Oil property diagnostic system for working machines and oil property diagnostic procedure for working machines

Abstract

An oil property sensor (1 (1a, 1b)) detects at least one dielectric constant and one resistance value as physical property values ​​of oil. A control unit (50) estimates a metal concentration or an iron concentration in the oil from the physical property values ​​based on a relationship between the physical property values ​​of the oil detected by the oil property sensor (1 (1a, 1b)) and the metal concentration or iron concentration in the oil.

Description

TECHNICAL AREA

[0001] The present disclosure relates to an oil property diagnostic system for working machines and an oil property diagnostic method for working machines. STATE OF THE ART

[0002] By identifying and analyzing the properties of oil used to operate machinery, it is possible to detect signs of malfunctions in the machinery. For example, JP 2016-113819 A (Patent Document 1) uses sensor-detected oil properties to determine whether an oil analysis involving oil sampling is necessary. If an oil analysis is deemed required, an oil sample is taken as soon as possible and analyzed by an oil analysis company. Subsequently, based on information from previous oil analyses, any abnormalities in the oil are identified, and the cause of these abnormalities is determined. List of oppositions patent literature

[0003] Patent Document 1: JP 2016-113819 A BRIEF DESCRIPTION OF THE INVENTION Technical Problem

[0004] In industrial machinery, it is necessary to constantly monitor the condition of the oil to prevent sudden breakdowns due to abnormal wear of internal parts and the like. However, patent document 1 requires oil analysis at an oil analysis company, and therefore the condition of the oil cannot be constantly monitored.

[0005] One objective of the present disclosure is to provide an oil property diagnostic system and an oil property diagnostic procedure for working machines that enable continuous monitoring of the metal and iron concentration in oil. Solution to the problem

[0006] An oil property diagnostic system for working machines according to the present disclosure includes an oil property sensor and a control unit. The oil property sensor detects at least one dielectric constant and a resistance value as physical property values ​​of the oil. The control unit estimates a metal concentration in the oil from the physical property value of the oil detected by the oil property sensor, based on a relationship between the physical property values ​​of the oil and the metal concentration in the oil.

[0007] Another oil property diagnostic system for working machines according to the present disclosure includes an oil property sensor and a control unit. The oil property sensor detects at least one dielectric constant and a resistance value as physical property values ​​of the oil. The control unit estimates an iron concentration in the oil from the physical property values ​​of the oil detected by the oil property sensor, based on a relationship between the physical property values ​​of the oil and the iron concentration in the oil.

[0008] An oil property diagnostic procedure for working machinery according to the present disclosure includes the following steps.

[0009] At least one dielectric constant and one resistance value are recorded as physical properties of the oil. The metal concentration in the oil is estimated from these recorded physical properties based on a relationship between the oil's physical properties and the metal concentration in the oil.

[0010] Another oil property diagnostic procedure for working machines according to the present disclosure includes the following steps.

[0011] At least one dielectric constant and one resistance value are recorded as physical properties of the oil. The iron concentration in the oil is estimated from these recorded physical properties based on a relationship between the oil's physical properties and the iron concentration in the oil. Advantageous effects of the invention

[0012] According to the present disclosure, it is possible to realize an oil property diagnostic system for working machines and an oil property diagnostic procedure for working machines that enable continuous monitoring of the metal and iron concentration in oil. BRIEF DESCRIPTION OF THE DRAWINGS Fig. Figure 1 is a diagram illustrating a configuration of a working machine according to an embodiment of the present disclosure. Fig. Figure 2 is a diagram illustrating an example of an oil circuit that supplies hydraulic oil to and from a hydraulic actuator (for example, a hydraulic cylinder). Fig. Figure 3 is a diagram illustrating another example of the oil circuit that supplies lubricating oil to a drive unit (for example, an engine). Fig. Figure 4 is a diagram illustrating a configuration of an oil property diagnostic system for the working machine according to an embodiment of the present disclosure. Fig. 5 is a functional block diagram of a control unit that is part of the system of Fig. 4 is used. Fig. Figure 6 is a flowchart illustrating an oil property diagnostic procedure for the working machine according to an embodiment of the present disclosure. Fig. Figure 7 is a diagram illustrating the coefficient of determination and an 80% error, which are evaluation indices for the accuracy of an estimation model (calculation equation). Fig. Figure 8 is a diagram illustrating the correlation coefficients with the soot concentration, which are evaluation indices for the accuracy of the estimation model (calculation equation). Fig. Figure 9 is a diagram illustrating a relationship between the physical properties of oil and rating indices relating to the iron concentration in the oil. Fig. Figure 10 is a diagram illustrating a relationship between the physical property values ​​of oil and rating indices in relation to the concentration of metals (iron + copper + chromium + aluminium + silicon + lead) in the oil. DESCRIPTION OF EXECUTION FORMS

[0013] One embodiment of the present disclosure is described in detail below with reference to the drawings.

[0014] In the description and drawings, identical or corresponding components are designated with identical reference numerals, and redundant descriptions are not repeated. Configurations may be omitted or simplified in the drawings to simplify the description. At least one part of the embodiment and one modification may be combined in any way.

[0015] In the following description, the forward-backward direction is the direction in which a boom 16 extends from the base to the tip in a plan view. The left-right direction is a direction orthogonal to the forward-backward direction in a plan view. The up-downward direction is a direction orthogonal to a plane that includes the forward-backward direction and the left-right direction, which are orthogonal to each other. Configuration of the working machine 10

[0016] Fig. Figure 1 is a diagram that schematically illustrates a configuration of a working machine according to an embodiment of the present disclosure. As in Fig. As shown in Figure 1, a working machine 10 of the present embodiment is, for example, a hydraulic excavator. However, the working machine 10 is not limited to a hydraulic excavator, but can be any working machine 10 that has a working attachment and uses oil during operation, such as a wheel loader, a bulldozer, a motor grader, or a dump truck.

[0017] The oil used to operate the working machine 10 is hydraulic oil, which is used to operate a hydraulic actuator (such as a hydraulic cylinder or a hydraulic motor), and lubricating oil, which is used to lubricate a lubrication unit (such as a motor). The lubrication unit is, for example, a lubrication unit of a drive unit, and the drive unit includes a drive source such as a motor.

[0018] Examples of oil used to operate the machine 10 include engine oil, hydraulic oil, swivel machine oil, final drive oil, axle oil, gear oil, and the like. The oil to be extracted varies depending on the model of the machine 10.

[0019] A hydraulic excavator 10, as an example of the working machine 10, has a main body 11 and a working device 12, which is operated by hydraulic pressure. The main body 11 includes a rotating body 13 and a travel body 15.

[0020] The chassis 15 has a pair of crawler tracks 15Cr and a drive motor 15M. The hydraulic excavator 10 can move by rotating the crawler tracks 15Cr. The drive motor 15M is provided as a drive source for the chassis 15.

[0021] The rotating body 13 is mounted on the transport body 15 and supported by the transport body 15. The rotating body 13 can be rotated relative to the transport body 15 about a rotary shaft RX by a rotary motor (not shown). The rotary shaft RX is a virtual straight line that serves as the center of rotation of the rotating body 13.

[0022] The rotary body 13 has a cabin 14. An operator's seat 14S, on which an operator sits, is provided in the cabin 14. The operator can enter the cabin 14 to operate the work device 12, rotate the rotary body 13 relative to the travel body 15, and operate the travel body 15 to move the hydraulic excavator 10.

[0023] The rotating body 13 has a drive source (for example, a motor). The drive source is located behind the cabin 14. The drive source is covered by an outer casing 13a.

[0024] The working device 12 is supported by the rotating body 13. The working device 12 has a boom 16, an arm 17, and a bucket 18. The working device 12 also has a boom cylinder 19a, an arm cylinder 19b, and a bucket cylinder 19c.

[0025] The boom 16 is rotatably connected to the main body 11. In particular, the base end of the boom 16 is rotatably connected to the rotating body 13 by a lower boom foot pin BF as a pivot point. The base end of the boom 16 is oriented in the left-right direction of the cabin 14. The arm 17 is rotatably connected to the boom 16. In particular, the base end of the arm 17 is rotatably connected to the tip end of the boom 16 by an upper boom pin BT as a pivot point. The bucket 18 is rotatably connected to the arm 17. In particular, the base end of the bucket 18 is rotatably connected to the tip end of the arm 17 by an upper arm pin AT as a pivot point.

[0026] The boom 16 can be driven by the boom cylinder 19a with respect to the main body 11. This drive allows the boom 16 to be rotated in the upward-downward direction with respect to the rotating body 13, with the lower boom pin BF as the pivot point.

[0027] The arm 17 can be driven by the arm cylinder 19b with respect to the boom 16. This drive allows the arm 17 to rotate in the upward-downward or forward-backward direction with respect to the boom 16, using the upper boom pin BT as its pivot point.

[0028] The bucket 18 can be driven by the bucket cylinder 19c with respect to the arm 17. This drive allows the bucket 18 to rotate in the up-down or back-and-forth direction with respect to the arm 17, with the upper arm pin AT as the pivot point.

[0029] The boom cylinder 19a, the arm cylinder 19b, the bucket cylinder 19c, the rotary motor, and the like are hydraulic actuators that operate, for example, using hydraulic oil. Furthermore, the drive unit, like a drive source (e.g., a motor), is configured to be lubricated with lubricating oil. In the present embodiment, the physical properties of the oil supplied to the hydraulic actuator or drive unit are recorded. At a minimum, the dielectric constant and the resistance value of the oil must be recorded as physical properties.

[0030] Furthermore, in the present embodiment, as described later, at least one of the metal and iron concentrations in the oil is estimated with a high degree of accuracy from the recorded physical properties of the oil. The degree of accelerated wear of the main components of the working machine 10 can be determined from the estimated metal and iron concentrations in the oil. For example, if monitoring of the engine oil reveals that the metal and iron concentrations in the oil are equal to or greater than the abnormality detection value, it is estimated that piston rings, cylinder liners, and the like are worn.

[0031] Furthermore, in the present embodiment, at least the metal concentration and the iron concentration in the oil are estimated based on the recorded physical property values ​​of the oil, thus enabling continuous monitoring of the metal concentration and the iron concentration in the oil.

[0032] Furthermore, in the present embodiment, if the metal concentration and the iron concentration in the oil are found to be equal to or greater than the abnormality detection value, a warning is issued to the surrounding area by means of a screen display, an audible signal, or the like. This prevents sudden failures due to abnormal wear of parts of the machine 10 or the like. Oil circuit

[0033] Next, with reference to Fig. 2 an example of an oil circuit for supplying and removing hydraulic oil from a hydraulic cylinder that serves as a hydraulic actuator, and an example of the arrangement of oil property sensors is described.

[0034] Fig. Figure 2 is a diagram illustrating an example of an oil circuit that supplies and removes hydraulic oil from a hydraulic actuator (for example, a hydraulic cylinder). As shown in Fig. Figure 2 shows an example of an oil circuit comprising a drive source 2, a main pump 3a, a main valve 4, a hydraulic actuator 5, an oil cooler 6a, an oil filter 7a and an oil tank 8.

[0035] The drive source 2 is, for example, a motor. The main pump 3a is operated by a drive force from the drive source 2. During operation, the main pump 3a pumps hydraulic oil upwards, which is stored in the oil tank 8. The hydraulic oil pumped upwards by the main pump 3a is supplied to the hydraulic actuator 5 via the main valve 4. The hydraulic oil supplied to the hydraulic actuator 5 is discharged via the main valve 4.

[0036] The hydraulic actuator is, for example, a hydraulic cylinder, but it can also be another type of actuator such as a hydraulic motor. The hydraulic actuator 5 is operated by supplying and discharging hydraulic oil. In a case where the hydraulic actuator 5 is, for example, a hydraulic cylinder, the hydraulic cylinder 5 expands and contracts as hydraulic oil is supplied and discharged.

[0037] The main valve 4 controls the amount of hydraulic oil or similar fluid supplied to and discharged from the hydraulic actuator 5. The hydraulic oil discharged from the hydraulic actuator 5 is cooled by the oil cooler 6a, filtered by the oil filter 7a, and then returned to the oil tank 8.

[0038] An example of the oil circuit further includes an oil property sensor 1a. The oil property sensor detects the properties of the oil flowing through the oil channel. These properties include, for example, temperature, viscosity, density, dielectric constant, and resistance. These oil properties can be detected by a variety of oil property sensors or by a single oil property sensor. For example, the oil property sensor 1a is located in the oil channel between the main pump 3a and the oil tank 8. The oil property sensor 1a could also be located in a different oil channel of the oil circuit.

[0039] Next, with reference to Fig. 3 another example of the oil circuit that supplies lubricating oil to an engine as a drive unit, and an example of the arrangement of oil property sensors is described.

[0040] Fig. Figure 3 is a diagram illustrating another example of the oil circuit that supplies lubricating oil to a drive unit (for example, an engine). As in Fig. As shown in Figure 3, another example of the oil circuit includes a lubrication pump 3b, an oil cooler 6b, an oil filter 7b, the oil tank 8 and a lubrication unit 9 of the drive unit.

[0041] The lubrication pump 3b is driven by a drive force from a drive source (not shown). When operating, the lubrication pump 3b pumps lubricating oil (oil) stored in oil tank 8 upwards. The lubricating oil pumped upwards by the lubrication pump 3b is cooled by the oil cooler 6b, filtered by the oil filter 7b, and then supplied to the lubrication unit 9 of the drive unit. After lubricating the lubrication unit 9 of the drive unit, the lubricating oil is returned to oil tank 8.

[0042] Another example of the oil circuit also includes an oil property sensor 1b. The oil property sensor 1b is, for example, arranged in an oil channel that connects the oil channel between the oil filter 7b and the lubrication unit 9 to the oil tank 8. The oil property sensor 1b can be arranged in a different oil channel of the oil circuit.

[0043] As in Fig. 2 and Fig. As shown in Figure 3, each of the oil property sensors 1a and 1b measures at least the dielectric constant and the resistance value of the oil as its physical properties. In addition to the dielectric constant and the resistance value of the oil, each of the oil property sensors 1a and 1b can also measure the viscosity and the density of the oil.

[0044] Each of the oil property sensors 1a and 1b is electrically connected to a control unit 50. Each of the oil property sensors 1a and 1b outputs a signal of the detected physical property value of the oil to the control unit 50.

[0045] In the present embodiment, the metal concentration and the iron concentration in the oil can be estimated with a high degree of accuracy by estimating the metal concentration and the iron concentration in the oil from at least the dielectric constant and the resistance value of the oil, which were detected by each of the oil property sensors 1a and 1b. Oil property diagnostic system of the working machine 10

[0046] Next, with reference to Fig. 4 an oil property diagnostic system for the working machine 10 according to an embodiment of the present disclosure is described.

[0047] Fig. Figure 4 is a diagram illustrating a configuration of an oil property diagnostic system for the working machine 10 according to an embodiment of the present disclosure. As in Fig. As shown in Figure 4, the oil property diagnostic system for the working machine 10 includes, for example, the working machine 10, a server 70 and a service computer 80.

[0048] The machine 10 comprises an oil property sensor 1, a control unit 50, and a monitor 60. The oil property sensor 1 is either one of the oil property sensors 1a and 1b, or both of them. The control unit 50 is electrically connected to the oil property sensor 1 and the monitor 60.

[0049] A signal indicating the physical property value of the oil as detected by the oil property sensor 1 is output to the control unit 50 of the machine 10. The control unit 50 estimates at least one of the metal and iron concentrations in the oil based on the detected physical property value. The control unit 50 determines whether the estimated metal and iron concentrations are equal to or greater than the abnormality detection value.

[0050] The control unit 50 sends a signal indicating the measurement result to the monitor 60. The monitor 60 displays an image based on the measurement result received from the control unit 50. The monitor 60 displays and announces a warning if at least one of the estimated metal concentrations and iron concentrations is equal to or greater than the abnormality measurement value. A loudspeaker can be used instead of the monitor 60. In this case, a warning can be issued as an audible signal.

[0051] The signal indicating the physical property value of the oil, as detected by oil property sensor 1, is output to server 70, which is located outside the machine 10. This signal can be transmitted wirelessly to server 70. Server 70 could, for example, be owned by a construction equipment manufacturer.

[0052] Server 70 has the same function as control unit 50. That is, server 70 estimates at least one of the metal and iron concentrations in the oil based on the measured physical properties of the oil and determines whether at least one of the estimated metal and iron concentrations exhibits an abnormal value. Server 70 then sends a signal indicating the result of this determination to service computer 80.

[0053] Meanwhile, server 70 can record the result of the investigation carried out by the control unit 50 of the work machine 10. In this case, server 70 can directly output the recorded investigation result to service computer 80.

[0054] Service computer 80, for example, is a computer owned by a representative of a construction equipment manufacturer or similar organization. Server 70 and service computer 80 are connected to each other, for example, via an intranet or the internet.

[0055] The service computer 80 can wirelessly transmit a signal indicating the diagnostic result received from the server 70 to the work machine 10. Furthermore, a representative of a construction equipment manufacturer or similar entity, who has confirmed the diagnostic result received by the service computer 80, can repair and inspect the work machine 10 based on this result.

[0056] Furthermore, a user who owns the work machine 10 can request repairs and inspections from a representative of a construction equipment manufacturer or similar entity based on the results displayed on the monitor 60. In this case, the representative of the construction equipment manufacturer or similar entity can repair and inspect the work machine 10 based on the request. Function blocks of control unit 50

[0057] Next, the functional blocks of control unit 50 will be described with reference to Fig. 5 described.

[0058] Fig. 5 is a functional block diagram of a control unit that is part of the system of Fig. 4 is used. As in Fig. As shown in Figure 5, the control unit 50 comprises a unit 51 for acquiring physical oil property values, a concentration estimation unit 52, a concentration determination unit 53, an output control unit 54, and the memory 55. The unit 51 for acquiring physical oil property values ​​acquires the physical oil property values ​​from the oil property sensor 1.

[0059] The concentration estimation unit 52 estimates at least one of the metal concentrations and iron concentrations in the oil from the recorded physical property value of the oil, based on the relationship between the physical property value of the oil and the metal concentration or iron concentration in the oil (relationship between physical property value and concentration). When performing the above estimation, the concentration estimation unit 52 refers to the above relationship between physical property value and concentration stored in memory 55. A procedure for estimating the iron concentration and metal concentration is described later.<Verfahren zum Schätzen von Eisenkonzentration und Metallkonzentration> described.

[0060] The concentration determination unit 53 determines whether the metal concentration and iron concentration in the oil are equal to or greater than the abnormality determination value. After performing the above determination, the concentration determination unit 53 refers to the abnormality determination value for iron concentration or the abnormality determination value for metal concentration stored in memory 55.

[0061] The output control unit 54 receives the result of the concentration measurement unit 53. Based on this result, the output control unit 54 sends a control signal to the monitor 60 and controls the display content of the monitor 60. If it is determined that at least one of the metal concentrations and the iron concentration in the oil is equal to or greater than the abnormality measurement value, the output control unit 54 issues a warning by displaying this fact on the monitor 60. Furthermore, if the metal concentration and iron concentration in the oil are less than the abnormality measurement value, the output control unit 54 displays on the monitor 60 that no abnormality is present.

[0062] Meanwhile, memory 55 can store the aforementioned relationship between physical property value and concentration, as well as the abnormality detection value, in advance (at the time of delivery of the machine 10). Furthermore, the aforementioned relationship between physical property value and concentration, as well as the abnormality detection value, can be stored in memory 55 after delivery of the machine 10 from outside the machine 10. The aforementioned relationship between physical property value and concentration, as well as the abnormality detection value, can be stored in memory 55 by operating an input device, such as a touch panel attached to the machine 10. Moreover, the device that issues a warning is not limited to the monitor 60 and can be a device that issues a warning by means of a loudspeaker tone or the like.

[0063] Meanwhile, the control unit 50 includes a processor, main memory, and storage. The processor is, for example, a central processing unit (CPU). The main memory includes, for example, non-volatile memory such as read-only memory (ROM) and volatile memory such as random-access memory (RAM).

[0064] The control unit 50 can be mounted on the machine 10 or located remotely from the machine 10. If the control unit 50 is located remotely from the machine 10, it can be wirelessly connected to the oil property sensor 1, the monitor 60, or similar devices. The control unit 50 can also be stored on the server 70, which is located remotely from the machine 10.

[0065] The control unit 50 reads a program stored in memory, loads it into main memory, and executes a predefined process according to the program. Furthermore, the program can be distributed to the control unit 50 via a network. Methods for estimating iron concentration and metal concentration

[0066] Next, a method for estimating the iron concentration from the iron and metal concentrations in the oil in the concentration estimation unit 52 is described. Fig. 5 described using the example of iron concentration.

[0067] The iron concentration in the oil is estimated using machine learning. In this machine learning process, the actual measured values ​​of the oil's physical properties (dielectric constant, viscosity, density, and resistivity) and the corresponding actual measured values ​​of the iron concentration in the oil are used as a training dataset. The actual measured values ​​of the oil's physical properties are acquired by oil property sensor 1. Oil property sensor 1 can acquire four physical properties of the oil, such as viscosity, density, dielectric constant, and resistivity. The actual measured values ​​of the iron concentration are acquired, for example, by an inductively coupled plasma analyzer (ICP analyzer).The actual measured values ​​of the physical properties of the oil and the corresponding actual measured values ​​of the iron concentration in the oil are each determined from used oil.

[0068] A large amount of the aforementioned training data is fed into a machine learning algorithm. The machine learning algorithm receives this large amount of training data as input and builds an estimation model. This estimation model is then formulated as a computational equation to calculate the iron concentration using the least squares method, based on the relationship between the physical property values ​​and the iron concentration.

[0069] For example, if two variables, namely a dielectric constant and a resistance value, are used as the physical properties of the oil, the calculation equation is expressed as iron concentration [ppm] = a (dielectric constant ε) + b (resistance value R) + c. In this calculation equation, the coefficients a, b, and c are determined by the machine learning algorithm. The above calculation equation is an equation that specifies the relationship between the physical properties of the oil and the iron concentration in the oil.

[0070] The iron concentration in the oil is calculated by measuring the iron content in the oil. Fig. The physical property values ​​of the oil, as shown in Figure 5 of the oil property sensor 1, are entered into the calculation equation created as described above. In this way, the iron concentration in the oil is estimated.

[0071] The metal concentration in the oil is estimated using the same procedure as for the iron concentration described above. The metal concentration in the oil is estimated as the total concentration of one or more metals selected from the group consisting of iron (Fe), copper (Cu), chromium (Cr), aluminum (Al), silicon (Si), and lead (Pb). For example, if two variables—a dielectric constant and a resistivity value—are used as the physical properties of the oil, the equation that expresses the relationship between the oil's physical properties and the metal concentration in the oil is given by the equation: Metal concentration [ppm] = a1 (dielectric constant ε) + b1 (resistivity value R) + c1. In this equation, the coefficients a1, b1, and c1 are determined by a machine learning algorithm. Oil property diagnostic procedure for working machinery

[0072] Next, with reference to Fig. 5 and Fig. 6 an oil property diagnostic method for the working machine according to an embodiment of the present disclosure is described.

[0073] Fig. Figure 6 is a flowchart illustrating an oil property diagnostic procedure for the working machine according to an embodiment of the present disclosure. As in Fig. 5 and Fig. As shown in Figure 6, the oil property sensor 1 detects the physical property values ​​of the oil. The control unit 50, which detects physical oil property values, detects the physical property values ​​of the oil from the oil property sensor 1 (step S1: Fig. 6) The physical properties of the oil include at least the dielectric constant and the resistance value of the oil, as described above, and may also include the viscosity and density of the oil in addition to the dielectric constant and the resistance value of the oil.

[0074] Unit 51 for recording physical oil property values ​​outputs the recorded physical property value to the concentration estimation unit 52. The concentration estimation unit 52 estimates at least one of the metal concentrations and iron concentration in the oil from the recorded physical property value of the oil based on the aforementioned relationship between physical property value and concentration (step S2: Fig. 6) The concentration estimation unit 52 refers to the above relationship between physical property value and concentration stored in memory 55 when at least one of the metal concentration and iron concentration in the oil is estimated.

[0075] The above relationship between physical property value and concentration is, for example, a calculation equation obtained through the above machine learning. The concentration estimation unit 52 estimates at least one of the metal concentrations and iron concentrations in the oil by inputting the physical property values ​​of the oil, as determined by unit 51 for the acquisition of physical oil property values, into the calculation equation obtained through the above machine learning.

[0076] The concentration estimation unit 52 outputs at least one of the estimated metal and iron concentrations in the oil to the concentration determination unit 53. The concentration determination unit 53 determines whether at least one of the detected metal and iron concentrations in the oil is equal to or greater than the abnormality determination value (step S3: Fig. 6).

[0077] The respective abnormality determination values ​​for the metal concentration and iron concentration are pre-stored in memory 55 of the control unit 50. After the above determination has been carried out, the concentration determination unit 53 refers to the abnormality determination value for the metal concentration or the abnormality determination value for the iron concentration that is stored in memory 55.

[0078] In a case where the concentration detection unit 53 determines that the metal and iron concentrations in the oil are lower than the abnormality detection value, steps S1, S2, and S3 are repeated. Meanwhile, even if the concentration detection unit 53 determines that the metal and iron concentrations in the oil are lower than the abnormality detection value, it can output the detection result to the monitor 60, and steps S1, S2, and S3 can be repeated. However, if the concentration detection unit 53 determines that at least one of the metal and iron concentrations in the oil is equal to or greater than the abnormality detection value, it outputs a signal to the output control unit 54 indicating the detection result.

[0079] Based on the recorded test result, the output control unit 54 sends a control signal to the monitor 60. If it is determined that at least one of the metal concentrations and iron concentrations in the oil is equal to or greater than the abnormality test value, the output control unit 54 issues a warning by displaying this fact on the monitor 60 (step S4: Fig. 6).

[0080] As described above, the oil property diagnostic procedure for working machines is implemented according to the present embodiment. Example

[0081] Next, the study conducted by the inventors on the relationship between the physical properties of the oil and the accuracy of the estimate will be presented, with reference to Fig. 7 to 10 described.

[0082] Fig. Figure 7 is a diagram illustrating the coefficient of determination and an 80% error, which are evaluation indices for the accuracy of an estimation model (calculation equation). Fig. Figure 8 is a diagram illustrating the correlation coefficients with the soot concentration, which are evaluation indices for the accuracy of the estimation model (calculation equation). Fig. Figure 9 is a diagram illustrating a relationship between the physical properties of oil and rating indices relating to the iron concentration in the oil. Fig. Figure 10 is a diagram illustrating a relationship between the physical property values ​​of oil and rating indices in relation to the concentration of metals (iron + copper + chromium + aluminium + silicon + lead) in the oil.

[0083] The inventors used an oil property sensor to determine the physical property values ​​(dielectric constant, viscosity, density, and resistivity) of each used motor oil (recovered from the market) with varying iron concentrations. The combination of measured physical property values ​​was varied, and the iron concentration in the oil was estimated for each combination. Specifically, the iron concentration in the oil was estimated using only one property value (one variable) from the four physical property values, a combination of two physical property values ​​(two variables), and a combination of three physical property values ​​(three variables).

[0084] The accuracy of the estimation for each combination of physical property values ​​was evaluated by comparing the estimated iron concentration obtained in this way with the actual measured iron concentration in the oil, determined by an ICP analyzer. The evaluation indices used in this assessment were (1) the coefficient of determination, (2) the 80% error, and (3) the correlation coefficient with the soot concentration. Each evaluation index is described below. (1) Coefficient of determination (= R²)

[0085] The coefficient of determination R² is calculated by the following equation (1), where yi is an i-th true value (actual measurement data) from N(n) data elements, yei is a value estimated by the regression equation, and yai is an average of the actual measurements in the entire dataset. The coefficient of determination R² means that the accuracy of the iron concentration estimation increases as the value approaches 1. That is, as in Fig. Figure 7 shows that the estimation accuracy increases when the data are closer to a straight line SL1, which represents a one-to-one relationship between the actual measured value for iron concentration and the estimated value for iron concentration. The coefficient of determination R2 is evaluated as an absolute value. [Math. 1] R2=1−∑i=1n(yi−yei)2∑i=1n(yi−yai)2 (2) 80% error (= RMSE × 1.25)

[0086] The mean squared error (RMSE), calculated by the following equation (2), represents the magnitude of the deviation between the actual measurement and the estimated value of the data. As with the standard deviation, approximately 68% of the data is considered to fall within the RMSE error range. Since approximately 80% of the data is considered to fall within the range of (RMSE × 1.25), (RMSE × 1.25) has consequently been defined as the 80% error and used as one of the evaluation indices. As in Fig. As shown in Figure 7, the 80% error represents the size of the error range ER, within which 80% of the data relating to the relationship between the actual measurement and the estimated value for the iron concentration lie with respect to the line SL1. The 80% error means that the accuracy of the estimate increases as the value decreases. [Math. 2] RMSE=1N∑i=1n(yi−yei)2 (3) Correlation coefficient (= r) between the actual measured value x of the soot concentration and the estimated value y of the iron concentration

[0087] The actual measured value of the soot concentration and the estimated value of the iron concentration are defined as (x, y), their averages are defined as (xa, ya), and the i-th value of n data points is defined as (xi, yi). Furthermore, the covariance of x and y is defined as sxy, the standard deviation of x is defined as sx, and the standard deviation of y is defined as sy. The correlation coefficient r is expressed at this time by the following equation (3). [Math. 3] r=sxysx×sy=1n∑i=1n(xi−xa)(yi−ya)1n∑i=1n(xi−xa)2×1n∑i=1n(yi−ya)2

[0088] The correlation coefficient r is a calculation of the degree of correlation between the estimated iron concentration obtained from an oil sample using the above procedure and the actual measured soot concentration in the same oil sample. The correlation coefficient r means that as its value approaches 1, it becomes increasingly difficult to distinguish the estimated iron concentration from the actual measured soot concentration, and the accuracy of the iron concentration estimate decreases. That is, as in Fig. Figure 8 shows that the estimation accuracy decreases as the data approach a straight line SL2, which represents a one-to-one relationship between the actual measured value for the soot concentration and the estimated value for the iron concentration. The correlation coefficient r is evaluated as an absolute value.

[0089] Soot, on the other hand, is a mixture of soot produced by fuel combustion and contained in the exhaust gas, and oil sludge, and is mixed, for example, with the engine oil. The soot concentration is determined by measuring the intensity of transmitted light using infrared spectroscopy and serves, for example, as an indicator of contamination in engine oil or similar substances. (4) Assessment result of the estimated value for the iron concentration in the oil

[0090] The results of the assessment of the iron concentration in the oil using the above assessment index are in Fig. 9 shown. As in Fig. As shown in Figure 9, samples (1) to (4) are evaluation results for one variable, samples (5) to (10) are evaluation results for two variables, and samples (11) to (14) are evaluation results for three variables. No special evaluation was performed for the single variable of viscosity of sample (2) and the single variable of density of sample (3). In one case where the iron concentration was estimated using at least the dielectric constant and resistivity of the oil as the physical property values ​​of the oils, as in samples (7), (12), and (13), the coefficient of determination was equal to or greater than 0.3, the 80% error was less than 27, and the correlation coefficient was less than 0.6. Thus, it was found that the estimation accuracy of the iron concentration was higher for samples (7), (12), and (13) than for the other samples (1) to (6), (8) to (11), and (14).

[0091] Based on this, it was found that by estimating the iron concentration using at least the dielectric constant and the resistivity value of the oil, the estimation accuracy of the iron concentration is higher than in a case where the iron concentration is estimated using physical property values ​​that do not include the combination of dielectric constant and resistivity value of the oil.

[0092] Furthermore, it was found that in each of the samples (7) and (12) the correlation coefficient was equal to or less than 0.4, indicating that the estimation accuracy of the iron concentration was higher than in the other samples.

[0093] Furthermore, in a case where three variables, i.e., a dielectric constant, a resistance value, and a viscosity of the oil, are used as the physical property values ​​of the oil, as in sample (12), it was found that the coefficient of determination is closer to 1 and the 80% error becomes smaller than in a case where two variables, i.e., a dielectric constant and a resistance value of the oil, are used, leading to a further improvement in the estimation accuracy of the iron concentration. (5) Assessment result of the estimated value for the metal concentration in the oil

[0094] The metal concentration (iron + copper + chromium + aluminum + silicon + lead) in the oil was estimated using the same procedure as for the iron concentration, and the accuracy of the estimated value was evaluated. The evaluation results are presented in Fig. 10 shown. As in Fig. As shown in Figure 10, in a case where the dielectric constant and the resistance value of the oil were used as physical property values ​​of the oil, as in sample (26), it was found that the coefficient of determination was equal to or greater than 0.07, the 80% error was less than 95, and the correlation coefficient was less than 0.88, indicating that the estimation accuracy of the metal concentration was higher than in the other samples (21) to (25) and (27).

[0095] Based on this, it was found that by estimating the metal concentration using at least the dielectric constant and the resistance value of the oil, the estimation accuracy of the metal concentration was higher than in a case where the metal concentration was estimated using physical property values ​​that do not include the combination of dielectric constant and resistance value of the oil.

[0096] The oil to be analyzed in this example contained at least iron, including copper, chromium, aluminum, silicon and lead. Effects

[0097] The effects of the present embodiment are described below.

[0098] If a machine experiences a sudden failure due to abnormal wear of an internal part or similar issue, a delay in detecting the fault can lead to a malfunction of the internal part, a machine downtime, or similar consequences. This malfunction, downtime, or similar issues result in significant losses for customers, such as delays in work schedules due to the resulting downtime. Therefore, there is a need to continuously monitor the concentration of metals, including iron, in the oil to detect sudden failures due to abnormal wear of internal parts as early as possible and prevent downtime.However, in patent document 1, in a case where it is determined that an oil analysis is necessary based on the oil properties detected by a sensor, the cause of the oil abnormality is determined by carrying out an oil analysis on an oil sample taken from the working machine using a precision analysis device of an oil analysis company or the like, and it is therefore not possible to constantly detect changes in the concentration of metals including iron in the oil.

[0099] In contrast, according to the present embodiment, as described in the Fig. 9 and Fig.As shown in Figure 10, at least the dielectric constant and the resistivity are recorded as physical properties of the oil, and the metal and iron concentrations in the oil are estimated from these recorded physical properties. This allows for a more accurate estimation of the metal and iron concentrations in the oil than in a case where the metal and iron concentrations are estimated using physical properties that do not include the combination of dielectric constant and resistivity.

[0100] Furthermore, when estimating both the metal and iron concentrations, the dielectric constant and the resistivity of the oil can be determined using a simple oil property sensor 1. This eliminates the need for a large device such as an ICP analyzer to determine the metal or iron concentration in the oil.

[0101] Furthermore, unlike an ICP analyzer or similar device, the simple oil property sensor 1 can be easily mounted on the machine 10. By mounting the simple oil property sensor 1 on the machine 10, the metal and iron concentrations in the oil can be continuously monitored. In addition, malfunctions (such as those caused by wear of internal parts) that occur suddenly at times other than oil sampling can be detected early, allowing for timely intervention (such as oil changes or part replacements) before the machine 10 is seriously damaged.

[0102] Furthermore, according to the present embodiment, the metal concentration is a total concentration of one or more metals selected from a group consisting of iron, copper, chromium, aluminum, silicon, and lead. This makes it possible to accurately estimate the total concentration of the aforementioned metals in the oil.

[0103] Furthermore, the metal concentration according to the present embodiment is an iron concentration. This makes it possible to accurately estimate the iron concentration in the oil. The metal concentration includes at least one iron concentration.

[0104] Furthermore, according to the present embodiment, the control unit 50 stores in advance the relationship between the physical properties of the oil and the metal concentration or iron concentration in the oil. This makes it possible to estimate the metal concentration or iron concentration in the oil based on the physical properties of the oil.

[0105] Furthermore, according to the present embodiment, the control unit 50 stores in advance an abnormality detection value for the metal or iron concentration in the oil, determined for each working machine 10, and determines, based on this abnormality detection value and the estimated metal or iron concentration, whether the oil is abnormal. By detecting oil abnormalities in this way, it becomes possible to prevent sudden malfunctions.

[0106] Furthermore, according to the present embodiment, the oil to be analyzed is the hydraulic oil or the engine oil of the working machine 10. This makes it possible to prevent sudden malfunctions in motors or hydraulic equipment such as a hydraulic actuator, a main pump and a main valve. Additional remarks

[0107] The embodiments described above include the following technical ideas. (Supplement 1)

[0108] An oil property diagnostic system for working machines, including: an oil property sensor configured to detect at least one dielectric constant and one resistance value as physical property values ​​of oil; and a control unit configured to estimate a metal concentration in the oil from the physical property values ​​of the oil detected by the oil property sensor, based on a relationship between the physical property values ​​of the oil and the metal concentration in the oil. (Supplement 2)

[0109] The oil property diagnostic system for working machinery according to Supplement 1, wherein the metal concentration is a total concentration of one or more metals selected from a group consisting of iron, copper, chromium, aluminium, silicon and lead. (Supplement 3)

[0110] The oil property diagnostic system for working machines according to Supplements 1 or 2, where the metal concentration is an iron concentration. (Supplement 4)

[0111] The oil property diagnostic system for working machines according to one of the amendments 1 to 3, wherein the control unit stores in advance a relationship between the physical property values ​​of the oil and the metal concentration in the oil. (Supplement 5)

[0112] The oil property diagnostic system for working machines according to one of the amendments 1 to 4, wherein the control unit stores in advance an abnormality detection value for the metal concentration in the oil, which is determined for each machine, and The control unit detected an abnormality in the oil based on the estimated metal concentration and the abnormality detection values. (Supplement 6)

[0113] Oil property diagnostic system for working machines according to one of the amendments 1 to 5, wherein the oil is hydraulic oil or engine oil of a working machine. (Supplement 7)

[0114] An oil property diagnostic system for working machines, including: an oil property sensor configured to detect at least one dielectric constant and one resistance value as physical property values ​​of oil; and a control unit configured to estimate the iron concentration in the oil from the physical properties of the oil detected by the oil property sensor, based on a relationship between the physical properties of the oil and the iron concentration in the oil. (Supplement 8)

[0115] An oil property diagnostic procedure for working machines, including: Determine at least one dielectric constant and one resistance value as physical property values ​​of oil and Estimating the metal concentration in the oil from the recorded physical properties of the oil based on a relationship between the physical properties of the oil and the metal concentration in the oil. (Supplement 9)

[0116] An oil property diagnostic procedure for working machines, including: Determining the dielectric constant and resistance value as physical properties of oil and Estimating the iron concentration in the oil from the recorded physical properties of the oil based on a relationship between the physical properties of the oil and the iron concentration in the oil.

[0117] It is understood that the embodiment disclosed herein is in every respect exemplary and not limiting. The scope of protection of the present invention is defined by the terms of the claims and not by the preceding description and is intended to include all modifications within the scope and meaning that correspond to the content of the claims. Reference symbol list

[0118] 1, 1a, 1b Oil property sensor, 2 Drive source, 3a Main pump, 3b Lubrication pump, 4 Main valve, 5 Hydraulic actuator, 6a, 6b Oil cooler, 7a, 7b Oil filter, 8 Oil tank, 9 Lubrication unit, 10 Working machine, 11 Main body, 12 Working attachment, 13 Rotating body, 13a Outer casing, 14 Cab, 14S Operator's seat, 15 Undercarriage, 15Cr Track, 15M Travel motor, 16 Boom, 17 Arm, 18 Bucket, 19a Boom cylinder, 19b Arm cylinder, 19c Bucket cylinder, 50 Control unit, 51 Unit for acquiring physical oil property values, 52 Concentration estimation unit, 53 Concentration determination unit, 54 Output control unit, 55 Memory, 60 Monitor, 70 Server, 80 Service computer, AT upper Arm pin, BF cantilever foot pin, BT upper cantilever pin, RX pivot shaft QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] JP 2016-113819 A [0002, 0003]

Claims

[1] Oil property diagnostic system for working machines, comprising: an oil property sensor configured to detect at least one dielectric constant and one resistance value as physical property values ​​of oil; and a control unit configured to estimate a metal concentration in the oil from the physical property values ​​of the oil detected by the oil property sensor, based on a relationship between the physical property values ​​of the oil and the metal concentration in the oil. [2] Oil property diagnostic system for working machines according to claim 1, wherein the metal concentration is a total concentration of one or more metals selected from the group consisting of iron, copper, chromium, aluminium, silicon and lead. [3] Oil property diagnostic system for working machines according to claim 2, wherein the metal concentration is an iron concentration. [4] Oil property diagnostic system for working machines according to claim 1, wherein the control unit stores in advance a relationship between the physical property values ​​of the oil and the metal concentration in the oil. [5] Oil property diagnostic system for working machines according to claim 1, wherein the control unit stores in advance an abnormality detection value for the metal concentration in the oil, which is determined for each working machine, and The control unit detected an abnormality in the oil based on the estimated metal concentration and the abnormality detection values. [6] Oil property diagnostic system for working machines according to claim 1, wherein the oil is hydraulic oil or engine oil of a working machine. [7] Oil property diagnostic system for working machines, comprising: an oil property sensor configured to detect at least one dielectric constant and one resistance value as physical property values ​​of oil; and a control unit configured to estimate the iron concentration in the oil from the physical properties of the oil detected by the oil property sensor, based on a relationship between the physical properties of the oil and the iron concentration in the oil. [8] Oil property diagnostic procedures for working machines, including: Determine at least one dielectric constant and one resistance value as physical property values ​​of oil and Estimating the metal concentration in the oil from the recorded physical properties of the oil based on a relationship between the physical properties of the oil and the metal concentration in the oil. [9] Oil property diagnostic procedures for working machines, including: Determine at least one dielectric constant and one resistance value as physical property values ​​of oil and Estimating the iron concentration in the oil from the recorded physical properties of the oil based on a relationship between the physical properties of the oil and the iron concentration in the oil.

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