Information processing device, machine learning device, inference device, information processing method, machine learning method, and inference method

Abstract

The objective is to provide an information processing device, a machine learning device, an inference device, an information processing method, a machine learning method, and an inference method that are capable of verifying the amount of fluid passing through a valve mechanism, verifying the presence or absence of unexpected fluid leakage from between the valve body and the valve seat, verifying the amount of leakage, and verifying signs of unexpected fluid leakage from the valve mechanism. An information processing device (250) of the present disclosure predicts the state of a valve mechanism, and obtains a valve mechanism state indicating the state of the valve mechanism based on input information that is a predetermined variation between a valve disc and a valve seat.

Description

[Technical field]

[0001] The present disclosure relates to an information processing device, a machine learning device, an inference device, an information processing method, a machine learning method, and an inference method. [Background technology]

[0002] Conventionally, a breather valve has been known that has a positive side valve seat having an outlet for a fluid in a container, and a positive side valve that can open and close the outlet depending on the pressure in the container (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-21652 A Summary of the Invention [Problem to be solved by the invention]

[0004] In a valve mechanism consisting of a valve body and a valve seat provided on a fluid flow path, due to the importance of managing the fluid flowing inside, it is required that the valve mechanism reliably closes the flow path, that the amount of fluid passing through the valve mechanism can be verified, that the presence or absence of unexpected fluid leakage from between the valve body and the valve seat can be verified, and that the amount of leakage can be verified in the unlikely event that the fluid leaks from the valve mechanism. Furthermore, in order to prevent fluid leakage from the valve mechanism due to an unexpected event or to minimize leakage, it is also required to verify signs of unexpected fluid leakage from the valve mechanism. In the case of handling volatile and flammable fluids, or fluids that are harmful to the human body and the environment, this requirement has become even greater due to the recent increase in consideration of animals, plants, and the environment. However, in conventional valve mechanisms, there is a problem in that it is not possible to verify the amount of fluid passing through the valve mechanism, the presence or absence of unexpected fluid leakage from between the valve body and the valve seat, the amount of leakage, and signs of unexpected fluid leakage from the valve mechanism until the flow path is closed and opened. This problem has not been solved at all even in the valve mechanism used in a breather valve that handles volatile fluids, as in the conventional example shown as an example of a valve mechanism, and exists as a general problem with valve mechanisms.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide an information processing device, a machine learning device, an inference device, an information processing method, a machine learning method, and an inference method that are capable of verifying the amount of fluid passing through a valve mechanism, verifying the presence or absence of unexpected fluid leakage from between the valve body and the valve seat, verifying the amount of leakage, and verifying signs of unexpected fluid leakage from the valve mechanism. [Means for solving the problem]

[0006] The information processing device according to the present disclosure is an information processing device that predicts the state of a valve mechanism, and obtains a valve mechanism state indicating the state of the valve mechanism based on input information that is a predetermined variable between a valve disc and a valve seat. Effect of the Invention

[0007] The information processing device, machine learning device, inference device, information processing method, machine learning method, and inference method disclosed herein can verify the amount of fluid passing through the valve mechanism, verify the presence or absence of unexpected fluid leakage from between the valve body and the valve seat, verify the amount of leakage, and verify signs of unexpected fluid leakage from the valve mechanism. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram showing a valve system according to a first embodiment. [Diagram 2] FIG. 2 is a schematic diagram showing the breather valve of FIG. 1. [Diagram 3] FIG. 3 is a top view showing the introduction portion of FIG. 2. [Figure 4] FIG. 3 is an enlarged view showing a portion including the detection device of FIG. 2. [Diagram 5] FIG. 3 is a top view of the valve contact member of FIG. 2. [Figure 6] FIG. 2 is a configuration diagram showing the machine learning device of FIG. 1. [Figure 7] FIG. 7 is a conceptual diagram showing elements of machine learning implemented by the machine learning device of FIG. 6. [Figure 8] FIG. 2 is a schematic diagram illustrating the information processing device of FIG. [Figure 9] FIG. 1 is a diagram illustrating the hardware configuration of a computer. [Figure 10] 2 is a flowchart showing a machine learning method performed by the machine learning device of FIG. 1. [Figure 11] 4 is a flowchart showing a learning valve mechanism state prediction method performed by the information processing device of FIG. 1. [Figure 12] FIG. 11 is a top view showing an introduction section according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment for carrying out the present disclosure will be described with reference to the drawings. Note that, in the following, the scope necessary for the explanation to achieve the object of the present disclosure is shown in a schematic manner, and the scope necessary for the explanation of the relevant part of the present disclosure will be mainly explained, and the parts that are omitted from the explanation will be referred to as publicly known techniques.

[0010] Embodiment 1 In the description of the valve system of the present disclosure, in the first embodiment, a valve system applied to a breather valve is used. However, the valve system of the present disclosure is not limited to applications to breather valves, and can be used appropriately in general valve mechanisms. The same applies to the valve systems in the second and third embodiments described below.

[0011] 1 is a schematic diagram showing a valve system 200 according to embodiment 1. The valve system 200 includes a breather valve 5, a machine learning device 240, an information processing device 250, and a network 270 connecting the above devices.

[0012] Figure 2 is a schematic diagram showing the breather valve 5 of Figure 1. The breather valve 5 is connected to the opening of the tank 4 via the upstream flange 4b. In Figure 2, a part of the breather valve 5 is shown in cross section on a plane including the axis of the flow path.

[0013] The tank 4 stores a fluid such as a flammable gas or liquid. Furthermore, the tank 4 also contains a fluid that is the volatilized version of the stored fluid. Examples of the flammable gas or liquid include fossil fuels and volatile gases. The shape of the tank 4 is a sphere, a cylinder, a rectangular solid, a cube, or the like.

[0014] In the first embodiment, the tank 4 is shaped such that the horizontal cross-sectional area of ​​the tank 4 decreases toward the upper end. An attachment portion 4a is provided at the upper end of the tank 4. A through hole that communicates with the inside of the tank 4 is formed in the attachment portion 4a. This allows the fluid inside the tank 4 to be efficiently discharged through the attachment portion 4a.

[0015] The base of the breather valve 5 is attached to the attachment portion 4a via the upstream flange 4b. In the present embodiment 1, the tank 4 is the upstream side, and the breather valve 5 is installed on the downstream side of the tank 4.

[0016] The breather valve 5 can discharge the fluid inside the tank 4 to the atmosphere according to the internal pressure of the tank 4, and can allow the atmosphere, which is the suction fluid, to flow into the tank 4. In other words, the breather valve 5 can adjust the pressure inside the tank 4.

[0017] In the first embodiment, the tank 4 and the breather valve 5 are installed outdoors and exposed to the atmosphere.

[0018] The breather valve 5 includes a main section 10, an intake section 20, and an exhaust section 40. The main section 10 is a T-shaped piping member. The ends of the T-shaped piping member of the main section 10 are an inlet side opening end 10a that opens vertically downward, an outlet side opening end 10b that opens vertically upward, and an intake side opening end 10c that opens horizontally.

[0019] The main section 10 is installed with the inlet opening end 10a on the lower side and the outlet opening end 10b on the upper side. A flow path leading from the inlet opening end 10a to the outlet opening end 10b is defined as a main flow path 110. The main section 10 is installed so that the axis of the main flow path 110 is along the vertical direction. The main flow path 110 extends toward the discharge section 40. The main flow path 110 extending to the discharge section 40 will be described later.

[0020] A flow path that branches off from the main flow path 110 and reaches the intake side opening end 10c is referred to as a branch flow path 111. The axis of the branch flow path 111 is aligned in the horizontal direction. The intake section 20 is connected to the intake side opening end 10c of the main section 10.

[0021] The intake section 20 has an intake section main body 21 and an intake valve mechanism 22 provided in the intake section main body 21. The intake section main body 21 is a tubular member having two open ends.

[0022] One end of the intake unit body 21 is a connection side opening end 21a that opens in the horizontal direction. The other end of the intake unit body 21 is formed with an intake port 21b that opens vertically downward. A flow path connecting the connection side opening end 21a and the intake port 21b is defined as an intake flow path 120. Here, the other end of the intake unit body 21 that constitutes the periphery of the intake port 21b is defined as an intake valve seat 21x.

[0023] The intake valve mechanism 22 has an intake valve 23 which is an opening / closing valve, an intake valve shaft 24 fixed to the intake valve 23, and an intake valve guide 25 installed in the intake unit main body 21. When the intake valve 23 comes into contact with an intake valve seat 21x which constitutes the periphery of the intake port 21b, the intake port 21b is closed. When a gap is generated between the intake valve 23 and the intake valve seat 21x, the intake port 21b is opened. That is, the intake valve 23 can open and close the intake flow path 120.

[0024] The intake valve guide 25 supports the intake valve shaft 24 so that it can move in the vertical direction. This allows the intake valve 23 to move between an intake close position and an intake open position as the intake valve shaft 24 moves in the vertical direction. The intake valve 23 is an open valve. The intake valve 23 can move toward the intake close position by dropping along the intake valve guide 25 due to the weight of the intake valve 23 and the intake valve shaft 24.

[0025] When the intake valve 23 is in the intake closing position, the intake valve 23 closes the intake port 21b. That is, the lower surface of the intake valve 23 is in contact with the intake valve seat 21x, so the intake flow path 120 is closed and communication between the intake flow path 120 and the outside of the breather valve 5 is cut off.

[0026] When the intake valve 23 moves upward against its own weight from the intake closed position to the intake open position, a gap is created between the intake valve 23 and the intake valve seat 21xb. In other words, the intake passage 120 is opened and communicates with the atmosphere outside the breather valve 5.

[0027] The intake side opening end 10c and the connection side opening end 21a are connected, and the intake section 20 is connected to the intake side opening end 10c of the main section 10. As a result, the intake flow path 120 inside the intake section main body 21 communicates with the branch flow path 111 inside the main section 10. That is, the intake flow path 120 extends horizontally from the branch flow path 111.

[0028] The discharge part 40 is connected to the outlet opening end 10b of the main part 10. The discharge part 40 has a discharge part main body 41, a vent cover 47, a discharge valve mechanism 50 installed on the vent cover 47, a detection device 60, and a valve contact member 70.

[0029] The exhaust body 41 is composed of a tubular inlet 42 and an air exhaust part 43 provided to surround one open end of the inlet 42. The exhaust body 41 is installed in a position in which the pipe of the inlet 42 is aligned vertically.

[0030] Fig. 3 is a top view showing the introduction portion 42 in Fig. 2. Fig. 4 is an enlarged view showing a portion including the detection device 60 in Fig. 2. Fig. 3 shows the top surface of the introduction portion 42, so the valve contact member 70 is not shown. The explanation will continue based on Figs. 2 to 4.

[0031] The downward opening end of introduction section 42 is referred to as discharge section inlet opening end 42a. The vertically upward opening end of introduction section 42 is referred to as valve side end 42b. The opening of valve side end 42b is referred to as valve side opening 42c, and the end face of valve side end 42b is referred to as main body end face 42d. Discharge section inlet opening end 42a is connected to outlet opening end 10b of main section 10.

[0032] One detector installation space 42e is formed in the introduction portion 42. The detector installation space 42e is a space that opens to the main body end surface 42d, passes through the wall of the introduction portion 42 from the opening in the main body end surface 42d, and opens to the outer periphery side of the introduction portion 42. A part of the detection device 60 is installed in the detector installation space 42e.

[0033] The detection device 60 has a detector 61 and wiring 62 that transmits a signal from the detector 61 to an input / output device (not shown). The detector 61 can detect a predetermined variable of a detection target.

[0034] At this time, the detector 61 can detect a predetermined variation of the detection target in the direction in which the detector 61 is pointed, and this direction is defined as the detection direction of the detector 61. In other words, the detection direction of the detector 61 is the direction in which the detector 61 can detect a predetermined variation of the detection target.

[0035] In the detector installation space 42e, one detector 61 and a part of the wiring 62 extending from the detector 61 are installed. The detector 61 is installed so that the detection direction of the detector 61 faces from the detector installation space 42e toward the outside of the main body end face 42d. In other words, the detector 61 can detect a predetermined variable of a detection target located beyond the end face on the valve side end face 42b side of the introduction part 42.

[0036] The wiring 62 is installed to extend outward from the outer periphery of the introduction part 42 through the detector installation space 42e. The wiring 62 is connected to an input / output device (not shown). The input / output device can transmit a predetermined variable detected by the detector 61 onto the network 270.

[0037] The network 270 is a wired or wireless information transmission network. The network 270 may be connected to the Internet and be capable of transmitting information. Devices connected to the network 270 can acquire the signal of the detector 61 transmitted onto the network 270. Other devices connected to the network 270 will be described later.

[0038] A valve contact member 70 is provided at the valve side end portion 42b. Fig. 5 is a top view showing the valve contact member 70 of Fig. 2. The description will continue with reference to Figs. 2 to 5.

[0039] The valve contact member 70 is an annular member. In the valve contact member 70, a cross section along a line along the radial direction of the annular shape is L-shaped. That is, the valve contact member 70 has a shape in which an annular flat plate material is bent perpendicularly to the plane around the entire circumference of its inner periphery. The valve contact member 70 is formed into a shape corresponding to the shape of the main body end face 42d.

[0040] The valve contact member 70 is installed in the introduction part 42 so as to cover the main body end face 42d of the introduction part 42 and a part of the inner circumferential surface of the introduction part 42 continuing from the main body end face 42d. Here, the surface of the valve contact member 70 facing the discharge valve 53 is referred to as a valve facing surface 70a. The discharge valve 53 will be described later.

[0041] The opening of the detector installation space 42e formed in the main body end surface 42d is closed by installing the valve contact member 70 in the introduction portion 42. That is, the detector 61 is installed in the detector installation space 42e covered by the valve contact member 70. The detection direction of the detector 61 is toward the exhaust valve 53 located beyond the valve facing surface 70a.

[0042] The valve contact member 70 is installed by being screwed to the introduction portion 42. The valve contact member 70 has attachment points for screwing provided on its outer periphery. However, the valve contact member 70 may be installed to the introduction portion 42 by any known method other than screwing. The valve contact member 70 is preferably made of a material that does not significantly reduce the detection capability of the detector 61. For example, the valve contact member 70 is made of stainless steel.

[0043] It is preferable to design the shape of the valve contact member 70 so as not to significantly reduce the detection ability of the detector 61. For example, by making the valve contact member 70 thinner, a significant reduction in the detection ability of the detector 61 can be prevented. As shown in FIG. 2-5, the inner circumferential surface of the introduction portion 42 where the detector installation space 42e is formed is formed to protrude further inward, and the wall thickness of the introduction portion 42 where the detector installation space 42e is formed is thicker than the wall thickness of the other portions. The valve contact member 70 is formed in a shape corresponding to the shape of the main body end surface 42d. However, this is not limited to this. For example, the wall thickness of the introduction portion 42 may be uniform, that is, the inner circumferential surface of the introduction portion 42 does not have a part protruding inward, and the inner circumferential surface of the introduction portion 42 viewed from the axial direction may be circular. Even in this case, the valve contact member 70 is formed in a shape corresponding to the shape of the main body end surface 42d.

[0044] Here, the valve side end 42b of the introduction portion 42 is the valve seat body 3. The valve seat body 3 may be cylindrical and form part of a flow path closed by the valve body, so that the valve side end 42b of the introduction portion 42 forms part of the discharge flow path 140. The body end surface 42d of the introduction portion 42 is the end of the flow path formed in the valve seat body 3, and is the body end surface of the valve seat body 3 that faces the valve body. The valve seat body 3 is formed with a detector installation space 42e.

[0045] Moreover, the valve seat main body 3, which is the valve side end 42b of the introduction part 42, and the valve contact member 70 are defined as the valve seat 2. Therefore, the valve seat 2 constitutes a part of the flow path. Moreover, the valve seat 2 and the detection device 60 are defined as the valve seat mechanism 1. In this case, the valve facing surface 70a of the valve contact member 70 is defined as the valve seat end surface 2a.

[0046] That is, the valve seat end face 2a faces the lower surface of the discharge valve 53, which is a valve body, and is in direct contact with the discharge valve 53 when the discharge valve 53 closes the discharge flow path 140. Furthermore, a gap is formed between the discharge valve 53 and the valve seat end face 2a, and when the discharge flow path 140 is opened, the fluid in the tank 4 is discharged through the gap between the discharge valve 53 and the valve seat end face 2a.

[0047] In addition, the detection direction is toward the discharge valve 53, which is a valve body, located beyond the valve seat end face 2a, in a state in which the detector 61 is installed in the detector installation space 42e.

[0048] Returning to Fig. 2, the explanation will continue. The atmosphere exhaust part 43 is formed so as to surround the valve side end part 42b of the introduction part 42. An opening is formed on the upper end side of the atmosphere exhaust part 43. The upper end side of the atmosphere exhaust part 43 is defined as the valve mechanism side opening end part 43b. Below the valve mechanism side opening end part 43b of the atmosphere exhaust part 43, i.e., in the body part of the atmosphere exhaust part 43, a plurality of discharge ports 43a that open in the horizontal direction are formed.

[0049] The vent cover 47 is connected to the valve mechanism side opening end 43b. The vent cover 47 closes the opening of the valve mechanism side opening end 43b. A known configuration can be used for connecting the vent cover 47 to the valve mechanism side opening end 43b. The vent cover 47 is provided with a discharge valve mechanism 50.

[0050] The exhaust valve mechanism 50 has a exhaust valve 53 which is a valve body capable of closing the valve side opening 42c, a exhaust valve shaft 54 ​​fixed to the exhaust valve 53, and a exhaust valve guide 55 installed on the vent cover 47.

[0051] With the vent cover 47 connected to the valve mechanism side opening end 43b, the exhaust valve guide 55 extends vertically downward from the vent cover 47. The exhaust valve guide 55 supports the exhaust valve shaft 54 ​​so as to be movable along the vertical direction.

[0052] The discharge valve 53 is movable between a discharge closed position and a discharge open position by the discharge valve shaft 54 ​​moving along the vertical direction. The discharge valve 53 is an open valve.

[0053] The discharge valve 53 can move toward the discharge closed position by dropping along the discharge valve guide 55 due to the weight of the discharge valve 53 and the discharge valve shaft 54 ​​.

[0054] When the exhaust valve 53 is in the exhaust closing position, the exhaust valve 53 closes the valve side opening 42c. Specifically, when the exhaust valve 53 is in the exhaust closing position, the surface of the exhaust valve 53 contacts the valve facing surface 70a of the valve contact member 70. At this time, the surface of the exhaust valve 53 and the valve facing surface 70a are in continuous contact around the valve side opening 42c.

[0055] In the first embodiment, the surface of the discharge valve 53 that contacts the valve facing surface 70a is the underside of the discharge valve 53. That is, the detection device 60 is installed to face the underside of the discharge valve 53, and can detect the state of the discharge valve 53.

[0056] When the discharge valve 53 moves against its own weight from the discharge closed position to the discharge open position, a gap is generated between the discharge valve 53 and the valve side opening 42c.

[0057] The discharge part 40 has a discharge flow passage 140 that extends from the discharge part inlet opening end 42a through the valve side opening 42c to the multiple discharge ports 43a. When the discharge valve 53 is in the discharge closed position, communication between the main flow passage 110 extending from the inside of the main part 10 and the discharge flow passage 140 is blocked.

[0058] When the exhaust valve 53 moves from the exhaust closed position toward the exhaust open position and a gap is created between the exhaust valve 53 and the valve side opening 42c, the main flow path 110 extending from the inside of the main section 10 and the exhaust flow path 140 become connected.

[0059] Next, a description will be given of the operation of the breather valve 5. The breather valve 5 operates based on the internal pressure of the tank 4.

[0060] First, when the internal pressure of the tank 4 is normal, the intake valve 23 and the exhaust valve 53 are both closed. In this state, intake from the intake valve 23 and exhaust from the exhaust valve 53 are not performed, and the internal pressure in the tank 4 is maintained.

[0061] Next, a case where the internal pressure of the tank 4 becomes low, which is lower than the normal pressure, will be described. In the intake section 20, since the internal pressure of the intake section 20 is low, the intake valve 23 is pushed by the atmosphere. Therefore, the intake valve 23 moves from the intake closed position toward the intake open position.

[0062] Specifically, the intake valve 23 is pushed by the atmosphere and rises against its own weight. The total weight of the intake valve 23 and the intake valve shaft 24 is set to a weight that rises when pushed by the atmosphere when the internal pressure of the intake section 20 falls below the normal pressure.

[0063] As the intake valve 23 rises against its own weight, the intake passage 120 is opened and the air enters the inside of the intake part 20 through the intake port 21b.

[0064] On the other hand, since the internal pressure of the discharge part 40 is low, the discharge valve 53 moves downward by its own weight and remains in the discharge closed position. That is, the valve-side opening 42c is maintained in a state where it is closed by the discharge valve 53.

[0065] As a result, air flows in through the intake port 21b, and the internal pressure of the tank 4 increases, causing the intake valve 23 to move to the intake closing position due to its own weight, closing the intake port 21b. In this way, the internal pressure of the tank 4 becomes the pressure set by the weight of the intake valve 23.

[0066] Next, a case where the internal pressure of the tank 4 is higher than the normal pressure will be described. At this time, since the internal pressure of the intake section 20 is high, the intake valve 23 remains in the intake closing position due to the internal pressure of the intake section 20 and its own weight. Therefore, the intake port 21b is closed.

[0067] In the discharge section 40, the internal pressure of the tank 4, the main section 10, and the intake section 20 is high, so the discharge valve 53 moves from the discharge closed position to the discharge open position. Specifically, the discharge valve 53 is pushed by the fluid inside the tank 4 and rises against its own weight. The total weight of the discharge valve 53 and the discharge valve shaft 54 ​​is set to a weight that rises against its own weight when the internal pressure of the main section 10 becomes equal to or higher than the normal pressure.

[0068] Therefore, the fluid in the tank 4 is discharged from the tank 4 through the main flow path 110, the valve side opening 42c, and the discharge flow path 140. That is, when the internal pressure of the discharge part 40 becomes higher than the normal pressure, the discharge valve mechanism 50 connects the main flow path 110 and the discharge flow path 140. This allows the fluid to be discharged from the inside of the tank 4 to the downstream side of the discharge valve mechanism 50 through the discharge flow path 140.

[0069] When the fluid in the tank 4 is discharged from the discharge portion 40, the discharge valve 53 eventually moves to the discharge closing position by its own weight based on the internal pressure of the tank 4, and closes the valve side opening 42c.

[0070] As a result, the internal pressure of the tank 4 becomes a pressure based on the weight of the discharge valve 53. In this manner, the breather valve 5 can maintain the pressure inside the tank 4 at a constant pressure.

[0071] As described above, the fluid inside the tank 4 includes the fluid inside the tank 4 and the fluid that has volatilized and gasified. Therefore, the discharged fluid also includes the fluid inside the tank 4 and the fluid that has volatilized and gasified.

[0072] Next, the detection device 60 will be described. The detector 61 can constantly detect the states of the discharge valve 53 and the valve seat 2. A predetermined variable of the detection target detected by the detector 61 can be recognized as the state of the valve mechanism, which is the state of the discharge valve 53 or the state between the discharge valve 53 and the valve seat 2.

[0073] For example, a distance sensor capable of measuring the distance to the underside of the discharge valve 53 can be used as the detector 61. In this case, the state of the discharge valve 53 that can be detected from the detection result of the detector 61 is the current distance to the discharge valve 53, i.e., the current position of the discharge valve 53.

[0074] The detector 61 does not have to be a distance sensor. For example, a vibration sensor facing the underside of the discharge valve 53 may be used as the detector 61. The vibration sensor may measure the vibration of the valve side end 42b facing the discharge valve 53. Even if a vibration sensor is used as the detector 61, the state of the valve mechanism can be detected in the same way as a distance sensor.

[0075] Also, for example, an acoustic sensor facing the underside of the discharge valve 53 may be used for the detector 61. The acoustic sensor may measure the sound between the discharge valve 53. The acoustic sensor can detect the operating sound of the discharge valve 53 and the sound of the fluid between the valve seat and the discharge valve 53. Even if an acoustic sensor is used for the detector 61, the state of the valve mechanism can be detected in the same way as a distance sensor. In the above, three types of sensors are exemplified as sensors used for the detector 61, but the types of sensors used for the detector 61 are not limited to the above three types, and various types of sensors can be used for the detector 61.

[0076] The machine learning device 240 is a device that operates as a main subject in the learning phase of machine learning. Fig. 6 is a configuration diagram showing the machine learning device 240 of Fig. 1. Fig. 7 is a conceptual diagram showing elements of machine learning performed by the machine learning device 240 of Fig. 6. The machine learning device 240 has a machine learning control unit 241, a machine learning communication unit 242, a learning data storage unit 243, and a machine learning model storage unit 244.

[0077] The machine learning control unit 241 generates a learning model 245 that provides output information corresponding to input information. The machine learning control unit 241 uses one or more pieces of learning data 246 to generate the learning model 245. The learning data 246 is composed of learning input information 201a and learning valve mechanism state 201b corresponding to the learning input information 201a.

[0078] Here, the input information is a predetermined variation of the detection target detected by the detector 61, and indicates a predetermined variation between the exhaust valve 53, which is a valve body, and the valve seat 2, which is detected from the valve seat 2. The valve mechanism state indicates the state of the valve mechanism, that is, the state of the exhaust valve 53, which is a valve body, and the valve seat 2, and the state between the exhaust valve 53 and the valve seat 2. The valve mechanism is composed of the exhaust valve 53, which is a valve body, and the valve seat 2, or the valve seat mechanism 1.

[0079] The learning data 246 is data used as training data, which is teacher data in supervised learning, verification data, and test data. The learning valve mechanism state 201b is data used as a correct answer label in supervised learning.

[0080] In addition, the learning input information 201a and the learning valve mechanism state 201b, which is the valve mechanism state corresponding to the learning input information 201a, are in a state in which the input information 200a and the valve mechanism state 200b, which is the valve mechanism state corresponding to the input information 200a, are stored in the learning data storage unit 243 as learning data 246.

[0081] The input information 200a and the valve mechanism state 200b are obtained by product tests such as various examinations and tests on the breather valve 5. The learning data storage unit 243 in which the input information 200a, the valve mechanism state 200b, the learning input information 201a, the learning valve mechanism state 201b, and the learning data 246 are stored will be described later.

[0082] The machine learning control unit 241 can cause the learning model 245 to learn the correlation between the learning input information 201a and the learning valve mechanism state 201b in one or more pieces of learning data 246. In this way, the machine learning control unit 241 can generate a learned learning model 245.

[0083] A neural network structure is adopted for the learning model 245. The learning model 245 has an input layer 245a, an intermediate layer 245b, and an output layer 245c. The input layer 245a has neurons whose number corresponds to the number of parameters of the learning input information 201a. The output layer 245c has neurons whose number corresponds to the number of conditions of the learning valve mechanism state 201b.

[0084] Between each layer, there are synapses (not shown) that connect each neuron. Each synapse can be assigned a weight.

[0085] The machine learning control unit 241 adjusts a weight parameter group consisting of the weights of each synapse by machine learning. The weight parameter group is reflected in the learning model 245.

[0086] In the machine learning control unit 241, each parameter of the learning input information 201a is input to each neuron of the input layer 245a, and a learning result valve mechanism state 201c indicating the state of the valve mechanism corresponding to the learning input information 201a is output through the learning model 245. The machine learning control unit 241 examines the learning result valve mechanism state 201c and adjusts the weight of each synapse based on the examination result.

[0087] When the learning model 245 is configured as a regression model, the learning valve mechanism state 201b is output as a numerical value normalized to a predetermined range (for example, 0 to 1). When the learning model 245 is configured as a classification model, the learning valve mechanism state 201b is output as a score (degree of certainty) for each class as a numerical value normalized to a predetermined range (for example, 0 to 1).

[0088] The learning data storage unit 243 can store a plurality of pieces of learning data 246 as a database. The specific configuration of the database constituting the learning data storage unit 243 can be designed as appropriate.

[0089] Input information 200a acquired in advance by product testing or the like and a valve mechanism state 200b corresponding to the input information 200a are input to the learning data storage unit 243. The input information 200a and the valve mechanism state 200b input to the learning data storage unit 243 are stored as learning input information 201a and learning valve mechanism state 201b, respectively. Therefore, the learning valve mechanism state 201b corresponds to the learning input information 201a. In this way, one piece of learning data 246 is stored.

[0090] The learning input information 201a and the learning valve mechanism state 201b are based on the input information 200a obtained by the detector 61 during product testing of the breather valve 5, etc., and the valve mechanism state 200b actually observed and measured during product testing, etc.

[0091] The above-mentioned product tests are carried out in the design, prototyping, and pre-shipment product inspection of the breather valve 5. A predetermined variable of the detection target detected by the detector 61 through various product tests is acquired as input information 200a.

[0092] In addition, the amount of fluid passing between the discharge valve 53 and the valve seat 2, the presence or absence of unexpected fluid leakage from between the valve body and the valve seat, the amount of leakage, and a sign of unexpected fluid leakage from between the valve body and the valve seat, which are observed or measured simultaneously with the detection of the variable by the product test, are acquired as the valve mechanism state 200b. In the product test, the breather valve 5 and the machine learning device 240 are connected so as to be able to exchange information, and the variables detected by the detector 61 during the product test are sequentially input as input information 200a to the learning data storage unit 243. Then, the valve mechanism state 200b corresponding to the input information 200a determined by the judgment of the operator or the like is input to the learning data storage unit 243. Note that the variables detected in the product test may be stored in a storage device not shown, and after determining the valve state corresponding to the variables, the operator may input the information to the learning data storage unit 243 as the input information 200a and the valve mechanism state 200b corresponding to the input information 200a. In this case, the breather valve 5 and the machine learning device 240 do not necessarily need to be connected to be able to exchange information.

[0093] In the product test, the detector 61 detects a predetermined variable of the detection target even when the opening degree of the discharge valve 53 is zero, that is, even when the flow path is closed by the discharge valve 53. That is, in the product test, the detector 61 constantly detects the variables of the detection target, so that time-series data of those variables can be obtained.

[0094] In the product test, the opening degree of the discharge valve 53 and the flow rate of the fluid discharged from the discharge valve 53 are actually measured using a measuring instrument or the like, and the amount of fluid passing between the discharge valve 53 and the valve seat 2 obtained based on the measurement results can be obtained as the valve mechanism state 200b.

[0095] In addition, an operator who can determine the state of the valve seat 2 and the discharge valve 53 can obtain the observation results of the discharge valve 53 and the valve seat 2, such as whether the state of the valve seat 2 and the discharge valve 53 is normal or abnormal, and if it is abnormal, what type of abnormal state it is, as the valve mechanism state 200b.

[0096] Furthermore, when the state between the discharge valve 53 and the valve seat 2 is abnormal, the presence or absence of fluid passing between the discharge valve 53 and the valve seat 2 is regarded as the presence or absence of unexpected fluid leakage, and the presence or absence of unexpected fluid leakage can be obtained as the valve mechanism state 200b.

[0097] Furthermore, when the state between the discharge valve 53 and the valve seat 2 is abnormal, the amount of fluid passing between the discharge valve 53 and the valve seat 2 is regarded as the amount of unexpected fluid leakage, and the amount of unexpected fluid leakage can be obtained as the valve mechanism state 200b.

[0098] Furthermore, when the state of the discharge valve 53 and the valve seat 2 is abnormal and the amount of fluid passing between the discharge valve 53 and the valve seat 2 is not zero, a sign of unexpected fluid leakage can be acquired as the valve mechanism state 200b. That is, when the state of the discharge valve 53 and the valve seat 2 is abnormal and the amount of fluid passing between the discharge valve 53 and the valve seat 2 is not zero, the valve mechanism state 200b is acquired as a sign of unexpected fluid leakage for time-series input information over a certain period of time when the amount of fluid passing between the discharge valve 53 and the valve seat 2 was zero prior to that.

[0099] Product tests are also conducted to reproduce the occurrence of defects. For example, there are product tests assuming that some foreign matter is trapped between the valve seat 2 and the discharge valve 53, or product tests conducted in a state in which at least one of the valve seat 2 and the discharge valve 53 is corroded. Furthermore, product tests may be conducted in which the valve mechanism is operated for a long period of time simulating actual operating conditions, and the state of aging deterioration and durability are confirmed.

[0100] In such product tests, it is determined that the condition of the exhaust valve 53 and the valve seat 2 is abnormal due to a malfunction or deterioration over time, and the presence or absence of unexpected fluid leakage, the amount of unexpected fluid leakage, and signs of unexpected fluid leakage can be obtained as the valve mechanism status 200b.

[0101] Furthermore, input information 200a obtained from not only product testing but also the currently operating breather valve 5 and information that can be obtained during maintenance inspections can be obtained as the valve mechanism status 200b. Alternatively, data created by simulation may be used.

[0102] In this way, the correlation between the input information 200a and the valve mechanism state 200b obtained by product testing or the like is valid because it is obtained as a result of using an actual device. Therefore, it is desirable to use such data as learning data 246 for machine learning.

[0103] The variables that are the input information 200a are specifically determined by the type of sensor used in the detector 61. Since the detector 61 can be a distance sensor, a vibration sensor, or an acoustic sensor, a predetermined variable corresponding to the type of the sensor is obtained as the input information 200a.

[0104] The valve mechanism status 200b may be indicated using a numerical value, an error code, etc. For example, if the valve mechanism status 200b is something that can be obtained quantitatively, such as a flow rate or a leakage amount, the valve mechanism status 200b may be indicated by the specific amount. For example, the valve mechanism status 200b may be indicated as 1 when the valve mechanism status is normal, and as 0 when the valve mechanism status is abnormal.

[0105] For example, when indicating a sign of an unexpected fluid leak, the absence of any sign of the leak may be indicated as 0, and the ability to detect the sign of the leak may be indicated as 1. Alternatively, the absence of any sign of an unexpected fluid leak may be indicated as 0, and the ability to detect the sign of the leak frequently and with a high probability may be indicated as 100, with any value in between being used.

[0106] The machine learning control unit 241 can extract any one or more pieces of learning data 246 from the multiple pieces of learning data 246 stored in the learning data storage unit 243, and use the extracted data for machine learning.

[0107] The machine learning model storage unit 244 is a database that stores the trained learning model 245 generated by the machine learning control unit 241, that is, the adjusted weighting parameter group.

[0108] The machine learning communication unit 242 is a communication interface unit. The machine learning communication unit 242 can transmit and receive various data by being connected to an external device via the network 270. The trained learning model 245 stored in the machine learning model storage unit 244 is provided to the information processing device 250 via the network 270, a storage medium, or the like.

[0109] In addition, in FIG. 6, the learning data storage unit 243 and the machine learning model storage unit 244 are shown as separate storage units, but these may be configured as a single storage unit.

[0110] 8 is a schematic diagram showing the information processing device 250 of FIG. 1. The information processing device 250 is a device that operates as a subject of the inference phase of machine learning. The information processing device 250 predicts a new valve mechanism state 202b corresponding to newly input prediction input information 202a, using a learning model 245 generated by the machine learning device 240. The prediction input information 202a is a variable acquired by the detector 61 in the currently operating breather valve 5. That is, the information processing device 250 obtains a new valve mechanism state 202b based on the prediction input information 202a in the currently operating breather valve 5.

[0111] The information processing device 250 has an information processing control unit 251, an information processing storage unit 255, and an information processing communication unit 256. The information processing control unit 251 has an information acquisition unit 252, an information prediction unit 253, and an output processing unit 254.

[0112] The information acquisition unit 252 acquires data acquired by the detector 61 and output from the detection device 60 as prediction input information 202a. The prediction input information 202a is input information for determining a new valve mechanism state. Here, the valve mechanism state corresponding to the prediction input information 202a is defined as a new valve mechanism state 202b.

[0113] The information prediction unit 253 predicts a new valve mechanism state 202b by inputting the prediction input information 202a acquired by the information acquisition unit 252 to the learning model 245. The output processing unit 254 outputs the new valve mechanism state 202b predicted by the information prediction unit 253 to the information processing communication unit 256.

[0114] The information prediction unit 253 can select and use one learning model 245 from among a plurality of learning models 245 stored in the information processing storage unit 255 .

[0115] The information processing storage unit 255 is a database that stores the trained learning models 245 used by the information prediction unit 253. The information processing storage unit 255 can store a plurality of trained learning models 245 input from the machine learning device 240.

[0116] The multiple learning models 245 are multiple trained models that differ, for example, in machine learning methods, types of data included in the learning input information 201a, types of data included in the learning valve mechanism status 201b, etc.

[0117] The information processing storage unit 255 may be substituted with a storage unit of an external computer such as a server-type computer or a cloud-type computer. In that case, the information prediction unit 253 can access the storage unit of the external computer to acquire the learning model 245.

[0118] The information processing and communication unit 256 is communicably connected to devices outside the valve system 200 via a network 270. The information processing and communication unit 256 is a communication interface unit that transmits and receives various data. The information processing and communication unit 256 can output the new valve mechanism state 202b output by the output processing unit 254 to the information processing and communication unit 256.

[0119] 9 is a diagram showing the hardware configuration of the computer 900. The machine learning device 240 and the information processing device 250 of the valve system 200 are configured by a general-purpose or dedicated computer 900.

[0120] The computer 900 includes a bus 910, a processor 912, a memory 914, an input device 916, an output device 917, a display device 918, a storage device 920, a communication interface unit 922, an external device interface unit 924, an input / output device interface unit 926, and a media input / output unit 928. Note that the above components may be omitted as appropriate depending on the application of the computer 900.

[0121] The processor 912 is composed of one or more arithmetic processing devices (CPU (Central Processing Unit), MPU (Micro-processing unit), DSP (digital signal processor), GPU (Graphics Processing Unit), etc.) and operates as a control unit that oversees the entire computer 900.

[0122] The memory 914 stores various data and programs 930, and is composed of, for example, a volatile memory (DRAM, SRAM, etc.) that functions as a main memory, a non-volatile memory (ROM), a flash memory, etc.

[0123] The input device 916 is, for example, a keyboard, a mouse, a numeric keypad, an electronic pen, etc., and functions as an input unit. The output device 917 is, for example, a sound output device including voice, a vibration device, etc., and functions as an output unit. The display device 918 is, for example, a liquid crystal display, an organic EL display, electronic paper, a projector, etc., and functions as an output unit.

[0124] The input device 916 and the display device 918 may be integrated, such as a touch panel display. The storage device 920 is configured, for example, with an HDD, an SSD (Solid State Drive), or the like, and functions as a storage unit. The storage device 920 stores various data necessary for the execution of the operating system and the program 930.

[0125] The communication interface unit 922 is connected to a network 940 such as the Internet or an intranet by wire or wirelessly, and functions as a communication unit that transmits and receives data to and from other computers in accordance with a predetermined communication standard. The network 940 may be the same as the network 270.

[0126] The external device interface unit 924 is connected to an external device 950 such as a camera, printer, scanner, or reader / writer by wire or wirelessly, and functions as a communication unit that transmits and receives data to and from the external device 950 in accordance with a predetermined communication standard.

[0127] The input / output device interface unit 926 is connected to input / output devices 960 such as various sensors and actuators, and functions as a communication unit that transmits and receives various signals and data between the input / output devices 960, such as detection signals from sensors and control signals to actuators.

[0128] The media input / output unit 928 is constituted by a drive device such as a DVD drive, a CD drive, etc., and reads and writes data from and to a medium 970, which is a storage medium such as a DVD or a CD.

[0129] In the computer 900 having the above configuration, the processor 912 loads a program 930 stored in the storage device 920 into the memory 914, executes the program, and controls each unit of the computer 900 via the bus 910. Note that the program 930 may be stored in the memory 914 instead of the storage device 920.

[0130] The program 930 may be recorded in a medium 970 in an installable file format or an executable file format, and provided to the computer 900 via the media input / output unit 928. The program 930 may be provided to the computer 900 by downloading it over a network 940 via the communication interface unit 922.

[0131] Furthermore, the computer 900 may realize various functions that are realized by the processor 912 executing the program 930 using hardware such as an FPGA or an ASIC.

[0132] The computer 900 is, for example, a stationary computer or a portable computer, and is an electronic device of any form. The computer 900 may be a client-type computer, a server-type computer, or a cloud-type computer. The computer 900 may be applied to devices other than the machine learning device 240 and the information processing device 250 of the valve system 200.

[0133] Next, the machine learning method will be described with reference to a flowchart shown in FIG 10, which illustrates the machine learning method performed by the machine learning device 240 in FIG 1.

[0134] First, the operator inputs one or more pieces of learning data 246 in advance and stores them in the learning data storage unit 243. The number of pieces of learning data 246 to be stored is set in consideration of the inference accuracy required for the finally obtained learning model 245. It is preferable to store a plurality of sets of learning data 246.

[0135] In the machine learning method, a learning model preparation step is performed as step S100. The machine learning control unit 241 prepares a pre-learning learning model 245. In the prepared learning model 245, the weight of each synapse is set to an initial value.

[0136] Next, a machine learning process is performed in step S110. In the machine learning process, first, a learning data acquisition process is performed in step S111. The machine learning control unit 241 randomly acquires one piece of learning data 246 from the multiple pieces of learning data 246 stored in the learning data storage unit 243.

[0137] Next, an inference result output step is performed as step S112. The machine learning control unit 241 inputs the learning input information 201a included in the acquired piece of learning data 246 to the input layer 245a of the prepared learning model 245. As a result, a learning result valve mechanism state 201c is output as an inference result from the output layer 245c of the learning model 245.

[0138] The learning result valve mechanism state 201c output as the inference result is generated by the learning model 245 before or during learning. Therefore, the learning result valve mechanism state 201c is different from the learning valve mechanism state 201b, which is the correct label included in the learning data 246.

[0139] Next, a weight adjustment step is performed in step S113. The machine learning control unit 241 compares the learning valve mechanism state 201b in the learning data 246 acquired in step S111, which is the correct label, with the learning result valve mechanism state 201c output as the inference result in step S112. Based on this comparison, the machine learning control unit 241 performs back propagation, which is a process of adjusting the weight of each synapse, to perform machine learning.

[0140] As a result, the machine learning control unit 241 causes the learning model 245 to learn the correlation between the learning input information 201a and the learning valve mechanism state 201b.

[0141] Next, a machine learning end determination step is performed as step S114. The machine learning control unit 241 determines whether a predetermined learning end condition is satisfied. This determination is performed, for example, based on an evaluation value of an error function based on the learning valve mechanism state 201b, which is the correct label, and the learning result valve mechanism state 201c, the remaining number of unlearned learning data 246 stored in the learning data storage unit 243, etc.

[0142] In step S114, if the machine learning control unit 241 determines that the learning end condition is not satisfied and to continue the machine learning, that is, if step S114 is No, the process returns to step S111. In this manner, the processes of steps S111 to S114 are performed multiple times on the unlearned learning data 246 for the learning model 245 being trained.

[0143] On the other hand, in step S114, if the machine learning control unit 241 determines that the learning end condition is satisfied and that the machine learning is to be ended, that is, if step S114 is Yes, the process proceeds to step S120.

[0144] Then, in step S120, a trained model storage step is performed. The machine learning control unit 241 stores the trained learning model 245 in which the weights of each synapse have been adjusted, i.e., the learning model 245 reflecting the adjusted weight parameter group, in the machine learning model storage unit 244. This ends the machine learning method.

[0145] Next, there will be described a method for predicting the new valve mechanism state 202b of the valve system 200 by the information processing device 250. Fig. 11 is a flowchart showing a learning valve mechanism state prediction method by the information processing device 250 of Fig. 1.

[0146] The learning valve mechanism state prediction method, which is an information processing method, is a method for determining the valve mechanism state indicating the state of the valve mechanism, and can obtain the valve mechanism state based on input information, which is a predetermined variable between the exhaust valve 53, which is the valve body, and the valve seat 2.

[0147] First, an input information for prediction acquisition step is performed as step S200. Variable data acquired by detection device 60 is input to information processing device 250 as input information for prediction 202a, whereby information acquisition unit 252 acquires input information for prediction 202a.

[0148] Next, a prediction step is performed as step S210. The information prediction unit 253 inputs the prediction use input information 202a acquired in step S200 to the learning model 245. As a result, the information prediction unit 253 predicts a new valve mechanism state 202b corresponding to the prediction use input information 202a.

[0149] Next, an output processing step is carried out as step S220. As the output processing, the output processing unit 254 displays the new valve mechanism state 202b generated in step S210 on a display screen (not shown) or the like. This completes the prediction of the new valve mechanism state 202b and its output. The new valve mechanism state 202b may be sent to a specified email address by being written in an email or the like.

[0150] Furthermore, since the detection device 60 constantly outputs a predetermined variable, the prediction input information acquisition step is carried out in accordance with this.

[0151] The present disclosure can also be provided in the form of a machine learning program, which is a program that causes computer 900 to function as each part of machine learning device 240, or a machine learning program, which is a program that causes computer 900 to execute each step of a machine learning method.

[0152] In addition, the present disclosure can also be provided in the form of a valve mechanism status output program, which is a program that causes the computer 900 to function as each part of the information processing device 250, or a valve mechanism status output program, which is a program that causes the computer 900 to execute each step of the information processing method of embodiment 1.

[0153] Furthermore, the present disclosure can be provided not only in the form of the information processing device 250, the information processing method, or the information processing program according to the first embodiment, but also in the form of an inference device, an inference method, or an inference program used to infer a valve mechanism state. In this case, the inference device, the inference method, or the inference program can include a memory 914 and a processor 912, of which the processor 912 can execute a series of processes.

[0154] The series of processes includes an information acquisition process (information acquisition step) for acquiring input information, an inference process (inference step) for inferring the valve mechanism state using the learning model 245 stored in the memory 914, and an output process (output step) for outputting the inferred valve mechanism state to an external device 950. Note that in the output process, the valve mechanism state may be output onto the network 940 via the communication interface unit 922.

[0155] The information processing device 250 according to the first embodiment predicts the state of the valve mechanism and can obtain the valve mechanism state indicating the state of the valve mechanism based on input information which is a predetermined variable between the discharge valve 53, which is the valve disc, and the valve seat 2. This makes it possible to verify the amount of fluid passing through the valve mechanism, the presence or absence of unexpected fluid leakage from between the valve disc and the valve seat, the amount of leakage, and signs of unexpected fluid leakage from the valve mechanism.

[0156] According to the information processing device 250 of the first embodiment, one or more detectors 61 capable of detecting a predetermined change in a detection target in a detection direction are provided. The valve seat 2 constitutes a part of a flow path, and the valve seat 2 has a valve seat end surface 2a with which the flow path is closed when the exhaust valve 53, which is a valve body, comes into contact. The valve seat 2 has one or more detector installation spaces 42e formed therein, and the detector 61 is installed in each detector installation space 42e. The detection direction is toward the exhaust valve 53, which is a valve body, located beyond the valve seat end surface 2a when the detector 61 is installed in the detector installation space 42e. This allows the detector 61 to detect a predetermined change between the valve seat 2 and the exhaust valve 53, which is a valve body. Therefore, it is possible to more accurately verify the amount of fluid passing through the valve mechanism, the presence or absence of unexpected fluid leakage from between the valve body and the valve seat, the amount of leakage, and the signs of unexpected fluid leakage from the valve mechanism.

[0157] According to the information processing device 250 of the first embodiment, at least one of the one or more detectors 61 uses any one of a distance sensor, a vibration sensor, and an acoustic sensor. Various types of sensors can be used for the detector 61, but by using any one of a distance sensor, a vibration sensor, and an acoustic sensor, a sensor that is generally available on the market and is easily available can be used as the detector 61. Therefore, the design, manufacture, and maintenance of the valve seat mechanism 1 can be easily handled.

[0158] According to the information processing device 250 of the first embodiment, the valve mechanism state includes at least one of the flow rate of the fluid flowing between the discharge valve 53 as a valve body and the valve seat 2, the presence or absence of unexpected fluid leakage from between the discharge valve 53 as a valve body and the valve seat 2, the amount of unexpected fluid leakage from between the discharge valve 53 as a valve body and the valve seat 2, and a sign of unexpected fluid leakage from between the discharge valve 53 as a valve body and the valve seat 2. This makes it possible to easily and quickly verify the state of the valve mechanism based on the output of the information processing device 250.

[0159] According to the information processing device 250 of the first embodiment, when input information is input, a valve mechanism state is determined using a learning model in which a correlation between the input information and the valve mechanism state is learned by machine learning, and the determined valve mechanism state is output. This allows a machine learning method based on accumulated information to be used to verify the state of the valve mechanism. This makes it possible to more accurately verify the amount of fluid passing through the valve mechanism, the presence or absence of unexpected fluid leakage from between the valve disc and the valve seat, the amount of leakage, and signs of unexpected fluid leakage from the valve mechanism.

[0160] According to the machine learning device 240 of the first embodiment, a learning model 245 for inferring a valve mechanism state indicating the state of the valve mechanism is generated. Also, a learning data storage unit 243 is provided for storing a plurality of sets of learning data 246 consisting of input information, which is a predetermined variable between the discharge valve 53, which is a valve body, and the valve seat 2, and a valve mechanism state corresponding to the input information. Also, a machine learning model storage unit is provided for inputting a plurality of sets of learning data 246, thereby making the learning model learn the correlation between the input information and the valve mechanism state, and storing the learned learning model 245. As a result, it is possible to generate and store the learning model 245 in which the correlation between the input information and the valve mechanism information is learned. Therefore, it is possible to use the learned learning model 245 to more accurately verify the amount of fluid passing through the valve mechanism, verify the presence or absence of unexpected fluid leakage from between the valve body and the valve seat, verify the amount of leakage, and verify the signs of unexpected fluid leakage from the valve mechanism.

[0161] The inference device according to the first embodiment includes a memory 914 and at least one processor 912, and infers a valve mechanism state indicating the state of the valve mechanism. The at least one processor 912 executes an information acquisition process for acquiring input information that is a predetermined variable between the discharge valve 53, which is a valve disc, and the valve seat 2, an inference process for inferring the valve mechanism state using a learning model 245 by machine learning stored in the memory 914 upon acquiring the input information, and an output process for outputting the inferred valve mechanism state. This makes it possible to verify the amount of fluid passing through the valve mechanism, the presence or absence of unexpected fluid leakage from between the valve disc and the valve seat, the amount of leakage, and a sign of unexpected fluid leakage from the valve mechanism.

[0162] According to the inference method of the first embodiment, the method is executed by an inference device including a memory 914 and at least one processor 912, and infers a valve mechanism state indicating the state of the valve mechanism. The at least one processor 912 also executes an information acquisition step of acquiring input information that is a predetermined variable between the discharge valve 53, which is a valve disc, and the valve seat 2. When the input information is acquired, an inference step of inferring the valve mechanism state is executed using a learning model 245 by machine learning stored in the memory 914. An output step of outputting the inferred valve mechanism state is also executed. This makes it possible to verify the amount of fluid passing through the valve mechanism, the presence or absence of unexpected fluid leakage from between the valve disc and the valve seat, the amount of leakage, and a sign of unexpected fluid leakage from the valve mechanism.

[0163] According to the information processing method of the first embodiment, a valve mechanism state indicating the state of the valve mechanism is determined. Also, the valve mechanism state is obtained based on input information which is a predetermined variable between the discharge valve 53, which is the valve disc, and the valve seat 2. This makes it possible to verify the amount of fluid passing through the valve mechanism, the presence or absence of unexpected fluid leakage from between the valve disc and the valve seat, the amount of leakage, and signs of unexpected fluid leakage from the valve mechanism.

[0164] According to the machine learning method of the first embodiment, a learning model 245 for inferring a valve mechanism state indicating the state of the valve mechanism is generated. In addition, in a state in which one or more pieces of learning data 246 are stored, which are composed of input information, which is a predetermined variable between the discharge valve 53, which is a valve body, and the valve seat 2, and a valve mechanism state corresponding to the input information, a machine learning process is executed in which a correlation between the input information and the valve mechanism state is learned by inputting a plurality of sets of learning data 246. In addition, a learned model storage process is executed in which the learning model 245 learned in the machine learning process is stored in the machine learning model storage unit 244. This makes it possible to verify the amount of fluid passing through the valve mechanism, verify the presence or absence of unexpected fluid leakage from between the valve body and the valve seat, verify the amount of leakage, and verify signs of unexpected fluid leakage from the valve mechanism.

[0165] In the first embodiment, a neural network is used as the learning model 245. However, the present invention is not limited to this. Other machine learning models may be used as the learning model 245. Examples of other machine learning models include tree types such as decision trees and regression trees, ensemble learning such as bagging and boosting, neural network types (including deep learning) such as recurrent neural networks, convolutional neural networks, and LSTM (Long Short Term Memory), clustering types such as hierarchical clustering, non-hierarchical clustering, k-nearest neighbors, and k-means, multivariate analysis such as principal component analysis, factor analysis, and logistic regression, and support vector machines. can be done.

[0166] Embodiment 2 To explain the valve system 200 of the present disclosure, the second embodiment also uses the valve system 200 used in the breather valve 5. However, the valve system 200 of the present disclosure is not limited to being applied to the breather valve 5, and can be used as appropriate in a general valve mechanism.

[0167] The information processing device 250 of the second embodiment differs from the valve system 200 of the first embodiment in that it outputs a new valve mechanism state 202b without using a learning model obtained by machine learning.

[0168] The information processing device 250 can output a new valve mechanism state 202b corresponding to the acquired input information for prediction 202a.

[0169] The information prediction unit 253 determines a new valve mechanism state 202b by a predetermined determination method from the prediction input information 202a input and acquired by the information acquisition unit 252. These determination methods do not use machine learning methods, and therefore do not use a learning model either. The output processing unit 254 can output the determined new valve mechanism state 202b to the information processing communication unit 256.

[0170] In the information processing device 250, the input information for prediction 202a acquired by the information acquiring unit 252 can also be sequentially stored in the information processing storage unit 255. This allows the stored input information for prediction 202a to be handled as time-series data. The information processing storage unit 255 in the first embodiment functions as a database for the learning model 245, but in the second embodiment, the information processing storage unit 255 functions as a storage device that can store information.

[0171] The predetermined determination method implemented by the information prediction unit 253 includes a method based on a comparison with stored waveform data, a method based on a threshold value to determine normality / abnormality, a method based on a calculation using coefficients to calculate physical quantities, and other well-known statistical analysis calculations.

[0172] The information processing storage unit 255 stores in advance information such as waveform data, thresholds, or coefficients required for the determination method implemented by the information prediction unit 253. The stored waveform data includes, for example, time series data of a predetermined variable detected by the detector 61 that may be a sign of an unexpected fluid leak.

[0173] The information prediction unit 253 can use this waveform data to implement a predetermined determination method. For example, the corresponding valve mechanism state can be determined based on whether the stored waveform data or features of the waveform data match the waveform data or features of the waveform data based on the prediction input information 202a. If they match, a new valve mechanism state 202b indicating a sign of unexpected fluid leakage can be determined.

[0174] In addition, the stored threshold values ​​include a state in which the discharge valve 53 opens the flow path for a certain period of time or more, and the time is stored as the threshold value, or a state in which the discharge valve 53 continues to perform micro-movements for a certain period of time or more, and the time is stored as the threshold value.

[0175] The information prediction unit 253 can use this threshold value to implement a predetermined determination method. For example, it determines whether the opening of the flow path by the discharge valve 53 or the slight movement of the discharge valve 53 continues for a time period equal to or greater than the threshold value based on the prediction input information 202a. If a time period equal to or greater than the threshold value has passed, it can determine a new valve mechanism state 202b indicating that the valve mechanism is abnormal.

[0176] The stored coefficients include those required for calculating physical quantities, such as coefficients required for calculating the flow rate of the fluid flowing out from between the discharge valve 53 and the valve seat 2.

[0177] The information prediction unit 253 can use this coefficient to implement a predetermined determination method. For example, the stored coefficient is used to calculate and determine the flow rate. The calculated flow rate can be used as the flow rate of the fluid flowing out of the valve mechanism to determine the new valve mechanism state 202b.

[0178] The information processing control unit 251 can determine the new valve mechanism state 202b based on the prediction input information 202a, using pre-stored waveform data, thresholds, and coefficients. According to the second embodiment, the pre-stored waveform data, thresholds, and coefficients are not limited to those described above, and can be selected as appropriate. In order to determine the new valve mechanism state 202b, the method is not limited to the above-described determination method, and well-known arithmetic and statistical methods can be used.

[0179] Since machine learning is not used in the second embodiment, the configurations relating to the learning model 245, the machine learning device 240, and the inference device in the first embodiment are not used. Other configurations of the second embodiment are similar to those of the first embodiment, and therefore description thereof will be omitted.

[0180] According to the information processing device 250 of the second embodiment, when input information is input, a valve mechanism state is determined from the input information based on a predetermined determination method that does not use a machine learning method, and the determined valve mechanism state is output. This makes it possible to verify the state of the currently installed valve mechanism based on past knowledge of the valve mechanism. Therefore, it is possible to more accurately verify the amount of fluid passing through the valve mechanism, the presence or absence of unexpected fluid leakage from between the valve disc and the valve seat, the amount of leakage, and signs of unexpected fluid leakage from the valve mechanism.

[0181] Embodiment 3 To explain the valve system 200 of the present disclosure, the third embodiment also uses the valve system 200 used in the breather valve 5. However, the valve system 200 of the present disclosure is not limited to being applied to the breather valve 5, and can be used as appropriate in a general valve mechanism.

[0182] The third embodiment differs from the first and second embodiments in that three detectors 61 are provided on the valve seat 2. Fig. 12 is a top view showing the introduction portion 42 according to the third embodiment.

[0183] The detection device 60 has three detectors 61, an input / output device (not shown), and wiring 62 for transmitting signals from each detector 61 to the input / output device. Three detector installation spaces 42e are formed in the introduction section 42. In each detector installation space 42e, one detector 61 and the wiring 62 connected to the detector 61 are installed.

[0184] The valve contact member 70 is formed in a shape corresponding to the shape of the main body end surface 42d. The openings of the multiple detector installation spaces 42e formed in the main body end surface 42d are closed by installing the valve contact member 70 in the introduction portion 42. That is, the multiple detectors 61 are installed in the detector installation space 42e covered with the valve contact member 70. Each of the multiple detectors 61 is installed along the valve facing surface 70a. As shown in FIG. 12, the inner circumferential surface of the introduction portion 42 where each detector installation space 42e is formed is formed to protrude further inward, and the thickness of the introduction portion 42 where the detector installation space 42e is formed is thicker than the thickness of the other portions. Also, the valve contact member 70 is formed in a shape corresponding to the shape of the main body end surface 42d. However, this is not limited to this. For example, the wall thickness of the introduction portion 42 may be uniform, i.e., the inner peripheral surface of the introduction portion 42 may not have any inwardly protruding portion, and the inner peripheral surface of the introduction portion 42 may be circular when viewed in the axial direction. Even in this case, the valve contact member 70 is formed in a shape corresponding to the shape of the main body end surface 42d.

[0185] Each of the multiple detectors 61 may be the same type of sensor, for example, all three detectors 61 may be distance sensors. Other configurations of the third embodiment are similar to those disclosed in the first embodiment, and therefore description thereof will be omitted.

[0186] In the third embodiment, three detectors 61 are installed in the introduction section 42. However, this is not limited to this. The number of detectors 61 arranged in the introduction section 42 may be two or more, i.e., multiple. Also, one or multiple detectors 61 may be installed in one detector installation space 42e. The detectors 61 of the valve seat mechanism 1 in the third embodiment are installed multiple times along the valve seat end surface 2a. This allows even slight tilts and wobble of the exhaust valve 53 to be examined in more detail. Therefore, the behavior of the valve body can be observed in more detail.

[0187] The multiple detectors 61 of the valve seat mechanism 1 in the third embodiment are sensors of a single type. This makes it possible to easily compare the signals obtained from the detectors 61. Therefore, even a slight tilt or wobble of the discharge valve 53 can be easily detected in more detail.

[0188] In addition, the three detectors 61 in the third embodiment are all the same type of sensor. However, this is not limited to this. For example, the types of sensors of the three detectors 61 may be one or more distance sensors and one or more vibration sensors. In other words, multiple types of sensors may be used for the multiple detectors 61.

[0189] In the valve seat mechanism 1 in the third embodiment, at least two detectors 61 are different types of sensors, which allows the behavior of the discharge valve 53 to be examined from multiple angles.

[0190] Moreover, the valve system 200 and the valve seat mechanism 1 in the first to third embodiments are provided in the breather valve 5. However, this is not limited to this. The valve system 200 and the valve seat mechanism 1 in the first to third embodiments can be provided in any valve mechanism that closes the flow path by closing the valve seat end face 2a of the valve seat 2 with a valve member and opens the flow path by providing a gap between the valve member and the valve seat 2. Examples of such valve mechanisms include a rotary valve such as a ball valve, and a diaphragm valve.

[0191] Furthermore, in the valve system 200 in the first to third embodiments, a predetermined variable detected by the detector 61 installed in the detector installation space 42e formed in the valve seat 2 is used as input information. However, this is not limited to this. The detector 61 may be installed anywhere in the valve seat 2 or the valve mechanism.

[0192] (Other embodiments) The present disclosure is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit and scope of the present disclosure. All such modifications are included in the technical concept of the present disclosure.

[0193] Various aspects of the present disclosure are summarized below as appendices.

[0194] (Appendix 1) An information processing device for predicting a state of a valve mechanism, determining a valve mechanism state indicating a state of the valve mechanism based on input information which is a predetermined variation between the valve disc and the valve seat; Information processing device. (Appendix 2) one or more detectors capable of detecting the predetermined variable of the detection target in a detection direction; The valve seat constitutes a part of a flow path, The valve seat has a valve seat end surface with which the valve body comes into contact to close the flow path, The valve seat is formed with one or more detector installation spaces, The detector is installed in each of the detector installation spaces, The detection direction is toward the valve body located beyond the valve seat end surface when the detector is installed in the detector installation space. 2. An information processing device according to claim 1. (Appendix 3) At least one of the one or more detectors uses any one of a distance sensor, a vibration sensor, and an acoustic sensor. 3. An information processing device according to claim 2. (Appendix 4) The valve mechanism state includes at least one of a flow rate of a fluid flowing between the valve disc and the valve seat, the presence or absence of unexpected fluid leakage from between the valve disc and the valve seat, an unexpected amount of fluid leakage from between the valve disc and the valve seat, and a sign of unexpected fluid leakage from between the valve disc and the valve seat. 4. The information processing device according to claim 1, (Appendix 5) When the input information is input, the valve mechanism state is determined using a learning model in which a correlation between the input information and the valve mechanism state is learned by machine learning, and the determined valve mechanism state is output. An information processing device according to any one of Supplementary Note 1 to Supplementary Note 4. (Appendix 6) A machine learning device that generates a learning model for inferring a valve mechanism state indicating a state of a valve mechanism, a learning data storage unit that stores a plurality of sets of learning data each including input information that is a predetermined variation between the valve disc and the valve seat and the valve mechanism state corresponding to the input information; a machine learning model storage unit that receives a plurality of sets of the learning data, causes a learning model to learn a correlation between the input information and the valve mechanism state, and stores the learned learning model; and Equipped with Machine learning device. (Appendix 7) An inference device for inferring a valve mechanism state indicative of a state of a valve mechanism, the inference device comprising: a memory; and at least one processor, The at least one processor includes an information acquisition process for acquiring input information, which is a predetermined variation between a valve body and a valve seat; an inference process for inferring the valve mechanism state using a machine learning model stored in the memory when the input information is acquired; an output process for outputting the inferred valve train state; Execute Reasoning device. (Appendix 8) 1. A method of information processing for determining a valve mechanism status indicative of a valve mechanism status, comprising: determining the valve mechanism state corresponding to input information which is a predetermined variation between the valve body and the valve seat; Information processing methods. (Appendix 9) 1. A machine learning method for generating a learning model for inferring a valve mechanism state indicative of a valve mechanism state, comprising: In a state where one or more pieces of learning data are stored, the learning data being composed of input information which is a predetermined variation between a valve disc and a valve seat, and the valve mechanism state corresponding to the input information, a machine learning process for inputting the learning data to cause a learning model to learn a correlation between the input information and the valve mechanism state; a learned model storage step of storing the learning model learned in the machine learning step in a machine learning model storage unit; Execute Machine learning methods. (Appendix 10) 1. A method of inferring a valve train state indicative of a valve train state, the method comprising: The at least one processor includes an information acquiring step of acquiring input information, the input information being a predetermined variation between a valve body and a valve seat; an inference step of inferring the valve mechanism state using a learning model based on machine learning stored in the memory when the input information is acquired; an output step of outputting the inferred valve train state; Execute Reasoning method [Explanation of symbols]

[0195] 1 valve seat mechanism, 2 valve seat, 2a valve seat end face, 3 valve seat body, 4 tank, 4a mounting portion, 4b upstream flange, 5 breather valve, 10 main portion, 10a inlet opening end, 10b outlet opening end, 10c intake side opening end, 20 intake portion, 21 intake body, 21a connection side opening end, 21b intake port, 21x intake valve seat, 22 intake valve mechanism, 23 intake valve (valve body), 24 intake valve shaft, 25 intake valve guide, 40 exhaust portion, 41 exhaust body, 42 introduction portion, 42a exhaust portion inlet opening end, 42b valve side end, 42c valve side opening, 42d body end face, 42e detector installation space, 43 atmospheric exhaust portion, 43a discharge port, 43b Valve mechanism side opening end, 47 vent cover, 50 exhaust valve mechanism, 53 exhaust valve (valve body), 54 exhaust valve shaft, 55 exhaust valve guide, 60 detection device, 61 detector, 62 wiring, 70 valve contact member, 110 main flow path, 111 branch flow path, 120 intake flow path, 140 exhaust flow path, 200 valve system, 200a input information, 200b valve mechanism state, 201a learning input information, 201b learning valve mechanism state, 201c learning result valve mechanism state, 202a prediction input information, 202b new valve mechanism state, 240 machine learning device, 241 machine learning control unit, 242 machine learning communication unit, 243 learning data storage unit, 244 machine learning model storage unit, 245 learning model, 245a input layer, 245b intermediate layer, 245c Output layer, 246 learning data, 250 information processing device, 251 information processing control unit, 252 information acquisition unit, 253 information prediction unit, 254 output processing unit, 255 information processing storage unit, 256 information processing communication unit, 270 network, 900 computer, 910 bus, 912 processor, 914 memory, 916 input device, 917 output device, 918 display device, 920 storage device, 922 communication interface unit, 924 external device interface unit, 926 input / output device interface unit, 928 media input / output unit, 930 program, 940 network, 950 external device, 960 input / output device, 970 media (non-transitory storage medium).

Claims

1. An information processing device for predicting a state of a valve mechanism, A valve mechanism state indicating a state of the valve mechanism is obtained based on input information which is a predetermined variation between a valve disc and a valve seat, one or more detectors capable of detecting the predetermined variable of the detection target in a detection direction; The valve seat constitutes a part of a flow path, The valve seat has a valve seat end surface with which the valve body comes into contact to close the flow path, The valve seat is formed with one or more detector installation spaces, The detector is installed in each of the detector installation spaces, the detection direction is toward the valve body located beyond the valve seat end face when the detector is installed in the detector installation space, The detector installation space is closed by the valve seat end surface. Information processing device.

2. At least one of the one or more detectors uses any one of a distance sensor, a vibration sensor, and an acoustic sensor. The information processing device according to claim 1 .

3. The valve mechanism state includes at least one of a flow rate of a fluid flowing between the valve disc and the valve seat, the presence or absence of unexpected fluid leakage from between the valve disc and the valve seat, an unexpected amount of fluid leakage from between the valve disc and the valve seat, and a sign of unexpected fluid leakage from between the valve disc and the valve seat. The information processing device according to claim 1 .

4. When the input information is input, the valve mechanism state is determined using a learning model in which a correlation between the input information and the valve mechanism state is learned by machine learning, and the determined valve mechanism state is output. The information processing device according to claim 1 .

5. A machine learning device that generates a learning model for inferring a valve mechanism state indicating a state of a valve mechanism, a learning data storage unit that stores a plurality of sets of learning data each including input information that is a predetermined variation between the valve disc and the valve seat and the valve mechanism state corresponding to the input information; a machine learning model storage unit that receives a plurality of sets of the learning data, causes a learning model to learn a correlation between the input information and the valve mechanism state, and stores the learned learning model; and Equipped with The valve seat constitutes a part of a flow path, The valve seat has a valve seat end surface with which the valve body comes into contact to close the flow path, The valve seat is formed with one or more detector installation spaces, A detector is installed in each of the detector installation spaces, Each of the detectors can detect the predetermined variable of the detection target in a detection direction, the detection direction is toward the valve body located beyond the valve seat end face when the detector is installed in the detector installation space, The detector installation space is closed by the valve seat end surface. Machine learning device.

6. An inference device for inferring a valve mechanism state indicative of a state of a valve mechanism, the inference device comprising: a memory; and at least one processor, The at least one processor includes an information acquisition process for acquiring input information, which is a predetermined variation between a valve body and a valve seat; an inference process for inferring the valve mechanism state using a machine learning model stored in the memory when the input information is acquired; an output process for outputting the inferred valve train state; The present invention relates to a method for performing The valve seat constitutes a part of a flow path, The valve seat has a valve seat end surface with which the valve body comes into contact to close the flow path, The valve seat is formed with one or more detector installation spaces, A detector is installed in each of the detector installation spaces, Each of the detectors can detect the predetermined variable of the detection target in a detection direction, the detection direction is toward the valve body located beyond the valve seat end face when the detector is installed in the detector installation space, The detector installation space is closed by the valve seat end surface. Reasoning device.

7. 1. A method of information processing for determining a valve mechanism status indicative of a valve mechanism status, comprising: A method for determining a valve mechanism state corresponding to input information which is a predetermined variable between a valve disc and a valve seat, comprising the steps of: The valve seat constitutes a part of a flow path, The valve seat has a valve seat end surface with which the valve body comes into contact to close the flow path, The valve seat is formed with one or more detector installation spaces, A detector is installed in each of the detector installation spaces, Each of the detectors can detect the predetermined variable of the detection target in a detection direction, the detection direction is toward the valve body located beyond the valve seat end face when the detector is installed in the detector installation space, The detector installation space is closed by the valve seat end surface. Information processing methods.

8. 1. A machine learning method for generating a learning model for inferring a valve mechanism state indicative of a valve mechanism state, comprising: In a state where one or more pieces of learning data are stored, the learning data being composed of input information which is a predetermined variation between a valve disc and a valve seat, and the valve mechanism state corresponding to the input information, a machine learning process for inputting the learning data to cause a learning model to learn a correlation between the input information and the valve mechanism state; a learned model storage step of storing the learning model learned in the machine learning step in a machine learning model storage unit; A method of carrying out the above-mentioned The valve seat constitutes a part of a flow path, The valve seat has a valve seat end surface with which the valve body comes into contact to close the flow path, The valve seat is formed with one or more detector installation spaces, A detector is installed in each of the detector installation spaces, Each of the detectors can detect the predetermined variable of the detection target in a detection direction, the detection direction is toward the valve body located beyond the valve seat end face when the detector is installed in the detector installation space, The detector installation space is closed by the valve seat end surface. Machine learning methods.

9. 1. A method of inferring a valve train state indicative of a valve train state, the method comprising: The at least one processor includes an information acquiring step of acquiring input information, the input information being a predetermined variation between a valve body and a valve seat; an inference step of inferring the valve mechanism state using a learning model based on machine learning stored in the memory when the input information is acquired; an output step of outputting the inferred valve train state; A method of carrying out the above-mentioned The valve seat constitutes a part of a flow path, The valve seat has a valve seat end surface with which the valve body comes into contact to close the flow path, The valve seat is formed with one or more detector installation spaces, A detector is installed in each of the detector installation spaces, Each of the detectors can detect the predetermined variable of the detection target in a detection direction, the detection direction is toward the valve body located beyond the valve seat end face when the detector is installed in the detector installation space, The detector installation space is closed by the valve seat end surface. Reasoning method.

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