Oil property diagnostic system for work machine and oil property diagnostic method for work machine

Abstract

Oil property sensors (1 (1a, 1b)) detect at least a dielectric constant and a resistance value as physical property values of oil. On the basis of the relationship between the physical property value of the oil detected by the oil property sensors (1 (1a, 1b)) and the metal concentration or iron concentration in the oil, the controller (50) estimates the metal concentration or iron concentration in the oil from the physical property value.

Description

Technical Field

[0001] This disclosure relates to a system and method for diagnosing the oiliness of machinery. Background Technology

[0002] By detecting and analyzing the properties of the oil used in operating machinery, it is possible to identify early signs of malfunctions in the machinery. For example, in Japanese Patent Application Publication No. 2016-113819 (Patent Document 1), the oil properties detected by sensors are used to determine whether oil analysis is required, which involves oil sampling. If oil analysis is deemed necessary, oil sampling is performed as quickly as possible, and the oil analysis is conducted by an oil analysis company. Then, based on past oil analysis information, any abnormalities in the oil are identified, and the cause of the abnormality is determined.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2016-113819 Summary of the Invention

[0006] The problem that the invention aims to solve

[0007] In operating machinery, it is desirable to constantly monitor the oil condition in order to prevent sudden failures due to abnormal wear of internal components. However, in Patent Document 1, since oil analysis by an oil analysis company is required, it is impossible to constantly monitor the oil condition.

[0008] The purpose of this disclosure is to provide an oil condition diagnostic system and method for working machinery that can continuously monitor the concentration of metals and iron in the oil.

[0009] Methods for solving problems

[0010] This disclosure discloses an oil condition diagnostic system for operating machinery, comprising an oil condition sensor and a controller. The oil condition sensor detects at least the dielectric constant and resistance value as physical property values ​​of the oil. The controller estimates the metal concentration in the oil based on the relationship between the physical property values ​​of the oil and the metal concentration in the oil, according to the physical property values ​​of the oil detected by the oil condition sensor.

[0011] Another oil condition diagnostic system for operating machinery disclosed herein includes an oil condition sensor and a controller. The oil condition sensor detects at least the dielectric constant and resistance value as physical property values ​​of the oil. The controller estimates the iron concentration in the oil based on the relationship between the physical property values ​​of the oil and the iron concentration in the oil, according to the physical property values ​​of the oil detected by the oil condition sensor.

[0012] The present disclosure provides a method for diagnosing the oiliness of operating machinery, which includes the following steps.

[0013] At least the dielectric constant and resistivity are obtained as physical property values ​​of the oil. Based on the relationship between the physical property values ​​of the oil and the metal concentration in the oil, the metal concentration in the oil is estimated based on the obtained physical property values.

[0014] Another method for diagnosing the oiliness of working machinery disclosed herein includes the following steps.

[0015] At least the dielectric constant and resistivity are obtained as physical property values ​​of the oil. Based on the relationship between the physical property values ​​of the oil and the iron concentration in the oil, the iron concentration in the oil is estimated based on the obtained physical property values.

[0016] Invention Effects

[0017] According to this disclosure, an oil condition diagnostic system and a method for diagnosing the oil condition of working machinery are available, which can continuously monitor the concentration of metals and iron in the oil. Attached Figure Description

[0018] Figure 1 This is a diagram showing the structure of a working machine according to one embodiment of the present disclosure.

[0019] Figure 2 This is a diagram illustrating an example of an oil circuit for supplying and discharging working oil to a hydraulic actuator (such as a hydraulic cylinder).

[0020] Figure 3 This is a diagram illustrating another example of an oil circuit that supplies lubricating oil to a drive unit (such as an engine).

[0021] Figure 4 This is a diagram showing the structure of an oil condition diagnostic system for a work machine according to one embodiment of the present disclosure.

[0022] Figure 5 yes Figure 4 The functional block diagram of the controller used in the system.

[0023] Figure 6 This is a flowchart illustrating a method for diagnosing the oiliness of a work machine according to one embodiment of the present disclosure.

[0024] Figure 7 It is a graph of the coefficient of determination and 80% error, which are used to illustrate the accuracy of the estimated model (calculation).

[0025] Figure 8 It is a graph of the correlation coefficient of soot concentration, which is used to illustrate and estimate the accuracy of the model (calculation).

[0026] Figure 9 This is a graph showing the relationship between the iron concentration in oil, representing the physical properties of the oil, and its evaluation index.

[0027] Figure 10 This is a graph showing the relationship between the concentration of metals (iron, copper, chromium, aluminum, silicon, and lead) in oil, representing the physical properties of the oil, and its evaluation index. Detailed Implementation

[0028] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0029] In the specification and accompanying drawings, the same or corresponding components are labeled with the same reference numerals and are not described repeatedly. Furthermore, for ease of explanation, structures are sometimes omitted or simplified in the drawings. Additionally, at least some of the embodiments and variations can be arbitrarily combined with each other.

[0030] It should be noted that, in the following context, the front-to-back direction refers to the direction in which the boom 16 extends from the base end to the front end when viewed from above. The left-to-right direction refers to the direction orthogonal to the front-to-back direction when viewed from above. The up-down direction is the direction orthogonal to the plane containing the mutually orthogonal front-to-back and left-to-right directions.

[0031] <Structure of Operation Machinery 10>

[0032] Figure 1 This is a diagram that schematically illustrates the structure of the working machinery in one embodiment of this disclosure. (See diagram for example.) Figure 1 As shown, the working machine 10 in this embodiment is, for example, a hydraulic excavator. However, the working machine 10 is not limited to a hydraulic excavator; it can be any working machine 10 that has a working device and uses oil during operation, such as a wheel loader, bulldozer, motor grader, or dump truck.

[0033] The oil used for operating the machinery 10 is working oil for hydraulic actuators (such as hydraulic cylinders, hydraulic motors, etc.) and lubricating oil for lubricating parts (such as engines). The lubricating parts are, for example, the lubrication parts of the drive unit, which includes drive sources such as engines.

[0034] The oils used for operating the machinery 10 include, for example, engine oil, working oil, slewing mechanism oil, final reduction gear oil, axle oil, and transmission oil. The oils collected vary depending on the model of the machinery 10.

[0035] As an example of a working machine 10, a hydraulic excavator 10 has a main body 11 and a working device 12 that operates hydraulically. The main body 11 has a rotating body 13 and a traveling body 15.

[0036] The traveling body 15 has a pair of tracks 15Cr and a travel motor 15M. The hydraulic excavator 10 is able to travel by rotating the tracks 15Cr. The travel motor 15M is provided as the drive source for the traveling body 15.

[0037] The rotating body 13 is mounted on and supported by the traveling body 15. The rotating body 13 is capable of rotating relative to the traveling body 15 about the rotation axis RX by a rotary motor (not shown). The rotation axis RX is an imaginary straight line that serves as the rotation center of the rotating body 13.

[0038] The slewing body 13 has a cab 14. Inside the cab 14 is a driver's seat 14S for the operator to sit in. The operator sits in the cab 14 and can operate the working device 12, rotate the slewing body 13 relative to the traveling body 15, and drive the hydraulic excavator 10 based on the traveling body 15.

[0039] The rotating body 13 has a drive source (e.g., an engine). The drive source is located behind the cab 14. The drive source is covered by an outer panel 13a.

[0040] The working device 12 is supported on the rotating body 13. The working device 12 has a boom 16, a stick 17, and a bucket 18. The working device 12 also has a boom cylinder 19a, a stick cylinder 19b, and a bucket cylinder 19c.

[0041] The boom 16 is rotatably connected to the main body 11. Specifically, the base end of the boom 16 is rotatably connected to the slewing body 13 with the boom base pin BF as the fulcrum. The base end of the boom 16 is positioned in the left-right direction of the cab 14. The stick 17 is rotatably connected to the boom 16. Specifically, the base end of the stick 17 is rotatably connected to the front end of the boom 16 with the boom top pin BT as the fulcrum. The bucket 18 is rotatably connected to the stick 17. Specifically, the base end of the bucket 18 is rotatably connected to the front end of the stick 17 with the stick top pin AT as the fulcrum.

[0042] The boom 16 can be driven relative to the body 11 via the boom cylinder 19a. With this drive, the boom 16 can rotate vertically relative to the rotating body 13 with the boom base pin BF as the fulcrum.

[0043] The stick 17 can be driven relative to the boom 16 via the stick cylinder 19b. With this drive, the stick 17 can rotate relative to the boom 16 in the vertical or longitudinal direction with the boom top pin BT as the fulcrum.

[0044] The bucket 18 can be driven relative to the stick 17 via the bucket cylinder 19c. With this drive, the bucket 18 can rotate relative to the stick 17 in the vertical or longitudinal direction with the top pin AT of the stick as the fulcrum.

[0045] The boom cylinder 19a, stick cylinder 19b, bucket cylinder 19c, and swing motor mentioned above are, for example, hydraulic actuators that operate using hydraulic fluid. Furthermore, the drive unit, such as the drive source (e.g., an engine), is configured to be lubricated using lubricating oil. In this embodiment, the physical properties of the oil supplied to the hydraulic actuator or drive unit are detected. The detected physical properties are at least the dielectric constant and resistivity of the oil.

[0046] Furthermore, in this embodiment, as described later, at least one of the metal concentration and iron concentration in the oil is accurately estimated based on the detected physical property values ​​of the oil. Based on the estimated metal or iron concentration in the oil, the degree of wear acceleration of the main components of the machine tool 10 can be determined. For example, if the monitoring of engine oil determines that both the metal concentration and iron concentration are above abnormal thresholds, it is estimated that wear has occurred on piston rings, cylinder liners, etc.

[0047] Furthermore, in this embodiment, at least one of the metal concentration and iron concentration in the oil is estimated based on the detected physical property values ​​of the oil, thus enabling continuous monitoring of the metal concentration and iron concentration in the oil.

[0048] Furthermore, in this embodiment, if the concentration of metal and iron in the oil is determined to be above abnormal thresholds, an alarm is issued to the surrounding area via visual display and sound. This prevents sudden malfunctions such as abnormal wear of components of the operating machinery 10.

[0049] <Oil Circuit>

[0050] Next, use Figure 2 This section describes an example of an oil circuit for supplying and discharging working oil relative to a hydraulic cylinder that acts as a hydraulic actuator, and an example of the configuration of an oil property sensor.

[0051] Figure 2 This is a diagram illustrating an example of an oil circuit for supplying and discharging working oil to a hydraulic actuator (such as a hydraulic cylinder). For example... Figure 2 As shown, one example of an oil circuit includes 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.

[0052] The drive source 2 is, for example, an engine. The main pump 3a operates by the driving force from the drive source 2. The main pump 3a draws working oil stored in the oil tank 8 by its operation. The working oil drawn by the main pump 3a is supplied to the hydraulic actuator 5 through the main valve 4. The working oil supplied to the hydraulic actuator 5 is discharged through the main valve 4.

[0053] The hydraulic actuator is, for example, a hydraulic cylinder, but it can also be other actuators such as a hydraulic motor. The hydraulic actuator 5 is actuated by supplying and discharging working oil. In the case where the hydraulic actuator 5 is, for example, a hydraulic cylinder, the hydraulic cylinder 5 extends and retracts by supplying and discharging working oil.

[0054] The main valve 4 controls the amount of working oil supplied to and discharged from the hydraulic actuator 5. The working oil discharged from the hydraulic actuator 5 is cooled by the oil cooler 6a and returned to the oil tank 8 after being filtered by the oil filter 7a.

[0055] One example of the oil circuit also includes an oil properties sensor 1a. The oil properties sensor detects the properties of the oil passing through the oil circuit. Oil properties include, for example, temperature, viscosity, density, dielectric constant, and resistivity. These oil properties can be detected by multiple oil properties sensors, or by a single oil properties sensor. The oil properties sensor 1a is, for example, located in the oil circuit between the main pump 3a and the oil tank 8. The oil properties sensor 1a can also be located in other oil circuits within the oil circuit.

[0056] Next, use Figure 3 This section describes another example of an oil circuit that supplies lubricating oil to the engine from the drive unit, and an example of the configuration of an oil condition sensor.

[0057] Figure 3 This is a diagram illustrating another example of an oil circuit that supplies lubricating oil to a drive unit (such as an engine). For example... Figure 3 As shown, another example of an oil circuit includes a lubrication pump 3b, an oil cooler 6b, an oil filter 7b, an oil tank 8, and a lubrication section 9 for the drive unit.

[0058] The lubrication pump 3b operates by a driving force from a drive source (not shown). The lubrication pump 3b draws lubricating oil stored in the oil tank 8. The lubricating oil drawn by the lubrication pump 3b is cooled by the oil cooler 6b, filtered by the oil filter 7b, and then supplied to the lubrication section 9 of the drive unit. After lubricating the lubrication section 9 of the drive unit, the lubricating oil returns to the oil tank 8.

[0059] Another example of an oil circuit includes an oil condition sensor 1b. The oil condition sensor 1b is, for example, configured in an oil circuit connecting the oil passage between the oil filter 7b and the lubrication unit 9 to the oil tank 8. The oil condition sensor 1b can also be configured in other oil passages of the oil circuit.

[0060] like Figure 2 and Figure 3 As shown, oil property sensors 1a and 1b are used to detect at least the dielectric constant and resistance value of oil as physical property values. In addition to the dielectric constant and resistance value, oil property sensors 1a and 1b can also detect the viscosity and density of oil.

[0061] Oil property sensors 1a and 1b are electrically connected to controller 50. Oil property sensors 1a and 1b output signals of the detected physical property values ​​of the oil to controller 50.

[0062] In this embodiment, the metal concentration and iron concentration in the oil are estimated based on the minimum dielectric constant and resistance value of the oil detected by the oil property sensors 1a and 1b, respectively, thereby enabling the estimation of the metal concentration and iron concentration in the oil with high accuracy.

[0063] <Oil Properties Diagnostic System for Operating Machinery 10>

[0064] Next, use Figure 4 An oil condition diagnostic system for the working machinery 10 in one embodiment of this disclosure will be described.

[0065] Figure 4 This is a diagram showing the structure of the oil condition diagnostic system for the working machinery 10 in one embodiment of this disclosure. (See diagram for details.) Figure 4 As shown, the oil condition diagnostic system of the operating machinery 10 includes, for example, the operating machinery 10, a server 70, and a service computer 80.

[0066] The operating machine 10 includes an oil condition sensor 1, a controller 50, and a monitor 60. The oil condition sensor 1 is either one of the aforementioned oil condition sensors 1a and 1b, or both. The oil condition sensor 1 and the monitor 60 are electrically connected to the controller 50.

[0067] A signal representing the physical property value of the oil detected by the oil properties sensor 1 is output to the controller 50 of the operating machine 10. Based on the obtained physical property value of the oil, the controller 50 estimates at least one of the metal concentration and iron concentration in the oil. The controller 50 determines whether the estimated metal concentration and iron concentration are each above an abnormality threshold.

[0068] The controller 50 outputs a signal indicating the determination result to the monitor 60. The monitor 60 displays an image based on the determination result obtained from the controller 50. If the monitor 60 displays a warning and issues an alarm if at least one of the estimated metal concentration and iron concentration exceeds the abnormal determination value, the monitor 60 may also use a speaker instead of the monitor 60. In this case, an audible warning may also be issued.

[0069] The signal representing the physical property value of the oil detected by the oil properties sensor 1 is output to a server 70 located outside the construction machinery 10. The signal representing the physical property value of the oil detected by the oil properties sensor 1 can be transmitted wirelessly to the server 70. The server 70 is, for example, a server owned by a construction machinery manufacturer.

[0070] Server 70 has the same functions as controller 50. That is, server 70 estimates at least one of the metal concentration and iron concentration in the oil based on the obtained physical property values ​​of the oil, and determines whether the estimated metal concentration and iron concentration are outliers. Server 70 outputs a signal indicating the determination result to server computer 80.

[0071] It should be noted that the server 70 can obtain the judgment result from the controller 50 of the operating machinery 10. In this case, the server 70 can directly output the obtained judgment result to the service computer 80.

[0072] Service computer 80 is, for example, a computer owned by an agent of a construction machinery manufacturer. Server 70 and service computer 80 are connected, for example, via an intranet or the Internet.

[0073] The service computer 80 can wirelessly transmit a signal representing the judgment result obtained from the server 70 to the construction machinery 10. Furthermore, dealerships of construction machinery manufacturers, etc., that have confirmed the judgment result obtained by the service computer 80 can repair or maintain the construction machinery 10 based on that judgment result.

[0074] Furthermore, users who own the construction machinery 10 can, based on the display results on the monitor 60, request repairs or maintenance from dealerships of the construction machinery manufacturer. In this case, dealerships of the construction machinery manufacturer can perform repairs or maintenance on the construction machinery 10 based on this request.

[0075] <Functional blocks of controller 50>

[0076] Next, use Figure 5 The function blocks of controller 50 are described.

[0077] Figure 5 yes Figure 4 The functional block diagram of the controller used in the system. For example... Figure 5 As shown, the controller 50 includes an oil physical property value acquisition unit 51, a concentration estimation unit 52, a concentration determination unit 53, an output control unit 54, and a memory 55. The oil physical property value acquisition unit 51 acquires the physical property values ​​of the oil from the oil property sensor 1.

[0078] The concentration estimation unit 52 estimates at least one of the metal concentration and iron concentration in the oil based on the relationship between the physical property values ​​of the oil and the metal concentration or iron concentration in the oil (physical property value-concentration relationship), according to the obtained physical property values ​​of the oil. When performing the above estimation, the concentration estimation unit 52 refers to the physical property value-concentration relationship stored in the memory 55. The method for estimating iron concentration and metal concentration will be described later in the section <Method for Estimating Iron Concentration and Metal Concentration>.

[0079] The concentration determination unit 53 determines whether at least one of the metal concentration and iron concentration in the oil is above an abnormal determination value. When performing the above determination, the concentration determination unit 53 refers to the abnormal determination value of iron concentration or the abnormal determination value of metal concentration stored in the memory 55.

[0080] The output control unit 54 obtains the determination result from the concentration determination unit 53. Based on the determination result of the concentration determination unit 53, the output control unit 54 outputs a control signal to the monitor 60 to control the display content of the monitor 60. If the output control unit 54 determines that at least one of the metal concentration and iron concentration in the oil is above the abnormal determination value, it issues an alarm by displaying this indication on the monitor 60. Conversely, if the metal concentration and iron concentration in the oil are below the abnormal determination value, the output control unit 54 displays "no abnormality" on the monitor 60.

[0081] It should be noted that the aforementioned physical property values-concentration relationships and anomaly determination values ​​can be pre-stored in the memory 55 (starting from when the machine 10 leaves the factory). Alternatively, the aforementioned physical property values-concentration relationships and anomaly determination values ​​can be stored in the memory 55 from outside the machine 10 after it leaves the factory. The aforementioned physical property values-concentration relationships and anomaly determination values ​​can be stored in the memory 55 by operating an input device such as a touch panel mounted on the machine 10. Furthermore, the device issuing the warning is not limited to the monitor 60; it can also be a device that issues an alarm through sound, such as a speaker.

[0082] It should be noted that the controller 50 includes a processor, main memory, and storage. The processor is, for example, a CPU (Central Processing Unit). The main memory includes, for example, non-volatile memory such as ROM (Read Only Memory) and volatile memory such as RAM (Random Access Memory).

[0083] The controller 50 can be mounted on the work machine 10 or disposed separately on the outside of the work machine 10. When the controller 50 is disposed separately on the outside of the work machine 10, the controller 50 can be wirelessly connected to the oil condition sensor 1, monitor 60, etc. The controller 50 can be stored on a server 70 located away from the work machine 10.

[0084] The controller 50 reads the program stored in the memory and expands it in the main memory, then executes the prescribed processing according to the program. Alternatively, the program can be distributed to the controller 50 via a network.

[0085] <Methods for estimating iron concentration and metal concentration>

[0086] Next, with Figure 5 The method of estimating the iron concentration in the oil and the iron concentration in the metal concentration in the concentration estimation section 52 is used as an example to illustrate the estimation method.

[0087] The iron concentration in the oil is estimated using machine learning. In machine learning, measured values ​​of the oil's physical properties (dielectric constant, viscosity, density, and resistivity) and the corresponding measured values ​​of the iron concentration in the oil are used as training data in sets. The measured values ​​of the oil's physical properties are detected by an oil properties sensor 1. The oil properties sensor 1 can, for example, detect viscosity, density, dielectric constant, and resistivity as the four values ​​of the oil's physical properties. The measured value of the iron concentration is detected, for example, by an ICP (Inductively Coupled Plasma) analyzer. The measured values ​​of the oil's physical properties and the corresponding measured values ​​of the iron concentration in the oil are detected from used oil samples.

[0088] Multiple sets of the aforementioned training data are input into a machine learning algorithm. The machine learning algorithm receives these training data as input and constructs an inference model. The inference model is formulated as a formula for calculating the iron concentration using the least squares method based on the relationship between physical property values ​​and iron concentration.

[0089] For example, when using dielectric constant and resistance as the two variables for the physical properties of oil, the formula is expressed as iron concentration [ppm] = a (dielectric constant ε) + b (resistance R) + c. In this formula, the coefficients of a, b, and c are determined by a machine learning algorithm. The above formula represents the relationship between the physical properties of oil and the iron concentration in the oil.

[0090] By Figure 5 The physical property values ​​of the oil detected by the oil property sensor 1 are input into the formula prepared as described above to calculate the iron concentration in the oil. In this way, the iron concentration in the oil is estimated.

[0091] The metal concentration in the oil is also estimated using the same method as for the iron concentration described above. The estimated metal concentration in the oil is 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). The formula representing the relationship between the physical properties of the oil and the metal concentration in the oil is, for example, when using dielectric constant and resistance as the two variables of the oil's physical properties, expressed as metal concentration [ppm] = a1 (dielectric constant ε) + b1 (resistance R) + c1. In this formula, the coefficients of a1, b1, and c1 are determined using a machine learning algorithm.

[0092] <Methods for Diagnosing the Oiliness of Operating Machinery>

[0093] Next, use Figure 5 and Figure 6 A method for diagnosing the oiliness of working machinery according to one embodiment of this disclosure will be described.

[0094] Figure 6 This is a flowchart illustrating a method for diagnosing the oiliness of working machinery according to one embodiment of this disclosure. For example... Figure 5 and Figure 6 As shown, the oil properties sensor 1 detects the physical property values ​​of the oil. The oil physical property value acquisition unit 51 of the controller 50 acquires the physical property values ​​of the oil from the oil properties sensor 1 (step S1: Figure 6 As mentioned above, the physical properties of oil include at least its dielectric constant and resistivity, and may also include its viscosity and density in addition to these properties.

[0095] The oil physical property value acquisition unit 51 outputs the acquired physical property values ​​to the concentration estimation unit 52. Based on the aforementioned physical property value-concentration relationship, the concentration estimation unit 52 estimates at least one of the metal concentration and iron concentration in the oil according to the acquired oil physical property values ​​(step S2:). Figure 6 When the concentration estimation unit 52 estimates at least one of the metal concentration and iron concentration in the oil, it refers to the above-mentioned physical property value-concentration relationship stored in the memory 55.

[0096] The physical property value-concentration relationship described above is, for example, a formula obtained through the aforementioned machine learning. The concentration estimation unit 52 inputs the physical property values ​​of the oil obtained from the oil physical property value acquisition unit 51 into the formula obtained through the aforementioned machine learning, thereby estimating at least one of the metal concentration and iron concentration in the oil.

[0097] The concentration estimation unit 52 outputs at least one of the estimated metal concentration and iron concentration in the oil to the concentration determination unit 53. The concentration determination unit 53 determines whether at least one of the obtained metal concentration and iron concentration in the oil is above an abnormality determination value (step S3: Figure 6 ).

[0098] The abnormal determination values ​​for metal concentration and iron concentration are pre-stored in the memory 55 of the controller 50. When performing the above determination, the concentration determination unit 53 refers to the abnormal determination value of metal concentration or the abnormal determination value of iron concentration stored in the memory 55.

[0099] If the concentration determination unit 53 determines that the metal concentration and iron concentration in the oil are less than the abnormal determination value, steps S1, S2, and S3 are repeated. It should be noted that even if the concentration determination unit 53 determines that the metal concentration and iron concentration in the oil are less than the abnormal determination value, steps S1, S2, and S3 can be repeated after the concentration determination unit 53 outputs the determination result to the monitor 60. On the other hand, if the concentration determination unit 53 determines that at least one of the metal concentration and iron concentration in the oil is above the abnormal determination value, the concentration determination unit 53 outputs a signal indicating the determination result to the output control unit 54.

[0100] The output control unit 54 outputs a control signal to the monitor 60 based on the obtained determination result. If the output control unit 54 determines that at least one of the metal concentration and iron concentration in the oil is above an abnormal determination value, it issues a warning by displaying this message on the monitor 60 (step S4). Figure 6 ).

[0101] Based on the above, the method for diagnosing the oiliness of the operating machinery in this embodiment is implemented.

[0102] <Example>

[0103] Next, use Figures 7-10 This invention relates to the research conducted by the inventors regarding the relationship between the physical properties of oil and the accuracy of estimation.

[0104] Figure 7 It is a graph of the coefficient of determination and 80% error, which are used to illustrate the accuracy of the estimated model (calculation). Figure 8 It is a graph of the correlation coefficient of soot concentration, which is used to illustrate and estimate the accuracy of the model (calculation). Figure 9 This is a graph showing the relationship between the iron concentration in oil, representing the physical properties of the oil, and its evaluation index. Figure 10 This is a graph showing the relationship between the concentration of metals (iron, copper, chromium, aluminum, silicon, and lead) in oil, representing the physical properties of the oil, and its evaluation index.

[0105] The inventors used an oil properties sensor to detect the physical properties (dielectric constant, viscosity, density, and resistivity) of used engine oils (oil recycled from the market) with different iron concentrations. By varying the combinations of the detected physical property values, the iron concentration in the oil was estimated for each combination. Specifically, the iron concentration in the oil was estimated based on only one of the four physical property values ​​(one variable), a combination of two physical property values ​​(two variables), and a combination of three physical property values ​​(three variables).

[0106] The estimation accuracy of each combination of physical property values ​​is evaluated by comparing the estimated iron concentration obtained in this way with the measured iron concentration in the oil detected by an ICP analyzer. The evaluation indices used in this evaluation are (1) the coefficient of determination, (2) the 80% error, and (3) the correlation coefficient with soot concentration. The evaluation indices are explained below.

[0107] (1) Coefficient of determination (=R) 2 )

[0108] If we set the i-th true value (the measured value) of the N(n) data points as yi, the value estimated by the regression equation as yei, and the average value of the entire measured data as yai, then the coefficient of determination R is calculated using the following formula (1). 2 Coefficient of determination R 2 The closer the value is to 1, the higher the accuracy of the estimated iron concentration. That is, if... Figure 7 As shown, the closer the data is to the straight line SL1, which represents a one-to-one relationship between the measured and estimated iron concentrations, the higher the estimation accuracy. Coefficient of Determination R0 2 Evaluation is based on absolute values.

[0109] [Equation 1]

[0110]

[0111] (2) 80% error (=RMSE×1.25)

[0112] The RMSE (Root Mean Squared Error), calculated using equation (2), represents the magnitude of the deviation between the measured and estimated values ​​of the data. Therefore, similar to the standard deviation, it is assumed that approximately 68% of the data converges within the RMSE error range. Thus, it is considered that approximately 80% of the data converges within the range of (RMSE × 1.25), and therefore (RMSE × 1.25) is defined as the 80% error and used as one of the evaluation indices. Figure 7 As shown, the 80% error refers to the magnitude of the error range ER that converges to 80% of the data representing the relationship between the measured and estimated iron concentrations relative to the straight line SL1. A smaller 80% error value indicates higher estimation accuracy.

[0113] [Equation 2]

[0114]

[0115] (3) Correlation coefficient between measured soot concentration x and estimated iron concentration y (=r)

[0116] Let the measured values ​​of soot concentration and the estimated values ​​of iron concentration be (x, y), their average values ​​be (xa, ya), and the i-th data in the n data points be (xi, yi). In addition, let the covariance of x and y be sxy, the standard deviation of x be sx, and the standard deviation of y be sy. The correlation coefficient r at this time is represented by the following equation (3).

[0117] [Equation 3]

[0118]

[0119] The correlation coefficient r is calculated by determining the degree of correlation between the estimated iron concentration obtained from the oil sample using the above method and the measured soot concentration in the same oil sample. The closer the correlation coefficient r is to 1, the more difficult it is to separate the estimated iron concentration from the measured soot concentration, meaning the accuracy of the iron concentration estimation decreases. That is, if... Figure 8 As shown, the closer the data is to the straight line SL2, which represents a one-to-one relationship between the measured value of soot concentration and the estimated value of iron concentration, the lower the estimation accuracy. The correlation coefficient r is evaluated as an absolute value.

[0120] It should be noted that soot is a mixture of coal and oil sludge produced by fuel combustion and contained in exhaust gases, such as when it gets mixed into engine oil. Soot concentration is measured by the intensity of transmitted light obtained through infrared spectroscopy, and it serves as an indicator of contamination, for example, in engine oil.

[0121] (4) Evaluation results of the estimated value of iron concentration in oil

[0122] The evaluation results of iron concentration in oils using the above evaluation indices are shown below. Figure 9 .like Figure 9 As shown, 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 conducted for the single variable of viscosity in sample (2) and the single variable of density in sample (3). When the dielectric constant and resistivity of the oil are used as physical properties to estimate the iron concentration, as in samples (7), (12), and (13), the coefficient of determination is greater than 0.3, the 80% error is less than 27, and the correlation coefficient is less than 0.6. Therefore, the estimation accuracy of iron concentration in samples (7), (12), and (13) is higher than that in other samples (1) to (6), (8) to (11), and (14).

[0123] Therefore, it can be seen that estimating iron concentration by using at least the dielectric constant and resistance value of oil results in higher accuracy compared to estimating iron concentration using a combination of physical properties that do not include the dielectric constant and resistance value of oil.

[0124] In addition, it can be seen that the correlation coefficients of samples (7) and (12) are below 0.4, and the estimation accuracy of iron concentration is higher than that of other samples.

[0125] In addition, it can be seen that when the three variables of the oil, namely dielectric constant, resistance value and viscosity, are used as the physical property values ​​of the oil, as in sample (12), the coefficient of determination is close to 1 and the error is reduced by 80% compared with the case where only two variables, dielectric constant and resistance value, are used, and the estimation accuracy of iron concentration is further improved.

[0126] (5) Evaluation results of the estimated values ​​of metal concentration in oil

[0127] The concentrations of metals (iron + copper + chromium + aluminum + silicon + lead) in oil were estimated using the same method as for iron concentration, and the accuracy of the estimated values ​​was evaluated. The evaluation results are presented below. Figure 10 .like Figure 10 As shown, when the dielectric constant and resistance value of oil are used as the physical property value of oil, as in sample (26), the coefficient of determination is greater than 0.07, the error is less than 95 in 80% of cases, and the correlation coefficient is less than 0.88. The estimation accuracy of metal concentration is higher than that of other samples (21)~(25) and (27).

[0128] Therefore, it can be seen that estimating metal concentration by using at least the dielectric constant and resistance value of oil results in higher accuracy compared to estimating metal concentration using a combination of physical properties that do not include the dielectric constant and resistance value of oil.

[0129] It should be noted that the oil analyzed in this embodiment contains at least iron from the following: iron, copper, chromium, aluminum, silicon, and lead.

[0130] <Effect>

[0131] The effects of this embodiment will be explained below.

[0132] In the case of sudden malfunctions in operating machinery, such as abnormal wear of internal components, the delay in detection can lead to malfunctions of internal components and shutdowns of the machinery. These malfunctions and shutdowns can cause significant losses to customers, such as delays in work schedules due to downtime. Therefore, in order to detect sudden malfunctions such as abnormal wear of internal components as early as possible and prevent downtime, it is desirable to always monitor the concentration of iron-containing metals in the oil. However, in Patent Document 1, when oil analysis is deemed necessary based on oil properties detected by sensors, oil samples taken from the operating machinery are analyzed using precision analytical equipment owned by an oil analysis company to determine the cause of the oil malfunction. Therefore, it is impossible to always monitor changes in the concentration of iron-containing metals in the oil.

[0133] In contrast, according to this embodiment, such as Figure 9 and Figure 10 As shown, at least the dielectric constant and resistance value are measured as physical property values ​​of the oil, and the concentration of metals and iron in the oil is estimated based on these measured physical property values. Therefore, compared to estimating the concentration of metals and iron in the oil using a combination of physical property values ​​that do not include the dielectric constant and resistance value of the oil, the concentration of metals and iron in the oil can be estimated with high accuracy.

[0134] Furthermore, in estimating either the metal concentration or the iron concentration mentioned above, the dielectric constant and resistivity of the oil can be detected using a simple oil property sensor 1. Therefore, large devices such as ICP analyzers are not required for detecting the metal or iron concentration in oil.

[0135] Furthermore, unlike ICP analysis devices, the simple oil condition sensor 1 is easy to mount on the machine tool 10. By mounting the simple oil condition sensor 1 on the machine tool 10, the metal and iron concentrations in the oil can be continuously monitored. In addition, it can detect sudden malfunctions (such as those caused by wear of internal components) that occur outside of oil removal times, and can be addressed in a timely manner (oil replacement, component replacement, etc.) before serious damage to the machine tool 10 occurs.

[0136] Furthermore, according to this embodiment, the metal concentration is the total concentration of one or more metals selected from the group consisting of iron, copper, chromium, aluminum, silicon, and lead. Therefore, the total concentration of the aforementioned metals contained in the oil can be estimated with high accuracy.

[0137] Furthermore, according to this embodiment, the metal concentration is the iron concentration. Therefore, the iron concentration contained in the oil can be estimated with high accuracy. The metal concentration includes at least the iron concentration.

[0138] Furthermore, according to this embodiment, the controller 50 pre-stores the relationship between the physical property values ​​of the oil and the metal concentration or iron concentration in the oil. Therefore, the metal concentration or iron concentration in the oil can be estimated based on the physical property values ​​of the oil.

[0139] Furthermore, according to this embodiment, the controller 50 pre-stores anomaly determination values ​​for the metal or iron concentration in the oil, determined for each machine 10, and determines oil anomalies based on these anomaly determination values ​​and the estimated metal or iron concentration. By detecting oil anomalies in this way, sudden malfunctions can be prevented.

[0140] Furthermore, according to this embodiment, the oil being analyzed is the working oil or engine oil of the machine tool 10. This allows for the prevention of sudden malfunctions in hydraulic equipment such as hydraulic actuators, main pumps, and main valves, or in the engine.

[0141] <Postscript>

[0142] The above-described implementation methods incorporate the following technical concepts.

[0143] (Note 1)

[0144] An oil condition diagnostic system for operating machinery, wherein,

[0145] The oil condition diagnostic system of the operating machinery includes:

[0146] An oil property sensor, which measures at least the dielectric constant and resistance value of oil as physical properties; and

[0147] The controller estimates the metal concentration in the oil based on the relationship between the physical property values ​​of the oil and the metal concentration in the oil, according to the physical property values ​​of the oil detected by the oil properties sensor.

[0148] (Note 2)

[0149] According to the oil condition diagnostic system for operating machinery described in Appendix 1, the metal concentration is the total concentration of one or more metals selected from the group consisting of iron, copper, chromium, aluminum, silicon, and lead.

[0150] (Note 3)

[0151] According to the oil condition diagnostic system for operating machinery described in Appendix 1 or Appendix 2, the metal concentration is iron concentration.

[0152] (Note 4)

[0153] An oil property diagnostic system for operating machinery according to any one of Annexes 1 to 3, wherein the controller stores in advance the relationship between the physical property values ​​of the oil and the metal concentration in the oil.

[0154] (Note 5)

[0155] The oil condition diagnostic system for operating machinery according to any one of Annexes 1 to 4, wherein anomaly determination values ​​for metal concentration in the oil determined for each of the operating machinery are pre-stored in the controller, and the controller determines the oil anomaly based on the estimated metal concentration and the anomaly determination values.

[0156] (Note 6)

[0157] The oil condition diagnostic system for operating machinery according to any one of Annexes 1 to 5, wherein the oil is the working oil or engine oil of the operating machinery.

[0158] (Note 7)

[0159] An oil condition diagnostic system for operating machinery, wherein,

[0160] The oil condition diagnostic system of the operating machinery includes:

[0161] An oil property sensor, which measures at least the dielectric constant and resistance value of oil as physical properties; and

[0162] The controller estimates the iron concentration in the oil based on the relationship between the physical properties of the oil and the iron concentration in the oil, according to the physical properties of the oil detected by the oil properties sensor.

[0163] (Postscript 8)

[0164] A method for diagnosing the oil properties of operating machinery, wherein,

[0165] The method for diagnosing the oiliness of the operating machinery includes the following steps:

[0166] As physical properties of oil, at least the dielectric constant and resistivity should be tested; and

[0167] Based on the relationship between the physical properties of the oil and the metal concentration in the oil, the metal concentration in the oil is estimated according to the obtained physical properties of the oil.

[0168] (Note 9)

[0169] A method for diagnosing the oil properties of operating machinery, wherein,

[0170] The method for diagnosing the oiliness of the operating machinery includes the following steps:

[0171] The dielectric constant and resistivity are measured as physical properties of oil; and

[0172] Based on the relationship between the physical properties of the oil and the iron concentration in the oil, the iron concentration in the oil is estimated according to the obtained physical properties of the oil.

[0173] The embodiments disclosed herein should be considered illustrative in all respects and not restrictive. The scope of the invention is shown by the technical solutions rather than the foregoing description and is intended to include all modifications within the meaning and scope of equivalent technical solutions.

[0174] Explanation of reference numerals in the attached figures:

[0175] 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 device; 13. Rotating body; 13a. Outer panel; 14. Cab; 14S. Driver's seat; 15. Running body; 15Cr. Track; 15M. Travel motor; 16. Boom; 17. Stick; 18. Bucket; 19a. Boom cylinder; 19b. Stick cylinder; 19c. Bucket cylinder; 50. Controller; 51. Oil physical property value acquisition unit; 52. Concentration estimation unit; 53. Concentration determination unit; 54. Output control unit; 55. Memory; 60. Monitor; 70. Server; 80. Service computer; AT. Stick top pin; BF. Boom base pin; BT boom top pin; RX slewing shaft.

Claims

1. A diagnostic system for the oil properties of operating machinery, wherein, The oil condition diagnostic system of the operating machinery includes: An oil property sensor, which measures at least the dielectric constant and resistance value of oil as physical properties; and The controller estimates the metal concentration in the oil based on the relationship between the physical property values ​​of the oil and the metal concentration in the oil, according to the physical property values ​​of the oil detected by the oil properties sensor.

2. The oil condition diagnostic system for operating machinery according to claim 1, wherein, The metal concentration is the total concentration of one or more metals selected from the group consisting of iron, copper, chromium, aluminum, silicon, and lead.

3. The oil condition diagnostic system for operating machinery according to claim 2, wherein, The metal concentration is the iron concentration.

4. The oil condition diagnostic system for operating machinery according to claim 1, wherein, The controller stores in advance the relationship between the physical property values ​​of the oil and the metal concentration in the oil.

5. The oil condition diagnostic system for operating machinery according to claim 1, wherein, The controller pre-stores anomaly detection values ​​for the metal concentration in the oil, determined for each of the operating machines. The controller determines the oil's abnormality based on the estimated metal concentration and the anomaly determination value.

6. The oil condition diagnostic system for operating machinery according to claim 1, wherein, The oil mentioned is the working oil or engine oil of the operating machinery.

7. A diagnostic system for the oil properties of operating machinery, wherein, The oil condition diagnostic system of the operating machinery includes: An oil property sensor, which measures at least the dielectric constant and resistance value of oil as physical properties; and The controller estimates the iron concentration in the oil based on the relationship between the physical properties of the oil and the iron concentration in the oil, according to the physical properties of the oil detected by the oil properties sensor.

8. A method for diagnosing the oiliness of operating machinery, wherein, The method for diagnosing the oiliness of the operating machinery includes the following steps: As physical properties of oil, at least the dielectric constant and resistivity must be obtained; and Based on the relationship between the physical properties of the oil and the metal concentration in the oil, the metal concentration in the oil is estimated according to the obtained physical properties of the oil.

9. A method for diagnosing the oiliness of operating machinery, wherein, The method for diagnosing the oiliness of the operating machinery includes the following steps: As physical properties of oil, at least the dielectric constant and resistivity must be obtained; and Based on the relationship between the physical properties of the oil and the iron concentration in the oil, the iron concentration in the oil is estimated according to the obtained physical properties of the oil.

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