Wear prediction device, wear prediction system, and wear prediction method

Abstract

The purpose of the present invention is to provide a wear prediction device (100) capable of highly accurately calculating the amount of wear in accordance with the characteristics of a lubricating fluid film. A wear prediction device (100) according to the present disclosure: reproduces the surface shapes of a first sliding member (31) and a second sliding member (34), which are attached to a sliding device (37), as a first surface (32) that is part of the first sliding member (31) and a second surface (35) that is part of the second sliding member (34) in a virtual space (45); and performs a diagnosis upon predicting, on the basis of the lubricating fluid film characteristics of a lubricating fluid, the amount of wear that will occur when the first surface (32) and the second surface (35) are separated and caused to slide.

Description

Wear prediction device, wear prediction system, and wear prediction method

[0001] This disclosure relates to a wear prediction device, a wear prediction system, and a wear prediction method.

[0002] When a sliding device having sliding members is operated, wear may occur in the sliding members. As wear progresses, the sliding device may become inoperable, so there is a need to understand the amount of wear on the sliding members of an operating sliding device and to quantify the lifespan of the sliding members. Patent Document 1 discloses a technique for generating multiple operating cycles, which represent average operating conditions, from automobile driving data, and calculating the wear of the piston rings per single cycle sample when the multiple generated operating cycles are used as a single cycle sample.

[0003] Japanese Patent Publication No. 2023-158615

[0004] However, conventional technologies do not take into account the action of lubricating fluids that lubricate sliding members, making it difficult to accurately predict wear caused by sliding in a lubricated environment.

[0005] This disclosure was made to solve the above-mentioned problems and aims to provide a wear prediction device that can calculate the amount of wear with high accuracy according to the characteristics of the lubricating fluid film. It also aims to provide a wear prediction system and a wear prediction method.

[0006] The wear prediction device according to this disclosure is a wear prediction device that predicts wear information due to sliding between a first sliding member and a second sliding member, and includes a data acquisition unit that acquires first surface shape data of the first surface which is at least a part of the surface of the first sliding member, and second surface shape data of the second surface which is at least a part of the surface of the second sliding member, as well as the operating time of the sliding device that slides the first sliding member and the second sliding member, the sliding speed corresponding to the operating time, the load on which the first sliding member is pressed against the second sliding member corresponding to the operating time, and the lubricating fluid film characteristics of the lubricating fluid interposed between the first sliding member and the second sliding member, and calculates the number of iterations which is the number of times the first surface is repeatedly moved in the surface direction by dividing the cumulative sliding distance calculated using the sliding speed and the operating time by the calculation unit distance, and The device comprises: a set repetition count setting unit; a wear amount calculation unit that sets the surface distance between the first surface and the second surface based on the lubrication fluid film characteristics at set time intervals, subtracts at least a portion of the interference height where the first surface and the second surface interfere from the surface height of at least one of the first surface and the second surface, and calculates the wear amount of at least one of the first sliding member and the second sliding member based on the surface height of the first surface and the surface height of the second surface before and after the subtraction; a determination unit that determines whether the cumulative number of movements of the first surface moved by the wear amount calculation unit has reached the repetition count, or whether the cumulative movement distance, which is the cumulative value of the movement distance of the first surface due to the movement, has reached the cumulative sliding distance; and a notification unit that notifies wear information including the wear amount calculation result and at least one of the determination results of the determination unit.

[0007] Furthermore, the wear prediction system according to this disclosure comprises a sliding device having a first sliding member, a second sliding member that slides with the first sliding member, and an operation control unit that controls the sliding operation of the first sliding member and the second sliding member, which exist in a real space that is a real spatial domain, and a wear prediction device according to this disclosure that calculates the amount of wear and the surface shape after wear due to sliding between the first sliding member and the second sliding member in a virtual space that is a virtual spatial domain, and the operation control unit controls the sliding speed and load of the sliding device based on the calculation result of the amount of wear.

[0008] Furthermore, the wear prediction method according to this disclosure is a wear prediction method for predicting wear information due to sliding between a first sliding member and a second sliding member, and includes the steps of acquiring first surface shape data of the first surface which is at least a part of the surface of the first sliding member, and second surface shape data of the second surface which is at least a part of the surface of the second sliding member, as well as acquiring the operating time of a sliding device that slides the first sliding member and the second sliding member, the sliding speed corresponding to the operating time, the load on which the first sliding member is pressed against the second sliding member corresponding to the operating time, and the lubricating fluid film characteristics of a lubricating fluid interposed between the first sliding member and the second sliding member, and calculating the number of iterations which is the number of times the first surface is repeatedly moved in the surface direction by dividing the cumulative sliding distance calculated using the sliding speed and the operating time by a set calculation unit distance, The system includes the steps of setting, setting the inter-surface distance between the first surface and the second surface based on the lubrication fluid film characteristics at set time intervals, subtracting at least a portion of the interference height where the first surface and the second surface interfere from the surface height of at least one of the first surface and the second surface, and calculating the amount of wear of at least one of the first sliding member and the second sliding member based on the surface height of the first surface and the surface height of the second surface before and after the subtraction, determining whether the cumulative number of movements in which the first surface was moved in the step of calculating the amount of wear has reached the number of iterations, or whether the cumulative movement distance, which is the cumulative value of the distance the first surface was moved by the movement, has reached the cumulative sliding distance, and notifying wear information including the calculation result of the amount of wear, and at least one of the determination results in the determination step.

[0009] According to this disclosure, in order to calculate the amount of wear on a first sliding member and a second sliding member that slide against each other with a lubricating fluid film, the amount of wear can be predicted with high accuracy according to the characteristics of the lubricating fluid film.

[0010] This is a block diagram showing an example of the configuration of a wear prediction device according to Embodiment 1. This is an explanatory diagram illustrating the data of the first surface according to Embodiment 1. This is an explanatory diagram showing the flow of the data acquisition unit according to Embodiment 1 acquiring the operating condition parameter set. This is an explanatory diagram showing the calculation flow of the wear amount calculation unit according to Embodiment 1. This is an explanatory diagram showing an example of sliding between the first surface and the second surface in a virtual space according to Embodiment 1. This is an explanatory diagram showing an example of the arrangement of the first surface and the second surface in a virtual space according to Embodiment 1. This is an explanatory diagram showing an example of the contact pressure distribution generated between the first sliding member and the second sliding member according to Embodiment 1. This is a block diagram showing an example of the configuration of a wear prediction device according to Embodiment 2. This is a diagram showing an example of lubrication fluid film characteristics calculated by fluid lubrication theory according to Embodiment 2. This is a block diagram showing an example of the configuration of a wear prediction device according to Embodiment 3. This is a block diagram showing an example of the configuration of a wear prediction device according to Embodiment 4. This is a block diagram showing an example of the configuration of a part of the wear prediction system according to Embodiment 4. This is a flowchart showing the generation flow of the surface shape data generation unit according to Embodiment 4. This is a block diagram showing an example of the configuration wear prediction system according to Embodiment 5. This is an explanatory diagram showing an example configuration of a wear prediction system corresponding to the digital twin platform system according to Embodiment 5. This is an explanatory diagram showing an example configuration of a wear prediction system corresponding to the digital twin platform system according to Embodiment 5. This is a flowchart showing the processing routine executed by the wear prediction device according to Embodiments 1 to 5. This is a schematic block diagram showing an example of a processing circuit that realizes each function of the wear prediction device according to Embodiments 1 to 5.

[0011] The embodiments described herein will be explained below with reference to the drawings. The same or corresponding parts in each drawing are denoted by the same reference numerals. In the description of the embodiments, the descriptions of the same or corresponding parts will be omitted or simplified as appropriate.

[0012] Embodiment 1. The wear prediction device 100 according to Embodiment 1 will be described with reference to the figures. Figure 1 is a block diagram showing an example of the configuration of the wear prediction device 100. The wear prediction device 100 predicts wear information due to sliding between a first sliding member 31 and a second sliding member 34. It acquires first surface shape data 33 of a first surface 32, which is at least a part of the surface of the first sliding member 31, and second surface shape data 36 of a second surface 35, which is at least a part of the surface of the second sliding member 34. It also acquires the operating time of the sliding device 37 that slides the first sliding member 31 and the second sliding member 34, the sliding speed corresponding to the operating time, the load on which the first sliding member 31 is pressed against the second sliding member 34, and the lubricating fluid film characteristics of the lubricating fluid interposed between the first sliding member 31 and the second sliding member 34. It calculates the number of iterations, which is the number of times the first surface 32 is repeatedly moved in the surface direction, by dividing the cumulative sliding distance calculated using the sliding speed and the operating time by the calculation unit distance. The system includes a count setting unit 12, a wear amount calculation unit 13 that sets the inter-surface distance 39 between the first surface 32 and the second surface 35 based on the lubrication fluid film characteristics at set time intervals, subtracts at least a portion of the interference height at which the first surface 32 and the second surface 35 interfere from the surface height of at least one of the first surface 32 and the second surface 35, and calculates the wear amount of at least one of the first sliding member 31 and the second sliding member 34 based on the surface height of the first surface 32 and the surface height of the second surface 35 before and after the subtraction, a determination unit 15 that determines whether the cumulative number of movements in which the first surface 32 has been moved by the wear amount calculation unit 13 has reached the number of repetitions, or whether the cumulative movement distance, which is the cumulative value of the movement distance of the first surface 32 due to the movement, has reached the cumulative sliding distance, and a notification unit 14 that notifies wear information including the wear amount calculation result and at least one of the determination results of the determination unit 15. Wear information includes the amount of wear, which is the change in the dimensions of the sliding member due to wear; the volume of wear, which is the change in the volume of the sliding member due to wear; the two-dimensional or three-dimensional surface shape of the sliding member after wear; and the surface roughness of the sliding member after wear (arithmetic mean roughness Sa, Ra, etc.). The wear prediction device 100 may also include a calculation data storage unit 20. The calculation data storage unit 20 stores the calculation data acquired by the data acquisition unit 11 and outputs the calculation data to the wear amount calculation unit 13.

[0013] This wear prediction device 100 inputs three-dimensional surface shape data of the first sliding member 31 and the second sliding member 34 that move and slide relative to each other. Based on the characteristics of the lubricating fluid film (hereinafter referred to as lubricating fluid film characteristics) of the lubricating fluid interposed between the surface of the first sliding member 31 and the surface of the second sliding member 34, it sets the distance between the first surface 32 and the second surface 35 as the inter-surface distance 39. The device then slides the first surface 32 and the second surface 35 in a virtual space (hereinafter referred to as virtual space 45) and calculates the amount of wear on the first surface 32 and the second surface 35 due to sliding. In this way, it can calculate the wear of the first sliding member 31 and the second sliding member 34 that slide with a lubricating fluid film interposed by the lubricating fluid, and can predict wear with high accuracy according to the characteristics of the lubricating fluid film. Here, the lubricating fluid is, for example, a gas such as air, or a liquid such as lubricating oil, water, or grease base oil.

[0014] The configuration of the wear prediction device 100 will now be described. The data acquisition unit 11 acquires from the outside first surface shape data 33 of at least a portion of the surface of the first sliding member 31, a first surface 32, and second surface shape data 36 of at least a portion of the surface of the second sliding member 34, a second surface 35. Figure 2 is an explanatory diagram showing the first surface shape data 33 as an example. As shown in Figure 2, the first surface shape data 33 and the second surface shape data 36 are discrete surface height data measured by a surface shape measuring instrument such as a laser microscope, white light interferometer, atomic force microscope, or stylus-type surface roughness meter. In Figure 2, the X and Y axes are coordinates that define the planar direction of the three-dimensional data measured by the surface shape measuring instrument. The X and Y axes are also the X and Y axes in the virtual space 45 arranged by the wear amount calculation unit 13, which will be described later. The size of one mesh is Δx (unit μm) and Δy (unit μm), and the number of meshes is n in the X-axis direction. x n in the Y-axis direction y There are n. When surface shape data is obtained by a surface shape measuring instrument, the mesh size is determined, for example, by the horizontal resolution of the surface shape measuring instrument. The first surface shape data 33 and the second surface shape data 36 are the size (n) in the surface direction of the first surface 32 and the second surface 35. y Δy, n xThe data is adjusted so that Δx) is the same. The data acquisition unit 11 uses a two-dimensional matrix (n) obtained by dividing the sliding surfaces (reference planes) of the first surface 32 and the second surface 35 into a finite number of elements (mesh, rectangular elements of the same shape with each side parallel to the X axis / Y axis) as the first surface shape data 33 and the second surface shape data 36. x ×n y A matrix is ​​defined, and matrix data is obtained in which surface height data corresponding to the Z-axis value is stored in each element of the matrix, and this data is output to the wear amount calculation unit 13.

[0015] Here, if the first surface shape data 33 and the second surface shape data 36 are discrete data in the planar direction, for example, the measurement interval in the planar direction of the first surface shape data 33 and the second surface shape data 36, ​​i.e., the length of the measurement mesh in the planar direction, is set as the calculation unit distance. If the first surface shape data 33 and the second surface shape data 36 acquired by the data acquisition unit 11 are continuous values, for example, the length obtained by dividing the planar size of the first surface 32 and the second surface 35 into an integer number is set as the calculation unit distance.

[0016] The first surface shape data 33 and the second surface shape data 36 measured by the surface shape measuring instrument may be those of the unused first sliding member 31 and second sliding member 34, or they may be data obtained by disassembling the sliding device 37 after operation, taking out at least one of the first sliding member 31 and the second sliding member 34, and measuring them with the surface shape measuring instrument.

[0017] By using the surface shapes of the first sliding member 31 and the second sliding member 34 actually used in the sliding device 37, the wear amount calculation described later can be performed with the actual surface shape as the initial state. The first surface shape data 33 and the second surface shape data 36 are two-dimensional or three-dimensional data that include height information of the irregularities of the first surface 32 and the second surface 35, with an arbitrary height as the zero point. Here, the data acquisition unit 11 acquires the data as a function of surface height with the position in the surface direction of the first surface 32 and the second surface 35 as an argument, and the first surface shape data 33 and the second surface shape data 36 may be continuous data.

[0018] The data acquisition unit 11, when the sliding surface (reference surface) can be considered an ideal curved surface, such as in a journal sliding bearing, acquires a sliding region that can be considered as contact between planes at a microscopic level, for example n y Δy = 500 μm, n x First surface shape data 33 and second surface shape data 36 with a size of Δx = 500 μm are obtained. Furthermore, when using a sliding surface (reference surface) that can be considered ideally curved as surface shape data, the first surface shape data 33 and second surface shape data 36 are obtained by performing a flattening process to remove curvature and waviness from the first surface 32 and second surface 35 using a high-pass filter such as a Gaussian filter or spline filter as a preprocessing step.

[0019] Next, the data acquisition unit 11 acquires the operating time when the sliding device 37 that slides the first surface 32 and the second surface 35 in the virtual space 45 is operated, the sliding speed associated with the operating time, and the sliding load associated with the operating time as a parameter set of operating conditions (hereinafter referred to as the operating condition parameter set). As shown in Figure 3, there are three patterns, for example, in which the data acquisition unit 11 acquires the operating condition parameter set, from (1) to (3) below.

[0020] (1) If acquisition route A is selected, simulated operation command information, which is simulated information of an operation command to be given to the operation control unit 42 that controls the operating conditions such as the rotation speed of the rotating shaft attached to the sliding device 37, is created based on past operating conditions (S101). The sliding device specification database 431 (hereinafter referred to as the sliding device specification DB), which is one of the databases 43 provided outside the wear prediction device 100, contains sliding device specification information such as the dimensions and physical properties of the first sliding member 31 and the second sliding member 34. The simulated operation command information and the sliding device specification information are input to a generation model (hereinafter referred to as the operating condition parameter set generation model) that generates an operating condition parameter set specific to the sliding device 37, and an operating condition parameter set is generated (S102). The data acquisition unit 11 acquires the operating condition parameter set generated by the operating condition parameter set generation model (S103). The data acquisition unit 11 divides the acquired operating condition parameter set into predetermined time intervals and generates an operating condition parameter subset (hereinafter referred to as the subset) (S104). Furthermore, the data acquisition unit 11 replaces each operating condition data within the subset with a fixed value to make it a constant (S105).

[0021] (2) If acquisition route B is selected, the data acquisition unit 11 acquires sliding environment data such as the first sliding member 31 and the second sliding member 34 measured by the sliding device 37, the ambient temperature of the atmosphere in which the lubricating fluid comes into contact, and operation history data (S106). The operation history data is actual data of operation commands given to the operation control unit 42 that controls the operating conditions such as the rotational speed of the rotating shaft attached to the sliding device 37. The sliding environment data and operation history data acquired from the sliding device, along with the sliding device specification information, are input into an operation condition parameter set generation model specific to the sliding device 37 to generate an operation condition parameter set (S108). After S108, S103 to S105 are performed.

[0022] (3) If acquisition route C is selected, the sliding device specification DB 431 includes previously measured sliding environment data, etc. The data acquisition unit acquires operation history data from the sliding device 37 (S107). Next, the sliding environment data included in the sliding device specification DB and the sliding device specification information are input into the operation condition parameter set generation model specific to the sliding device 37 to generate an operation condition parameter set (S108). After S108, S103 to S105 are performed. That is, the operation condition parameter set may be a virtual value or a value based on the recorded history of operation conditions specific to the sliding device 37. As in the case of acquisition route C, if only actual data of operating commands given to the operation control unit 42, which controls operating conditions such as the rotational speed of the rotating shaft attached to the sliding device 37, is acquired from the sliding device 37, the number of various sensors attached to the sliding device 37 can be kept to a minimum, and the amount of wear of the first sliding member 31 and the second sliding member 34 of the sliding device 37 can be predicted by the wear prediction device 100.

[0023] The preparation of subsets in the data acquisition unit 11 will now be described. The data acquisition unit 11 specifies a sampling time (Δt) for the operating condition parameter set, divides the time series data into subsets with a width of Δt, and outputs them to the wear amount calculation unit 13. Each subset becomes the unit for executing the simulation. The sampling time Δt does not have to be constant for each division, as long as it is greater than or equal to the time resolution of the time series data of the operating conditions. In addition, the sampling time Δt when the sliding speed and load change over time may be relatively smaller than the sampling time Δt during steady-state operation when the sliding speed and load are constant.

[0024] The slip velocity included in the divided subset is a fixed value replaced, for example, with the value corresponding to the first time in the subset. Furthermore, it may be a fixed value replaced with the value corresponding to the last time in the subset, the average value within the subset's time, etc. The load included in the divided subset may, similar to the slip velocity, be a fixed value replaced with the value corresponding to the first time in the subset, the value corresponding to the last time in the subset, or the average value within the subset's time. The time interval of the data within the subset is a set time obtained by dividing the calculation unit distance by the slip velocity.

[0025] Next, the data acquisition unit 11 acquires the lubricating fluid film characteristics of the lubricating fluid interposed between the first sliding member 31 and the second sliding member 34. The lubricating fluid film characteristics are, for example, the minimum lubricating fluid film thickness 38 of the lubricating fluid film formed between the first sliding member 31 and the second sliding member 34. If the sliding speed and load within the subset are fixed values, then the lubricating fluid film characteristics are also fixed values. If the sliding speed and load are time-series values ​​that change based on time-series data, then the lubricating fluid film characteristics are also time-series values ​​that change based on the sliding speed and load. Of the lubricating fluid film characteristics, the sliding speed within the subset, and the load, at least one may be a fixed value, while the other may be a variable value. If the fluctuations in sliding speed and load are small, by fixing the minimum lubricating fluid film thickness 38, the number of time-series data acquired by the data acquisition unit 11 can be reduced, thus shortening the time required for data acquisition processing.

[0026] The data acquisition unit 11 may acquire the first surface shape data 33 and the second surface shape data 36, ​​the subset, and the lubrication fluid film characteristics simultaneously, or it may acquire them at different timings.

[0027] The iteration count setting unit 12 receives a subset from the data acquisition unit 11 and sets the number of times the first surface 32 is moved in the surface direction at each calculation unit distance in the wear amount calculation unit 13, i.e., the iteration count. Here, if the first surface shape data 33 and the second surface shape data 36 received from the data acquisition unit 11 are continuous data, the iteration count setting unit 12 determines the calculation unit distance based, for example, on the size of the first surface 32 represented by the first surface shape data 33 and the second surface 35 represented by the second surface shape data 36. Alternatively, the calculation unit distance may be determined based on the surface width, area, etc., of the contact area (also called the true contact area) between the first surface 32 and the second surface 35 reproduced in the virtual space 45.

[0028] The iteration count setting unit 12 calculates the cumulative sliding distance, which is the sum of the sliding distances due to the relative motion of the first sliding member 31 and the second sliding member 34, using the time information and the sliding speeds in the subset acquired by the data acquisition unit 11. If the sliding speeds in the subset are replaced with fixed values, the cumulative sliding distance is calculated by multiplying the sliding speed by the time from the beginning to the end of the subset. Then, the number of times the first surface 32 is moved in the planar direction, i.e., the number of iterations, is calculated by dividing the cumulative sliding distance by the calculation unit distance. If the value obtained by dividing the cumulative sliding distance by the calculation unit distance is not an integer, the iteration count setting unit 12 corrects it to an integer, for example, by rounding to the first decimal place.

[0029] The wear amount calculation unit 13 receives a subset, first surface shape data 33, second surface shape data 36, ​​and lubrication fluid film characteristics from the data acquisition unit 11. In addition, as shown by the dashed line in Figure 1, the wear calculation unit may also receive the number of iterations, which is the number of times the first surface 32 is moved, from the iteration count setting unit 12 when calculating the wear amount based on the operating condition data within one subset.

[0030] At time t1, the wear amount calculation unit 13 acquires the lubrication fluid film characteristics, for example, the minimum lubrication fluid film thickness 38, acquired by the data acquisition unit 11, as shown in Figure 4 (S201). Once the data for the minimum lubrication fluid film thickness 38 is acquired, the wear amount calculation of the subset associated with the lubrication fluid film characteristics is started (S202). For example, the position of the average surface of the surface height distribution of the first surface 32 and the second surface 35 is calculated (S203). For example, the surface based on the average value of the surface height distribution of the first surface 32 is set as the first average surface 40, and the surface based on the average value of the surface height distribution of the second surface 35 is set as the second average surface 41.

[0031] Next, the wear amount calculation unit 13 sets, for example, the minimum lubrication fluid film thickness 38 as the inter-surface distance 39, which is the distance between the first average surface 40 and the second average surface 41, and places the first surface 32 and the second surface 35 apart (S204). Figure 5 is a two-dimensional schematic diagram showing the state in which a part of the first surface 32 and a part of the second surface 35 interfere in the virtual space 45 at an arbitrary operating time. Also in Figure 5, the first surface shape data and the second surface shape data are continuous data obtained by a function of surface height with the surface position of the first surface 32 and the second surface 35 as arguments. In Figure 5, for convenience, multiple dividing lines are displayed in the X-axis direction at intervals of the calculation unit distance determined by the iteration count setting unit. Since the dividing lines correspond to the mesh when the surface shape data is discrete data, the dividing lines will be described as a mesh. When the first surface 32 and the second surface 35 are placed facing each other, in reality the surface roughness does not interfere with the roughness of the other surface. However, in the virtual space 45, depending on the relationship between the surface roughness and the minimum lubrication fluid film thickness, the first and second surfaces may interfere with each other, as shown in the meshes in the three shaded areas of Figure 5. In the example shown in Figure 5, the three interference areas are each caused by the interference of three meshes.

[0032] Here, when arranging the first surface 32 and the second surface 35 on the virtual space 45, the wear amount calculation unit reverses the front and back sides of the first surface 32 and places it on the virtual space 45, as shown in Figure 6, in order to make the roughness protrusions of the first surface 32 and the roughness protrusions of the second surface 35 face each other. As a process of reversing the front and back sides, the wear amount calculation unit reverses the sign of the surface height value of the first surface shape data 33. Alternatively, the wear amount calculation unit may reverse the sign of the value of the second surface shape data 36.

[0033] When the wear amount calculation unit 13 places the first surface 32 and the second surface 35 in the virtual space 45, it adjusts the orientation of at least one of the first surface 32 and the second surface 35 in the virtual space 45 so that the relationship between the roughness direction and the sliding direction of the first surface 32 and the second surface 35 matches the relationship between the roughness direction of the sliding surfaces of the first sliding member 31 and the second sliding member 34 incorporated into the actual sliding device 37 and the sliding direction of the first sliding member 31 and the second sliding member 34. For example, the sliding direction in the virtual space 45 may be arranged so that it is roughly parallel to the grinding marks and the sliding direction. In this case, surface shape data is acquired for the surface of the first surface 32 and the second surface 35 placed in the virtual space 45 over a range greater than the length in the X-axis direction and the length in the Y-axis direction, in order to perform a rotational transformation in the X-axis-Y-axis plane. The adjustment of the relationship between the roughness direction and the sliding direction may be performed by either the wear amount calculation unit 13 or the data acquisition unit 11. If surface shape data is measured in advance outside the wear prediction device 100, the data acquisition unit 11 may acquire surface shape data measured based on the sliding direction in the virtual space 45.

[0034] The wear amount calculation unit 13 extracts meshes having interference heights where protrusions on the first surface 32 and protrusions on the second surface 35 interfere, as shown in the meshes indicated by the three shaded interference areas in Figure 5 (S205). The interference height is the length in the height direction of the surface of each mesh where the first surface 32 and the second surface 35 interfere. The wear amount calculation unit 13 performs a subtraction process on the interfering mesh by subtracting at least a portion of the interference height from the surface height of the mesh having the interference height of at least one of the first surface 32 and the second surface 35 (S206). After subtracting at least a portion of the interference height from the mesh, the first surface shape data 33 for the worn first surface 32 and the second surface shape data 36 for the worn second surface 35 may be stored in the calculation data storage unit or output to the outside via the notification unit described later. Furthermore, the surface roughness, such as the arithmetic mean roughness Sa, and the line roughness, such as the arithmetic mean roughness Ra, of the first and second surfaces after the subtraction process may be further calculated and stored, or output externally via the notification unit described later.

[0035] The wear amount calculation unit 13 performs a subtraction process on all meshes where the first surface 32 and the second surface 35 are interfering, and then calculates and predicts the wear amount of at least one of the first sliding member 31 and the second sliding member 34 based on the distribution of surface heights of the first surface 32 and the second surface 35 before subtraction and after subtraction. Specifically, the first wear amount of the first surface 32 is calculated as the difference between the first average surface 40 obtained by averaging the surface heights of the first surface 32 before subtraction and the first average surface 40 obtained by averaging the surface heights of the first surface 32 after subtraction. Similarly, the second wear amount of the second surface 35 is calculated as the difference between the second average surface 41 obtained by averaging the surface heights of the second surface 35 before subtraction and the second average surface 41 obtained by averaging the surface heights of the second surface 35 after subtraction. Here, the total amount of mesh heights subtracted from the interference height on the first surface 32 may be used as the first wear volume, and the total amount of mesh heights subtracted from the interference height on the second surface 35 may be used as the second wear volume. When the processes from S201 to S206 are performed, the wear amount calculation unit 13 counts the number of times the first surface 32 has been moved as 1, and 1 is added to the cumulative number of movements. Also, when the processes from S201 to S206 are performed, a distance equivalent to the calculation unit distance is added to the cumulative movement distance.

[0036] When the wear amount calculation unit 13 finishes subtracting the meshes having interference heights of the first surface 32 and the second surface 35 at time t1 of the subset, if the number of times the first surface 32 has been moved by a calculation unit distance in the surface direction (hereinafter referred to as the cumulative number of movements) has not reached the number of iterations set by the iteration count setting unit 12 (NO in S207), the first surface 32 is moved by a calculation unit distance in the surface direction (S208), and the wear amount calculation for the next time t2 in the subset is performed. If the calculation unit distance corresponds to the length of the side of one mesh of a rectangular shape, one mesh portion of the first surface shape data 33 is moved in the negative direction of the X axis. At the left and right ends of the first surface shape data 33 in the X direction, for example, periodic boundary conditions are set, so that the height data of one column in the Y direction that extends beyond the left and right ends in the X direction after moving one mesh portion is moved to the right end (positive direction of the X axis in Figure 2). If NO in S207, the wear calculation processing for time t2 in the subset is then performed. When moving the first surface 32 in the planar direction, if the calculation speed is increased, the wear amount calculation process is not performed even if there is interference between the first surface 32 and the second surface 35 during the process of moving the first surface 32. When the cumulative number of movements reaches the number of iterations (YES in S207), that is, when the wear amount calculation has been performed for each time point included in the subset, the wear amount calculation for one subset is terminated (S209). If the number of completed subsets does not match the number of subsets after division by the data acquisition unit 11 (NO in S210), the calculation for the next subset is performed (S211). When the number of completed subsets matches the number of subsets after division by the data acquisition unit 11, the wear amount calculation for the acquired operating condition parameter set is completed (YES in S210).

[0037] As explained in S209, the determination unit 15 terminates the calculation processing of the subset acquired by the data acquisition unit 11 in the wear amount calculation unit 13 based on the cumulative number of movements or cumulative distance of movements received from the wear amount calculation unit 13. That is, the determination unit 15 determines whether or not the desired wear amount calculation has been performed. If it is determined that the desired wear amount calculation has been performed, it may send a signal to the wear amount calculation unit 13 to terminate the wear amount calculation processing.

[0038] Here, when the determination unit 15 makes a determination using the integrated number of movements, it determines whether or not the integrated number of movements obtained by moving the first surface 32 in the wear amount calculation unit 13 has reached the number of repetitions. When the integrated number of movements has reached the number of repetitions, the wear amount calculation process using one subset by the wear amount calculation unit 13 may be terminated.

[0039] When the determination unit 15 makes a determination using the integrated movement distance, it determines whether or not the integrated movement distance, which is the integrated value of the movement distances due to the movement of the first surface 32, has reached the integrated sliding distance. When the integrated movement distance has reached the integrated sliding distance, the process of calculating the wear amount using one subset of the operating condition parameters by the wear amount calculation unit 13 is terminated.

[0040] Therefore, the determination unit 15 can appropriately terminate the wear amount calculation based on at least either the integrated number of movements or the integrated movement distance.

[0041] Each time the calculation at each time within the subset is completed, the notification unit 14 receives and outputs the first wear amount, the first wear volume of the first surface 32, the second wear amount, and the second wear volume of the second surface 35 from the wear calculation unit. The notification unit 14 may output the first surface shape data and the second surface shape data after wear as calculation results based on the wear amount. Further, the notification unit 14 may output the surface roughness (calculated average roughness Ra, Sa, etc.) of the first surface and the second surface after wear in the virtual space 45.

[0042] Also, the notification unit 14 may output the first wear amount, the first wear volume, the second wear amount of the second surface 35, and the second wear volume corresponding to each time within the subset, and may output the first wear amount, the first wear volume of the first surface 32, the second wear amount of the second surface 35, and the second wear volume each time the calculation of the subset is completed.

[0043] Here, an example where the first surface moves only in one direction has been described, but it may make a reciprocating movement showing a predetermined amplitude. Also, although the second surface 35 is made stationary and the first surface 32 is moved for wear calculation, the first surface 32 may be made stationary and the second surface 35 may be moved, or the first surface 32 and the second surface 35 may be moved.

[0044] Thus, the wear prediction device 100 predicts wear information due to sliding between the first sliding member 31 and the second sliding member 34, and acquires first surface shape data 33 of the first surface 32, which is at least a part of the surface of the first sliding member 31, and second surface shape data 36 of the second surface 35, which is at least a part of the surface of the second sliding member 34, as well as the operating time of the sliding device 37 that slides the first surface 32 and the second surface 35, the sliding speed corresponding to the operating time, the load on which the first surface 32 is pressed against the second surface 35 corresponding to the operating time, and the lubricating fluid film characteristics of the lubricating fluid interposed between the first sliding member 31 and the second sliding member 34, and the number of iterations setting unit 12 calculates the number of times the first surface 32 is repeatedly moved in the surface direction by dividing the cumulative sliding distance calculated using the sliding speed and the operating time by the calculation unit distance, and at set time intervals, based on the lubricating fluid film characteristics, the first surface 32 and the second The wear amount calculation unit 13 calculates the amount of wear on at least one of the first sliding member 31 and the second sliding member 34 based on the surface heights of the first surface 32 and the second surface 35, by setting a surface distance 39 between the two surfaces 35, subtracting at least a portion of the interference height at which the first surface 32 and the second surface 35 interfere from the surface height of at least one of the first surface 32 and the second surface 35, and determining whether the cumulative number of movements in which the first surface 32 is moved by the wear amount calculation unit 13 has reached the number of repetitions, or whether the cumulative movement distance, which is the cumulative value of the movement distance of the first surface 32 due to the movement, has reached the cumulative sliding distance, and a notification unit 14 notifies at least one of the determination result of the determination unit and wear information including the amount of wear.

[0045] In this example, the data acquisition unit 11 acquires the minimum lubricating fluid film thickness 38 as a lubricating fluid film characteristic. However, the data acquisition unit 11 may also acquire the lubricating fluid film pressure distribution and output it to the wear amount calculation unit 13. The lubricating fluid film pressure distribution is the pressure distribution of the lubricating fluid film generated between the first sliding member 31 and the second sliding member 34, and includes the contact surfaces of the first surface 32 and the second surface 35. That is, the lubricating fluid film pressure distribution is the lubricating fluid film pressure distribution over the entire sliding surface where the first sliding member 31 and the second sliding member 34 contact and slide. The wear amount calculation unit 13 adjusts the inter-surface distance 39 so that the sum of the lubricating fluid film reaction force based on the lubricating fluid film pressure that has the effect of separating the first surface 32 and the second surface 35, and the deformation reaction force due to the elastoplastic deformation of the first surface 32 and the second surface 35 generated at the part where the first surface 32 and the second surface 35 interfere, balances the partial load applied to the first surface 32 and the second surface 35. Furthermore, the data acquisition unit 11 acquires the first Young's modulus (hereinafter referred to as Young's modulus) and the first plastic flow pressure (indicating the compressive modulus of the first sliding member 31), and the second Young's modulus (indicating the Young's modulus of the second sliding member 34) and the second plastic flow pressure (indicating the plastic flow pressure), in order to determine the deformation reaction force of the surface, and outputs these to the wear amount calculation unit 13.

[0046] The adjustment operation of the inter-surface distance 39 by the wear amount calculation unit 13 using the lubrication fluid film pressure distribution will be explained. First, the inter-surface distance 39 is initially set to zero, for example, and the first surface 32 and the second surface 35 are positioned. The reaction force due to the contact between the first surface 32 and the second surface 35 is calculated. Figure 7 is an explanatory diagram showing the schematic of the theoretical contact pressure distribution that occurs between the first sliding member 31 and the second sliding member 34 when a cylindrical first sliding member 31 is inserted into the inner diameter of a cylindrical second sliding member 34 and the first sliding member 31 is pressed against the cylinder in the radial direction of the second sliding member 34, with the inner circumference of the second sliding member 34 and the outer circumference of the first sliding member 31 expanded. The theoretical contact pressure distribution is calculated using, for example, Hertz's elastic contact theory. As shown in Figure 7, when the first sliding member 31 is pressed against the second sliding member 34, pressure is not generated over the entire circumferential surface, but rather the contact pressure distribution occurs only in the angular range that can be theoretically determined by Hertz's contact theory. The wear amount calculation unit 13 calculates, for example, the average value of the contact pressure in the contact pressure distribution. Here, even if the maximum value of the contact pressure in the contact pressure distribution is calculated, the partial load applied to the first surface 32 and the second surface 35 is determined by multiplying the surface area of ​​the first surface 32 or the second surface 35 by the average value of the contact pressure.

[0047] Furthermore, the wear amount calculation unit 13 calculates the lubricating fluid film reaction force obtained by integrating the lubricating fluid film pressure distribution, which has the effect of separating the first surface 32 and the second surface 35, over the entire contact surface by multiplying the surface area of ​​the first surface 32 or the second surface 35 by the average value of the lubricating fluid film pressure distribution. For the portion where the first surface 32 and the second surface 35 are interfering, the wear amount calculation unit 13 calculates the deformation reaction force due to the strain of the first surface 32 using the first Young's modulus, and the deformation reaction force due to the strain of the second surface 35 using the second Young's modulus. If there are multiple interfering portions, the wear amount calculation unit 13 calculates the deformation reaction force due to elastic deformation by summing the deformation reaction forces of the interfering portions. Furthermore, if the first surface shape data 33 and the second surface shape data 36 are discrete data, and the stress corresponding to the strain of the mesh interfering between the second surface 35 and the first surface 32 is greater than or equal to the first plastic flow pressure, the deformation reaction force may be calculated by replacing the stress with the first plastic flow pressure. For the second surface 35 as well, if the stress corresponding to the strain of the mesh where the first surface 32 and the second surface 35 are interfering is greater than or equal to the second plastic flow pressure, the deformation reaction force may be determined by replacing this stress with the second plastic flow pressure.

[0048] By performing this process, when the lubrication fluid film pressure distribution is acquired as a lubrication fluid film characteristic and output to the wear amount calculation unit 13, the partial load, lubrication fluid film reaction force, and deformation reaction force based on elastoplastic deformation are determined, and the inter-surface distance 39 at which the partial load balances with the sum of the lubrication fluid film reaction force and deformation reaction force can be finely adjusted. This allows for the prediction of wear amount considering not only the lubrication fluid film characteristics but also the contact state between the first surface 32 and the second surface 35. Here, the data acquisition unit 11 may acquire the minimum lubrication fluid film thickness 38 in addition to acquiring the lubrication fluid film pressure distribution. By using the minimum lubrication fluid film thickness 38 as the initial value of the inter-surface distance 39, the balance calculation process between the partial load and the sum of the lubrication fluid film reaction force and deformation reaction force can be performed at high speed.

[0049] Furthermore, the data acquisition unit 11 may acquire the virtual operating time of the sliding device 37, the virtual sliding speed of the sliding part corresponding to the virtual operating time, and the virtual load on which the first sliding member 31 is pressed against the second sliding member 34 corresponding to the virtual operating time. The virtual operating time, virtual sliding speed, and virtual load may be created based on operating history data acquired from the sliding device. The wear amount calculation unit 13 can then calculate the remaining lifespan of the sliding device 37 when it is operated under operating conditions assumed by virtual data such as the virtual operating time, virtual sliding speed, and virtual load, by calculating the operating time until the cumulative value of the wear amount reaches a preset wear amount threshold as the remaining lifespan.

[0050] Embodiment 2. The wear prediction device 100 according to Embodiment 2 will be described with reference to the figures. Embodiment 1 described an example of calculating the amount of wear when the lubrication fluid film characteristics are known, but Embodiment 2 differs in that the lubrication fluid film characteristics are determined based on the sliding speed, load, etc. acquired by the data acquisition unit 11 and the amount of wear is calculated. The following will mainly describe the differences from Embodiment 1, and descriptions of the same or corresponding parts will be omitted as appropriate.

[0051] As shown in Figure 8, the wear prediction device 100 includes a lubrication fluid film characteristic calculation unit 16 that calculates lubrication fluid film characteristics such as the fluid characteristics of the lubrication fluid film interposed between the first sliding member 31 and the second sliding member 34. Fluid characteristics include, for example, the lubrication fluid film thickness distribution, the minimum lubrication fluid film thickness 38 which is the minimum value of the lubrication fluid film thickness in the lubrication fluid film thickness distribution, and the lubrication fluid film pressure distribution. The wear prediction system 1000 includes the wear prediction device 100 and a database 43.

[0052] One of the databases 43, the sliding device specification DB 431, stores data used for calculations of lubrication fluid film characteristics, including at least one of the following: the diameter of the rotating shaft when the first sliding member 31 is a rotating shaft and the second sliding member 34 is a sliding bearing, the diameter of the sliding bearing, the axial length of the sliding bearing, and physical property information of the lubrication fluid. Another database 43, the lubrication fluid physical property database (hereinafter referred to as the lubrication fluid physical property DB), stores physical property information of the lubrication fluid, including a fixed value for the viscosity of the lubrication fluid, as calculation data used for calculations of lubrication fluid film characteristics.

[0053] The lubrication fluid film characteristic calculation unit 16 receives from the data acquisition unit 11 the sliding speed of the sliding mechanism corresponding to the operating time, and the load on which the first sliding member 31 is pressed against the second sliding member 34, corresponding to the operating time. The data acquisition unit 11 also acquires the diameter of the rotating shaft, the diameter of the sliding bearing, the axial length of the sliding bearing, and the viscosity of the lubrication fluid from an external database 43 and outputs them to the lubrication fluid film characteristic calculation unit 16. The lubrication fluid film characteristic calculation unit 16 calculates the lubrication fluid film characteristics of the lubrication fluid interposed between the first sliding member 31 and the second sliding member 34 based on the diameter of the rotating shaft, the diameter of the sliding bearing, the axial length of the sliding bearing, the viscosity of the lubrication fluid, and the sliding speed received from the data acquisition unit 11, as well as fluid lubrication theory such as the Reynolds equation for calculating the characteristics of the lubrication fluid. Here, the lubrication fluid film characteristics may also be calculated using the bearing clearance, which is the difference between the diameter of the rotating shaft and the diameter of the sliding bearing.

[0054] The lubrication fluid film characteristics calculation unit 16 calculates the lubrication fluid film characteristics for each operating condition, which consists of the operating time included in the subset divided by the data acquisition unit 11, the sliding speed of the sliding motion corresponding to the operating time, and the load applied to the first sliding member 31 against the second sliding member 34 corresponding to the operating time. If the sliding speed and load within the subset are replaced with fixed values, the lubrication fluid film characteristics can be calculated based on the sliding speed and load corresponding to, for example, the first operating time within the subset, rather than calculating the lubrication fluid film characteristics for each operating time. Furthermore, when the wear amount calculation unit 13 has finished calculating one subset, the lubrication fluid film characteristics calculation unit 16 calculates the lubrication fluid film characteristics based on, for example, the sliding speed and load corresponding to the first operating time within the next subset in the operating condition parameter set. The lubrication fluid film characteristics calculation unit 16 outputs the calculation result of the lubrication fluid film characteristics to the wear amount calculation unit 13.

[0055] In this way, by determining the lubrication fluid film characteristics that change based on operating conditions such as sliding speed and load, and outputting them to the wear amount calculation unit 13, the wear amount can be calculated with high accuracy because the lubrication fluid film characteristics due to changes in operating conditions can be taken into consideration.

[0056] Furthermore, the minimum lubrication fluid thickness 38 may be calculated using a trained model that infers the minimum lubrication fluid thickness 38 from the sliding speed, load, rotating shaft diameter, bearing diameter, and viscosity of the lubrication fluid. As shown in Figure 9, when the first sliding member 31 is a rotating shaft and the second sliding member 34 is a sliding bearing into which the rotating shaft is inserted, the minimum lubrication fluid thickness 38 of the lubrication fluid interposed between the first sliding member 31 and the second sliding member 34 changes with changes in sliding speed, load, or viscosity during the sliding of the first sliding member 31 and the second sliding member 34. In addition, the minimum lubrication fluid thickness 38 on the vertical axis of Figure 9 also changes depending on the diameter and axial length of the rotating shaft and sliding bearing, so the minimum lubrication fluid thickness 38 may be determined using a trained model that infers the minimum lubrication fluid thickness 38 from the sliding speed, load, rotating shaft diameter, bearing diameter, and viscosity. By using a trained model that infers the minimum lubrication fluid thickness 38, the calculation processing in the lubrication fluid film characteristic calculation unit 16 can be performed at high speed.

[0057] Embodiment 3. The wear prediction device 100 according to Embodiment 3 will be described with reference to the figures. In Embodiment 2, an example was described in which the viscosity of the lubricating fluid was assumed to be a fixed value and the lubricating fluid film characteristics were calculated, but Embodiment 3 differs in that it determines the estimated viscosity of the lubricating fluid. The following will mainly describe the differences from Embodiments 1 and 2, and descriptions of the same or corresponding parts will be omitted as appropriate.

[0058] As shown in Figure 10, the wear prediction device 100 includes a viscosity calculation unit 17 that calculates the estimated viscosity of the lubricating fluid and outputs it to the wear amount calculation unit 13, and a lubricating fluid film characteristic calculation unit 16 that inputs the estimated viscosity calculated by the viscosity calculation unit 17 as viscosity. The wear prediction system 1000 includes the wear prediction device 100 and a database 43.

[0059] One of the databases 43 is the Lubrication Fluid Properties DB, which stores lubrication fluid property information, including a dataset that associates ambient temperature, lubrication fluid composition, and lubrication fluid viscosity, as calculation data used to calculate the estimated viscosity of the lubrication fluid interposed between the first sliding member and the second sliding member.

[0060] The viscosity calculation unit 17 acquires the ambient pressure of the ambient fluid in contact with at least a portion of the lubricating fluid, measured by a pressure sensor attached to the sliding device 37, and the ambient temperature of the ambient fluid, measured by a temperature sensor attached to the sliding device 37. Based on the measured ambient pressure and ambient temperature and the physical property information of the lubricating fluid, the viscosity calculation unit 17 calculates the estimated viscosity of the lubricating fluid and outputs the estimated viscosity to the lubricating fluid film characteristic calculation unit 16. The composition of the lubricating fluid is, for example, a lubricating oil with at least one of the following as a base oil: polyol ester, polyvinyl ether, alkylbenzene, and mineral oil. Here, the viscosity calculation unit 17 may also acquire the ambient temperature, which has been acquired by the data acquisition unit 11 as an operating condition parameter set, from the data acquisition unit 11, as described in Figure 3. When the viscosity calculation unit 17 acquires the ambient temperature and ambient pressure, it may acquire them directly from the pressure sensor and temperature sensor of the sliding device 37 wirelessly or via wired connection, or it may acquire them from the data acquisition unit 11 as described above.

[0061] The operation of the viscosity calculation unit 17 in calculating the estimated viscosity of the lubricating fluid will now be described. If the data acquisition unit 11 acquires the ambient temperature at the same time as the operating condition parameter set, the viscosity calculation unit receives the ambient temperature from the data acquisition unit. The viscosity calculation unit 17 transmits the ambient temperature received from the data acquisition unit to the lubricating fluid properties DB, which is one of the externally provided databases 43, and receives the viscosity of the lubricating fluid corresponding to the transmitted ambient temperature. The viscosity calculation unit outputs the viscosity received from the lubricating fluid properties DB as the estimated viscosity calculation result to the lubricating fluid film properties calculation unit 16. That is, the viscosity calculation unit 17 calculates the estimated viscosity of the lubricating fluid based on the ambient temperature corresponding to the time in the operating condition parameter set and the lubricating fluid properties DB. If the sliding velocity and load in the subset are replaced with fixed values, the viscosity calculation is performed using the ambient temperature corresponding to any time in the subset. For example, if the sliding velocity and load in the subset are replaced with the sliding velocity and load corresponding to the first time in the subset, the viscosity calculation unit 17 acquires the ambient temperature corresponding to that first time and calculates the estimated viscosity.

[0062] Although it has been explained that the lubrication fluid properties DB is located outside the wear prediction device 100, it may also be provided inside the wear prediction device 100.

[0063] In this way, by calculating the estimated viscosity of the lubricating fluid according to sliding environment data such as ambient temperature and ambient pressure, and then using the estimated viscosity to calculate the lubricating fluid film characteristics in the lubricating fluid film characteristics calculation unit 16, the physical properties of the lubricating fluid film interposed between the first sliding member 31 and the second sliding member 34 can be accurately grasped, thereby improving the accuracy of wear amount calculation that takes the lubricating fluid film into consideration.

[0064] The data acquisition unit 11 may further acquire the ambient pressure of the ambient fluid that is in contact with at least a portion of the lubricating fluid measured by the pressure sensor, the composition of the ambient fluid, and the composition of the lubricating fluid. The viscosity calculation unit 17 may calculate the estimated dissolved viscosity of the mixed fluid in which the ambient fluid is dissolved in the lubricating fluid based on the composition of the ambient fluid, ambient pressure, ambient temperature, and composition of the lubricating fluid acquired from the data acquisition unit 11, and the physical property information of the lubricating fluid, and output the estimated dissolved viscosity to the lubricating fluid film property calculation unit 16. Here, the lubricating fluid physical property DB, which is one of the databases 43, is physical property information of the lubricating fluid that includes a dataset that associates the composition of the ambient fluid and the combination of the composition of the lubricating fluid with the ambient pressure, ambient temperature, and the dissolved viscosity of the mixed fluid of the lubricating fluid and the ambient fluid in which the ambient fluid is dissolved. Specifically, the viscosity calculation unit 17 transmits the composition of the ambient fluid, ambient pressure, ambient temperature, and composition of the lubricating fluid received from the data acquisition unit 11 to the lubricating fluid properties DB, which is one of the externally provided databases 43, and receives the estimated dissolution viscosity of the mixed fluid corresponding to the transmitted ambient fluid composition, ambient pressure, ambient temperature, and composition of the lubricating fluid. The viscosity calculation unit outputs the viscosity received from the lubricating fluid properties DB as the calculation result of the estimated dissolution viscosity to the lubricating fluid film properties calculation unit 16. The composition of the ambient fluid is, for example, at least one of air, fluorine compounds, hydrocarbon compounds, or carbon oxides, and may also be a mixed fluid of these. The state of the ambient fluid is gas, liquid, or a mixture of gas and liquid. By considering that the dissolution viscosity of the mixed fluid in which the ambient fluid is dissolved in the lubricating fluid changes with ambient temperature and ambient pressure, differences in the composition of the ambient fluid, ambient pressure, ambient temperature, and composition of the lubricating fluid can be reflected in the viscosity used by the lubricating fluid film properties calculation unit. Therefore, depending on the combination of the composition of the ambient fluid and the lubricating fluid included in the lubricating fluid properties DB, the amount of wear of the first sliding member 31 and the second sliding member 34 in various sliding environments can be predicted with high accuracy.

[0065] Furthermore, the viscosity calculation unit 17 may calculate a corrected estimated viscosity from the hue measurement value using pre-stored correlation data between hue value and viscosity, based on the hue measurement value of the lubricating fluid measured by a color sensor attached to the sliding device 37, and output it to the lubricating fluid film characteristics calculation unit 16. The hue measurement value measured by the color sensor may be output to the viscosity calculation unit 17 wirelessly or via a wired connection, or it may be output to the data acquisition unit 11 and then output from the data acquisition unit 11 to the viscosity calculation unit 17. By correcting the estimated viscosity of the lubricating fluid based on the hue of the lubricating fluid used in the sliding device 37, the estimated viscosity can be output to the lubricating fluid film characteristics calculation unit 16 while considering the deterioration state of the lubricating fluid of the sliding device 37 operating in real space 46, and the amount of wear of the first sliding member 31 and the second sliding member 34 can be calculated with high accuracy based on the lubricating fluid film characteristics that take the deterioration state into consideration.

[0066] Embodiment 4. The wear prediction device 100 according to Embodiment 4 will be described with reference to the figures. In the wear prediction devices 100 of Embodiments 1 to 3, surface shape data corresponding to the first surface 32 and the second surface 35 was created by a surface shape measuring instrument. Embodiment 4 describes a wear prediction device 100 that generates surface shape data for the first surface 32 and the second surface 35 using artificial intelligence (learning model). Furthermore, in the wear prediction device 100 of this embodiment, the processing performed by the iteration count setting unit 12, the wear amount calculation unit 13, the notification unit 14, and the determination unit 15 is the same as in Embodiment 1, except that a learning model is used when generating surface shape data, so the explanation will be omitted.

[0067] In order to create first surface shape data 33 and second surface shape data 36 more easily than when actually measuring the surface shapes of the first sliding member 31 and the second sliding member 34, the wear prediction device 100 according to this embodiment is equipped with a surface shape data generation unit 18 as shown in Figure 11.

[0068] The wear prediction system 1000 according to Embodiment 4 comprises a wear prediction device 100 and an artificial intelligence device 19, as shown in Figure 12. The wear prediction device 100 generates surface shape generation data for a first surface 32 and a second surface 35 using an artificial intelligence device 19 having artificial intelligence (AI) functions. The wear prediction device 100 comprises a surface shape data generation unit 18 that exchanges information with the artificial intelligence device 19, and a data acquisition unit 11 that acquires surface shape generation data as first surface shape data 33 and second surface shape data 36 from the surface shape data generation unit 18. The artificial intelligence device 19 refers to artificial intelligence and its operating environment that has intelligent functions such as reasoning and judgment. The artificial intelligence device 19 is composed of a trained model storage unit 192 and a model control unit 191.

[0069] The surface shape data generation unit 18 acquires at least one of the first surface roughness of the first sliding member 31 and the second surface roughness of the second sliding member 34 as a surface roughness specification value. The surface shape data generation unit 18 has a generation control unit 181 that outputs the acquired surface roughness specification value to the artificial intelligence device 19, and acquires surface shape generation data corresponding to the surface roughness specification value from the artificial intelligence device 19 and outputs it to the wear amount calculation unit 13. That is, when the first surface roughness of the first sliding member 31 is input as the surface roughness specification value, the surface shape data generation unit 18 uses the artificial intelligence device 19 to acquire surface shape generation data corresponding to the first surface roughness and outputs it to the data acquisition unit 11. Also, when the second surface roughness of the second sliding member 34 is input as the surface roughness specification value, the surface shape data generation unit 18 uses the artificial intelligence device 19 to acquire surface shape generation data corresponding to the second surface roughness and outputs it to the data acquisition unit 11.

[0070] The first surface roughness may be composed of one type of surface roughness data or a plurality of types of surface roughness data. Also, the second surface roughness may be composed of one type of surface roughness data or a plurality of types of surface roughness data. For example, the first surface roughness and the second surface roughness are, for example, at least any one of the minimum autocorrelation length Sal, the root mean square height Sq, the skewness Ssk, and the kurtosis Skw. Also, the first surface roughness and the second surface roughness are at least any one of the roughness information in the lateral direction (the surface plane direction) and the index indicating the longitudinal direction (the direction intersecting the surface). The roughness information in the lateral direction is, for example, the minimum autocorrelation length Sal, the surface texture direction Std, etc. Also, the roughness information in the longitudinal direction is, for example, the root mean square height Sq, the skewness Ssk, the kurtosis Skw, etc. By the generation control unit 181 acquiring the roughness information in the lateral direction and the roughness information in the longitudinal direction and transmitting the roughness information to the artificial intelligence device 19 to generate the surface shape generation data, the first surfaces 32 and the second surfaces 35 of the first sliding member 31 and the second sliding member 34 with different surface specifications of the finish machining can be easily generated. The first surface roughness and the second surface roughness may include roughness information regarding the directionality of roughness such as isotropy and anisotropy. Here, the first surface roughness and the second surface roughness may be values stored inside or outside the wear prediction device 100.

[0071] Here, the minimum autocorrelation length Sal represents the horizontal distance in the direction in which the autocorrelation function decays most rapidly to a specific value s (default is 0.2), and is an index that can determine whether there is a location where the surface height changes rapidly. The root mean square height Sq is an index corresponding to the standard deviation of the distance from the average plane. The skewness Ssk is an index representing the symmetry of the surface height distribution. The kurtosis Skw is an index representing the sharpness of the surface height distribution. The surface texture direction Std is an index representing the directionality of the surface unevenness (such as the direction of the grain) in terms of an angle.

[0072] As shown in FIG. 2, the surface shape generation data is, for example, the size in the plane direction of the first surface 32 and the second surface 35 (n y Δy, n xThis is data adjusted so that Δx) is the same. Furthermore, the surface shape generation data is a two-dimensional matrix (n) obtained by dividing the sliding surfaces (reference planes) of the first surface 32 and the second surface 35 into a finite number of elements (mesh, rectangular elements of the same shape with each side parallel to the X / Y axis). x ×n y This is matrix data in which a matrix is ​​defined and each element of the matrix stores data for surface height (surface roughness) corresponding to the Z-axis value. Surface shape generation data may also be continuous data represented by a function or the like.

[0073] The generation control unit 181 is an interface that can exchange information with an external system. The generation control unit 181 receives the first surface roughness and the second surface roughness as specified surface roughness values ​​from an external surface roughness input unit 21. The surface roughness input unit 21 may be located inside the wear prediction device 100.

[0074] The generation control unit 181 inputs the acquired surface roughness specification value to the artificial intelligence device 19, thereby obtaining surface shape generation data corresponding to the surface roughness specification value from the artificial intelligence device 19. In other words, the generation control unit 181 inputs the surface roughness specification value to the artificial intelligence device 19 in order to obtain surface shape generation data corresponding to the surface roughness specification value from the artificial intelligence device 19. The generation control unit 181 outputs the acquired surface shape generation data to the data acquisition unit 11. The data acquisition unit 11 may be provided inside the surface shape data generation unit 18.

[0075] As shown in Figure 12, the artificial intelligence device 19 comprises a trained model storage unit 192 and a model control unit 191. The artificial intelligence device 19 is a model and its operating environment configured to output surface shape generation data corresponding to a specified surface roughness value when a specified surface roughness value is input. When the artificial intelligence device 19 receives a specified surface roughness value such as a first surface roughness and a second surface roughness from the generation control unit 181, it outputs surface shape generation data based on the specified surface roughness value and a trained model described later.

[0076] The trained model storage unit 192 stores the trained model. The trained model includes model information, which will be described later. The trained model may also include model parameters, which are information that defines the behavior of the model, such as constraints, weighting variables, and evaluation functions.

[0077] Examples of models include NN (Neural Network), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), VAE (Variational Autoencoder), GAN (Generative Adversarial Networks), Diffusion model, Transformer, LLM (Large Language Model), VLM (Visual Language Model), and BERT (Bidirectional Encoder Representations from... The models may also be called Transformers, GPT (Generative Pre-trained Transformer), or CLIP (Contrastive Language Image Pre-training). Note that the above models are not mutually exclusive; for example, LLM, VLM, BERT, and GPT are included in Transformers. Also, for example, Transformers are included in NNs. Furthermore, the learning algorithm and model may be a combination of multiple types. Models also include what are called multimodal models, which are trained by combining multiple different types of data.

[0078] When the model control unit 191 receives a specified surface roughness value, it outputs surface shape generation data corresponding to the specified surface roughness value to the generation control unit, based on the specified surface roughness value and the trained model. In other words, when the model control unit 191 receives a specified surface roughness value, it generates and outputs surface shape generation data corresponding to the specified surface roughness value using the model shown by the trained model.

[0079] The trained model and other information used by the artificial intelligence device 19 may be prepared in advance, or they may be acquired via the network 44 as needed.

[0080] Next, using Figure 13, we will explain the process for obtaining surface shape generation data using the surface shape data generation unit 18.

[0081] In step S301, the generation control unit 181 acquires at least one of the first surface roughness and the second surface roughness as a surface roughness specification value and outputs it to the model control unit 191.

[0082] In step S302, the generation control unit 181 inputs the surface roughness specification value to the artificial intelligence device 19 and generates surface shape generation data.

[0083] In step S303, the generation control unit 181 outputs the surface shape generation data obtained by the artificial intelligence device 19 to the data acquisition unit 11.

[0084] In step S304, the data acquisition unit 11 acquires the surface shape generation data output from the generation control unit 181 and outputs it to the wear amount calculation unit 13 as at least one of the first surface shape data 33 and the second surface shape data 36. The wear prediction device 100 can calculate the wear amount without actually preparing the first sliding member 31 by using the surface shape generation data corresponding to the first surface roughness generated by the artificial intelligence device 19 as the first surface shape data 33. Furthermore, the wear prediction device 100 can calculate the wear amount without actually preparing the second sliding member 34 by using the surface shape generation data corresponding to the second surface roughness generated by the artificial intelligence device 19 as the second surface shape data 36.

[0085] This makes it easy to generate a large number of surface shape generation data with different surface roughnesses. Therefore, when wear prediction is needed, it is not necessary to manufacture the first sliding member 31 and the second sliding member 34 and measure their surface shapes. Instead, multiple wear prediction devices 100 can perform wear amount calculations in parallel for cases with different initial roughnesses.

[0086] In this way, by generating surface shape data for the first surface 32 and the second surface 35 using artificial intelligence, simulated surfaces of the first sliding member 31 and the second sliding member 34 with various surface finishes can be easily generated, and the amount of wear can be calculated. If it is acceptable to replace the surface of the sliding member used for wear prediction with a simulated surface, using artificial intelligence eliminates the need to actually manufacture the first sliding member 31, the second sliding member 34, and other components.

[0087] The generation control unit 181 may also return an inquiry to the surface roughness input unit 21, as shown in Figure 14. The generation control unit 181 interacts with the surface roughness input unit 21, that is, it repeatedly inputs and outputs information related to the surface roughness specification value to the surface roughness input unit 21, in order to obtain surface shape generation data that better matches the requirements of the surface roughness input unit 21. If the generation control unit 181 cannot form a surface that satisfies the input surface roughness specification value, it returns a message to the surface roughness input unit 21 suggesting a modification of the surface roughness specification value. When the generation control unit 181 receives a response from the surface roughness input unit 21 to the message suggesting a modification of the surface roughness specification value, it generates a command indicating the addition, modification, or cancellation of the content indicated by the already input surface roughness specification value and inputs it to the artificial intelligence device 19. Inquiries may also be made via chat. In this way, in order to improve the accuracy of input to the artificial intelligence device 19, the system returns a query to the user and updates the input information based on the response. This makes it possible to generate surface shape generation data based on an appropriate surface roughness specification value, even if there is a defect in the surface roughness specification value entered in the surface roughness input unit 21.

[0088] Furthermore, the artificial intelligence device 19 may further include a reference information storage unit 193, as shown in Figure 15. The artificial intelligence device 19 may also be a model and its operating environment configured to generate and output surface shape generation data based on surface roughness, other information accessible to the artificial intelligence device 19, and a trained model, when a surface roughness specification value is input from the generation control unit 181. The reference information storage unit 193 stores reference information that associates the surface roughness specification value with reference surface shape data corresponding to the surface roughness specification value. The reference information storage unit 193 may consist of multiple databases connected via a network 44. The reference information is information referenced by the model control unit 191 in order to output surface shape generation data. The reference information includes, for example, surface shape reference data measured by a surface shape measuring instrument, and the minimum autocorrelation length Sal, root mean square height Sq, skewness Ssk, and kurtosis Sku obtained based on the surface shape generation data.

[0089] When a surface roughness specification value is input to the model control unit 191, it generates and outputs surface shape generation data based on the surface roughness specification value and reference information. For example, when a surface roughness specification value is input to the model control unit 191, it searches the reference information storage unit 193 and obtains surface shape reference data corresponding to the surface roughness specification value. That is, the model control unit 191 generates surface shape generation data based on the surface roughness specification value, the trained model, and the surface shape reference data. In order to improve the validity of the surface shape generation data generated by the artificial intelligence device 19, by having the artificial intelligence device 19 generate surface shape generation data based on information obtained by searching for specified information, it is possible to generate surface shape generation data that is closer to the actual surface shape after processing, and if this surface shape generation data is used in wear amount calculation, the accuracy of wear amount prediction can be improved.

[0090] Furthermore, although this embodiment describes an example of acquiring three-dimensional surface shape generation data, two-dimensional surface shape generation data may also be generated by the artificial intelligence device 19 using two-dimensional line roughness. For example, a three-dimensional surface shape can be generated by duplicating the two-dimensional surface height in the surface direction.

[0091] Embodiment 5. The wear prediction device 100 according to Embodiment 5 will be described with reference to the figures. While Embodiments 1 to 4 described the functions of the wear prediction device 100, Embodiment 5 will describe an example of a wear prediction system 1000 equipped with the wear prediction device 100. The following description will focus on the differences from Embodiments 1 to 4, and descriptions of identical or corresponding parts will be omitted as appropriate.

[0092] The wear prediction system 1000 according to this embodiment, as shown in Figure 16, comprises a sliding device 37 and a wear prediction device 100 described in embodiments 1 to 4, which is connected to the sliding device 37 via a network 44. The sliding device 37 exists in a real space 46, which is a real-world spatial domain, and includes a first sliding member 31, a second sliding member 34 that slides with the first sliding member 31, and an operation control unit 42 that controls the sliding operation of the first sliding member 31 and the second sliding member 34. The wear prediction device 100 calculates the amount of wear of at least one of the first sliding member 31 and the second sliding member 34 in a virtual space 45, which is a virtual spatial domain. The operation control unit 42 controls the sliding speed and load of the sliding device 37 based on the calculation result of the amount of wear. The database 43 and the wear prediction device 100, as well as the operation control unit 42 and the sliding device 37, are connected by wired or wireless means.

[0093] Furthermore, the wear prediction system 1000 may also include a database 43 that stores calculation data necessary for each calculation in the wear prediction device 100. The database 43 stores data used as calculation data for at least one of the following calculations in the wear prediction device 100: the lubrication fluid film characteristics, the estimated viscosity of the lubrication fluid interposed between the first sliding member and the second sliding member, the estimated dissolution viscosity of the mixed fluid formed when the ambient fluid in contact with the lubrication fluid is dissolved in the lubrication fluid, the amount of wear, and the surface shape after wear. This data includes at least one of the following: the diameter of the rotating shaft when the first sliding member 31 is a rotating shaft and the second sliding member 34 is a sliding bearing, the diameter of the sliding bearing, the axial length of the sliding bearing, the composition of the ambient fluid, the composition of the lubrication fluid, the physical properties information of the lubrication fluid, an operating condition generation model that generates sliding velocity and load, and sliding environment data of the sliding device 37. Here, the physical properties information of the lubrication fluid includes a dataset that associates ambient pressure, ambient temperature, and viscosity of the lubrication fluid. Furthermore, the physical property information of the lubricating fluid may include a dataset that associates the composition of the ambient fluid and combinations of the lubricating fluid's composition with the ambient pressure, ambient temperature, and the dissolved viscosity of the lubricating fluid dissolved in the ambient fluid and the mixed fluid of the ambient fluid.

[0094] Figure 17 is an explanatory diagram showing an example configuration of a wear prediction system 1000 corresponding to the digital twin platform system according to Embodiment 5. As shown in Figure 17, the wear prediction system 1000 is a system that realizes a so-called digital twin by realizing tangible assets such as sliding devices 37 that exist in a real space 46, which is a real-world spatial domain, as electronic assets in a virtual space 45, which is a virtual spatial domain. This wear prediction system 1000 is configured by connecting the real space 46 to a wear prediction device 100. Specifically, the wear prediction device 100 collects assets that exist in the real space 46 from the real space 46 and reproduces the assets that exist in the real space 46 in the virtual space 45 based on the collected data. The virtual space 45 is also called the metaverse. In Figure 17, the virtual space 45 is depicted as existing independently outside the wear prediction device 100, but in reality, the virtual space 45 is constructed within the wear prediction device 100. The wear prediction device 100 is also connected to the real space 46 via a network 44 or the like. Here, a digital twin refers to the wear prediction device 100 collecting data from the real space 46 and recreating the asset from which the data was collected in the virtual space 45 based on the collected data; in other words, recreating the real space 46 in the virtual space 45.

[0095] In the real space 46, as shown in Figure 17, there exists, for example, a user 461 of the sliding device a and a manufacturer 462 of the sliding device 37 that possesses the sliding device b. The manufacturer 462 provides know-how and tools for properly operating the sliding device a (for example, a control program to control the sliding device a, a function addition program to add functions to the sliding device a, etc.). Know-how and tools may be provided to the user 461 from the manufacturer 452, 462 with human intervention in the real space 46, or they may be provided to the user 461 via the virtual space 45 described later.

[0096] In the virtual space 45, there are users 451 and 452, which are the counterparts of user 461 and manufacturer 462 existing in the real space 46, as electronic assets. User 451 in the virtual space 45 has a sliding device a that is the counterpart to the sliding device a of user 461 in the real space 46, and manufacturer 452 in the virtual space 45 has a sliding device b that is the counterpart to the sliding device b of manufacturer 462 in the real space 46, as well as know-how, operating programs, etc.

[0097] The virtual space 45 of the wear prediction system 1000 is provided in the wear prediction device 100. This wear prediction device 100 is provided on a cloud 47, as shown in Figure 18. Figure 18 is a diagram showing an example of the arrangement of the wear prediction device 100 that realizes the wear prediction system 1000. The cloud 47 is connected to the users 461 and manufacturers 462 of the sliding device a in the real space 46 via a network 44, and is configured to provide data obtained from the real space 46 to the users 451 and manufacturers 452 of the virtual space 45, and to provide data obtained from the virtual space 45 to the users 461 and manufacturers 462 of the real space 46.

[0098] Manufacturers 452 and 462 provide services to improve the function and performance of the user 461's sliding device a. For example, manufacturers 452 and 462 provide the data stored in the aforementioned database 43 regarding the sliding device a to the user 461's operating control unit of the sliding device a. This allows user 461 to understand the remaining lifespan of the first sliding member 31 and the second sliding member 34 based on the predicted wear amount of the first sliding member 31 and the second sliding member 34 according to the material specifications of the sliding device a. In addition, manufacturers 452 and 462 can provide the user 461 with an operating program for the sliding device a that improves its function and performance, after confirming its effectiveness in the sliding device a or sliding device b in the virtual space 45.

[0099] If the user agrees to use the virtual space 45, the wear prediction device 100 in the cloud 47 may transmit sliding environment data such as ambient pressure, sliding speed, load, etc., measured and recorded at the user's sliding device a in the real space 46 at the time when the user 461 wants to know the lifespan of the sliding device a, to predict the amount of wear of the first sliding member 31 and the second sliding member 34 at that time, and further calculate the remaining lifespan based on virtual operating conditions. If there is a request to extend the remaining lifespan, the manufacturers 452 and 462 may select an operating program that can extend the remaining lifespan of the sliding device 37 and provide it to the operation control unit 42 of the sliding device 37 in the real space 46.

[0100] The sliding device a is the sliding device 37 described in Embodiments 1 to 4, and is, for example, a compressor for compressing a refrigerant. The first sliding member 31 is, for example, a rotating shaft attached to the compressor and supported so as to be rotatable, the second sliding member 34 is a sliding bearing into which the rotating shaft is inserted, the lubricating fluid is refrigerant oil that lubricates the rotating shaft and the sliding speed, and the ambient fluid is the refrigerant. If the compressor is a scroll compressor, the combination of the first sliding member 31 and the second sliding member 34 may be, for example, the first sliding member 31 being the tooth tip of an orbiting scroll and the second sliding member 34 being the tooth root of a fixed scroll. If the compressor is a rotary compressor, the first sliding member 31 may be a vane that separates a high-pressure space and a low-pressure space in the refrigerant compression mechanism, and the second sliding member 34 may be a vane groove (also called a vane slot) into which the vane is fitted.

[0101] The sliding device 37 is not limited to a compressor, but may be, for example, a wind turbine generator or turbine generator having a sliding bearing. It may also be a mechanical device having gears that slide. Furthermore, the sliding members of the sliding device 37 in the real space that correspond to the first sliding member 31 and the second sliding member 34 of the sliding device 37 in the virtual space may be made of metal or resin. The mechanical device that predicts the amount of wear using the wear prediction device 100 and the wear prediction system 1000 should be capable of obtaining the lubrication fluid film characteristics of the lubrication fluid interposed between the first sliding member 31 and the second sliding member 34 by at least one of theoretical and experimental methods. Here, theoretical methods refer to methods using fluid lubrication theory such as the Reynolds equation or elastohydrodynamic lubrication theory such as the Dowson-Higginson equation, and experimental methods refer to measuring the lubrication fluid film pressure using a pressure sensor or measuring the lubrication fluid film thickness using a displacement sensor.

[0102] In this way, the wear prediction device 100 acquires operating history data and other information from the operating sliding device a using a digital twin, and calculates the amount of wear, allowing the user of the sliding device a to easily understand the amount of wear on the device. Furthermore, by predicting the amount of wear in the future based on hypothetical operating conditions and adding it to the current amount of wear, the cumulative value of wear since the start of operation can be quantified, and the operating time until a preset wear threshold is reached can be calculated as the remaining lifespan. If there is a need to extend the remaining lifespan, the remaining lifespan of the sliding device a can be extended by providing an operating program to the operation control unit 42 of the sliding device a that reduces the sliding speed, load, etc., caused by the sliding between the first sliding member 31 and the second sliding member 34 of the sliding device a.

[0103] Embodiment 6. The operation of the wear prediction device 100 described in Embodiments 1 to 5 will be explained. Figure 19 is a flowchart showing the processing routine executed by the wear prediction device 100.

[0104] The wear prediction device 100 acquires surface shape data, operating time, sliding speed and load corresponding to the operating time, and lubrication fluid film characteristic data (step S401). The load indicates the load at which the first sliding member 31 is pressed against the second sliding member 34, corresponding to the operating time. The surface shape data consists of first surface shape data 33 of the first surface 32, which is at least a part of the surface of the first sliding member 31, and second surface shape data 36 of the second surface 35, which is at least a part of the surface of the second sliding member 34. Next, the cumulative sliding distance calculated using the sliding speed and operating time is divided by a set calculation unit distance to calculate the number of iterations, which is the number of times the first surface 32 is repeatedly moved in the surface direction (step S402). Then, at set time intervals, the inter-surface distance 39 between the first surface 32 and the second surface 35 is set based on the lubrication fluid film characteristics, at least a portion of the interference height at which the first surface 32 and the second surface 35 interfere is subtracted from the surface height of at least one of the first surface 32 and the second surface 35, and the amount of wear of at least one of the first sliding member 31 and the second sliding member 34 is calculated based on the surface heights of the first surface 32 and the second surface 35 before and after the subtraction (step S403). Once the amount of wear is calculated, the calculated result of the amount of wear is output (step S404). Each time the calculation of the amount of wear for one operating time is completed, it is determined whether the number of calculations performed in S403 has reached the number of iterations, or whether the travel distance, which is the cumulative travel distance due to movement, has reached the cumulative sliding distance (step S404). If the answer in S404 is YES, wear information including the calculation result of the amount of wear and the determination result in S404 are output and notified, and the wear amount calculation process by the wear amount calculation unit 13 is terminated (step S405). If NO is obtained in S404, the first surface 32 is moved in the planar direction, and the process returns to S403 (S406). Furthermore, the sliding speed and load during the sliding between the first sliding member 31 and the second sliding member 34 of the sliding device 37 may be controlled based on the calculation result of the amount of wear.

[0105] Here, each function of the wear prediction device 100 is realized by a processing circuit. Figure 20 is a schematic diagram showing an example of a processing circuit that realizes each function of the wear prediction device 100. The wear prediction device 100 has a processor, memory, communication I / F (interface) 50, etc. For example, a CPU (Central Processing Unit) is used as the processor. The memory sends and receives data to and from the processor and stores the data. The operation history data and sliding environment data measured and recorded by each sensor of the sliding device 37 are acquired by the data acquisition unit 11 via the communication I / F 50. The calculations, processing, and judgments of the data acquisition unit 11, the repetition count setting unit 12, the wear amount calculation unit 13, the notification unit 14, and the judgment unit 15 are executed by the processor. Parameters used for calculations, programs 52 related to calculations and other processing are stored in the memory. The calculation results are transmitted to the operation control unit 42 via the communication I / F 50.

[0106] The processor and memory may be shared by a single unit, or there may be multiple units. The processor may also include logic circuits using, for example, ASICs (Application Specific Integrated Circuits), ICs (Integrated Circuits), DSPs (Digital Signal Processors), FPGAs (Field Programmable Gate Arrays), and various signal processing circuits. Multiple processors of the same or different types may be provided, allowing each process to be divided and executed by multiple arithmetic processing units.

[0107] Multiple memory components may include, for example, RAM (Random Access Memory) configured to allow reading and writing of data from the processor, ROM (Read Only Memory) configured to allow reading of data from the processor, and a hard disk drive (HDD).

[0108] Each function of the wear prediction device 100 is realized by the processor executing software or program 52 stored in memory and cooperating with the hardware. The setting data to be set for the wear prediction device 100 may be stored in memory as part of the software or program 52, or it may be made available for user input. A non-temporary recording medium 51 on which the program 52 for the wear prediction device 100 is recorded may be distributed and installed in the memory of the wear prediction device 100.

[0109] In this way, the first sliding member 31 and the second sliding member 34, which move and slide relative to each other, are given three-dimensional surface shape data, and the distance between the first surface 32 and the second surface 35 is set as the inter-surface distance 39 based on the lubricating fluid film characteristics of the lubricating fluid interposed between the surface of the first sliding member 31 and the surface of the second sliding member 34. The first surface 32 and the second surface 35 are then slid in the virtual space 45, and the amount of wear on the first surface 32 and the second surface 35 due to sliding is calculated. In this way, the amount of wear on the first sliding member 31 and the second sliding member 34, which slide with a lubricating fluid film interposed by the lubricating fluid, is calculated, and the amount of wear can be predicted with high accuracy according to the characteristics of the lubricating fluid film.

[0110] While this disclosure describes various exemplary embodiments, the various features, aspects, and functions described in one or more embodiments are not limited to the application of a particular embodiment, but are applicable individually or in various combinations to the embodiments. Accordingly, countless variations not illustrated are conceivable within the scope of the art disclosed herein. These include, for example, modifying, adding or omitting at least one component, or even extracting at least one component and combining it with a component from another embodiment.

[0111] 11 Data acquisition unit, 12 Iteration count setting unit, 13 Wear amount calculation unit, 14 Notification unit, 15 Judgment unit, 16 Lubrication fluid film characteristic calculation unit, 17 Viscosity calculation unit, 18 Surface shape data generation unit, 181 Generation control unit, 19 Artificial intelligence device, 191 Model control unit, 192 Trained model storage unit, 193 Reference information storage unit, 20 Calculation data storage unit, 21 Surface roughness input unit, 100 Wear prediction device, 1000 Wear prediction system

Claims

1. A wear prediction device for predicting wear information due to sliding between a first sliding member and a second sliding member, comprising: a data acquisition unit that acquires first surface shape data of a first surface which is at least a part of the surface of the first sliding member, and second surface shape data of a second surface which is at least a part of the surface of the second sliding member, as well as the operating time of a sliding device that slides the first sliding member and the second sliding member, the sliding speed corresponding to the operating time, the load on which the first sliding member is pressed against the second sliding member corresponding to the operating time, and the lubricating fluid film characteristics of a lubricating fluid interposed between the first sliding member and the second sliding member; and an iteration count setting unit that calculates and sets the number of iterations, which is the number of times the first surface is repeatedly moved in the surface direction, by dividing the cumulative sliding distance calculated using the sliding speed and the operating time by the calculation unit distance. A wear prediction device comprising: a wear amount calculation unit that, at set time intervals, sets the surface distance between the first surface and the second surface based on the lubrication fluid film characteristics, subtracts at least a portion of the interference height at which the first surface and the second surface interfere from the surface height of at least one of the first surface and the second surface, and calculates the amount of wear of at least one of the first sliding member and the second sliding member based on the surface height of the first surface and the surface height of the second surface before and after the subtraction; a determination unit that determines whether the cumulative number of movements in which the first surface has been moved by the wear amount calculation unit has reached the number of iterations, or whether the cumulative movement distance, which is the cumulative value of the movement distance of the first surface due to the movement, has reached the cumulative sliding distance; and a notification unit that notifies the wear information including the calculation result of the wear amount, and at least one of the determination results of the determination unit.

2. The wear prediction device according to claim 1, wherein the repetition count setting unit corrects the value obtained by dividing the cumulative sliding distance by the calculation unit distance to an integer to determine the number of repetitions.

3. The wear prediction device according to claim 1 or 2, wherein the data acquisition unit acquires the minimum lubrication fluid film thickness of the lubrication fluid as the lubrication fluid film characteristics, and the wear amount calculation unit sets the distance between the first average plane obtained by averaging the surface heights of the first surface shape data and the second average plane obtained by averaging the surface heights of the second surface shape data as the inter-surface distance, and sets the inter-surface distance as the minimum lubrication fluid film thickness.

4. The wear prediction device according to any one of claims 1 to 3, wherein the data acquisition unit acquires the lubrication fluid film pressure distribution of the lubrication fluid as the lubrication fluid film characteristics, and acquires the first Young's modulus and the second Young's modulus, respectively, representing the Young's moduli of the first sliding member and the second sliding member, and the wear amount calculation unit calculates the deformation reaction force generated by the elastoplastic deformation of at least a portion of the first surface and the second surface occurring on a portion of the surface having the interference height, using at least one of the first Young's modulus and the second Young's modulus, and sets the distance between the surfaces such that the sum of the lubrication fluid film reaction force and the deformation reaction force generated between the first surface and the second surface due to the lubrication fluid film pressure distribution balances the partial load applied to the first surface and the second surface.

5. A wear prediction device according to any one of claims 1 to 4, comprising a lubrication fluid film characteristic calculation unit that calculates the lubrication fluid film characteristics, which are the characteristics of the lubrication fluid film when the lubrication fluid is interposed between the first sliding member and the second sliding member, wherein the data acquisition unit acquires the diameter of the rotating shaft, the diameter of the sliding bearing, the axial length of the bearing, and the viscosity of the lubrication fluid as calculation data to be used in the calculation of the lubrication fluid film characteristic calculation unit when the first sliding member is a rotating shaft and the second sliding member is a sliding bearing into which the rotating shaft is inserted, and the lubrication fluid film characteristic calculation unit calculates the lubrication fluid film characteristics based on the diameter of the rotating shaft, the diameter of the sliding bearing, the axial length of the sliding bearing, the viscosity of the lubrication fluid, the sliding velocity, and a fluid lubrication theory for calculating the characteristics of the lubrication fluid.

6. The wear prediction device according to claim 5, comprising a viscosity calculation unit for calculating the estimated viscosity of the lubricating fluid, wherein the data acquisition unit acquires, as calculation data used in the calculation of the lubricating fluid film characteristics calculation unit, the ambient pressure of the ambient fluid in contact with at least a portion of the lubricating fluid measured by a pressure sensor, and the ambient temperature of the ambient fluid measured by a temperature sensor, and the viscosity calculation unit calculates the estimated viscosity of the lubricating fluid based on the ambient pressure and the ambient temperature, and outputs the estimated viscosity to the lubricating fluid film characteristics calculation unit.

7. The wear prediction device according to claim 6, wherein the data acquisition unit further acquires the composition of the ambient fluid and the composition of the lubricating fluid, the viscosity calculation unit calculates the estimated dissolved viscosity of the mixed fluid in which the ambient fluid is dissolved in the lubricating fluid based on the composition of the ambient fluid, the ambient pressure, the ambient temperature, and the composition of the lubricating fluid acquired from the data acquisition unit, and outputs the estimated dissolved viscosity to the lubricating fluid film characteristic calculation unit.

8. The wear prediction device according to claim 7, wherein the atmospheric fluid is at least one of air, a fluorine compound, a hydrocarbon compound, and a carbon oxide, and the lubricating fluid is at least one of a polyol ester, a polyvinyl ether, an alkylbenzene, and a mineral oil.

9. The wear prediction device according to any one of claims 6 to 8, wherein the data acquisition unit acquires the hue measurement value of the lubricating fluid measured by a color sensor, and the viscosity calculation unit calculates the corrected estimated viscosity from the hue measurement value using correlation data between hue value and viscosity that has been measured and stored in advance, based on the hue measurement value, and outputs it to the lubricating fluid film characteristic calculation unit.

10. The wear prediction device according to any one of claims 1 to 9, wherein the data acquisition unit acquires the first surface shape data and the second surface shape data measured by the surface shape measuring instrument.

11. A wear prediction device according to any one of claims 1 to 10, comprising a surface shape data generation unit having a generation control unit that acquires at least one of the first surface roughness of the first sliding member and the second surface roughness of the second sliding member as a surface roughness specified value, outputs the acquired surface roughness specified value to an artificial intelligence device, and acquires surface shape generation data corresponding to the surface roughness specified value from the artificial intelligence device and outputs it to the data acquisition unit, wherein the data acquisition unit acquires the surface shape generation data from the surface shape data generation unit as at least one of the first surface shape data and the second surface shape data.

12. The wear prediction device according to claim 11, wherein at least one of the first surface roughness and the second surface roughness includes at least one of the minimum autocorrelation length Sal, and the root mean square height Sq, skewness Ssk, and kurtosis Sku.

13. The wear prediction device according to any one of claims 1 to 12, wherein the data acquisition unit acquires the virtual operating time of the sliding device, the virtual sliding speed in the sliding between the first sliding member and the second sliding member corresponding to the virtual operating time, and the virtual load on which the first sliding member is pressed against the second sliding member corresponding to the virtual operating time, and the wear amount calculation unit calculates the operating time until the cumulative value of the wear amount reaches a preset wear amount threshold as the remaining life time.

14. A sliding device having a first sliding member, a second sliding member that slides with the first sliding member, and an operation control unit that controls the sliding operation of the first sliding member and the second sliding member, which exist in a real space which is a real spatial domain; and a wear prediction device according to any one of claims 1 to 13 that calculates the amount of wear and the surface shape after wear due to sliding between the first sliding member and the second sliding member in a virtual space which is a virtual spatial domain, wherein the operation control unit controls the sliding speed and load of the sliding device based on the calculation result of the amount of wear.

15. The wear prediction system according to claim 14, further comprising an artificial intelligence device that generates surface shape generation data corresponding to a specified surface roughness value input from the generation control unit of the wear prediction device.

16. A wear prediction system according to claim 14 or claim 15, comprising a database that stores data used for calculations of at least one of the following: the lubrication fluid film characteristics, the estimated viscosity of the lubrication fluid interposed between the first sliding member and the second sliding member, the estimated dissolution viscosity of the mixed fluid obtained by dissolving the ambient fluid in contact with the lubrication fluid, the amount of wear, and the surface shape after wear, the diameter of the rotating shaft when the first sliding member is a rotating shaft and the second sliding member is a sliding bearing, the diameter of the sliding bearing, the axial length of the sliding bearing, the composition of the ambient fluid, the composition of the lubrication fluid, physical property information of the lubrication fluid, an operating condition generation model that generates the sliding velocity and the load, and sliding environment data of the sliding device.

17. The wear prediction system according to any one of claims 14 to 16, wherein the sliding device is a compressor for compressing a refrigerant, the first sliding member is a rotating shaft attached to the compressor and rotatably supported, the second sliding member is a sliding bearing into which the rotating shaft is inserted, the lubricating fluid is refrigerant oil for lubricating the rotating shaft and the sliding bearing, and the ambient fluid in contact with the lubricating fluid is the refrigerant.

18. A wear prediction method for predicting wear information due to sliding between a first sliding member and a second sliding member, comprising the steps of: acquiring first surface shape data of a first surface which is at least a part of the surface of the first sliding member, and second surface shape data of a second surface which is at least a part of the surface of the second sliding member, and acquiring the operating time of a sliding device that slides the first sliding member and the second sliding member, the sliding speed corresponding to the operating time, the load on which the first sliding member is pressed against the second sliding member corresponding to the operating time, and the lubricating fluid film characteristics of a lubricating fluid interposed between the first sliding member and the second sliding member; and calculating and setting the number of iterations which is the number of times the first surface is repeatedly moved in the surface direction by dividing the cumulative sliding distance calculated using the sliding speed and the operating time by a set calculation unit distance. A wear prediction method comprising: setting the inter-surface distance between the first surface and the second surface based on the lubrication fluid film characteristics at set time intervals; subtracting at least a portion of the interference height at which the first surface and the second surface interfere from the surface height of at least one of the first surface and the second surface; calculating the amount of wear of at least one of the first sliding member and the second sliding member based on the surface height of the first surface and the surface height of the second surface before and after the subtraction; determining whether the cumulative number of movements of the first surface in the step of calculating the amount of wear has reached the number of iterations, or whether the cumulative movement distance, which is the cumulative value of the movement distance of the first surface due to the movement, has reached the cumulative sliding distance; and notifying at least one of the wear information including the calculation result of the amount of wear and the determination result in the determination step.

19. The wear prediction method according to claim 18, further comprising the step of controlling the sliding speed and load of the sliding device based on the calculation result of the amount of wear.

Images