectrum data using a dimensionality reduction algorithm, and generating an estimation model of the oil type.

[0020] Another aspect of the present invention is a method for estimating oil type using a diagnostic system including an information processing device that implements the oil type estimation model, in which the oil type is expressed by values ​​of at least two principal components, and in which diagnostic system absorption spectrum data of a diagnostic object obtained by transmitting light of at least a portion of wavelengths between 800 nm and 2500 nm through the diagnostic object oil is input to the diagnostic system, values ​​based on the diagnostic object absorption spectrum data are input to the estimation model, and the type of the diagnostic object oil is estimated from the estimation model.

[0021] Another aspect of the present invention is an oil type estimation system including an information processing device that implements the estimation model.

[0022] According to the present invention, it is possible to provide a technology for identifying the type of oil. Problems, configurations, effects, and the like other than those described above will become clear from the following description of the embodiments.

[0023] 1 is a schematic diagram of an optical sensor that measures near-infrared absorption spectra. FIG. 2 is a flow diagram showing the process of generating a discrimination map used in a type estimation model for estimating oil type. FIG. 3 is a graph showing the results of principal component analysis of lubricant oil. FIG. 4 is a schematic diagram of an optical sensor that measures near-infrared absorption spectra. FIG. 5 is a flow diagram showing the process of determining the type of lubricant oil and monitoring and diagnosing its condition. FIG. 6 is a graph showing the results of principal component analysis of lubricant oil. FIG. 7 is a block diagram of an oil diagnostic system according to an embodiment. FIG. 8 is a flow diagram of an oil diagnostic method according to an embodiment. FIG. 9 is a flow diagram of oil property estimation according to an embodiment. FIG. 10 is a graph showing the relationship between usage time and oil viscosity, total acid number, and antioxidant concentration. FIG. 11 is a graph of an example of a near-infrared spectrum. FIG. 12 is a flow diagram of an estimation model generation process according to an embodiment. FIG. 13 is a graph showing the second derivative of an example of a near-infrared spectrum. FIG. 14 is a flow diagram of an oil property estimation process according to an embodiment. FIG. 15 is a graph showing the consistency between predicted values ​​and measured values ​​using the estimation model according to an embodiment. FIG. 16 is a schematic diagram of a lubricant monitoring system for a wind turbine generator according to an embodiment. FIG. 17 is a conceptual diagram of a rotating part equipped with a lubricant sensor. 1 is a flow chart showing a lubricant diagnosis process according to an embodiment of the present invention;

[0024] 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 without departing from the concept or purpose of the present invention.

[0025] 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 redundant explanations may be omitted.

[0026] 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.

[0027] The terms "first," "second," "third," etc. used 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 numbers used in one context do 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.

[0028] 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.

[0029] The publications, patents, and patent applications cited in this specification are incorporated herein by reference in their entirety. In this specification, elements expressed in the singular include the plural unless otherwise clearly indicated in the context. In this specification, the wavelength range of 800 nm to 2500 nm is referred to as the near-infrared region.

[0030] A typical embodiment uses an information processing system including an input device, an output device, a processing device, and a storage device, and the information processing system includes an estimation model generation unit that generates an estimation model, and the estimation model generation unit receives 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 an additive, and a value reflecting the oil characteristics, and generates an estimation model using a value based on the absorption spectrum as an explanatory variable and a value reflecting the oil type as a response variable.

[0031] Another aspect of the present invention is a method for determining oil type 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 that contains a base oil and additives is input to the diagnostic system, a value based on the oil type is input to the estimation model, and a value that reflects the characteristics of the oil to be diagnosed is obtained from the estimation model.

[0032] Another aspect of the present invention is an oil type diagnosis system including an information processing device that implements the above estimation model.

[0033] <Types of lubricating oils and additives> There are various types of lubricating oils, including engine oil, turbine oil, hydraulic oil, bearing oil, sliding surface oil, gear oil, compressor oil, and cutting oil.

[0034] Lubricating oils are composed of base oils and additives. Base oils, also called base oils, make up 80-90% of lubricating oils, and the performance of lubricating oils is determined by the base oil. Base oils are classified into mineral oils and synthetic oils. The American Petroleum Institute (API) classifies base oils into five groups. Mineral oils are oils distilled and refined from petroleum, and are inexpensive.

[0035] Mineral oils are further classified into paraffinic oil and naphthenic oil. Paraffinic base oils are often used in relatively low-cost lubricants, but they have inferior performance compared to synthetic oils and are not suitable for use at high temperatures or in harsh environments. Paraffinic oil is a base oil in which the paraffin carbon number of the contained components is 50% or more. Paraffinic base oils tend to crystallize under low temperature conditions, so pour point depressants are added. Naphthenic base oil is a base oil in which 30% or more naphthenic compounds are present. Although it has a low viscosity index, it has high solubility and excellent low-temperature fluidity.

[0036] Synthetic oils are base oils produced by chemical synthesis and are classified into synthetic hydrocarbon oils, ester oils, ether oils, silicone oils, and fluorinated oils. Synthetic oils are used in automotive brake fluids, metalworking oils, and industrial and automotive lubricants, and have a wider usable temperature range than mineral oils. Therefore, they are used under conditions where lubricants using mineral base oils are prone to performance problems, such as low temperatures, high temperatures, high shear stress, resin resistance, and vacuum conditions. However, they are more expensive than mineral oils.

[0037] Synthetic hydrocarbon oils include polyalphaolefins (PAO), polybutene, alkylbenzenes, and cycloalkanes. Synthetic hydrocarbon oils generally have high viscosity indexes and thermal stability (especially at low temperatures). They also have low evaporation loss. They have excellent resistance to rubber and resins, making them suitable for a wide range of applications, but are not suitable for use with natural rubber or ethylene propylene diene rubber (EPDM). Ester oils are compounds of fatty acids and alcohols, or fatty acids and glycerin. Ester oils include monoesters, diesters (DOS), polyol esters, phosphate esters, and silicate esters. Ester oils generally have excellent lubricating properties and thermal stability, and are characterized by a wide operating temperature range.

[0038] Ether oils include polyalkylene glycols (PAGs) and phenyl ethers. PAGs, in particular, have excellent lubricity and a wide viscosity range. Silicone oils include polysiloxanes and silicate esters. Silicone oils have excellent heat, water, and cold resistance. They are resistant to sludge formation even when degraded, are hygroscopic, low-toxicity, colorless, and odorless, making them suitable for a wide range of applications. Fluorine oils are primarily composed of trifluoroethylene. They have excellent heat resistance and oxidation stability, but are expensive. The performance of lubricating oils, which are dependent on the base oil, includes lubricity (wear resistance), low torque, low-temperature resistance, heat resistance, and resin resistance.

[0039] As such, there are many types of base oil, and the composition and purity vary from product to product depending on the crude oil used as the raw material and the manufacturer.

[0040] Additives include antioxidants, rust inhibitors, antifoaming agents, viscosity index improvers, oiliness improvers, extreme pressure additives, detergent dispersants, pour point depressants, emulsifiers, etc. For many oils, the degree of additive consumption through use can be used to determine when to change the oil or as an indicator of mechanical abnormalities. The concentration of additives can be quantified by measuring changes in absorbance in the near-infrared region.

[0041] <Function of additives> Lubricating oils are composed of base oils and additives. Base oils include mineral oils made from petroleum, high-performance synthetic oils, bio-oils made from plants, and biodegradable oils. Lubricating oil deterioration is an oxidation reaction involving oxygen. Antioxidants, a typical additive, are added to prevent base oil oxidation, and base oil oxidation usually begins when the antioxidants are depleted to a certain extent. When base oil oxidation begins, changes in viscosity occur, causing changes in the thickness of the lubricating film and other deterioration in lubrication properties, so an oil change is generally recommended. Therefore, by monitoring the level of antioxidant consumption, it is possible to estimate the remaining life of the lubricating oil.

[0042] Among additives, extreme-pressure additives and anti-friction agents function to prevent wear on the sliding surfaces of parts. Consumption of these additives accelerates part wear, so the remaining life of a lubricant is sometimes determined in terms of the concentration of the additives. Therefore, by monitoring the consumption of the additives, it is possible to estimate the remaining life of a lubricant. The lubricant's life is determined when either the amount or concentration of the antioxidant, the additive, or the anti-friction agent reaches a threshold value. At this point, an oil change is generally recommended.

[0043] Other additives such as rust inhibitors, antifoaming agents, viscosity index improvers, oiliness improvers, detergents and dispersants, pour point depressants, and emulsifiers are also known to be consumed with use of lubricating oil, and thresholds are sometimes set based on the functionality of the lubricating oil and used as indicators for determining when to change the oil. The type of oil is basically determined by the combination of base oil and additives.

[0044] <Identifying types and products using near-infrared absorption spectra> Through research by the inventors, it has been discovered that it is possible to distinguish between lubricating oils that have similar apparent colors and viscosities, which has previously been difficult to distinguish, and to determine which manufacturer's product a lubricating oil of unknown viscosity or viscosity is similar to, by measuring near-infrared light absorption spectra at wavelengths from 800 nm to 2500 nm using an optical sensor.

[0045] 1 is a diagram showing one form of optical sensor. The optical sensor 700 incorporates a light source 101 that emits at least a portion of near-infrared light with wavelengths of, for example, 800 nm to 3 μm (3000 nm), and a detector 103 that can detect the light emitted from the light source 101. The light emitted from the light source 101 (indicated by the arrow in the figure) passes through the lubricating oil 102 to be measured, and the transmitted light is measured by the detector 103. It is sufficient for the optical sensor 700 to obtain an absorption spectrum of at least a portion of the wavelengths of near-infrared light with wavelengths of 800 nm to 3 μm (3000 nm).

[0046] Near-infrared spectroscopy measures the vibrational energy of atomic bonds in organic compounds. The near-infrared wavelength range only corresponds to overtones and combination tones of the fundamental tone, and the fundamental tone is not detected, so absorption intensity is low. The reason for the low absorption is that overtones and combination tones are forbidden transitions with a low probability of occurring.

[0047] The advantage of near-infrared spectroscopy, which measures low-probability forbidden transition absorption, is its excellent transparency and is less susceptible to the concentration saturation problem that occurs with ultraviolet-visible spectroscopy and mid-infrared spectroscopy. This means that there is no need to make the optical path length extremely short when measuring liquids such as lubricating oil, and an optical path length of approximately 2 mm to 20 mm can be selected.

[0048] 2 is a flow diagram showing the process S1320 for generating a discrimination map used in a type estimation model for estimating the type of oil. First, a near-infrared absorption spectrum is obtained for the oil to be identified using the optical sensor shown in FIG. 1. This near-infrared absorption spectrum is input (S1321). An example of the near-infrared absorption spectrum is shown in FIG. 12.

[0049] Spectral analysis for quantifying additives in lubricating oil involves the following typical spectral preprocessing: In the first step, smoothing is performed to remove spectral noise (S1322). For example, a central moving average method is applied to a selected interval of approximately 3 to 20 adjacent points.

[0050] In the second step, a second-order derivative filter is applied to the spectrum, which allows for the extraction of wavelengths with large spectral changes and the extraction of peaks from the complex overlapping spectra with unclear peak positions, which is typical of near-infrared absorption spectra (S1323). Either of these two steps can be performed first. An example of a twice-differentiated near-infrared absorption spectrum is shown in Figure 14.

[0051] Next, multivariate analysis is performed on the preprocessed spectrum (S1324). To identify the type and product of lubricant, we use principal component analysis (PCA), a type of unsupervised learning method using near-infrared absorption spectra, which contain a wealth of information about the composition of lubricants. PCA is a method applied when quantitative data indicating the relationship between n samples and p variables is given. It uses dimensionality reduction to extract a small number of less correlated synthetic variables that best represent the overall variability from a large number of correlated p variables. Using a dimensionality reduction algorithm such as PCA makes it easier to identify oils using near-infrared absorption spectra.

[0052] Principal component analysis can be performed using the following procedure. The near-infrared absorption spectra of genuine oil, lubricating oils with known product names, and lubricating oils to be identified are measured, and a two-dimensional or three-dimensional discrimination map is created using principal component analysis with the spectral data (S1325). This discrimination map is then implemented in a type estimation model. The near-infrared absorption spectrum of lubricating oils with unknown product names or whose authenticity is to be determined (the oil to be diagnosed) is then measured, and principal component analysis is performed using the spectral data. Based on the degree of similarity with lubricating oils with known product names, the product name of the lubricating oil to be diagnosed and its authenticity are identified. The processing of the oil to be diagnosed is essentially the same as the processing in S1321 to S1324 in Figure 2.

[0053] As the type estimation model, it is also possible to use an estimation model that uses the above-mentioned discrimination map and is machine-learned using spectral data as an explanatory variable and the discrimination map score as a target variable.

[0054] In addition to principal component analysis, methods that can be used to identify the type of lubricant and determine its authenticity include support vector machines, k-nearest neighbor methods, cluster analysis, quantum circuit learning, and discriminant analysis.

[0055] In this way, it is possible to identify the type of lubricating oil and determine its authenticity whether two or more types of oil samples are the same product and brand.

[0056] The procedure for identifying the type of lubricant oil will be explained using the example of principal component analysis mentioned above. Distance can be used to quantify the mutual similarity of the results of principal component analysis using near-infrared absorption spectrum data of multiple types of oil. Distances that can be used to explain similarity include Euclidean distance, Chebyshev distance, and Mahalanobis distance. These distances (d between the i-th sample and the j-th sample) i,j ) is defined as follows: m is the dimension number of the component.

[0057] The Euclidean distance is expressed by Equation 1.

[0058] ...(Formula 1)

[0059] The Chebyshev distance is expressed by Equation 2.

[0060] ...(Formula 2)

[0061] The Mahalanobis distance is expressed by Equation 3.

[0062] ...(Formula 3)

[0063] A method for identifying the type of oil of unknown type will be described. For example, if there are known types of oils A, B, C, D, E, F, and G, and an unknown type of oil Z, where oil Z is one of oils A to F, near-infrared absorption spectra of these oils in the wavelength range of 1300 nm to 2500 nm are acquired, and principal component analysis is performed using the acquired spectral data to plot the scores of the first and second principal components. The oil with the score closest to oil Z is determined to be an oil of the same type as oil Z.

[0064] Figure 3 shows the results of principal component analysis of lubricating oils (the discrimination map created in Figure 2). A discrimination map in which known types of oils A to G are plotted using two-dimensional principal components is compared with the plot of unknown oil Z, which was subjected to a similar principal component analysis. According to the results of the principal component analysis in Figure 3, the plots for oil Z and oil B almost overlap, indicating that oil Z is the same type of oil as oil B.

[0065] Even for the same type of oil, characteristics may vary depending on the conditions of use. In such cases, for example, for a known type of oil A, near-infrared absorption spectra are obtained for multiple samples, such as new oil, deteriorated oil, oil containing a small amount of water, and oil containing a small amount of foreign matter, and principal component analysis is performed to determine the distribution, i.e., probability density, of oil A, and the Mahalanobis distance where oil A is distributed.

[0066] Furthermore, for other oils of known type (B to F), the Mahalanobis distances that can be distributed are calculated using the same procedure as for oil A. For each oil score, a distribution range that includes a predetermined percentage of samples, such as 95%, is calculated. Then, for example, if the analysis result of an unknown type of oil falls within any of the 95% distribution ranges A to H, it is determined to match that oil. Alternatively, if it falls within a predetermined distance from the outer edge of the 95% distribution range, it is determined to match that oil.

[0067] Alternatively, a method can be used in which the score of an oil of unknown type and the score of an oil of known type are calculated, and the oil with the smallest distance from the unknown oil is determined to be the same as the unknown oil.

[0068] Alternatively, a method can be used in which if the distance between the score of an unknown oil and the score of a known oil is closer than a predetermined distance, the unknown oil is determined to be the same as the known oil.

[0069] <Measurement Method Using Optical Sensor> The near-infrared absorption spectrum measurement of the lubricating oil is performed by an optical sensor 700 having 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 lubricating oil to be measured is measured by a detector 703, which detects the light intensity of light emitted from a light source 701 after it passes through the lubricating oil 702.

[0070] FIG. 1 shown above shows one form of sensor, but it is also possible to have the light emitted from the light source 701 pass through the lubricating oil 702 and enter the detector 703 using means such as reflection by a mirror, focusing by a lens, or passing through a prism.

[0071] 4 is a diagram showing another form of sensor. In the optical sensor 700A, a light source 101 and a detector 103 are arranged on the same plane and are in contact with the lubricating oil 102, and a reflector 104 is installed on the opposite side, and light emitted from the light source 101 (indicated by the arrow in the figure) is reflected by the reflector 104 and received by the detector 103. Even in this form, a lens, a mirror, or a prism may be installed along the optical path.

[0072] <Target lubricants> The target is lubricants containing base oil and additives. The base oil is mineral oil or synthetic oil, and the additives are one or more additives selected from antioxidants, rust inhibitors, antifoaming agents, viscosity index improvers, oiliness improvers, extreme pressure additives, detergents and dispersants, pour point depressants, emulsifiers, etc. Types of lubricants 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, and cleaning oil.

[0073] <Lubricant type determination and subsequent actions> Figure 5 shows an example flow of an oil maintenance use case that utilizes the oil type determination of the embodiment. When the lubricant oil in a generator or the like is changed (S401), an optical sensor 700A is installed in the oil tank or oil piping, as described below, and the near-infrared absorption spectrum of the lubricant oil is acquired immediately after the lubricant oil change to measure the oil to be identified (S402). As an alternative method, the optical sensor 700 or 700A can also be used to acquire the near-infrared absorption spectrum of a small amount of lubricant oil sampled from the machine.

[0074] Separately, near-infrared absorption spectra of lubricating oils that should be used, genuine oils, and lubricating oils that are likely to be misused are obtained using the method described in Figure 2, and a discrimination map for various lubricating oils is created by principal component analysis (S403).

[0075] Using the near-infrared absorption spectrum data measured from the lubricant oil filled at the time of replacement and the near-infrared absorption spectrum data used to create the discrimination maps for various lubricants, principal component analysis is performed in the same manner as when creating the discrimination map (S404). By comparing the results of the principal component analysis with the discrimination map, it is possible to confirm whether the lubricant oil filled at the time of replacement is the lubricant oil that should have been used or the genuine oil (S405). If it does not match the genuine oil, the machine owner, maintenance company, machine sales company, etc. are notified (S407) (authenticity determination process).

[0076] If it is confirmed as a result of the authenticity determination process that the lubricating oil filled at the time of replacement is the same as the lubricating oil that should have been used or the genuine oil, the machine is allowed to continue operating (S406).

[0077] If the authenticity determination process reveals that the lubricant filled during the replacement is not the same as the lubricant that should have been used or the genuine oil, the machine owner is notified to replace the lubricant with the lubricant that should have been used without operating the machine (S407).

[0078] Following the authenticity determination process, it is effective to monitor the deterioration and contamination status of the lubricant when the machine is operated. The near-infrared absorption spectrum data immediately after refilling and the near-infrared absorption spectrum of the lubricant during use, for example, once per hour, are acquired and stored (S408). Then, using a machine learning technique called Partial Least Squares Regression (PLS), the consumption of additives in the lubricant can be measured (S409) and the next replacement time can be predicted (S410). Evaluation of additive amounts using PLS will be described later with reference to FIG. 13.

[0079] The amount of additives can also be evaluated using the technology disclosed in Patent Document 2 (JP 2022-118670 A). As described above, the type of lubricant can be determined and the condition can be monitored and diagnosed.

[0080] An expected application example will be described in association with the example flow in Figure 5. This example involves comparing replaced oil with one type of genuine oil. The engine oil is replaced in a 150-kVA diesel generator that is supposed to use genuine engine oil X (S401). Engine oil Y, which was filled during the replacement, and genuine engine oil X are sampled, and near-infrared absorption spectra in the wavelength range of 800 nm to 2500 nm are acquired using a compact device equipped with optical sensor 700A shown in Figure 4. Ten measurements are taken for each oil. Principal component analysis is performed using the acquired spectral data (S404), and the results are plotted with the first principal component on the horizontal axis and the second principal component on the vertical axis.

[0081] 6 shows an example of the results of principal component analysis of lubricating oil. As preprocessing for the principal component analysis, second-order differentiation of the spectral data and smoothing of 11 neighboring points were performed.

[0082] The results of the principal component analysis show that genuine engine oil X and engine oil Y filled at the time of change are plotted overlapping, indicating that they are the same lubricating oil (S405). This result is notified to the machine owner, maintenance company, and machine dealer (S406), and the diesel generator is operated. Thereafter, the near-infrared absorption spectrum of the engine oil filled at the time of change is obtained once a day, and partial least squares regression analysis is performed using the near-infrared absorption spectrum data to quantify the antioxidant concentration in the engine oil (S408).

[0083] After 1000 hours, it was confirmed that the antioxidant concentration in the engine oil filled at the time of replacement had returned to 30% of the initial value (S409), so the owner of the machine, the maintenance company, and the sales company of the machine were notified to replace the engine oil filled at the time of replacement (S410), and the engine oil in the diesel generator was replaced three days later (S401).

[0084] Immediately after the oil change (S401), a small amount of the newly filled engine oil is sampled, and a near-infrared absorption spectrum in the wavelength range of 800 nm to 2500 nm is obtained using a small device equipped with optical sensor 700A (S402). Principal component analysis is performed using the obtained spectral data (S404), and it is confirmed that genuine engine oil X and the newly filled engine oil Y are the same lubricating oil (S405), and the diesel generator is then operated.

[0085] An example of a possible application is explained below, in association with the example flow in Figure 5. In this example, replaced oil is compared with multiple types of oil. Seven types of gear oil are commercially available for wind turbine gearboxes. It is known that if a different gear oil product is filled when replacing these gear oils, additives may precipitate or the gearbox may malfunction.

[0086] For the seven types of gear oils A to G, near-infrared absorption spectra in the wavelength range of 1400 nm to 2000 nm are obtained using a small device equipped with the optical sensor 700 shown in Figure 1 (S402). Using the obtained near-infrared absorption spectrum data, smoothing of nine neighboring points is performed as preprocessing, and then principal component analysis is performed (S404), and the results are plotted with the first principal component on the horizontal axis and the second principal component on the vertical axis.

[0087] 7 shows an example of the results of principal component analysis of lubricating oil. The gear oil in the speed-up gear of wind turbine H was replaced (S401), and the near-infrared absorption spectrum of the filled gear oil was obtained (S402). Gear oil A was to be used as the genuine oil in wind turbine H. Principal component analysis was performed (S404) on the near-infrared absorption spectrum data of the filled gear oil together with the near-infrared absorption spectrum data of gear oils A to G (S403). It was found that the filled gear oil was the same as gear oil A (S405).

[0088] The results and an instruction to change the gear oil in the speed increaser of wind turbine H were notified to the owner of wind turbine H, the maintenance company for wind turbine H, and the sales company for wind turbine H (S410), and one week later the maintenance company for wind turbine H changed the oil (S401). The second gear oil was confirmed to be the same as gear oil A by principal component analysis using near-infrared absorption spectrum data, and wind turbine H began operation the following day.

[0089] An example of an expected application will be described below. The optical sensor 700 shown in Figure 1 was installed in the engine oil piping of a diesel generator with an output of 300 kW so that the near-infrared absorption spectrum of the oil could be collected. The near-infrared absorption spectrum is input to the following diagnostic system as needed.

[0090] <Diagnostic System Configuration> Figure 8 shows an example of a system used to diagnose engine oil in this embodiment. The diagnostic system 1200 in this embodiment is configured using a regular 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 memory and a magnetic disk device. This diagnostic system 1200 has the function of determining the type of oil and the function of monitoring the consumption of antioxidants in the oil.

[0091] The storage device 1240 includes an estimation model generation unit 1250 and an oil type estimation unit 1260. In this embodiment, the estimation model generation unit 1250 and the oil type estimation unit 1260 are included in the same device, but they can also be configured as separate devices. In this embodiment, a monitoring unit 1262 is optionally attached to the oil type estimation unit 1260, making it possible to monitor the properties of the oil during operation.

[0092] 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 .

[0093] The oil type estimation unit 1260 includes a preprocessing unit 1252 B, an estimation model 1261 , and a monitoring unit 1262 .

[0094] <Outline of Processing by Diagnostic System> Figure 9 shows an example of engine oil diagnosis using diagnostic system 1200. First, measurement data is acquired from actual engine oil, and a measurement database 1251 is generated. The measurement database stores near-infrared absorption spectrum data measured with an optical sensor such as that shown in Figure 1 or 4 for oils to be compared (S1310). For example, near-infrared absorption spectrum data for genuine oil M, the engine oil to be used, gear oil N made by the same manufacturer as genuine oil M, and gear oil L made by a different manufacturer are stored in measurement database 1251.

[0095] As explained in FIG. 2, the preprocessing unit 1252A of the estimation model generation unit 1250 performs smoothing (S1322) and differentiation (S1323) of the measured data, prepares preprocessed near-infrared absorption spectrum data, and stores it in the training database 1253 as training data.

[0096] As described in FIG. 2, the multivariate analysis unit 1254 uses principal component analysis (S1324) to create an oil type discrimination map such as that shown in FIG. 3 from the training data (S1325), and generates an estimation model 1261 (S1320).

[0097] Using the obtained estimation model 1261, the oil type estimation unit 1260 estimates the type of oil using the method described above (S1330). That is, the diagnostic object absorption spectrum data is analyzed using a dimension reduction algorithm, and multivariate analysis using principal component analysis is performed to express the type of diagnostic object oil using values ​​of at least two principal components. This is then compared with an oil type estimation model that expresses the oil type using values ​​of at least two principal components. The results of the oil type estimation S1330 are automatically notified to relevant parties by email.

[0098] If the oil type is confirmed to be the same as the oil that should be used as a result of the oil type estimation, the diagnostic system notifies the diesel generator that permission to operate, and then proceeds to the oil property estimation process. The monitoring unit 1262 collects actual measurement data every 12 hours and performs the oil property estimation process (S1340).

[0099] <Oil property estimation process by diagnostic system> Figure 10 is a flow diagram of the oil property estimation process executed by the monitoring unit 1262. Actual oil measurement data is collected at any time using an optical sensor such as that shown in Figure 1 or 4 (S1341), and the oil property is estimated (S1342). The results of the oil property estimation process are automatically notified to relevant parties by email.

[0100] In this example, the viscosity of the new engine oil was 20 cP, and it was recommended to change the oil when the viscosity reached 25 cP with use. The viscosity of this engine oil increased due to the consumption of the antioxidant. The engine oil contained a phenolic antioxidant, and its concentration in the new oil was 3.5 wt%.

[0101] The diesel generator was operated continuously. Every 12 hours, the near-infrared absorption spectrum of the engine oil was measured using the optical sensor shown in Figure 1. The wavelength resolution of the near-infrared absorption spectrum was 5 nm. For remote oil monitoring using an optical sensor, the technology described in Patent Document 2 and JP 2020-12690 A cited therein can be applied. Below, we explain the technique using machine learning.

[0102] <Processing for Estimating Oil Properties Using Machine Learning> A process for estimating oil properties using machine learning will be described. Oil is used in an actual usage environment, and data on changes in properties over time is obtained. For example, the concentration of antioxidants in sampled engine oil is quantified using high-performance liquid chromatography (HPLC). The viscosity of the engine oil is also measured using any known method. The measured near-infrared absorption spectrum, antioxidant concentration, and viscosity are linked to time information and recorded in the actual measurement database 1251. Note that the time interval for obtaining near-infrared absorption spectrum data is an example, and any known method for analyzing oil properties may be selected.

[0103] As an example of data representation in the actual measurement database, a graph showing the relationship between usage time and oil viscosity, total acid number, and antioxidant concentration is shown in Figure 11. By preparing such relationships as reference data, viscosity can be calculated from antioxidant concentration.

[0104] Furthermore, the near-infrared absorption spectrum of the engine oil was measured using the optical sensor shown in Figure 1. These data were also linked to time and stored in the actual measurement database 1251.

[0105] Figure 12 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. The curves show the spectra after different periods of use.

[0106] Next, explanatory variables are generated using the measured near-infrared absorption spectrum, and an oil property estimation model is generated using the antioxidant concentration measured using HPLC as the response variable. In generating the oil property estimation model, model generation using known machine learning, in which explanatory variables are used as inputs and response variables are used as outputs, can be used. In this example, PLS regression analysis was performed.

[0107] 13 is a flow diagram of the oil property estimation model generation process S3320. This process is performed by the estimation model generation unit 1250, which is a general computer, and the processing unit 1230 executes software.

[0108] First, the preprocessing unit 1252A reads out data of the near-infrared absorption spectrum at a certain time point (S3321) from the actual measurement database 1251. The preprocessing of the near-infrared absorption spectrum in the preprocessing unit 1252A includes smoothing by the Savitzky-Golay method (SG method) using data from nine adjacent points (S3322) and second-order differentiation of the spectrum (S3323).

[0109] Figure 14 shows a graph of the results of differentiating the spectrum twice. The curves represent the derivatives of the spectrum at different times after use. Differentiation can emphasize the difference 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.

[0110] 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.

[0111] The input of the near-infrared absorption spectrum (S3321), smoothing (S3322), and second-order differentiation of the spectrum (S3323) in FIG. 12 are basically the same processes as the input of the near-infrared absorption spectrum (S1321), smoothing (S1322), and second-order differentiation of the spectrum (S1323) in FIG. 2.

[0112] When estimating oil properties using machine learning, the multivariate analysis unit 1254 has an optional PLS regression analysis function in addition to multivariate analysis. PLS regression analysis is performed using the explanatory variable x and the objective variable y (S3324) to generate an oil property estimation model 1261. In PLS regression analysis, a large amount of data (light intensity at multiple wavelengths) is integrated into principal components, and a regression line is derived by two-dimensionally plotting the principal components and the objective variable. After the PLS regression analysis, cross-validation was performed (S1325). The generated property estimation model is implemented in the monitoring unit 1262.

[0113] As mentioned above, when constructing an oil type estimation model using machine learning, similar machine learning can be performed using the twice-differentiated near-infrared absorption spectrum as the explanatory variable x and the score of the discrimination map dimensionally reduced by principal component analysis as the objective variable y.

[0114] 15 is a flow diagram of the oil property estimation process S1340 using the generated property estimation model. This process is performed by the monitoring unit 1262, which is a general computer, through software processing.

[0115] First, the near-infrared absorption spectrum of the engine oil whose properties are to be determined is acquired and input from the input device 1210 of the diagnostic system 1200 (S1341). The near-infrared absorption spectrum is acquired by the optical sensor shown in Fig. 1 or 3. One of the features of the present embodiment is that the near-infrared absorption spectrum can be collected contactlessly and remotely.

[0116] The near-infrared absorption spectrum is subjected to preprocessing such as smoothing and second differentiation by the preprocessing unit 1252B in the same manner as when the property estimation model was generated in Fig. 13 (S1342). The monitoring unit 1262 inputs the preprocessed near-infrared absorption spectrum into the oil property estimation model to estimate the antioxidant concentration (S1343).

[0117] The monitoring unit 1262 can have a characteristic conversion table (not necessarily in table form, but may be data showing the relationship between antioxidant concentration and viscosity as shown in FIG. 11 ) that converts antioxidant concentration, viscosity, and total acid value into each other using all or part of the actual measurement database 1251.

[0118] The viscosity of the engine oil is estimated based on the antioxidant concentration estimated by the oil property estimation model and the characteristic conversion table (S1344). Using the results of this analysis, it was determined that the viscosity of the engine oil would reach 25 cP when the antioxidant concentration was reduced to 40% of the initial concentration.

[0119] Figure 16 is a graph showing the consistency between predicted and measured values ​​of antioxidant concentration using the property estimation model of the monitoring unit 1262. The reference value is the actual measured value of the sample, and the PLS regression equation is obtained as a result of analysis using the PLS model. The predicted value is the result predicted from the actual measured value using this regression equation (estimation model), and if the reference value and the predicted value match well, a good PLS model has been constructed. Using the calibration curve shown in Figure 15, the concentration of a sample with an unknown additive concentration can be quantified.

[0120] 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 total acid number, concentration, 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 adopted.

[0121] In the above example, the near-infrared absorption spectrum was used as the explanatory variable x and the antioxidant concentration as the response variable y, but values ​​reflecting other oil characteristics (such as the concentration of other additives or the duration of use) can also be used as the response variable. Furthermore, learning can also be performed using viscosity and total acid number, which indicate oil properties, directly as response variables.

[0122] In this embodiment, the configurations of the first to third embodiments are applied to a system and method for monitoring lubricating oil in a wind power generator. This embodiment is a system for monitoring lubricating oil supplied to a mechanical drive unit of a wind power generator. This system includes a diagnostic system 1200 shown in FIG. 8.

[0123] The storage device in the monitoring system stores near-infrared absorption spectrum data of various oils required for identifying and judging lubricating oils, as well as additive concentration data that stores the concentrations of additives in lubricating oils in chronological order as references, and the diagnostic system 1200 estimates the time at which the additive concentration in the lubricating oil, determined from the near-infrared absorption spectrum of the lubricating oil, will reach a predetermined threshold value.

[0124] (1. Overall System Configuration) Figure 17 shows a schematic diagram of a lubricant monitoring system for a wind power generator having 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 system 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.

[0125] 17 , 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.

[0126] 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 Figure 8, and the system of this embodiment is capable of remote oil monitoring.

[0127] (2. Sensor Arrangement) Fig. 18 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 speed increaser 33 or any other general part where mechanical contact occurs, and is not particularly limited.

[0128] The optical sensor 304 is disposed in the lubricating oil flow path etc. in order to detect the state of the lubricating oil. Specific examples of the optical sensor 304 are shown in FIG.

[0129] In this embodiment, a transparent measurement 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 measurement unit 303. An optical sensor 304 is then installed in the measurement unit 303. The measurement unit 303 is not provided in the main lubricant flow path in order to adjust the flow rate of the lubricant in the measurement 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.

[0130] 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. The optical sensor 304 acquires the near-infrared absorption spectrum by transmitting near-infrared light from the near-infrared light source through the oil and detecting the near-infrared light that has transmitted through the oil with a light-receiving element that has sensitivity according to the wavelength. 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 estimate the type of lubricating oil and determine the amount of remaining additives in the lubricating oil, and to perform deterioration diagnosis and remaining life diagnosis.

[0131] Lubricating oil deteriorates in quality with use and no longer performs its original function. Therefore, maintenance such as replacement is required depending on the deterioration of quality. To determine the timing of such maintenance, it is useful for efficient maintenance management to be able to remotely monitor data collected by an optical sensor 304 installed on-site. Furthermore, filling with a lubricating oil different from the one that should be used during an oil change can cause precipitation of base oil and additives, so determining the type of oil when changing is useful for efficient maintenance management.

[0132] The data collected by the optical sensor 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.

[0133] However, for analyses that require equipment for measurement, such as LC (liquid chromatography), FT-IR (Fourier transform infrared spectroscopy), and NMR (nuclear magnetic resonance), it is necessary to collect lubricant samples as appropriate and analyze them using separately provided equipment. The results of these LC, FT-IR, and NMR measurements are also stored as data in the central server 240, and it is desirable to aggregate the data and take this data into consideration when understanding the properties of the lubricant.

[0134] The aggregated data may include not only data related to lubricants but also data indicating the operating status of the wind turbine. For example, the wind turbine output value (the higher the value, the faster the lubricant deteriorates), the actual operating time (the longer the value, the faster the lubricant deteriorates), the machine temperature (the higher the value, the faster the lubricant deteriorates), the shaft rotation speed (the faster the lubricant deteriorates), etc. These data can be collected from sensors with known configurations installed at various locations on the wind turbine or from control signals from the device.

[0135] (3. Lubricant Diagnosis Flow) Figure 19 is a flow diagram showing the lubricant diagnosis process according to this embodiment. The process shown in Figure 19 is performed under the control of any of the server 210, aggregation server 220, or central server 240 in Figure 17. 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).

[0136] 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 repeated process, and the start timing is set by a timer or the like, for example, starting at midnight every day (S601). Alternatively, the central server 240 can perform the processing at any timing in response to an operator's instruction.

[0137] 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.

[0138] If it is time to change the lubricating oil, the lubricating oil is changed in step S603. Since changing the lubricating oil is normally performed by a worker, the central server 240 displays and notifies the worker when and what to change.

[0139] If it is not time to change the lubricant, in process 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 an optical sensor, oil temperature, oil pressure, particle concentration in the lubricant, and the like that 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 characteristics of the lubricant, for example, by comparing the oil temperature, oil pressure, and particle concentration in the lubricant obtained from the sensor with predetermined thresholds.

[0140] 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. If the lubricating oil has been changed, the type of oil is subsequently determined using the method described above.

[0141] From the perspective of preventive and planned maintenance of wind turbines, it is desirable to detect when an incorrect lubricant is mistakenly filled during an oil change and replace it with the correct lubricant, and to perform predictive diagnosis of lubricant deterioration based on changes in the concentration of additives contained in the lubricant before it is determined that an abnormality has occurred.

[0142] In this embodiment, the time-series near-infrared absorption spectra measured by the optical sensor are stored and the degree of deterioration of the lubricant is estimated based on the stored spectra. The replacement time estimation result obtained in step S607 can be displayed as the lubricant diagnosis result (steps S608 and S609).

[0143] 20 shows an example of the display of the results of process S608. Since it is predicted that the viscosity will reach 200 in 50 months, the new replacement time can be set to a time before that (for example, half a month before). One cycle of processing ends in process S610, and in process S602 of the next cycle, judgment processing is performed according to the new replacement time.

[0144] As described above, according to the technology described in the embodiments, an optical sensor having a light source and a detector that detects light emitted from the light source is used to obtain the absorption spectrum of the oil in the wavelength range of 800 nm to 2500 nm, and data based on the absorption spectrum is input into an estimation model implemented in an information processing device to predict the type of oil, and based on the prediction result, the result is notified and a determination is made as to whether the machine using the oil can be operated.

[0145] According to this embodiment, the optical sensor accurately determines whether the correct lubricant has been filled when filling or changing the oil, and after confirming that the correct lubricant has been filled, the machine's operation and the condition of the lubricant can be monitored. This prevents machine breakdowns caused by the use of counterfeit lubricant or the use of the wrong type of lubricant.

[0146] Furthermore, the above-described embodiment enables remote oil detection and diagnosis, thereby reducing labor and costs. Furthermore, by applying dimension reduction using principal component analysis to the near-infrared absorption spectrum, the near-infrared absorption spectrum can be adapted for machine learning.

[0147] 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.

[0148] Diagnostic system 1200, estimation model generation unit 1250, oil type estimation unit 1260, actual measurement data collection process S1310, type estimation model generation process S1320, oil type estimation process S1330

Claims

1. A method for generating an estimation model of an oil type, using an information processing system equipped with an input device, an output device, a processing device, and a storage device, wherein the information processing system has an estimation model generation unit that generates an estimation model, and the estimation model generation unit takes in absorption spectrum data obtained by transmitting light with at least a portion of wavelengths between 800 nm and 2500 nm through oil of known type, analyzes the absorption spectrum data using a dimensionality reduction algorithm, and generates an estimation model of the oil type.

2. The method for generating an oil type estimation model according to claim 1, wherein the dimension reduction algorithm performs multivariate analysis using principal component analysis.

3. The method for generating an oil type estimation model according to claim 1, wherein the dimension reduction algorithm analyzes values ​​obtained by differentiating the absorption spectrum data one or more times.

4. The method for generating an oil type estimation model according to claim 1, wherein the oil type estimation model expresses the oil type using values ​​of at least two principal components.

5. A method for generating an oil type estimation model as described in claim 4, wherein the oil type estimation model is an estimation model generated by machine learning using the absorption spectrum data as an explanatory variable and the values ​​of the principal components as a target variable.

6. The method for generating an estimation model of an oil type according to claim 4, wherein the estimation model generation unit acquires the absorption spectrum data for a plurality of samples of the same type of oil whose type is known.

7. A method for estimating an oil type using a diagnostic system comprising an information processing device that implements the estimation model described in claim 4, comprising: inputting, into said diagnostic system, diagnostic object absorption spectrum data obtained by transmitting light of at least a portion of wavelengths between 800 nm and 2500 nm through the diagnostic object oil; inputting values ​​based on said diagnostic object absorption spectrum data into said estimation model; and estimating the type of said diagnostic object oil from said estimation model.

8. The method for estimating the type of oil according to claim 7, wherein the diagnostic object absorption spectrum data is analyzed using a dimension reduction algorithm, the dimension reduction algorithm performs multivariate analysis using principal component analysis, and the type of the diagnostic object oil is expressed by the values ​​of at least two principal components.

9. The method for estimating oil type as described in claim 8, wherein when the distance between the value of the principal component representing the oil to be diagnosed and the value of the principal component of an oil of known type represented by the oil type estimation model is within a predetermined range, the oil to be diagnosed is estimated to be the same type as the oil of known type.

10. The method for estimating oil type according to claim 9, wherein the distance is at least one selected from the group consisting of Euclidean distance, Chebyshev distance, and Mahalanobis distance.

11. The method for estimating the type of oil according to claim 7, wherein the diagnostic object absorption spectrum data is differentiated one or more times and the differentiated value is input to the estimation model.

12. The method for estimating oil type as described in claim 7, wherein, if the estimated type of oil to be diagnosed is not the same as a predetermined oil type, the user is notified of the estimated result of the type of oil to be diagnosed and is instructed to at least one of check the oil to be diagnosed and replace the oil to be diagnosed.

13. The method for estimating oil type as described in claim 7, wherein if the estimated type of oil to be diagnosed is the same as a predetermined oil type, the user is notified and instructed to allow and start operation of the machine filled with the oil to be diagnosed.

14. The method for estimating oil type according to claim 13, further comprising notifying or instructing a user to permit or start operation of a machine filled with the oil to be diagnosed, and then monitoring the oil properties of the oil to be diagnosed.

15. An oil type estimation system comprising an information processing device that implements the estimation model described in claim 1.

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