Diagnostic system

Abstract

The present invention provides a diagnostic device that is capable of accurately diagnosing a deterioration state of a grease.　This diagnostic system comprises: an input unit that receives input of a diagnostic oil fraction, which is measurement data of the oil fraction of a grease to be diagnosed, and a diagnostic oil separation value, which is measurement data of the grease to be diagnosed for an oil separation value indicating the ease of separation of a base oil from the grease; a storage unit that stores a calibration model for calculating an oil separation value serving as a reference corresponding to the oil fraction of the grease; a calculation unit that obtains a reference oil separation value corresponding to the diagnostic oil fraction on the basis of the calibration model, and calculates a diagnostic difference between the reference oil separation value and the diagnostic oil separation value; and a determination unit that determines, on the basis of the diagnostic difference, a deterioration state of the grease to be diagnosed.

Description

Diagnostic system

[0001] This disclosure relates to a grease diagnostic system.

[0002] Patent Document 1 discloses a method for evaluating the deterioration of grease. According to this evaluation method, the degree of deterioration of the grease, as indicated by consistency, can be evaluated based on the area covered when the grease to be evaluated is spread thinly.

[0003] Japanese Patent Publication No. 2014-190752

[0004] However, generally speaking, various properties of grease, including consistency, are greatly influenced by the oil content of the grease. The evaluation method described in Patent Document 1 does not reflect the influence of the oil content. Therefore, the accuracy of the evaluation results may not be good.

[0005] This disclosure was made to solve the aforementioned problems. The purpose of this disclosure is to provide a diagnostic system that can accurately diagnose the degradation state of grease.

[0006] The diagnostic system according to this disclosure includes: an input unit that receives input of a diagnostic oil content, which is measurement data of the oil content of the grease to be diagnosed, and a diagnostic oil separation value, which is measurement data of the grease to be diagnosed regarding the ease with which the base oil is separated from the grease; a storage unit that stores a calibration model for calculating a standard oil separation value corresponding to the oil content of the grease; a calculation unit that determines a standard oil separation value corresponding to the diagnostic oil content based on the calibration model and calculates a diagnostic difference between the standard oil separation value and the diagnostic oil separation value; and a determination unit that determines the deterioration state of the grease to be diagnosed based on the diagnostic difference.

[0007] The diagnostic system according to this disclosure includes an input unit that accepts input of a diagnostic oil content, which is measurement data of the oil content of the grease to be diagnosed; a diagnostic oil separation value, which is measurement data of the grease to be diagnosed regarding the ease with which the base oil is separated from the grease; measurement data of the operating temperature in the environment in which the grease to be diagnosed was used when the diagnostic oil content was measured; and the type of the grease to be diagnosed; and a determination unit that determines the deterioration state of the grease to be diagnosed from the diagnostic oil content, the diagnostic oil separation value, the type of grease to be diagnosed, and the operating temperature input to the input unit.

[0008] The diagnostic system according to this disclosure includes an input unit that receives input of a diagnostic oil content, which is measurement data of the oil content of the grease to be diagnosed, and a diagnostic oil separation value, which is measurement data of the grease to be diagnosed, indicating the ease with which the base oil can be separated from the grease; a storage unit that stores information of a normal range, with the oil content and oil separation value of the grease as its dimensions; and a determination unit that determines the deterioration state of the grease to be diagnosed by comparing the combination of the diagnostic oil content and the diagnostic oil separation value with the normal range.

[0009] According to this disclosure, the diagnostic device determines the deterioration state of the grease to be diagnosed using an index that reflects a combination of the oil content and oil separation value of the grease to be diagnosed. Therefore, the deterioration state of the grease can be diagnosed with high accuracy.

[0010] This is a functional block diagram of the diagnostic system in Embodiment 1. This is a graph showing the diagnostic difference calculated by the diagnostic system in Embodiment 1. This is a flowchart showing an example of the operation of the diagnostic system in Embodiment 1. This is a flowchart showing an example of the operation of the diagnostic system in Embodiment 1. This is a flowchart showing an example of the operation of the diagnostic system in Embodiment 1. This is a functional block diagram of the diagnostic system in Embodiment 1. This is a perspective view showing the colorimeter of the diagnostic system in Embodiment 1. This is a perspective view showing the colorimeter of the diagnostic system in Embodiment 1. This is an image diagram showing each value indicating the hue calculated by the diagnostic system in Embodiment 1. This is an image diagram showing each value indicating the hue calculated by the diagnostic system in Embodiment 1. This is a flowchart showing an example of the operation of the diagnostic system 1 in Embodiment 1. This is a functional block diagram of the diagnostic system in a first modified example of Embodiment 1. This is a flowchart showing an example of the operation of the diagnostic system in a first modified example of Embodiment 1. This is a functional block diagram of the diagnostic system in a second modified example of Embodiment 1. This is a flowchart showing an example of the operation of the diagnostic system in a second modified example of Embodiment 1. This is a diagram showing an example of the normal range used by the diagnostic system in Embodiment 2. This is a flowchart showing an example of the operation of the diagnostic system in Embodiment 2. This is a diagram showing the upper and lower limit models stored in the diagnostic system in Embodiment 3. This is a flowchart showing an example of the operation of the diagnostic system in Embodiment 3. This is a diagram showing the absorption part of the diagnostic system in Embodiment 4. This is a diagram showing the absorption section of the diagnostic system in Embodiment 4. This is a functional block diagram of the diagnostic system in Embodiment 4. This is a graph showing the diagnostic difference calculated by the diagnostic system in Embodiment 4. This is a flowchart showing the measurement work performed in the absorption section of the diagnostic system in Embodiment 4. This is a flowchart showing an example of the operation of the diagnostic system in Embodiment 4. This is a functional block diagram of the diagnostic system in a modified example of Embodiment 4. This is a flowchart showing an example of operation in a modified example of Embodiment 4. This is a hardware configuration diagram of the diagnostic device of the diagnostic system in Embodiments 1 to 4.

[0011] The embodiments for implementing this disclosure will be described with reference to the attached drawings. In each drawing, the same or corresponding parts are denoted by the same reference numerals. The explanation of such parts will be simplified or omitted as appropriate.

[0012] Embodiment 1. Figure 1 is a functional block diagram of the diagnostic system in Embodiment 1. Figure 2 is a graph showing the diagnostic difference calculated by the diagnostic system in Embodiment 1. Figures 3 to 5 are flowcharts showing examples of the operation of the diagnostic system in Embodiment 1.

[0013] The diagnostic system 1 shown in Figure 1 diagnoses the deterioration state of lubricating grease. Besides the simple decrease in base oil due to use, lubricating grease can also deteriorate in ways that involve changes in the properties of at least one of its main components: the thickener, base oil, and additives. Hereafter, deterioration involving changes in the properties of grease components such as the thickener and base oil will also be referred to as abnormal deterioration. In particular, we will explain the diagnosis of abnormal deterioration, including the aggregation of the thickener, chemical changes such as oxidation of grease components (including hydrogen substitution; hereinafter referred to as "oxidation"), and the aggregation of the base oil. Note that abnormal deterioration may also include cases where foreign matter is mixed into the grease. Deterioration that is not abnormal, such as the simple decrease in base oil due to use, will also be referred to as normal deterioration.

[0014] For example, the diagnostic system 1 determines whether or not abnormal deterioration has occurred in the grease being diagnosed. The diagnostic system 1 may also determine what type of abnormal deterioration has occurred in the grease being diagnosed. The diagnostic system 1 may also determine the degree of the abnormal deterioration in the grease being diagnosed.

[0015] For example, the grease to be diagnosed is used in motor bearings, etc. The equipment in which the grease is used can be any equipment. In one example of diagnostic system 1, a worker takes a sample of the grease sealed in the equipment in question and uses it to diagnose its deterioration.

[0016] The diagnostic system 1 comprises an analyzer 2, a thermometer 3, and a diagnostic device 10. Each component of the diagnostic system 1 performs its respective function as part of the diagnostic system 1.

[0017] The analyzer 2 measures the properties of the grease under diagnosis and outputs the measurement data. The analyzer 2 measures the oil content of the grease under diagnosis. The analyzer 2 outputs the diagnostic oil content as measurement data for the oil content of the grease under diagnosis.

[0018] The oil content is primarily the proportion of base oil to the total grease. Hereafter, the oil content will be calculated as the quotient obtained by dividing the mass of base oil contained in the grease by the total mass of the grease. Note that the oil content may also be the volume ratio of base oil to the total volume of grease. The oil content may also be the same value as the residual oil percentage. That is, the oil content may be the ratio of the current base oil to the amount of base oil in unused grease.

[0019] The analyzer 2 measures the oil separation value of the grease under diagnosis. The analyzer 2 outputs the diagnostic oil separation value as measurement data of the oil separation value of the grease under diagnosis.

[0020] The oil separation value is an index value indicating the ease with which oil can be separated from grease. The oil separated from grease is mainly base oil. The oil separation value may also be expressed as the degree of oil separation. The degree of oil separation is the quotient obtained by dividing the amount of oil separated, which is the mass of base oil separated from the grease when a specified test is performed, by the total mass of grease before separation. In this embodiment, the oil separation value represents the oil separation rate. The degree of oil separation may be measured by a test specified in a general standard. The oil separation value may be any measurement value that indicates the ease with which base oil can be separated from grease. For example, the oil separation value may be the amount of base oil adsorbed by the filter paper that came into contact with the grease being tested.

[0021] The analysis device 2 may be divided into a device for measuring the oil content and a device for measuring the oil separation value. Alternatively, the analysis device 2 may be a device that accepts various test data for measuring the oil separation value and calculates the oil separation value.

[0022] The analyzer 2 may be located in a building separate from the building in which the equipment under diagnosis is used. Alternatively, the analyzer 2 may be located in the same building as the equipment under diagnosis. If located in the same building, the analyzer 2 may have a data storage unit 2a and a communication unit 2b. The data storage unit 2a stores measurement data of the grease under diagnosis. The communication unit 2b communicates with external equipment via a network. The communication unit 2b transmits the measurement data of the grease under diagnosis to the external equipment. For example, the analyzer 2 automatically samples the grease under diagnosis at a predetermined interval and creates and stores measurement data including the diagnostic oil content and diagnostic oil separation value.

[0023] Thermometer 3 is attached to the location where the grease under diagnosis is used. Thermometer 3 measures the temperature of the location where the grease under diagnosis is used at the time the grease under diagnosis is sampled, and this temperature is recorded as the operating temperature. Thermometer 3 may also continuously measure the temperature of the location where the grease under diagnosis is used.

[0024] The diagnostic device 10 determines the deterioration state of the grease under diagnosis based on various information, including measurement data of the grease under diagnosis, as part of the grease diagnosis. For example, the diagnostic device 10 is a terminal used by maintenance workers. The diagnostic device 10 may be installed in a building separate from the building in which the equipment using the grease under diagnosis is located. The diagnostic device 10 can communicate with the analysis device 2 and the thermometer 3. The diagnostic device 10 includes an input unit 11, a storage unit 12, a calculation unit 13, a determination unit 14, and an output unit 15 as its functions.

[0025] The input unit 11 may be software that accepts information input, or it may be an interface that accepts information input, such as a communication terminal or a keyboard. The input unit 11 accepts various types of information input. The input unit 11 accepts input of the diagnostic oil content and diagnostic oil separation value of the grease to be diagnosed. The input unit 11 may also accept input of measurement data from the thermometer 3, such as the operating temperature and the type of grease to be diagnosed.

[0026] The memory unit 12 stores various types of information. Specifically, the memory unit 12 stores calibration models in advance. The calibration model is a model for calculating a reference oil separation value, which is a standard oil separation value corresponding to a certain oil content for a grease. The calibration model may be expressed as a function. For example, the calibration model is a calibration curve in a two-dimensional plane between the oil content and the oil separation value. That is, the calibration model is a linear function that shows the relationship between the oil content and the reference oil separation value. The following describes an example where the calibration model is a linear function. For example, the calibration model is set in advance based on the measurement results of the relationship between the oil content and the oil separation value of a standard grease that has not undergone any abnormal type of deterioration. If no abnormal type of deterioration has occurred, generally, the higher the oil content, the larger the oil separation value.

[0027] Furthermore, the calibration model does not have to be a linear function; it can be any function other than a linear function, or it can be an algorithm that combines several functions, as long as it is a model in which a single reference oil separation value is determined for each oil content.

[0028] The calibration model may be multiple individual calibration models, each different for each type of grease. In this case, the diagnostic device 10 uses an individual calibration model corresponding to the type of grease being diagnosed as the calibration model.

[0029] The calculation unit 13 calculates various values ​​based on the information input to the input unit 11 and the information stored in the storage unit 12. For example, the calculation unit 13 determines a reference oil separation value corresponding to the diagnostic oil content based on the calibration model. The calculation unit 13 calculates the diagnostic difference, which is the difference obtained by subtracting the determined reference oil separation value from the diagnostic oil content. The calculation unit 13 may also use as the calibration model an individual calibration model corresponding to the type of grease being diagnosed from among the multiple individual calibration models stored in the storage unit 12.

[0030] The determination unit 14 determines the deterioration state of the grease to be diagnosed using the information input to the input unit 11, the information calculated by the calculation unit 13, etc. For example, the determination unit 14 determines the deterioration state of the grease to be diagnosed based on the diagnostic difference. The determination by the determination unit 14 can be performed based on various methods. In addition, the deterioration state can be output with content corresponding to the method adopted.

[0031] The output unit 15 is an interface that outputs the determination result by the determination unit 14 to an external device. For example, the output unit 15 displays the determination result by the determination unit 14 on a display. The output unit 15 may store the determination result in a data server not shown in the drawings.

[0032] Next, the diagnostic difference calculated in the present embodiment will be described with reference to FIG. 2. The vertical axis of the graph in FIG. 2 is the oil separation value. The horizontal axis is the oil content rate. The calibration curve Ls, which is a calibration model, is shown in the graph. The points Ps1 and Ps2 are the points used for the calculation of the calibration model.

[0033] Three sets of the diagnostic oil content rate and the diagnostic oil separation value are shown in the graph. Each set is indicated by points P1, P2, and P3. The measured diagnosed grease and the measured situation for each are not related.

[0034] The diagnostic difference D1 corresponds to the point P1. When the diagnostic difference D1 is calculated, first, the reference oil separation value corresponding to the diagnostic oil content rate indicated by the point P1 in the calibration model is calculated. Then, the difference between the diagnostic oil separation value indicated by the point P1 and the reference oil separation value is calculated as the diagnostic difference D1. The diagnostic differences D2 and D3 are calculated in the same manner.

[0035] The diagnostic difference is the result of comparing the case where no abnormal deterioration has occurred with the diagnosed grease. When abnormal deterioration occurs, the oil separation value with respect to the oil content rate becomes a value different from the general relationship depending on the type of the deterioration. The relationship between the abnormal deterioration and the diagnostic difference will be described in detail using Table 1 below.

[0036]

[0037] Table 1 shows the relationship between the types of deterioration obtained by the inventors and various measurement data. Table 1 shows the diagnostic oil content rate, the diagnostic oil separation value, the reference oil separation value, the diagnostic difference, and the presence or absence of discoloration for samples X1 to X4 and S1 to S9, respectively. The case where discoloration has occurred means that the color of the diagnosed grease is different from that of the normal one. When discoloration has occurred, it is regarded as having discoloration, and a '+' is described in the table.

[0038] Samples X1 to X4 are samples used for calculating the reference model and are samples without abnormal deterioration. In particular, sample X1 is new grease. Also, samples X2 to X4 may be new grease with an adjusted oil content rate. In this example, the bleeding values of samples X1 to X4 respectively match the reference bleeding values.

[0039] Samples S1 and S2 are samples in which only the aggregation of the thickener occurs as abnormal deterioration. When the aggregation of the thickener occurs, the holding power of the base oil in the grease decreases, so the base oil is likely to flow out. In this case, the bleeding value with respect to the oil content rate becomes larger than the case where it is used as a reference. That is, the diagnostic difference becomes a positive value.

[0040] Sample S3 is a sample in which only the oxidation of the grease components such as the base oil occurs as abnormal deterioration. When at least one of the thickener, base oil, and additives, which are the grease components, is oxidized, particularly when the oxidation of the base oil occurs, the base oil deteriorates and the polymerization of the base oil occurs. In this case, the base oils bond to each other and condense in a sludge-like form, so the base oil in the grease becomes more likely to flow out from the thickener. In this case, the diagnostic difference becomes a positive value. Also, in this case, the diagnostic difference is often smaller than that of a sample in which only the aggregation of the thickener occurs. Furthermore, when the oxidation of the grease components such as the base oil occurs, the grease and the flowed-out base oil are discolored. For example, the grease and the base oil turn brown. Note that discoloration of the grease may also occur when foreign matter is mixed into the grease by being used.

[0041] Sample S4 is a sample in which only the aggregation of the base oil occurs as abnormal deterioration. When the aggregation of the base oil occurs, the base oil in the grease is less likely to flow out from the thickener. In this case, the bleeding value with respect to the oil content rate becomes smaller than the case where it is used as a reference. That is, the diagnostic difference becomes a negative value.

[0042] Sample S5 is a sample in which abnormal deterioration has occurred, specifically oxidation of the grease components such as the base oil and aggregation of the base oil. In sample S5, the diagnostic difference is a negative value. The diagnostic difference for sample S5 is larger than that for sample S4. In other words, the value obtained by adding the positive difference due to oxidation of the grease components to the negative diagnostic difference when aggregation of the base oil occurs is the diagnostic difference when oxidation of the grease components and aggregation of the base oil occur.

[0043] Samples S6 and S8 are samples in which abnormal deterioration has occurred, specifically oxidation of the grease components such as the base oil and aggregation of the thickener. Sample S8 has a lower diagnostic oil content than sample S6. In particular, the diagnostic difference for sample S6 is a positive value. The diagnostic difference for sample S6 is larger than that for sample S1. That is, the value obtained by adding the positive differential influence due to oxidation of the grease components to the positive diagnostic difference when aggregation of the thickener occurs is the diagnostic difference when both aggregation of the thickener and oxidation of the grease components occur.

[0044] Sample S7 is a sample in which abnormal degradation has occurred, specifically the aggregation of the thickener and the base oil. In sample S7, the diagnostic difference is a negative value. The diagnostic difference for sample S7 is larger than that for sample S4.

[0045] Sample S9 exhibits abnormal degradation, including thickening agent aggregation, oxidation of grease components such as base oil, and base oil aggregation. Furthermore, sample S9 has a low oil content. The effect of base oil aggregation is particularly pronounced in sample S9.

[0046] The determination unit 14 of the diagnostic device 10 determines the deterioration state of the grease under diagnosis based on the diagnostic difference, which is the trend of the diagnostic oil separation value relative to the diagnostic oil content. Several methods for this determination can be adopted depending on the information used. The first to third examples of the determination will be described below.

[0047] Figure 3 is a flowchart showing a first example of the determination. In the first example, the determination unit 14 determines whether or not abnormal deterioration has occurred based on the absolute value of the diagnostic difference.

[0048] In step S101, the analyzer 2 measures the diagnostic oil content and the diagnostic oil separation value. Then, in step S102, the input unit 11 receives the measured data, which are the diagnostic oil content and the diagnostic oil separation value.

[0049] Subsequently, in step S103, the calculation unit 13 calculates a reference oil separation value corresponding to the diagnostic oil content.

[0050] Subsequently, in step S104, the calculation unit 13 calculates the diagnostic difference by subtracting the reference oil removal value from the diagnostic oil removal value.

[0051] Subsequently, in step S105, the determination unit 14 determines whether the absolute value of the diagnostic difference exceeds a specified first threshold. The first threshold is an arbitrary positive value that indicates abnormal deterioration has occurred. The first threshold may be a value that changes in accordance with the diagnostic oil content. For example, the first threshold may become larger as the diagnostic oil content increases.

[0052] In step S105, if the absolute value of the diagnostic difference is greater than the first threshold, in step S106, the determination unit 14 determines that abnormal deterioration has occurred.

[0053] Subsequently, in step S107, the diagnostic device 10 stores the judgment content. The output unit 15 outputs the judgment content.

[0054] After that, the flowchart operation ends.

[0055] In step S105, if the absolute value of the diagnostic difference is less than or equal to the first threshold, in step S108, the determination unit 14 determines that no abnormal deterioration has occurred, i.e., that the deterioration is normal. Subsequently, the operations from step S107 onward are performed.

[0056] In the first example, the diagnostic system 1 determines whether or not abnormal deterioration has occurred. Therefore, the operator can determine whether or not a noteworthy deterioration state is present simply by measuring the oil content and oil separation value of the grease being diagnosed.

[0057] Figure 4 is a flowchart showing a second example of the determination process. In the second example, the determination unit 14 narrows down the types of abnormal deterioration to a certain extent based on the diagnostic difference.

[0058] Steps S101 to S106 are the same as the flowchart in Figure 3. After step S106, in step S109, the determination unit 14 determines the type of abnormal deterioration based on the diagnostic difference.

[0059] Specifically, if the diagnostic difference is a negative value, the determination unit 14 determines that at least base oil aggregation has occurred as a type of abnormal deterioration. If the diagnostic difference is a positive value, the determination unit 14 determines that at least one of the following has occurred as a type of abnormal deterioration: aggregation of the thickener or oxidation of the grease components such as base oil.

[0060] Furthermore, the determination unit 14 may determine that aggregation of the thickener has occurred as a type of abnormal deterioration if the diagnostic difference is greater than the second threshold. The second threshold is greater than the first threshold. This is because, basically, the increase in the diagnostic difference due to aggregation of the thickener is greater than the increase in the diagnostic difference due to oxidation of the grease components. Subsequently, the same operations as in the flowchart of Figure 3 are performed from step S107 onwards.

[0061] Furthermore, the operation of step S109 may be performed after step S104. That is, the comparison between the absolute value of the diagnostic difference and the first threshold does not need to be performed. In this case, if the diagnostic difference is zero, the determination unit 14 may determine that no abnormal deterioration has occurred.

[0062] In the second example, the diagnostic system 1 determines the type of abnormal deterioration. In particular, the diagnostic device 10 can easily detect whether or not base oil aggregation has occurred. Furthermore, the diagnostic device 10 can easily detect whether thickener aggregation has occurred by making a determination based on a second threshold.

[0063] Figure 5 is a flowchart showing a third example of the determination. In the third example, the input unit 11 receives input information about the color of the grease to be diagnosed. The color information of the grease to be diagnosed includes information about discoloration of the grease to be diagnosed. For example, the worker may input to the input unit 11 that the grease to be diagnosed has discolored as color information. Alternatively, measurement data indicating discoloration may be input to the input unit 11 as color information.

[0064] Steps S101 to S104 are generally the same as the flowchart in Figure 3. However, in step S102, the input unit 11 is further input with information about the color of the grease being diagnosed.

[0065] After step S104, in step S110, the determination unit 14 determines the type of abnormal deterioration based on the diagnostic difference and the color information of the grease being diagnosed.

[0066] Specifically, if the color information of the grease being diagnosed includes information indicating that discoloration has occurred, the determination unit 14 determines that at least one of the following is occurring as abnormal deterioration: oxidation of the grease components such as the base oil or contamination of foreign matter. Furthermore, in this case, if the diagnostic difference is a negative value, the determination unit 14 determines that aggregation of the base oil is occurring as abnormal deterioration.

[0067] In the third example, the diagnostic system 1 uses color information to determine the type of abnormal deterioration. In particular, the diagnostic device 10 can easily detect that at least one of the following has occurred: oxidation of the grease components or contamination of foreign matter. The diagnostic device 10 can also determine, depending on the presence or absence of oxidation of the grease components, that aggregation of the thickener and aggregation of the base oil have occurred. In this way, the diagnostic system 1 can determine the type of abnormal deterioration of the grease being diagnosed from both qualitative and quantitative viewpoints. As a result, the diagnostic system 1 can improve the accuracy of the diagnosis.

[0068] Furthermore, if the color information of the grease being diagnosed includes information indicating that discoloration has occurred, the determination unit 14 may calculate a value obtained by subtracting a set differential influence value from the diagnostic difference. If the value obtained by subtracting a pre-set differential influence value from the diagnostic difference is a positive value, the determination unit 14 determines that aggregation of the thickener has occurred as an abnormal deterioration. If the value obtained by subtracting a differential influence value from the diagnostic difference is a negative value, the determination unit 14 determines that aggregation of the base oil has occurred as an abnormal deterioration.

[0069] If the color information includes information indicating that no discoloration has occurred, the determination unit 14 determines that oxidation of the grease components has not occurred. In this case, if the diagnostic difference is a negative value, the determination unit 14 determines that aggregation of the base oil has occurred as an abnormal deterioration. Furthermore, if the information includes information indicating that no discoloration has occurred and the diagnostic difference is a positive value, the determination unit 14 determines that aggregation of the thickener has occurred as an abnormal deterioration.

[0070] Furthermore, if no discoloration has occurred in the color information and the absolute value of the diagnostic difference is smaller than the first threshold, the determination unit 14 may determine that no abnormal deterioration has occurred.

[0071] After step S110, the same operation as in the flowchart in Figure 3, step S107, is performed. Then, the operation in the flowchart ends.

[0072] Note that the operation in step S110 may be performed instead of step S109 in the flowchart of Figure 4. In this case, color information is input in step S102. That is, the determination unit 14 may perform a determination using the color information after determining that abnormal deterioration has occurred.

[0073] In addition, in the first to third examples, the determination unit 14 may determine the degree of abnormal deterioration based on the magnitude of the absolute value of the diagnostic difference or the absolute value of the value obtained by subtracting the difference influence value from the diagnostic difference. For example, the determination unit 14 determines that the larger the absolute value of the diagnostic difference, the greater the degree of abnormal deterioration and the more advanced the deterioration is.

[0074] Furthermore, as an example of an embodiment, the diagnostic system 1 may include a measuring unit for measuring the hue of the grease to be diagnosed. Hue is a concept that more quantitatively indicates the color of the grease to be diagnosed. Figure 6 is a functional block diagram of the diagnostic system in Embodiment 1. Figures 7 and 8 are perspective views showing the color measuring instrument of the diagnostic system in Embodiment 1. Figures 9 and 10 are illustrative diagrams showing the values ​​representing the hue calculated by the diagnostic system in Embodiment 1. Figure 11 is a flowchart showing an example of the operation of the diagnostic system 1 in Embodiment 1.

[0075] As shown in Figure 6, the diagnostic system 1 further includes a color measuring device 4 as a function of the measurement unit. The color measuring device 4, as a color sensor relating to hue, measures the RGB values ​​of the reflected light of the target, as shown in Figure 7. Alternatively, as shown in Figure 8, the color measuring device 4 may measure the RGB values ​​of the transmitted light after it has passed through the target. The color measuring device 4 creates color information that includes the measured RGB values ​​and values ​​indicating the hue calculated from the RGB values. The input unit 11 receives the color information created by the color measuring device 4.

[0076] As shown in Figure 7 or Figure 8, the color measuring device 4 comprises a light 4a, a photodetector 4b, and an analyzer 4c. The light 4a emits white light. For example, the light 4a is an LED.

[0077] The photodetector 4b is a light sensor, and is a color sensor capable of detecting RGB values. As shown in the figure, for example, the photodetector 4b can detect RGB values ​​as integer values ​​between 0 and 255.

[0078] The analyzer 4c creates color information from the RGB values ​​detected by the photodetector 4b. In this process, the analyzer 4c calculates ΔE based on the detected RGB values. ＲＧＢ Alternatively, calculate at least one of the MCDs.

[0079] As shown in Figure 9 and the following equation (1), ΔE ＲＧＢ This is a value calculated from the RGB values. That is, ΔE ＲＧＢis the distance from the point indicating the detected RGB value to white (0, 0, 0) in a three-dimensional space with each of the R value, G value, and B value as dimensions. ΔE ＲＧＢ The larger the ＲＧＢ is, the closer the measurement target is to black.

[0080]

[0081] As shown in FIG. 10, the maximum color difference MCD (Maximum Color Difference) is the maximum value of the differences between the values of two colors among the detected RGB values. For example, when the RGB value is (170, 114, 49), the difference between the R value and the B value is the largest. In this case, MCD = 170 - 49 = 121.

[0082] FIG. 11 is a graph showing the relationship between the degree of oxidation of the composition of grease such as base oil and ΔE ＲＧＢ or MCD. The horizontal axis of the graph is the degree of oxidation of the composition of the grease. On the horizontal axis, the further to the right side of the paper, the greater the degree of oxidation of the composition of the grease. The vertical axis is ΔE ＲＧＢ or MCD. The dashed line L1 is ΔE ＲＧＢ . The solid line L2 is MCD.

[0083] The more the composition of the grease oxidizes, the larger ΔE ＲＧＢ becomes. Also, when the degree of oxidation of the composition of the grease reaches a certain level, the value of MCD takes the maximum value. For example, the graph of MCD is mountain-shaped with the maximum value at the apex. For example, the calculation unit 13 calculates a differential influence value based on the value of ΔE ＲＧＢ or MCD. Specifically, the calculation unit 13 calculates a larger differential influence value as ΔE ＲＧＢ becomes larger.

[0084] In the diagnostic system 1, at least one of ΔE ＲＧＢ or MCD may be used. The calculation unit 13 may use the value included in the color information of ΔE ＲＧＢ or MCD. Also, when both ΔE ＲＧＢ and MCD are included in the color information, the calculation unit 13 may calculate a differential influence value based on a function with ΔE ＲＧＢ and MCD as variables.

[0085] In the fourth example of a judgment using the results from the measurement unit, the operation is generally the same as that shown in the flowchart of Figure 5. In step S104, the calculation unit 13 may calculate the difference influence value based on the color information along with the diagnostic difference.

[0086] Furthermore, in step S110, the determination unit 14 may determine the type of abnormal deterioration using the differential influence value calculated by the calculation unit 13. Alternatively, the determination unit 14 may determine that discoloration has occurred based on the RGB values ​​included in the color information.

[0087] For example, the determination unit 14 may determine, based on the differential influence value, whether oxidation of the grease components, such as the base oil, has occurred as an abnormal deterioration. Specifically, the determination unit 14 may determine that oxidation of the grease components has occurred as an abnormal deterioration if the differential influence value is greater than a specified third threshold. Alternatively, the determination unit 14 may determine that foreign matter has been introduced if the differential influence value is greater than the third threshold.

[0088] For example, the determination unit 14 may determine that aggregation of the thickener or the base oil has occurred as an abnormal deterioration, based on the difference obtained by subtracting the differential influence value from the diagnostic difference. Specifically, if the value obtained by subtracting the calculated differential influence value from the diagnostic difference is positive, the determination unit 14 may determine that aggregation of the thickener has occurred as an abnormal deterioration. If the value obtained by subtracting the differential influence value from the diagnostic difference is negative, the determination unit 14 may determine that aggregation of the base oil has occurred as an abnormal deterioration.

[0089] In the fourth example, the diagnostic system 1 further utilizes hue information to determine the type of abnormal deterioration. Specifically, hue can be measured more accurately and quantitatively than by visual inspection using the color measuring device 4, which is a color sensor. The diagnostic device 10 calculates the difference in influence value based on the hue. Therefore, the diagnostic system 1 can more accurately reflect the influence of the degree of oxidation of the grease components in the judgment result. In addition, workers performing maintenance on the grease under diagnosis do not need to perform visual inspections, and can more easily obtain hue measurement data.

[0090] In this example, the calculation unit 13 may further calculate the difference in effect value based on information about the type of grease being diagnosed. By further using information about the type of grease being diagnosed, the effect of the degree of oxidation of the grease's components can be reflected more accurately.

[0091] According to Embodiment 1 described above, the diagnostic system 1 comprises an input unit 11, a storage unit 12, a calculation unit 13, and a determination unit 14. The calculation unit 13 calculates a diagnostic difference based on a combination of the diagnostic oil content ratio and the diagnostic oil release value of the grease to be diagnosed. The diagnostic difference is a value that reflects the combination of the diagnostic oil content ratio and the diagnostic oil release value. The determination unit 14 determines the deterioration state of the grease to be diagnosed based on the diagnostic difference. Therefore, the diagnostic system 1 can accurately diagnose the deterioration state of the grease.

[0092] Furthermore, by utilizing the diagnostic difference, the diagnostic system 1 can reflect in its judgment changes in the base oil retention capacity of the grease being diagnosed due to abnormal deterioration of the thickener, etc., while excluding the influence of the oil content on the oil separation value. As a result, the diagnostic system 1 can improve the accuracy of its diagnosis.

[0093] Furthermore, the diagnostic system 1 may also be equipped with a communication unit 2b as a function. The communication unit 2b transmits measurement information regarding the grease being diagnosed to an external device. Therefore, the measurement results of various information about the grease being diagnosed used in the equipment can be monitored by an external device.

[0094] Next, we will describe some modifications of the main examples of Embodiment 1 described so far. Figure 12 is a functional block diagram of the diagnostic system in the first modification of Embodiment 1. Figure 13 is a flowchart showing an example of operation in the first modification of Embodiment 1.

[0095] As shown in Figure 12, in the first modified example, the diagnostic device 10 further includes a prediction unit 16 and a creation unit 17 as functions. The prediction unit 16 predicts the future progression of the deterioration state of the grease to be diagnosed. The prediction unit 16 may also further predict the lifespan of the grease to be diagnosed based on the predicted progression.

[0096] As an example, the prediction unit 16 may predict the progression of the deterioration state of the grease under diagnosis based on the deterioration state of the grease under diagnosis determined by the determination unit 14 and the operating temperature input to the input unit 11. That is, the type and extent of deterioration that may occur in the grease under diagnosis in the future is predicted from the current deterioration state and the expected operating temperature. In this case, the prediction unit 16 may also use information about the type of grease under diagnosis.

[0097] As another example, the input unit 11 receives input of the cumulative usage time of the grease under diagnosis at the time the diagnostic oil content is measured. The prediction unit 16 may predict the progression of the deterioration state of the grease under diagnosis based on the deterioration state of the grease under diagnosis determined by the determination unit 14 and the usage time entered into the input unit 11. From the current deterioration state and the usage time, the degree of deterioration so far can be estimated. The prediction unit 16 predicts the type and extent of deterioration that may occur in the grease under diagnosis in the future by extrapolating the estimated degree of deterioration so far based on the current deterioration state. At this time, the prediction unit 16 may further use information on the type of grease under diagnosis.

[0098] As yet another example, the input unit 11 receives input of usage information regarding the operating environment of the equipment in which the grease under diagnosis is used. The usage information includes the equipment in which the grease under diagnosis is used, the frequency of operation of the equipment, and the cumulative operating time of the equipment, all associated with the operating environment. The prediction unit 16 may predict the progression of the deterioration state of the grease under diagnosis based on the deterioration state of the grease under diagnosis determined by the determination unit 14 and the usage information input to the input unit 11. The type and extent of deterioration that may occur in the grease under diagnosis in the future are predicted from the current deterioration state and the future operating environment. At this time, the prediction unit 16 may also use information about the type of grease under diagnosis.

[0099] Furthermore, the prediction unit 16 may predict the progression of the deterioration state of the grease under diagnosis based on at least two of the following: operating temperature, cumulative operating time, or usage information, and the deterioration state of the grease under diagnosis determined by the determination unit 14. In this case, the prediction unit 16 may also use information on the type of grease under diagnosis.

[0100] The prediction unit 16 predicts the lifespan of the grease being diagnosed based on the progression of its deterioration state. For example, the prediction unit 16 defines the lifespan of the grease being diagnosed as the period until the deterioration state meets the specified replacement conditions.

[0101] The creation unit 17 creates a maintenance plan, including a plan to replace the grease under diagnosis, based on the progression of the grease's deterioration state predicted by the prediction unit 16. The maintenance plan includes the date and time when replacement of the grease under diagnosis is recommended.

[0102] The input unit 11 may also accept input of information regarding the operating pattern of the equipment using the grease under diagnosis. The creation unit 17 may create a maintenance plan that includes a plan to change at least one of the operating pattern or operating temperature of the equipment using the grease under diagnosis.

[0103] In the flowchart of Figure 13, for example, steps S101 to S107 are performed in the same way as in the flowchart of Figure 5, which is the third example of the determination. In this case, in step S102, the input unit 11 receives at least one input from among the measured data of the operating temperature, the cumulative operating time, or the operating information. Note that the same operations as in the first or second example of the determination may be performed. In this case, the same operations are performed up to step S107 of the first example of the determination, or up to step S107 of the second example of the determination.

[0104] After step S107, in step S111, the prediction unit 16 predicts the progression of the deterioration state of the grease under diagnosis. At this time, the prediction unit 16 predicts the progression of the deterioration state of the grease under diagnosis based on the measured operating temperature data, cumulative operating time, or one or more items of the operating information that the input unit 11 received as input in step S102, and the deterioration state.

[0105] Subsequently, in step S112, the creation unit 17 creates a maintenance plan for the grease to be diagnosed based on the progression of the deterioration state predicted in step S111.

[0106] After that, the flowchart operation ends.

[0107] According to the first modification of Embodiment 1 described above, the diagnostic system 1 further includes a prediction unit 16 as a function. The diagnostic system 1 can predict the deterioration state of the grease to be diagnosed based on various information. As a result, future maintenance plans can be reviewed. In addition, the usage conditions of the grease to be diagnosed can be reviewed.

[0108] Furthermore, the diagnostic system 1 is further equipped with a creation unit 17 as a function. The diagnostic system 1 creates a maintenance plan for the grease under diagnosis based on the predicted deterioration state. Therefore, the diagnostic system 1 can assist maintenance personnel in creating maintenance plans. As a result, the diagnostic system 1 can achieve reductions in maintenance time, increased maintenance efficiency, and reduced resource consumption such as grease.

[0109] Next, a second modified example of Embodiment 1 will be described. Figure 14 is a functional block diagram of the diagnostic system in the second modified example of Embodiment 1. Figure 15 is a flowchart showing an example of the operation of the diagnostic system in the second modified example of Embodiment 1.

[0110] As shown in Figure 14, the diagnostic device 10 further includes a model storage unit 18 as a function. In the second modified example, the diagnostic device 10 does not need to include a calculation unit 13.

[0111] The model storage unit 18 stores a determination model. The determination model is a model for inferring the deterioration state of the grease to be diagnosed from the diagnostic oil content, diagnostic oil release value, type of grease to be diagnosed, and operating temperature. Alternatively, the determination model may be a model for inferring the deterioration state of the grease to be diagnosed from the diagnostic oil content, diagnostic oil release value, type of grease to be diagnosed, operating temperature, and color information of the grease to be diagnosed. The inferred deterioration state of the grease to be diagnosed may be the state determined in Embodiment 1. That is, the determination model may infer whether or not there is abnormal deterioration. The determination model may also infer the type of abnormal deterioration.

[0112] The judgment model may be a pre-trained model trained using machine learning. In this machine learning, combinations of diagnostic oil content, diagnostic oil release value, type of grease to be diagnosed, operating temperature, and degradation state of the grease to be diagnosed may be used as training data. Color information may also be associated with the training data.

[0113] The determination unit 14 uses a determination model to determine the deterioration state of the grease to be diagnosed based on the diagnosed oil content, the diagnosed oil separation value, the type of grease to be diagnosed, and the usage time.

[0114] In the flowchart of Figure 15, step S101 is the same as step S101 in Figure 3. After step S101, in step S113, the input unit 11 receives information on the diagnostic oil content, diagnostic oil separation value, type of grease to be diagnosed, and operating temperature. At this time, the input unit 11 may also receive information on the color of the grease to be diagnosed.

[0115] Subsequently, in step S114, the determination unit 14 uses a determination model to determine the deterioration state of the grease to be diagnosed from the diagnostic oil content, diagnostic oil release value, type of grease to be diagnosed, and usage time. At this time, the determination unit 14 may use a determination model to determine the deterioration state of the grease to be diagnosed from color information in addition to the diagnostic oil content, diagnostic oil release value, type of grease to be diagnosed, and usage time.

[0116] Subsequently, in step S107, the determined result is stored. The output unit 15 outputs the determination result from step S114.

[0117] After that, the flowchart operation ends.

[0118] In the second modification of Embodiment 1 described above, the diagnostic system 1 determines the deterioration state of the grease to be diagnosed based at least on a combination of the diagnostic oil content and the diagnostic oil release value. Therefore, the diagnostic system 1 can accurately diagnose the deterioration state of the grease. Furthermore, the determination unit 14 can determine the deterioration state of the grease to be diagnosed without using the diagnostic difference. In the second modification, the storage unit 12 does not store the calibration model. Also, no calculations are performed by the calculation unit 13. Therefore, the diagnostic system 1 can determine the deterioration state of the grease to be diagnosed more easily.

[0119] Embodiment 2. Figure 16 shows an example of a normal range used in the diagnostic system in Embodiment 2. Figure 17 is a flowchart showing an example of the operation of the diagnostic system in Embodiment 2. Note that parts that are the same as or equivalent to parts in Embodiment 1 are denoted by the same reference numerals. Descriptions of such parts are omitted.

[0120] In Embodiment 2, the memory unit 12 stores information about the normal range. The normal range is the range of possible combinations of oil content and oil separation value that can be considered to represent normal deterioration.

[0121] Figure 16 is a graph representing an example of a normal region Z in a two-dimensional space with oil content and oil separation value as its dimensions. In this example, the normal region Z takes the shape of a triangle. One vertex of the triangle is a point on the linear function representing the calibration model. For example, the sides of the triangle that do not include that vertex are line segments where the oil content is 1. Note that the normal region Z may take any shape other than a triangle. The perimeter of the normal region Z may be a combination of graphs corresponding to any function.

[0122] The determination unit 14 determines the deterioration state of the grease under diagnosis by comparing the combination of the diagnosed oil content and the diagnosed oil release value with the normal region. In the example graph in Figure 16, the determination unit 14 determines the deterioration state of the grease under diagnosis based on the positional relationship between point P4, which represents the combination of the diagnosed oil content and the diagnosed oil release value, and the normal region Z. Specifically, for example, the determination unit 14 determines that normal deterioration has occurred if point P4 is included within the normal region Z. The determination unit 14 determines that abnormal deterioration has occurred if point P4 is not included within the normal region Z.

[0123] Furthermore, if point P4 is not located within the normal region Z, the determination unit 14 may determine the degree of abnormal deterioration based on the distance between point P4 and the normal region Z. If point P4 is not located within the normal region Z, the determination unit 14 may determine the type of abnormal deterioration based on the position of point P4 relative to the normal region Z. In this case, the determination unit 14 may determine that base oil aggregation has occurred if point P4 is located at a position with a small oil separation value relative to the normal region Z.

[0124] The determination unit 14 may also determine the deterioration state of the grease to be diagnosed by combining the determination based on the diagnostic difference shown in Embodiment 1 and the determination using the normal region Z.

[0125] The determination unit 14 may determine the deterioration state of the grease being diagnosed by comparing at least one of the diagnostic oil content ratio or the diagnostic oil separation value with a normal range. For example, the normal range may be a range indicating the upper and lower limits of one of the oil content ratio or oil separation value, and a range in which the other of the oil content ratio or oil separation value can take any value.

[0126] The memory unit 12 may store information for multiple individual normal areas corresponding to the type of grease. The determination unit 14 may use the individual normal area corresponding to the type of grease being diagnosed from among the multiple individual normal areas as the normal area for determination.

[0127] In the flowchart shown in Figure 17, the operations of steps S201 and S202 are the same as the operations of steps S101 and S102 in the flowchart of Figure 3 in Embodiment 1.

[0128] After step S202, in step S203, the determination unit 14 determines the deterioration state of the grease to be diagnosed by comparing the combination of the diagnostic oil content and diagnostic oil separation value input in step S202 with the normal range stored in the storage unit 12.

[0129] Subsequently, in step S204, the determination result is stored and output, similar to step S107 in the flowchart of Figure 3 in Embodiment 1.

[0130] After that, the flowchart operation ends.

[0131] According to Embodiment 2 described above, the diagnostic system 1 determines the deterioration state of the grease to be diagnosed by comparing the combination of the diagnosed oil content and the diagnosed oil separation value with the normal range. Therefore, the diagnostic system 1 can accurately diagnose the deterioration state of the grease.

[0132] The determination unit 14 may perform the determination in Embodiment 2 based on the determination model stored in the model storage unit 18. In this case, the determination model may be a model for inferring the deterioration state of the grease to be diagnosed from the diagnostic oil content, the diagnostic oil release value, and the normal range. Also, similar to the second modification of Embodiment 1, the determination model may be a trained model learned by machine learning. In this machine learning, a combination of the diagnostic oil content, the diagnostic oil release value, the normal range, and the deterioration state of the grease to be diagnosed may be used as training data. Further information such as the type of grease to be diagnosed may be associated with the training data.

[0133] In the second embodiment, the determination unit 14 of the diagnostic system 1 may determine the deterioration state of the grease being diagnosed by comparing the combination of the diagnosed oil content and the diagnosed oil separation value with the normal range, without using the diagnostic difference. In this case, the diagnostic system 1 does not need to have a calculation unit 13 as a function.

[0134] Embodiment 3. Figure 18 is a diagram showing the upper and lower limit models stored in the diagnostic system in Embodiment 3. Figure 19 is a flowchart showing an example of the operation of the diagnostic system in Embodiment 3. Note that parts that are the same as or corresponding to parts in Embodiment 1 or 2 are denoted by the same reference numerals. The description of such parts is omitted.

[0135] In Embodiment 3, the storage unit 12 stores upper and lower limit models. The upper and lower limit models include an upper limit model and a lower limit model. The upper limit model is a model that calculates an upper limit oil separation value, which indicates an arbitrarily defined upper limit of oil separation value, in correspondence with the oil content. For example, the upper limit model is a function with the oil content as a variable. The lower limit model is a model that calculates a lower limit oil separation value, which indicates an arbitrarily defined lower limit of oil separation value, in correspondence with the oil content. For example, the lower limit model is a function with the oil content as a variable. The following describes the case where the upper limit model and the lower limit model are linear functions.

[0136] Figure 18 is a graph showing the relationship between oil content and oil separation value. The figure shows the upper limit line Lu, which is the upper limit model, and the lower limit line Ll, which is the lower limit model. The upper limit line Lu, the lower limit line Ll, and the calibration curve Ls intersect at a single point P5. The slopes of the upper limit line Lu and the lower limit line Ll are different from the slope of the calibration curve Ls. Specifically, the slope of the upper limit line Lu is greater than the slope of the calibration curve Ls. The slope of the lower limit line Ll is less than the slope of the calibration curve Ls.

[0137] The relationship between these lines means that the ease with which the oil release value of grease changes differs depending on the oil content. In other words, if the type of abnormal deterioration is the same, the higher the oil content of the grease, the wider the range over which the oil release value of the grease can change. For example, suppose we have sample A with an oil content of 0.70 and sample B with an oil content of 0.40, and assume that the diagnostic difference for sample A and the diagnostic difference for sample B are equal. In this case, sample B has undergone more advanced abnormal deterioration than sample A. In other words, sample B is the grease that should be replaced with priority over sample A.

[0138] In this way, by normalizing the diagnostic difference according to the variability of the grease's oil release value, the degree of abnormal deterioration can be quantified as a deterioration state. The upper and lower oil release values ​​indicate the variability of the grease's oil release value for each oil content. The calculation unit 13 calculates a first degree of deterioration as the degree of abnormal deterioration by normalizing the diagnostic difference with the upper or lower oil release value. Calculation examples are shown in Tables 2 and 3 below. Table 2 shows the values ​​used to calculate the degree of deterioration for each sample. Table 3 shows the first degree of deterioration, the second degree of deterioration, and the third degree of deterioration.

[0139]

[0140]

[0141] Table 2 shows the calculation results for each sample with different oil content and oil separation values. Table 2 also shows the upper limit oil separation value, lower limit oil separation value, and reference oil separation value for each sample at the diagnostic oil content.

[0142] At a given oil content, the distance from the standard oil separation value to the upper limit oil separation value, or the distance from the standard oil separation value to the lower limit oil separation value, represents the ease with which the oil separation value of the grease changes. The calculation unit 13 normalizes the diagnostic difference using either the difference obtained by subtracting the standard oil separation value from the upper limit oil separation value, or the difference obtained by subtracting the standard oil separation value from the lower limit oil separation value.

[0143] Specifically, when the diagnostic difference is positive at a certain diagnostic oil content, the calculation unit 13 normalizes the diagnostic difference and divides it by the difference obtained by subtracting the standard oil removal value from the upper limit oil removal value at that diagnostic oil content, and the quotient obtained is the first degree of deterioration. When the diagnostic difference is negative at a certain diagnostic oil content, the calculation unit 13 normalizes the diagnostic difference and divides it by the difference obtained by subtracting the lower limit oil removal value from the standard oil removal value at that diagnostic oil content, and the quotient obtained is the first degree of deterioration.

[0144] A higher first degree of deterioration indicates a relatively large diagnostic difference within the range in which the grease oil release value can change. Note that the first degree of deterioration is a value calculated using the diagnostic oil release value, standard oil release value, upper limit oil release value, and lower limit oil release value, and does not necessarily have to be calculated using the above method, as long as the value increases as the abnormal deterioration progresses.

[0145] Furthermore, the calculation unit 13 calculates the second degree of deterioration and the third degree of deterioration. The second degree of deterioration indicates the amount of decrease in the oil content of the grease under diagnosis from the initial oil content. For example, the second degree of deterioration is the difference obtained by subtracting the diagnosed oil content from 1. Note that the second degree of deterioration does not have to be calculated using the above method, as long as it is a value that increases as the diagnosed oil content decreases.

[0146] Figure 18 shows point P6, which represents the combination of the diagnostic oil content and the diagnostic oil release value. The first degree of deterioration is the value obtained by dividing the diagnostic difference De1 in the figure by the difference De2 between the upper limit oil release value and the standard oil release value. In the figure, the second degree of deterioration is the value shown by the difference De3.

[0147] The third degree of deterioration is a degree of deterioration that comprehensively indicates the degree of abnormal deterioration and the degree of decrease in oil content. The calculation unit 13 calculates the third degree of deterioration, which is determined by the second degree of deterioration and the first degree of deterioration. For example, the third degree of deterioration is the sum of the first degree of deterioration and the second degree of deterioration. However, the calculation method for the third degree of deterioration does not have to be as described above, as long as it is calculated from the first degree of deterioration and the second degree of deterioration using a function that increases monotonically as the first degree of deterioration increases and also increases monotonically as the second degree of deterioration increases.

[0148] The determination unit 14 determines that the greater the first degree of deterioration, the more abnormal the deterioration of the grease being diagnosed is progressing. The determination unit 14 determines that the greater the second degree of deterioration, the more the base oil is decreasing. The determination unit 14 determines that the greater the third degree of deterioration, the more overall deterioration of the grease being diagnosed is progressing. The determination unit 14 may also determine the deterioration state of the grease being diagnosed by combining at least one of the diagnostic difference, the color information of the grease being diagnosed, the type of grease being diagnosed, and the operating temperature with at least one of the first degree of deterioration, the second degree of deterioration, and the third degree of deterioration.

[0149] For example, the prediction unit 16 uses at least one of the determination results from the determination unit 14, which is the type of abnormal deterioration, the first degree of deterioration, the second degree of deterioration, or the third degree of deterioration, to predict the progression of the deterioration state of the grease being diagnosed.

[0150] The operation from step S301 to step S304 in Figure 19 is the same as the operation from step S101 to step S104 in Figure 3, etc., in Embodiment 1. After step S304, in step S305, the calculation unit 13 calculates the first degree of deterioration, the second degree of deterioration, and the third degree of deterioration.

[0151] Subsequently, in step S306, the determination unit 14 determines the deterioration state of the grease to be diagnosed based on the first or third deterioration degree. The determination unit 14 may also determine the deterioration state of the grease to be diagnosed based on other information.

[0152] Subsequently, in step S307, the same operation as in step S107 in the flowchart shown in Figure 3 is performed.

[0153] Subsequently, in step S308, the prediction unit 16 predicts the progression of the deterioration state of the grease under diagnosis. Then, in step S309, the creation unit 17 creates a maintenance plan for the grease under diagnosis. After that, the flowchart operation ends.

[0154] According to Embodiment 3 described above, the calculation unit 13 calculates a first degree of deterioration. The determination unit 14 determines that the greater the first degree of deterioration, the more the abnormal deterioration of the grease under diagnosis is progressing. In this way, the diagnostic system 1 can quantitatively diagnose the degree of abnormal deterioration of the grease under diagnosis by normalizing the diagnostic difference.

[0155] Furthermore, the calculation unit 13 calculates a third degree of deterioration. The determination unit 14 determines that the greater the third degree of deterioration, the more advanced the deterioration of the grease being diagnosed. In this way, the diagnostic system 1 can quantitatively diagnose the overall degree of deterioration of the grease being diagnosed by utilizing the third degree of deterioration, which includes the effects of the first degree of deterioration and the second degree of deterioration.

[0156] The calculation unit 13 may also use a function that results in a smaller value for the third degree of deterioration as the first degree of deterioration and the second degree of deterioration increase. In this case, the determination unit 14 may determine that the deterioration of the grease being diagnosed is progressing as the third degree of deterioration decreases. Even in this case, the diagnostic system 1 can quantitatively diagnose the overall degree of deterioration of the grease being diagnosed by using the third degree of deterioration, which includes the effects of the first degree of deterioration and the second degree of deterioration.

[0157] Embodiment 4. Figures 20 and 21 are diagrams showing the absorption section of the diagnostic system in Embodiment 4. Figure 22 is a functional block diagram of the diagnostic system in Embodiment 4. Figure 23 is a graph showing the diagnostic difference calculated by the diagnostic system in Embodiment 4. Figure 24 is a flowchart showing the measurement work performed in the absorption section of the diagnostic system in Embodiment 4. Figure 25 is a flowchart showing an example of the operation of the diagnostic system in Embodiment 4. Note that parts that are the same as or equivalent to parts in Embodiments 1, 2, or 3 are denoted by the same reference numerals. Descriptions of such parts are omitted.

[0158] In Embodiment 4, as shown in Figures 20 and 21, the diagnostic system 1 further comprises an absorbent material 30. That is, the diagnostic system 1 has the function of an absorbent section due to the absorbent material 30. The absorbent material 30 may be included in the configuration of the analytical apparatus 2, which is not shown.

[0159] The absorbent material 30 is a component that absorbs oil from objects such as grease that it comes into contact with. The absorbent material 30 mainly absorbs base oil from the grease that it comes into contact with. In the example shown in Figure 20, the absorbent material 30 is disc-shaped. Figure 21 is a cross-sectional view of the absorbent material 30 along line A-A' in Figure 20. Note that the shape of the absorbent material 30 is not limited to a disc shape. The absorbent material 30 includes an installation portion 31 and an absorption portion 32.

[0160] The installation portion 31 is a part of the surface of the absorbent material 30. Grease G1 is installed in the installation portion 31. For example, grease G1 whose volume is determined by measuring with a measuring jig 33 having a cylindrical cavity is installed in the installation portion 31. When the grease G1 is installed in the installation portion 31, it is in contact with the absorbent portion 32.

[0161] The absorbent portion 32 may be a part other than the installation portion 31 of the absorbent material 30. The absorbent portion 32 absorbs oil from the grease G1 it comes into contact with. The absorbent portion 32 absorbs more base oil the easier it is for the base oil of the grease it comes into contact with to separate. In other words, the absorbent portion 32 absorbs more base oil the greater the oil separation value of the grease. In the absorbent portion 32, the absorbed oil spreads out planarly in proportion to the amount of oil absorbed. The area of ​​this spread oil is also called the oil absorption area G2. The greater the amount of oil absorbed by the absorbent portion 32, the larger the oil absorption area G2 in the absorbent material 30 becomes.

[0162] The input unit 11 shown in Figure 22 accepts the diagnostic oil absorption area as a diagnostic oil release value. The diagnostic oil absorption area is measurement data of the oil absorption area formed when the grease under diagnosis comes into contact with the absorbent material 30. For example, the diagnostic oil absorption area is measured by the analyzer 2.

[0163] In Embodiment 4, the diagnostic system 1 uses the oil absorption area as the oil separation value. For example, the calibration model stored in the memory unit 12 is a model for calculating the reference oil absorption area, which is a reference oil absorption area corresponding to the diagnostic oil fraction. The reference oil absorption area is the oil absorption area when the reference grease comes into contact with the absorbent material 30.

[0164] In the graph in Figure 23, the vertical axis represents the oil absorption area. As shown in Figure 23, the calculation unit 13 detects the difference between the diagnostic oil absorption area and the standard oil absorption area as the diagnostic difference. The determination unit 14 determines the deterioration state of the grease under diagnosis from the diagnostic difference using the same method as in Embodiments 1 to 3.

[0165] Figure 24 shows the procedure for measuring the oil absorption area. In step S401, the worker weighs the grease to be diagnosed by filling the cavity of the weighing jig 33 with the collected grease. For example, the grease to be diagnosed is weighed to have a specified volume and specified surface area. In addition, the diagnostic oil content is measured for another portion of the grease to be diagnosed.

[0166] Subsequently, in step S402, the grease to be diagnosed is placed from the measuring jig 33 onto the installation portion 31 of the absorbent material 30 by a worker.

[0167] Subsequently, in step S403, the absorbent material 30 is left at a specified separation temperature for a specified period of time.

[0168] Subsequently, in step S404, the oil absorption area of ​​the absorption portion 32 is measured. After that, the flowchart process is completed.

[0169] The flowchart in Figure 25 is an example of a diagnosis performed by the diagnostic system 1. In step S405, the measurement operation shown in the flowchart in Figure 24 is performed.

[0170] Subsequently, in step S406, the input unit 11 receives input of the diagnostic oil percentage and the diagnostic oil absorption area, which is the diagnostic oil separation value. Then, in step S407, the calculation unit 13 determines the reference oil absorption area corresponding to the diagnostic oil percentage based on the calibration model.

[0171] Subsequently, in step S408, the calculation unit 13 calculates the diagnostic difference, which is the difference between the diagnostic oil absorption area and the standard oil absorption area. Then, in step S409, the determination unit 14 determines the deterioration state of the grease to be diagnosed based on the diagnostic difference calculated in step S408.

[0172] Subsequently, in step S410, the determination result of the determination unit 14 is stored and output. After that, the operation of the flowchart ends.

[0173] Furthermore, after this flowchart, the progression of the deterioration state may be predicted. A maintenance plan may also be created.

[0174] The diagnostic system 1 described in Embodiment 4 above includes the following configurations as Appendix 1. (Appendix 1) A diagnostic system comprising: an absorbent part that absorbs oil from grease it comes into contact with, wherein the oil absorption area, which is the area over which the absorbed oil is spread, increases in proportion to the amount absorbed; an input unit that accepts input of a diagnostic oil percentage, which is measurement data of the oil percentage of the grease to be diagnosed; a diagnostic oil absorption area, which is measurement data of the oil absorption area when the absorbent part comes into contact with the grease to be diagnosed; and the type of the grease to be diagnosed; a storage unit that stores a calibration model for calculating the oil absorption area when a reference grease comes into contact with the absorbent part in proportion to the oil percentage; a calculation unit that determines a reference oil absorption area corresponding to the diagnostic oil percentage based on the calibration model and calculates a diagnostic difference between the diagnostic oil absorption area and the reference oil absorption area; and a determination unit that determines the deterioration state of the grease to be diagnosed based on the diagnostic difference calculated by the calculation unit.

[0175] Thus, the diagnostic system 1 is equipped with an absorbent material 30 as the function of the absorption section. In the diagnostic system 1, the oil absorption area can be used as the oil separation value by the absorbent material 30. Maintenance workers can easily perform the task of determining the oil separation value. Furthermore, the diagnostic system 1 determines the deterioration state based on the combination of the diagnostic oil content and the diagnostic oil separation value, which is the diagnostic oil absorption area. For this reason, the diagnostic system 1 can accurately diagnose the deterioration state of the grease.

[0176] Next, we will describe modifications of the main examples of Embodiment 4 described so far. Figure 26 is a functional block diagram of the diagnostic system in a modified example of Embodiment 4. Figure 27 is a flowchart showing an example of operation in a modified example of Embodiment 4.

[0177] The color measuring device 4 shown in Figure 26 measures the color of the grease to be diagnosed in the absorbent material 30. Specifically, the color measuring device 4 is installed in the installation section 31 and measures the color of the grease to be diagnosed before the oil is absorbed as the installation color. The color measuring device 4 also measures the color of the grease to be diagnosed absorbed in the absorption section 32 as the absorption color. Note that the installation color may be measurement data of the color of the grease to be diagnosed after the oil has been absorbed.

[0178] The input unit 11 accepts input of the type of grease to be diagnosed, the diagnostic oil absorption area, as well as information on the installation color, absorption color, and installation area. The installation area is the area of ​​the grease to be diagnosed installed on the installation part 31. The installation area may be measured by the analyzer 2 using the same method as the oil absorption area, or a fixed value may be set. Furthermore, the input unit 11 may also receive input of at least one of the separation temperature or the standing time when the base oil is absorbed by the absorbent material 30. Note that the separation temperature and standing time may be set in the diagnostic device 10 in advance.

[0179] The model storage unit 18 stores the judgment model. In the modified embodiment of the fourth embodiment, the judgment model is a model for determining the deterioration state of the grease to be diagnosed from the installation color, installation area, absorption color, diagnostic oil absorption area, and type of grease to be diagnosed. Here, the combination of installation color and absorption color, and the absorption color itself, reflect the state of the components of the grease to be diagnosed. In addition, the ratio of installation area to oil absorption area reflects the oil content. That is, since this information serves as an indicator that reflects the oil content, inputting the diagnostic oil content becomes unnecessary when determining the deterioration state by inputting the installation color, installation area, and diagnostic oil absorption area.

[0180] The judgment model may be a pre-trained model learned by machine learning, similar to the second modified example of Embodiment 1. In this machine learning, combinations of installation color, installation area, absorption color, diagnostic oil absorption area, type of grease to be diagnosed, and deterioration state of the grease to be diagnosed may be used as training data. Further information such as separation temperature and standing time may be associated with the training data.

[0181] The determination unit 14 uses a determination model to determine the deterioration state of the grease to be diagnosed based on the installation color, installation area, absorption color, diagnostic oil absorption area, and type of grease to be diagnosed, which are input to the input unit 11.

[0182] In step S411 of the flowchart in Figure 27, the installation color, installation area, absorption color, and diagnostic oil absorption area of ​​the grease to be diagnosed are measured. Then, in step S412, the installation color, installation area, absorption color, diagnostic oil absorption area, and type of grease to be diagnosed are input to the input unit 11.

[0183] Subsequently, in step S413, the determination unit 14 determines the deterioration state of the grease to be diagnosed based on the information input in step S412.

[0184] Subsequently, in step S410, the determination result of the determination unit 14 is stored and output. After that, the operation of the flowchart ends.

[0185] Furthermore, after this flowchart, the progression of the deterioration state may be predicted. A maintenance plan may also be created.

[0186] The diagnostic system 1 described in the modified embodiment 4 above includes the following configurations as shown in Appendix 2. (Appendix 2) A diagnostic system comprising: an absorbent part including an installation part and an absorbent part that absorbs oil from grease installed on the installation part, wherein the oil absorption area, which is the area over which the absorbed oil has spread, increases in proportion to the amount absorbed; an input unit that receives input of the installation color of the grease to be diagnosed installed on the installation part, the installation area of ​​the grease to be diagnosed installed on the installation part, the absorption color of the grease to be diagnosed absorbed by the absorbent part, the diagnostic oil absorption area which is measurement data of the oil absorption area of ​​the grease to be diagnosed absorbed by the absorbent part, and the type of the grease to be diagnosed; and a determination unit that determines the deterioration state of the grease to be diagnosed from the installation color, the installation area, the absorption color, the diagnostic oil absorption area, and the type of grease to be diagnosed input to the input unit.

[0187] In this way, the diagnostic system 1 determines the deterioration state of the grease being diagnosed using an index that reflects a combination of an index value that reflects the oil content and the diagnostic oil absorption area. Therefore, the diagnostic system 1 can accurately diagnose the deterioration state of the grease.

[0188] Furthermore, based on the installation color and absorption color information, the degree of abnormal deterioration of the separated base oil and the solid portion of the grease being diagnosed can be individually determined.

[0189] Next, an example of the hardware constituting the diagnostic device 10 will be described using Figure 28. Figure 28 is a hardware configuration diagram of the diagnostic device of the diagnostic system in Embodiments 1 to 4.

[0190] Each function of the diagnostic device 10 can be realized by a processing circuit. For example, the processing circuit includes at least one processor 100a and at least one memory 100b. For example, the processing circuit includes at least one dedicated hardware 200.

[0191] When the processing circuit comprises at least one processor 100a and at least one memory 100b, each function of the diagnostic device 10 is realized by software, firmware, or a combination of software and firmware. At least one of the software and firmware is written as a program. At least one of the software and firmware is stored in at least one memory 100b. At least one processor 100a realizes each function of the diagnostic device 10 by reading and executing the program stored in at least one memory 100b. At least one processor 100a is also called a central processing unit, processing unit, arithmetic unit, microprocessor, microcomputer, or DSP. For example, at least one memory 100b is a non-volatile or volatile semiconductor memory such as RAM, ROM, flash memory, EPROM, EEPROM, magnetic disk, flexible disk, optical disk, compact disk, minidisc, DVD, etc.

[0192] If the processing circuit includes at least one dedicated hardware 200, the processing circuit may be implemented as, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof. For example, each function of the diagnostic device 10 may be implemented by a processing circuit. For example, each function of the diagnostic device 10 may be implemented together by a processing circuit.

[0193] For each function of the diagnostic device 10, some may be implemented by dedicated hardware 200, and others by software or firmware. For example, the function of the determination unit 14 may be implemented by a processing circuit as dedicated hardware 200, while functions other than those of the determination unit 14 may be implemented by at least one processor 100a reading and executing a program stored in at least one memory 100b.

[0194] In this way, the processing circuit realizes each function of the diagnostic device 10 using hardware 200, software, firmware, or a combination thereof.

[0195] Although not shown in the diagram, the functions of the analysis device 2 and the color measuring device 4 are also realized by processing circuits equivalent to those that realize the functions of the diagnostic device 10.

[0196] Furthermore, at least some of the functions of the diagnostic device 10 may be implemented on a cloud server. In this case, the processing circuit is composed of multiple sub-circuits. Each of the multiple sub-processing circuits is provided in one of the multiple devices that make up the cloud server. Each of the multiple devices that make up the cloud server may be located in a different building.

[0197] As described above, the diagnostic system 1 described herein can be used to diagnose the deterioration state of grease.

[0198] 1 Diagnostic system, 2 Analysis device, 2a Data storage unit, 2b Communication unit, 3 Thermometer, 4 Color measuring device, 4a Light, 4b Photodetector, 4c Analyzer, 10 Diagnostic device, 11 Input unit, 12 Storage unit, 13 Calculation unit, 14 Judgment unit, 15 Output unit, 16 Prediction unit, 17 Creation unit, 18 Model storage unit, 30 Absorbent material, 31 Installation unit, 32 Absorption unit, 33 Weighing jig, 100a Processor, 100b Memory, 200 Hardware

Claims

1. A diagnostic system comprising: an input unit that receives input of a diagnostic oil content, which is measurement data of the oil content of the grease to be diagnosed, and a diagnostic oil separation value, which is measurement data of the grease to be diagnosed regarding the ease of separation of the base oil from the grease; a storage unit that stores a calibration model for calculating a standard oil separation value corresponding to the oil content of the grease; a calculation unit that determines a standard oil separation value corresponding to the diagnostic oil content based on the calibration model and calculates a diagnostic difference between the standard oil separation value and the diagnostic oil separation value; and a determination unit that determines the deterioration state of the grease to be diagnosed based on the diagnostic difference.

2. The diagnostic system according to claim 1, wherein the determination unit determines that abnormal deterioration has occurred when the absolute value of the diagnostic difference is greater than a first threshold.

3. The diagnostic system according to claim 2, wherein the determination unit determines that at least one of the following is occurring as abnormal deterioration: aggregation of the thickener or oxidation of the components of the grease, when the diagnostic difference is a positive value.

4. The diagnostic system according to claim 2 or 3, wherein the determination unit determines that base oil aggregation has occurred as an abnormal deterioration when the diagnostic difference is a negative value.

5. The diagnostic system according to any one of claims 1 to 4, wherein the input unit receives input information on the color of the grease to be diagnosed when the diagnostic oil content is measured, and the determination unit determines the deterioration state of the grease to be diagnosed based on the diagnostic difference and the color information of the grease to be diagnosed.

6. The diagnostic system according to claim 5, wherein the color information of the grease to be diagnosed includes information regarding discoloration of the grease to be diagnosed, and the determination unit determines that at least one of oxidation of the grease components or contamination of foreign matter has occurred as abnormal deterioration when the color information of the grease to be diagnosed includes information indicating that discoloration has occurred.

7. The diagnostic system according to claim 6, wherein the determination unit determines that aggregation of the thickener has occurred as an abnormal deterioration when the color information of the grease to be diagnosed includes information indicating that discoloration has occurred, and the difference obtained by subtracting the difference impact value due to discoloration from the diagnostic difference is a positive value, and determines that aggregation of the base oil has occurred as an abnormal deterioration when the color information of the grease to be diagnosed includes information indicating that discoloration has occurred, and the difference obtained by subtracting the difference impact value due to discoloration from the diagnostic difference is a negative value.

8. The diagnostic system according to any one of claims 1 to 7, wherein the input unit receives measurement data of the operating temperature in the environment in which the grease to be diagnosed was used when the diagnostic oil content was measured, and inputs the type of the grease to be diagnosed, and the determination unit determines the deterioration state of the grease to be diagnosed from the diagnostic oil content, the diagnostic oil separation value, the type of grease to be diagnosed, and the operating temperature input to the input unit.

9. The diagnostic system according to any one of claims 1 to 8, wherein the storage unit stores information on a normal range having the oil content and oil separation value of the grease as dimensions, and the determination unit determines the deterioration state of the grease to be diagnosed by comparing at least one of the diagnostic oil content or the diagnostic oil separation value with the normal range.

10. The diagnostic system according to any one of claims 1 to 9, wherein the storage unit stores upper and lower limit models for which an upper limit oil separation value indicating an upper limit of the oil separation value with respect to the oil content and a lower limit oil separation value indicating a lower limit of the oil separation value with respect to the oil content are calculated from the oil content, the calculation unit calculates a first degree of deterioration by normalizing the diagnostic difference using either the difference obtained by subtracting the standard oil separation value from the upper limit oil separation value corresponding to the diagnostic oil content, or the difference obtained by subtracting the standard oil separation value from the lower limit oil separation value corresponding to the diagnostic oil content, and the determination unit determines that the greater the first degree of deterioration, the more the abnormal deterioration of the grease under diagnosis is progressing.

11. The diagnostic system according to claim 10, wherein the calculation unit calculates a second degree of deterioration, which indicates the amount of decrease in the oil content of the grease under diagnosis from the initial oil content, and a third degree of deterioration determined by the first degree of deterioration, and the determination unit determines that the deterioration of the grease under diagnosis is progressing as the third degree of deterioration is larger or smaller.

12. The diagnostic system according to any one of claims 1 to 11, wherein the storage unit stores a plurality of individual calibration models that differ depending on the type of grease as the calibration model, the input unit receives input of information on the type of grease to be diagnosed, and the calculation unit calculates the reference oil separation value by using the individual calibration model corresponding to the type of grease to be diagnosed from the plurality of individual calibration models as the calibration model.

13. A diagnostic system according to any one of claims 1 to 12, further comprising: a prediction unit for predicting the progression of the deterioration state of the grease to be diagnosed, wherein the input unit receives input of at least one of the following: measurement data of the operating temperature in the environment in which the grease to be diagnosed was used or the cumulative operating time of the grease to be diagnosed at the time the diagnostic oil content is measured; and the prediction unit predicts the progression of the deterioration state of the grease to be diagnosed based on at least one of the operating temperature or operating time received by the input unit and the deterioration state determined by the determination unit.

14. A diagnostic system according to any one of claims 1 to 12, further comprising: a prediction unit for predicting the progression of the deterioration state of the grease to be diagnosed, wherein the input unit receives input usage information relating the equipment in which the grease to be diagnosed is used, the operating frequency of the equipment, and the cumulative operating time of the equipment, and the prediction unit predicts the progression of the deterioration state of the grease to be diagnosed based on the deterioration state determined by the determination unit and the usage information.

15. The diagnostic system according to claim 13 or claim 14, further comprising: a creation unit that creates a maintenance plan including a plan to replace the grease under diagnosis based on the progression of the deterioration state of the grease under diagnosis predicted by the prediction unit.

16. The diagnostic system according to any one of claims 1 to 15, further comprising: a communication unit located in the same building as the equipment in which the grease to be diagnosed is used, and which transmits measurement information relating to the grease to be diagnosed, including the diagnostic oil content and the diagnostic oil separation value, to an external location.

17. A diagnostic system comprising: an input unit that accepts input of a diagnostic oil content, which is measurement data of the oil content of the grease to be diagnosed; a diagnostic oil separation value, which is measurement data of the grease to be diagnosed regarding the ease of separation of the base oil from the grease; measurement data of the operating temperature in the environment in which the grease to be diagnosed was used when the diagnostic oil content was measured; and the type of the grease to be diagnosed; and a determination unit that determines the deterioration state of the grease to be diagnosed from the diagnostic oil content, the diagnostic oil separation value, the type of grease to be diagnosed, and the operating temperature input to the input unit.

18. A diagnostic system comprising: an input unit that accepts input of a diagnostic oil content, which is measurement data of the oil content of the grease to be diagnosed, and a diagnostic oil separation value, which is measurement data of the grease to be diagnosed, indicating the ease with which the base oil can be separated from the grease; a storage unit that stores information of a normal range, with the oil content and oil separation value of the grease as its dimensions; and a determination unit that determines the deterioration state of the grease to be diagnosed by comparing the combination of the diagnostic oil content and the diagnostic oil separation value with the normal range.

Images