JP6783487B1 - Machine learning equipment, data processing systems, inference equipment and machine learning methods - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a machine learning device or the like capable of accurately grasping an abnormality and a sign of an abnormality in a solenoid unit without depending on an operator's experience or the like. A machine learning device 200 is applied to an electromagnetic valve 1 including a solenoid unit 3, and is associated with input data including control parameters of the solenoid unit 3 and the temperature of the electromagnetic valve 1. A learning data set storage unit 202 that stores a plurality of sets of learning data sets composed of output data composed of diagnostic information of the solenoid unit 3; input data and output data by inputting a plurality of sets of learning data sets. It includes a learning unit 203 that learns a learning model that infers a correlation with; and a trained model storage unit 204 that stores a trained model. [Selection diagram] Fig. 4

Description

The present invention relates to a machine learning device, a data processing system, an inference device, and a machine learning method for performing an abnormality diagnosis of a valve system.

Conventionally, a fluid pressure drive valve that opens and closes a main valve by controlling a drive fluid with a solenoid valve is known.
For example, Patent Document 1 describes a fluid pressure drive valve used for piping of plant equipment, which is a ball valve (main valve) in which the drive fluid is controlled by a solenoid valve in an emergency such as when an abnormality occurs in the equipment.
An emergency shutoff valve device that shuts off the fluid flowing through the pipe by closing the pipe is disclosed.

JP-A-2009-97539

In the solenoid valve used for the fluid pressure drive valve used in the plant equipment as shown in Patent Document 1, unexpected abnormality does not occur in order to improve the operating rate and reliability of the entire plant equipment. Is preferable. Therefore, in such a solenoid valve, it is desired to realize not only post-maintenance for grasping the abnormality when an abnormality occurs but also predictive maintenance for grasping the sign of the abnormality.

Here, the signs of abnormality of the solenoid valve can be expressed as various events, but the causal relationship between the events that can occur in the solenoid valve and the signs of abnormality has not been clearly specified. As a result, the predictive maintenance of the solenoid valve is carried out based on the judgment depending on the experience (including tacit knowledge) of the worker, and there is a problem that the accuracy differs depending on the worker in charge.

In view of the above-mentioned problems, the present invention is a machine learning device and data processing for accurately grasping abnormalities and signs of abnormalities in a solenoid valve (hereinafter, these are collectively referred to as "abnormalities" in the present invention). It is an object of the present invention to provide a system, an inference device and a machine learning method.

In order to achieve the above object, the machine learning device according to the first aspect of the present invention is, for example, FIG.
As shown in 4, it is applied to the electromagnetic valve 1 provided with the solenoid unit 3, and corresponds to the input data including the control parameter of the solenoid unit 3 and the temperature of the electromagnetic valve 1 and the input data. A learning data set storage unit 202 that stores a plurality of sets of learning data sets composed of output data composed of the attached diagnostic information of the solenoid unit 3; by inputting a plurality of sets of the learning data sets. It includes a learning unit 203 that learns a learning model that infers a correlation between the input data and the output data; and a trained model storage unit 204 that stores the learning model learned by the learning unit 203.

According to the machine learning device of the present invention, it becomes possible to provide a learned model capable of estimating the presence or absence of an abnormality in the solenoid valve based on various information and the like that can be acquired during steady operation of the solenoid valve. .. Therefore, by using this trained model, it becomes possible to estimate the abnormality generated in the solenoid valve with high accuracy without depending on the experience of the operator.

It is a schematic diagram which shows an example of the fluid pressure drive valve to which the machine learning apparatus and the like which concerns on one Embodiment of this invention are applied. It is a schematic diagram which shows an example of the solenoid valve to which the machine learning apparatus and the like which concerns on one Embodiment of this invention are applied. It is a block diagram which shows an example of the solenoid valve to which the machine learning apparatus and the like which concerns on one Embodiment of this invention are applied. It is a schematic block diagram of the machine learning apparatus which concerns on one Embodiment of this invention. It is a figure which shows the structural example (supervised learning) of the data used in the machine learning apparatus which concerns on one Embodiment of this invention. It is a figure which shows the structural example (unsupervised learning) of the data used in the machine learning apparatus or the like which concerns on one Embodiment of this invention. It is a figure which shows the example of the neural network model for supervised learning carried out in the machine learning apparatus which concerns on one Embodiment of this invention. It is a flowchart which shows the example of the machine learning method which concerns on one Embodiment of this invention. It is a schematic block diagram which shows the data processing system which concerns on one Embodiment of this invention. It is a flowchart which shows the example of the data processing process by the data processing system which concerns on one Embodiment of this invention.

Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. In addition, it should be noted
Hereinafter, the range necessary for the explanation for achieving the object of the present invention is schematically shown, the range necessary for the explanation of the relevant part of the present invention will be mainly described, and the parts where the description is omitted are known techniques. It shall be due to.

Before explaining the machine learning device, the data processing system, the inference device, and the machine learning method according to the embodiment of the present invention, first, the fluid pressure drive valve to which the machine learning device and the like are applied will be described below.

(Fluid pressure drive valve)
FIG. 1 is a schematic view showing an example of a fluid pressure drive valve 10 according to an embodiment of the present invention. The fluid pressure drive valve 10 in the present embodiment is installed in, for example, a pipe 100 through which various gases, oil, etc. flow in the plant equipment, and shuts off the flow of the pipe 100 at the time of an emergency stop when an abnormality occurs in the plant equipment. It can be used as an emergency shutoff valve. The installation location and application of the fluid pressure drive valve 10 are not limited to the above examples.

The fluid pressure drive valve 10 shown in FIG. 1 includes a main valve 11 arranged in the middle of the pipe 100 and a main valve 11.
A fluid pressure type drive device 12 that opens and closes the main valve 11 by driving the valve shaft 13 connected to the drive fluid according to the fluid pressure of the drive fluid, and controls the supply and discharge of the drive fluid to the drive device 12. It is provided with a solenoid valve 1 having a function.

Instrumentation air (hereinafter, simply referred to as “air”) A is adopted as the driving fluid used for the fluid pressure drive valve 10. The air A is supplied from the air supply source 14 to the solenoid valve 1 via the first air pipe 140, and further to the drive device 12 via the second air pipe 141. Further, the fluid pressure drive valve 10 includes a communication cable 150 for transmitting and receiving various data between the external device 15 and the solenoid valve 1, and a power cable 160 for supplying electric power from the external power supply 16 to the solenoid valve 1. Is connected. The driving fluid is not limited to the above-mentioned air A, and may be another gas or a liquid (for example, oil).

The external device 15 is a device for transmitting and receiving various information to and from the fluid pressure drive valve 10, and includes, for example, a computer for plant management (including a local server and a cloud server).
), A diagnostic computer used by an operator (maintenance inspector), or an external storage unit such as a USB memory or an external HDD. The external device 15 can also be connected to a machine learning device 200, which will be described later, to transmit various data constituting a learning data set. Further, the external device 15 is a GUI (Graphical) for notifying an operator or the like that an abnormality has occurred and the content thereof when an abnormality has occurred in the fluid pressure drive valve 10.
It is equipped with a notification means including a User Interface) or the like. The external device 1
Wireless communication may be used for communication between 5 and the solenoid valve 1.

As the drive system of the fluid pressure drive valve 10 of the present embodiment, an airless close system is adopted. Therefore, during steady operation, the main valve 11 is opened by supplying (air supply) air A from the air supply source 14 to the drive device 12 via the solenoid valve 1, and during emergency stop or test operation, The main valve 11 is closed by discharging (exhausting) air A from the drive device 12 via the solenoid valve 1. The fluid pressure drive valve 10 may adopt an airless open system. In that case, the fluid pressure drive valve 10 is closed by supplying air A to the drive device 12, and the air A is discharged from the drive device 12. The main valve 11 is closed.

A ball valve is adopted as the main valve 11. As a specific configuration of the main valve 11, a valve box 110 arranged in the middle of the pipe 100 and a ball-shaped valve body 111 rotatably provided in the valve box 110 are provided. Further, a first end portion 130A of the valve shaft 13 is connected to the upper portion of the valve body 111. The valve body 111 rotates in the valve box 110 according to the rotational drive of the valve shaft 13 from 0 degrees to 90 degrees, and the main valve 11 can be switched between a fully open state (state shown in FIG. 1) and a fully closed state. .. The valve used as the main valve 11 is not limited to the ball valve, and may be, for example, a butterfly valve or other on / off valve.

The drive device 12 employs a single-acting air cylinder mechanism arranged between the main valve 11 and the solenoid valve 1. As a specific configuration of the drive device 12, a cylindrical cylinder 12
0, a pair of pistons 122A and 122B provided in the cylinder 120 so as to be reciprocally linearly movable and connected via a piston rod 121, a coil spring 123 provided on the first piston 122A side, and a second piston. An air supply / exhaust port 124 formed on the piston 122B side,
A valve shaft 13 and a piston rod 1 arranged so as to penetrate the cylinder 120 along the radial direction.
It is provided with a transmission mechanism 125 provided at a portion orthogonal to 21. The drive device 1
2 is not limited to the single-acting type, and may be configured in another form such as a double-acting type.

The first piston 122A is urged by the coil spring 123 to operate the main valve 11 in the closing direction. Further, the second piston 122B presses the main valve 11 so as to operate in the opening direction (against the urging force of the coil spring 123) by the air A (air supply) supplied from the air supply / discharge port 124. It is a thing. Further, the transmission mechanism 125 is a rack and pinion mechanism,
It is composed of a link mechanism, a cam mechanism, and the like, and converts the reciprocating linear motion of the piston rod 121 into a rotary motion and transmits it to the valve shaft 13.

The valve shaft 13 is formed in a shaft shape and is arranged so as to penetrate the drive device 12 in a rotatable state. The first end 130A of the valve shaft 13 is connected to the main valve 11 and the valve shaft 13
The second end 130B of the above is pivotally supported by the solenoid valve 1. The valve shaft 13 may have a plurality of shafts connected via a coupling or the like.

The solenoid valve 1 has a function of controlling the supply and discharge of air A to the drive device 12, and is, for example, a three-way solenoid valve of a normally closed type (“open” when energized, “closed” when not energized) at two positions. It is configured as. The solenoid valve 1 has a spool portion 2 that switches the flow path through which the air A flows inside the housing portion 6 that functions as a housing of the indoor type or explosion-proof type solenoid valve 1, and an energized state (
It is provided with a solenoid unit 3 that displaces the spool unit 2 according to (when energized or de-energized). The solenoid valve 1 is not limited to the above-mentioned type of three-way solenoid valve, and may be a three-position solenoid valve, a normally open type, a four-way solenoid valve, or the like, and any combination thereof. It can be composed of various formations based on. Further, in the present embodiment, the solenoid valve 1 is used as a pilot valve in the fluid pressure drive valve 10, but the solenoid valve 1
Applications are not limited to this.

The spool portion 2 includes an input port 20 connected to the air supply source 14 via the first air pipe 140, and an output port 21 connected to the drive device 12 via the second air pipe 141.
It is provided with an exhaust port 22 for exhausting exhaust gas from the drive device 12.

The solenoid unit 3 displaces the spool unit 2 so as to communicate between the input port 20 and the output port 21 when energized, and communicates between the output port 21 and the exhaust port 22 when the power is off. , The spool portion 2 is displaced.

According to the series of configurations described above, when the solenoid valve 1 is energized, the air A (air supply) from the air supply source 14 is the first air pipe 140, the input port 20, the output port 21, and the second.
The second piston 122B is pressed and the coil spring 123 is compressed by flowing in the order of the air pipe 141 and being supplied to the air supply / discharge port 124. Then, the piston rod 121 and the transmission mechanism 125 are moved by the amount that the piston rod 121 moves according to the compression of the coil spring 123.
When the valve shaft 13 is rotationally driven via the above, the valve body 111 rotates in the valve box 110, and the main valve 11 is operated in a fully open state.

On the other hand, when the solenoid valve 1 is in a non-energized state, the air A (exhaust) in the cylinder 120 becomes
The pressing pressure of the second piston 122B is reduced and the coil spring 123 is compressed by flowing from the air supply / exhaust port 124 to the second air pipe 141, the output port 21 and the exhaust port 22 in this order and being discharged to the outside air. Restore from state. Then, when the valve shaft 13 is rotationally driven via the transmission mechanism 125 by the amount that the piston rod 121 moves in response to the restoration of the coil spring 123, the valve body 111 rotates in the valve box 110, and the main valve 11 rotates. It is operated to the fully closed state.

FIG. 2 is a cross-sectional view showing an example of a solenoid valve 1 according to an embodiment of the present invention. As shown in FIG. 2, the solenoid valve 1 according to the present embodiment includes a plurality of sensors 4 for acquiring the state of each portion of the solenoid valve 1 and a plurality of sensors in addition to the spool portion 2 and the solenoid portion 3 described above. It includes a substrate 5 on which at least one of the four is mounted, a spool portion 2, a solenoid portion 3, a plurality of sensors 4, and an accommodating portion 6 accommodating the substrate 5.

The accommodating portion 6 is adjacent to the first accommodating portion 60 accommodating the spool portion 2 and the first accommodating portion 60, and also accommodates the solenoid unit 3, the plurality of sensors 4, and the substrate 5. 61
And a terminal box 62 to which the communication cable 150 and the power cable 160 are connected. The first accommodating portion 60 and the second accommodating portion 61 are made of a metal material such as aluminum.

The first accommodating portion 60 has openings (not shown) that function as input ports 20, output ports 21, and exhaust ports 22, respectively.

The second housing portion 61 includes a cylindrical housing 610 in which both ends (first housing end portion 610a and second housing end portion 610b) are open, a body 611 arranged inside the housing 610, and a second housing portion 61. A solenoid cover 612 that covers the solenoid portion 3 fixed to the housing end portion 610a of 1 from the outside air, and a terminal box cover 613 that covers the terminal box 62 fixed to the second housing end portion 610b from the outside air are provided.

The housing 610 has a shaft insertion port 610c formed in the lower portion thereof and into which the second end 130B of the valve shaft 13 is inserted, a body insertion port 610d formed in the upper portion thereof into which the body 611 is inserted, and a second. It has a cable insertion port 610e formed on the housing end portion 610b side of the above and into which the communication cable 150 and the power cable 160 are inserted.

The first accommodating portion 60 and the second accommodating portion 61 are made to penetrate the body 611.
A first flow path 63 that branches from the input side flow path 26 and communicates between the input side flow path 26 and the first pressure sensor 40, and an output side flow path 27 that branches from the output side flow path 27. A second flow path 64 communicating with the second pressure sensor 41 and a spool flow path 65 through which air A for interlocking the spool portion 2 and the solenoid portion 3 are formed are formed.

The spool portion 2 includes a spool hole 23 formed in a second accommodating portion 61 that functions as a spool case, a spool valve 24 that is movably arranged in the spool hole 23, and a spool that urges the spool valve 24. The spring 25, the input side flow path 26 communicating between the input port 20 and the spool hole 23, the output side flow path 27 communicating between the output port 21 and the spool hole 23, the exhaust port 22 and the spool hole 23. It is provided with an exhaust flow path 28 that communicates between the two.

The solenoid unit 3 is arranged in a solenoid case 30, a solenoid coil 31 housed in the solenoid case 30, a movable iron core 32 movably arranged in the solenoid coil 31, and a fixed state in the solenoid coil 31. A fixed iron core 33 and a solenoid spring 34 for urging the movable iron core 32 are provided.

When the solenoid valve 1 is switched from the non-energized state to the energized state, the solenoid coil 31 generates an electromagnetic force when the coil current flows through the solenoid coil 31 in the solenoid unit 3, and the movable iron core is generated by the electromagnetic force. When the 32 is sucked into the fixed iron core 33 against the urging force of the solenoid spring 34, the flow state of the air A flowing through the spool flow path 65 is switched. Then, in the spool portion 2, the flow state of the air A flowing through the spool flow path 65 is switched, so that the spool valve 24 is moved against the urging force of the spool spring 25, so that the input port 20 and the exhaust are exhausted. The state of communicating with the port 22 can be switched to the state of communicating with the input port 20 and the output port 21.

The substrate 5 includes a first substrate 50 arranged so that the substrate surfaces 500A and 500B are arranged along the valve shaft 13 inserted from the shaft insertion port 610c, and a second substrate 51 arranged close to the terminal box 62. And a third substrate 52 arranged close to the solenoid unit 3.

Of the substrate surfaces 500A and 500B of the first substrate 50, the body 611, the solenoid unit 3, and the third substrate 52 are arranged on the first substrate surface 500A side. First substrate surface 500A
On the second board surface 500B side opposite to the side, the second board 51 and the terminal box 62
Is placed.

Sensors 4 are arranged at appropriate positions on the first substrate 50, the second substrate 51, and the third substrate 52. Examples of the sensor 4 include a first pressure sensor 40 that measures the fluid pressure of air A flowing through the input side flow path 26 and the first flow path 63, and an output side flow path 27 and a second flow path 64. The second pressure sensor 41 that measures the fluid pressure of the air A flowing through the surface and the rotation angle when the valve shaft 13 is rotationally driven are measured, and valve opening information of the main valve 11 is acquired according to the rotation angle. Includes a main valve opening sensor 42.

The main valve opening sensor 42 is composed of, for example, a magnetic sensor, measures the magnetic strength generated by the permanent magnet 131 attached to the second end 130B of the valve shaft 13, and measures the magnetic strength. Correspondingly, the valve opening information of the main valve 11 is acquired. The main valve opening sensor 42 is the first substrate surface 5 of the first substrate 5 arranged along the valve shaft 13 inserted from the shaft insertion port 610c.
Of 00A, it is preferable that the valve shaft 13 is placed at a position facing the outer circumference of the valve shaft 13. As a result, in the accommodating portion 6, the main valve opening sensor 42 mounted on the first substrate 50 and the second end portion 130B of the valve shaft 13 are brought close to each other without wasting the arrangement space. It becomes possible to arrange the valve opening degree accurately.

FIG. 3 is a block diagram showing an example of the solenoid valve 1 according to the embodiment of the present invention. As shown in FIG. 3, the solenoid valve 1 has, as an example of an electrical configuration, a communication for communicating with a control unit 7 that controls the solenoid valve 1 and an external device 15 in addition to the above-mentioned substrate 3 and sensor 4. Part 8 and external power supply 16
A power supply circuit unit 9 connected to is provided.

The plurality of sensors 4 measure the supply voltage to the solenoid unit 3 in addition to the above-mentioned first pressure sensor 40, second pressure sensor 41, and main valve opening sensor 42 as a sensor group for measuring the physical quantity of each part. The voltage sensor 43, the current / resistance sensor 44 that measures the current value when the solenoid unit 3 is energized and the resistance value when the solenoid unit is not energized, and the inside of the accommodating unit 6 that houses the temperature of the electromagnetic valve 1, particularly the solenoid unit 3. A temperature sensor 45 for measuring the temperature and a magnetic sensor 46 for measuring the strength of the magnetism generated by the solenoid unit 3 are provided.

In addition, the plurality of sensors 4 measure at least one of the total energization time for the solenoid unit and the current energization continuous time as the operation time of the solenoid unit 3 as a sensor group for acquiring information on the operation history of each unit. Meter (timer) 47, solenoid valve 1, drive device 12
And an operation counter (counter) 48 for counting the number of operations of each of the main valves 11.

Further, these sensors 40 to 48 are not limited to those in which each sensor is individually provided as described above, and the other sensor is individually provided by the specific sensor having the function of the other sensor. It does not have to be. For example, the magnetic sensor 46 measures the magnetic strength generated by the solenoid unit 3, and the current / resistance sensor 44 obtains the current value when the solenoid unit 3 is energized based on the magnetic strength. It does not have to be provided individually. Further, the microcontroller 70 may have a built-in sensor function or a part of the sensor function. For example, the microcontroller 70 has a built-in operating time meter 47 and an operation counter 48. , The operation time meter 47 and the operation counter 48 may not be provided separately.

The control unit 7 processes the information indicating the state of each part of the solenoid valve 1 acquired by the plurality of sensors 4, and also controls the microcontroller 70 and the solenoid unit 3 that control each part of the solenoid valve 1.
It is provided with a valve test switch 71 that controls the energized state of the main valve 11 and opens and closes the main valve 11 during a test operation.

The microcontroller 70 is a CPU (Central Processing Unit).
Processor (not shown) such as it), ROM (Read Only Memory), R
It includes a memory configured by AM (Random Access Memory) or the like. The microcontroller 70 can include a function of realizing the data processing system 300 described later in the present embodiment.

The valve test switch 71 receives a command from the microcontroller 70 when a predetermined test operation condition is satisfied, and executes a stroke test of the fluid pressure drive valve 10 as a test operation.

The stroke test is performed, for example, by either a full stroke test or a partial stroke test. The full stroke test is executed by operating the main valve 11 in the fully open state by switching from the energized state to the non-energized state, and by switching from the non-energized state to the energized state in the fully closed state to return to the fully open state. Will be done. In the partial stroke test, a predetermined opening degree is obtained by switching the main valve 11 from the energized state to the non-energized state in the fully open state without operating the main valve 11 in the fully closed state (that is, without stopping the plant equipment). It is executed by partially closing up to and returning to the fully open state by switching from the non-energized state to the energized state in the partially closed state.

As the test operation conditions, for example, the execution time or a specific designated date and time according to the execution frequency (for example, once a year) specified as a set value by the administrator has arrived, or the execution command from the external device 15 has arrived. When the test operation button (not shown) provided on the solenoid valve 1 is operated by the administrator, the test operation (stroke test) should be executed so that the test operation condition is satisfied. Just do it.

(Machine learning device)
In the fluid pressure drive valve 10 having the above-mentioned series of configurations, by providing the above-mentioned plurality of sensors 4, for example, during steady operation and unsteady operation (for example, during test operation in which opening / closing operation is performed or during emergency stop). ), Various information on the fluid pressure drive valve 10 can be acquired. Therefore, the following is machine learning for learning an inference model (learned model) capable of estimating diagnostic information of the fluid pressure drive valve 10 based on information (state variable) that can be acquired from the fluid pressure drive valve 10. The device 200 will be described. The machine learning device 200 referred to here.
Is not only provided as a device that operates independently, but also a program for causing an arbitrary processor to execute the operation described below, or a non-temporary storage for one or more instructions for executing the operation. Includes those provided in the form of a typical computer-readable medium.

FIG. 4 is a schematic block diagram of the machine learning device 200 according to the embodiment of the present invention. As shown in FIG. 4, the machine learning device 200 according to the present embodiment includes a learning data set acquisition unit 201, a learning data set storage unit 202, a learning unit 203, and a trained model storage unit 204. I have.

The learning data set acquisition unit 201 is an interface unit for acquiring a plurality of data constituting the learning (training) data set from various devices connected via, for example, a wired or wireless communication line. Here, examples of various devices connected to the learning data set acquisition unit 201 include a worker computer PC1 used by a worker of an external device 15 and a fluid pressure drive valve 10. Although FIG. 4 shows an example in which the external device 15 and the computer PC 1 are connected separately, the external device 1
5 and the worker computer PC 1 may be configured by the same computer.
In the learning data set acquisition unit 201, detection data of a plurality of sensors 4 of the fluid pressure drive valve 10 is acquired as input data from, for example, an external device 15, and diagnostic information of the fluid pressure drive valve 10 associated with the input data is acquired. Can be acquired as output data from, for example, the worker computer PC1. Then, the input data and the output data associated with each other constitute one learning data set described later.

FIG. 5 is a diagram showing a configuration example (supervised learning) of data used in the machine learning device 200 according to the embodiment of the present invention. FIG. 6 shows a machine learning device 2 according to an embodiment of the present invention.
It is a figure which shows the composition example (unsupervised learning) of the data used in 00. It should be noted that FIGS. 5 and 6 are appropriately referred to in the description of the data processing system and the inference device.

As shown in FIGS. 5 and 6, the learning data set includes at least the control parameters of the solenoid unit 3 and the temperature of the solenoid valve 1 as input data, and the solenoid valve 1 including the solenoid valve 1 as output data. It contains the diagnostic information of the above, and refers to a data set to be used in machine learning described later. The details of these various data will be described below as an example, but the present invention is not limited thereto.

In the present embodiment, the temperature of the solenoid valve 1 refers to the value of the internal temperature of the accommodating portion 6 accommodating the solenoid portion 3, and can be obtained by the temperature sensor 45 described above.

The control parameters of the solenoid unit 3 are various parameter information for controlling the solenoid unit 3, specifically, the supply voltage supplied to the solenoid coil 31, the current value when the solenoid coil 31 is energized, and It is preferable to include at least one of the operating times of the solenoid unit 3. Of these, the supply voltage supplied to the solenoid coil 31 can be acquired by the voltage sensor 43 described above, and the current value when the solenoid coil 31 is energized can be acquired by the current / resistance sensor 44 described above, and the operating time of the solenoid unit 3 Can be obtained by the above-mentioned total operating time 47.

The control parameters of the solenoid unit 3 constituting the input data and the temperature of the solenoid valve 1 may each be composed of one data (time point data) at a specific time point, and FIGS. As shown in parentheses of 6, it may be composed of a plurality of data (time series data) acquired at a plurality of different time points within a predetermined period. When each data is composed of time-series data, the time-series data of the control parameter of the solenoid unit 3 and the time-series data of the temperature of the electromagnetic valve 1 each have the same sampling period and the same. It may be acquired at a plurality of time points in phase (without a phase difference), or at least one of the sampling period and the phase may be different. In order to effectively improve the accuracy of inference, the latter time series data format is preferable.

The diagnostic information of the solenoid unit 3 is information indicating whether or not any abnormality has occurred in the solenoid unit 3 when an abnormality diagnosis is performed on the solenoid unit 3, and various data formats can be used. Can be adopted. Abnormalities are not only ex post facto abnormalities such that the occurrence of the abnormality was found at the time of the abnormality diagnosis, but also within the permissible range that is judged to be normal at the time of the abnormality diagnosis, but future abnormalities. It also includes signs of anomalies such as those foreseen.

For example, as shown in FIG. 5, the diagnostic information as one aspect can be composed of information indicating whether the solenoid unit 3 is normal (no abnormality) or abnormal (with abnormality). .. In this case, the diagnostic information is classified into two values. For example, the value indicating that the solenoid unit 3 is in a normal state is set to "0", and the value indicating that the solenoid unit 3 is in an abnormal state is set to "0".
As 1 ”, the corresponding value may be input by the operator using the working computer PC1 in a form associated with the input data. In this case, information on the specific content of the abnormality is not always necessary.

In addition, among the above-mentioned diagnostic information, the information indicating that the solenoid unit 3 is abnormal may include the specific content of the abnormality as shown by the broken line in FIG. Specific details of the abnormality include, for example, a defect in the air A circuit, an abnormality in the supply pressure of the air A from the air supply source 14, an abnormality in the supply voltage in the solenoid unit 3, deterioration / damage of the solenoid coil 31, and an electric circuit. It includes a short circuit, abnormal heat generation of the solenoid valve 1, malfunction of the iron core, and the like. In this case, the diagnostic information is classified into a plurality of values (3 or more), and for example, a value indicating that the solenoid unit 3 is in a normal state is set to "0".
The value indicating that the solenoid unit 3 is an abnormality corresponding to the malfunction of the main valve 11 is set to "1".
, The value indicating that the abnormality corresponds to the solenoid unit 3 is set to "2", and similarly, the value is uniquely determined in advance according to the content of each abnormality, and then the operator uses the work computer PC1. The corresponding value may be input in a form associated with the input data. By setting the diagnostic information in this way, not only the presence or absence of the occurrence of the abnormality but also the specific content information of the abnormality when the abnormality occurs (abnormality 1 / abnormality 2 / ... / abnormality n shown in FIG. 6). It is possible to prepare a learning data set that also includes (). The diagnostic information as one aspect described above is used in the case of supervised learning (see FIG. 5) in the machine learning described below.

Further, as the diagnostic information of the solenoid unit 3, other than the above-mentioned information can be adopted. For example, the diagnostic information as another aspect can be information indicating only that the solenoid unit 3 is normal and not abnormal, as shown in FIG. In this case, since the diagnostic information includes only information indicating that the solenoid unit 3 is normal, the learning data set including this diagnostic information as output data inevitably includes the case where the solenoid unit 3 is normal. Only a dataset consisting of input data and output data. Therefore, since the output data of the training data set in this case is always the same, it can be understood by those skilled in the art that the training data set does not necessarily have the output data as data. There will be. The diagnostic information as another aspect is used when unsupervised learning (see FIG. 6) is performed in the machine learning described below.

Further, as shown in FIG. 5, the diagnostic information as another aspect is that the solenoid unit 3 is in a normal state (no abnormality) and the remaining life of the solenoid unit 3 is a preset period (for example, several hours). It can be composed of information indicating that the condition is abnormal (abnormal) within several days, months, years, etc.). In this case, the diagnostic information is classified into two values, for example, the value indicating that the solenoid unit 3 is in a normal state is set to "0", and the remaining life of the solenoid unit 3 is an abnormal state within a preset period. The value indicating that the value is "1" may be set, and the corresponding value may be input by the operator using the working computer PC1 in a form associated with the input data. In this case, information on the specific content of the abnormality is not always necessary.

In addition, among the above diagnostic information, the information indicating that the remaining life of the solenoid unit 3 is an abnormality within a preset period also includes the specific contents of the remaining life, as shown by the broken line in FIG. It may be included. In this case, the diagnostic information is classified into a plurality of values (at least 3 or more), and the solenoid unit 3 is in a normal state, and the remaining life of the solenoid unit 3 is within a preset period of the plurality of remaining lives. It is composed of information indicating one of the abnormalities that corresponds to.

For example, the value indicating that the solenoid unit 3 is in a normal state is set to "0", and the solenoid unit 3 is set.
The value indicating that the remaining life of the solenoid unit 3 corresponds to the first period (for example, 6 months or more and less than 1 year) is "1", and the remaining life of the solenoid unit 3 is shorter than the first period. Work using the work computer PC1 after setting the value indicating that the abnormality corresponds to (for example, less than 6 months) to "2", and similarly setting the value uniquely in advance according to the content of each abnormality. The corresponding value may be input by a person in association with the input data. By setting such diagnostic information, not only the presence or absence of an abnormality, but also the specific information on the remaining life when an abnormality occurs (
Corresponds to the abnormality 1 / abnormality 2 / ... / abnormality n shown in FIG. ) Can be prepared as a training data set. The life prediction information as one aspect described above is used in the case of supervised learning (see FIG. 5) in the machine learning described below.

Further, as the life prediction information of the solenoid unit 3, other than the above-mentioned information can be adopted. For example, the life prediction information as another aspect is the solenoid unit 3 as shown in FIG.
Can be information indicating only that is normal and not abnormal. In this case, since the life prediction information includes only information indicating that the solenoid unit 3 is normal, the solenoid unit 3 is inevitably normal in the learning data set including this life prediction information as output data. Only the data set consisting of the input data and the output data of the case. Therefore, since the output data of the training data set in this case is always the same, it can be understood by those skilled in the art that the training data set does not necessarily have the output data as data. There will be. The life prediction information as another aspect is used when unsupervised learning (see FIG. 6) is performed in the machine learning described below.

As an option, the input data in the training dataset also includes a fluid pressure drive valve 10
Total operating time, operating time since the last power was turned on to the fluid pressure drive valve 10, the number of times the main valve 11 was operated, the number of times the drive device 12 was operated, the number of times the solenoid unit 3 was operated, and the main valve. 11
The opening and closing time of can be selectively included. Of these, the total operating time of the fluid pressure drive valve 10 and the operating time since the last power supply to the fluid pressure drive valve 10 can be obtained by the above-mentioned total operating time 47, and the number of times the main valve 11 is operated. The number of times the drive device 12 is operated and the number of times the solenoid unit 3 is operated can be obtained by the above-mentioned operation counter 48, and the opening / closing time of the main valve 11 can be obtained by using a timer or the like (not shown). Increasing the types of input data generally contributes to improving the estimation accuracy of the trained model obtained after machine learning, but adopting input data with a low degree of correlation with diagnostic information does not help. On the contrary, it may hinder the improvement of the estimation accuracy of the trained model. Therefore, the number and types of data to be adopted as input data should be appropriately selected in consideration of the state of the fluid pressure drive valve 10 to which the trained model is applied.

The learning data set storage unit 202 associates a plurality of data for forming the learning data set acquired by the learning data set acquisition unit 201 with the related input data and output data into one learning data set. It is a database for storing. The specific configuration of the database that constitutes this learning data set storage unit can be adjusted as appropriate. For example, in FIG. 4, for convenience of explanation, the training data set storage unit 202 and the trained model storage unit 204 described later are shown as separate storage means, but these are used as a single storage medium (database). ) Can also be configured.

The learning unit 203 correlates the input data and the output data included in the plurality of learning data sets by executing machine learning using the plurality of learning data sets stored in the learning data set storage unit 202. It generates a trained model that trains relationships. In this embodiment, as will be explained in detail later, supervised learning using a neural network is adopted as a specific method of machine learning. However, the specific method of machine learning is not limited to this, and other learning methods may be adopted as long as the correlation between input and output can be learned from the training data set. It is possible. For example, ensemble learning (random forest, boosting, etc.) can also be used.

The trained model storage unit 204 is a database for storing the trained model generated by the training unit 203. The trained model stored in the trained model storage unit 24 is applied to the actual system via a communication line including the Internet or a storage medium, if requested. The specific application mode of the trained model to the actual system (data processing system 300) will be described in detail later.

Next, the learning unit 2 using the plurality of training data sets obtained as described above.
The learning method in 03 will be described focusing on supervised learning. FIG. 7 is a diagram showing an example of a neural network model for supervised learning implemented in the machine learning device according to the embodiment of the present invention. The neural network in the neural network model shown in FIG. 7 includes l neurons (x1 to xl) in the input layer, m neurons (y11 to y1m) in the first intermediate layer, and n neurons in the second intermediate layer. Neuron (y21
It is composed of ~ y2n) and o neurons (z1 to zo) in the output layer.
The first intermediate layer and the second intermediate layer are also called hidden layers, and the neural network may have a plurality of hidden layers in addition to the first intermediate layer and the second intermediate layer. Alternatively, only the first intermediate layer may be used as the hidden layer. In addition, in FIG. 7,
An example is a neural network model in which a plurality (o) output layers are set. For example, when the above-mentioned diagnostic information is specified from one value, that is, the number of teacher data described later is 1. When the number is (t1 only), the number of neurons in the output layer can also be one (z1 only).

In addition, nodes connecting the neurons between the layers are stretched between the input layer and the first intermediate layer, between the first intermediate layer and the second intermediate layer, and between the second intermediate layer and the output layer. A weight wi (i is a natural number) is associated with each node.

The neural network in the neural network model according to the present embodiment learns the correlation between the control parameters of the solenoid unit 3 and the temperature of the solenoid valve 1 and the diagnostic information of the solenoid unit 3 by using the learning data set. To do. Specifically, the control parameters of the solenoid unit 3 as state variables and the temperature of the electromagnetic valve 1 are associated with the neurons in the input layer, and the values of the neurons in the output layer are output from a general neural network. In the method of calculating the value, that is, the value of the output side neuron is associated with the value of the input side neuron connected to the neuron and the node connecting the output side neuron and the input side neuron. It is calculated as the sum of a number sequence of multiplication values with wi by using a method performed on all neurons other than the neurons in the input layer. When inputting the above state variables to neurons in the input layer, the format in which the information acquired as the state variables is input can be appropriately set in consideration of the accuracy of the generated trained model and the like. it can. Specifically, preprocessing can be performed on specific input data in order to adjust the number of neurons corresponding to each input data or to adjust the value to correspond to the neurons.

Then, the values of o neurons z1 to zo in the calculated output layer, that is, one or more diagnostic information in the present embodiment and one or more diagnostic information constituting a part of the learning data set. The teacher data t1 to t 1 to be composed of the above are compared with each other to obtain an error, and the weight wi associated with each node is adjusted so that the obtained error becomes small (back pro vacation).

Then, when a predetermined condition such as repeating the above-mentioned series of steps a predetermined number of times or the error becoming smaller than the permissible value is satisfied, the learning is terminated and the neural network model (of the neural network model) All the weights wi) associated with each of the nodes are stored in the trained model storage unit 204 as a trained model.

(Machine learning method)
In connection with the above, the present invention provides a machine learning method. The machine learning method according to the present invention will be described below with reference to FIGS. 5 (learning phase), 6 (learning phase), 7 and 8. FIG. 8 is a flowchart showing an example of a machine learning method according to an embodiment of the present invention. The machine learning method shown below will be described based on the machine learning device 200 described above, but the premise configuration is not limited to the machine learning device 200. Also,
This machine learning method is realized by using a computer, but various computers can be applied, for example, an external device 15, a working computer PC1.
Alternatively, computer devices constituting the microcontroller 70, server devices arranged on the network, and the like can be mentioned. Regarding the specific configuration of this computer, for example, communication for communicating with an arithmetic unit consisting of at least a CPU, a GPU, etc., a storage device composed of a volatile or non-volatile memory, etc., and a network or other devices. With the device
Those including a bus connecting each of these devices can be adopted.

In supervised learning as a machine learning method according to the present embodiment, as a preliminary preparation for starting machine learning, a desired number of learning data sets (see FIG. 5) are first prepared, and a plurality of prepared data sets are prepared. The learning data sets are stored in the learning data set storage unit 202 (step S11). The number of training data sets prepared here may be set in consideration of the inference accuracy required for the finally obtained trained model.

Several methods can be adopted to prepare the learning data set used for this supervised learning. For example, when an abnormality occurs in a specific solenoid unit 3, or when an operator recognizes a sign of an abnormality, various information during steady operation of the hydraulic pressure drive valve 10 at that time is obtained by using a plurality of sensors 4 and the like. The input data and output data (for example, the output in this case) that constitute the learning data set are acquired and the worker identifies and inputs the diagnostic information using the work computer PC1 or the like in the form of associating with these information. The value of the data is "1"
) And prepare. Then, by repeating such work, a method of preparing a desired number of training data sets can be adopted. In addition to these methods, various methods such as acquiring a learning data set by positively creating an abnormal state in the solenoid unit 3 are adopted as a method for preparing the learning data set. Can be done. However, since various information of the solenoid unit 3 often has a tendency peculiar to each of the solenoid units 3, the target for acquiring the data constituting the learning data set is already learned obtained through machine learning described later. It is preferable to collect only from one solenoid unit 3 to which the model will be applied. Further, the learning data set is not only composed of input / output data when an abnormality occurs, but also input data and output data (for example, when no abnormality occurs, that is, in the normal state of the solenoid unit 3). , The value of the output data in this case includes a predetermined number of training data sets composed of "0").

When step S11 is completed, then learning in the learning unit 203 is started.
A neural network model before training is prepared (S12). The pre-learning neural network model prepared here has, for example, the structure shown in FIG. 7, and the weight of each node is set as an initial value. Then, for example, one learning data set is randomly selected from the plurality of learning data sets stored in the learning data set storage unit 202 (step S13), and the input data in the one learning data set is selected. Is input to the input layer (see FIG. 7) of the prepared pre-learning neural network model (step S14).

Here, since the value of the output layer (see FIG. 7) generated as a result of step S14 is generated by the neural network model before training, the value is different from the desired result in most cases, that is, , A value indicating information different from the correct diagnostic information. Therefore, next, machine learning is performed using the diagnostic information as teacher data in one learning data set acquired in step S13 and the value of the output layer generated in step S13 (step S15). .. The machine learning performed here is, for example, compared with the diagnostic information constituting the teacher data and the value of the output layer, and is associated with each node in the neural network model before learning so as to obtain a preferable output layer. It may be a process of adjusting the weight (back propagation). The number and format of the values output to the output layer of the neural network model before learning are the same numbers and formats as the teacher data in the training data set as the learning target.

To give a concrete example of machine learning here, suppose that the diagnostic information constituting the teacher data is "0" when it is normal and "1" when it is abnormal (binary classification). When the value of the output data in one learning data set selected in step S13 is "1", the value of the output layer is a predetermined value of 0 to 1, specifically, For example, "0.
A value such as "63" is output. Therefore, in step S15, if the same input data is input to the input layer, the value obtained by the neural network model being trained approaches "1" to each node of the neural network model being trained. Adjust the associated weights.

When machine learning is performed in step S15, whether or not it is necessary to continue machine learning is determined based on, for example, the remaining number of unlearned learning data sets stored in the learning data set storage unit 202. (Step S16). Then, when the machine learning is continued (No in step S16), the process returns to step S13, and when the machine learning is finished (Yes in step S16), the process proceeds to step S17. When the machine learning is continued, the steps S13 to S15 are performed a plurality of times on the neural network model being trained by using the unlearned learning data set. The accuracy of the finally generated trained model generally increases in proportion to this number of times.

When the machine learning is finished (Yes in step S16), the neural network generated by adjusting the weights associated with each node by a series of steps is stored in the trained model storage unit 204 as a trained model. (Step S17), a series of learning processes is completed. The trained model stored here can be applied to and used in the data processing system 300 described later.

In the learning process and machine learning method of the machine learning device described above, in order to generate one trained model, a plurality of machine learning processes are repeatedly executed for one (pre-learning) neural network model. To improve the accuracy of the data processing system 30
It explains what to get a trained model that is sufficient to apply to zero. However, the present invention is not limited to this acquisition method. For example, a plurality of trained models that have undergone machine learning a predetermined number of times are stored in the trained model storage unit 204 as one candidate, and a data set for validation is input to the plurality of trained model groups. The output layer (values of neurons) may be generated, and the accuracy of the values specified in the output layer may be compared and examined to select one of the best trained models to be applied to the data processing system 300. .. The validity judgment data set may be any data set that is similar to the learning data set used for learning and is not used for learning.

As described above, by applying the machine learning device and the machine learning method according to the present embodiment, from various data acquired by a plurality of sensors 4 provided at appropriate positions of the solenoid valve 1,
It is possible to obtain a trained model capable of accurately deriving diagnostic information indicating whether or not an abnormality (including an ex post facto abnormality and a sign of an abnormality) occurs.

In the learning method and machine learning method of the machine learning device 200, "supervised learning" has been described, but as a method for generating a trained model, other known "supervised learning" such as a convolutional neural network (CNN) is used. , And as the diagnostic information constituting the output data, the diagnostic information according to the above-mentioned other aspects, that is, the information indicating only that the hydraulic pressure drive valve 10 is normal without being abnormal. Learning data set including (Fig. 6)
reference. ) May be used for "unsupervised learning". By using "unsupervised learning"
Even if the diagnostic information in the output data associated with the input data is only the information on the normal state of the electromagnetic valve 1, as shown in the "learning phase" of FIG. 6, the input data and the output data A trained model can be obtained by learning the correlations that represent the characteristics of the normal state. In this case, at the time of inference in the data processing system 300 described later,
The inference of diagnostic information can be realized by regarding the input data that is determined not to match the characteristics of the normal state by a predetermined amount as the non-normal state, that is, the abnormal state. As a specific method of this "unsupervised learning", for example, a known method using an autoencoder or the like simplified in FIG. 6 can be used, and detailed description thereof will be omitted here.

(Data processing system)
Next, with reference to FIG. 9, an application example of the machine learning device 200 and the trained model generated by the machine learning method described above will be described. FIG. 9 is a schematic block diagram showing a data processing system according to an embodiment of the present invention.

As the data processing system 300 according to the present embodiment, an embodiment mounted in the microcontroller 70 of the fluid pressure drive valve 10 described above will be exemplified. It is also possible to apply at least a part of the data processing system 300 to other devices, for example, other devices connected to the external device 15 and the fluid pressure drive valve 10.

The data processing system 300 includes an input data acquisition unit 301 and an inference unit 3.
02, a trained model storage unit 303, and a notification unit 304 are included at least.

The input data acquisition unit 301 is an interface unit that is connected to a plurality of sensors 4 included in the solenoid valve 1 and acquires data output by each sensor 4. The input data acquisition unit 301 acquires at least the control parameters of the solenoid unit 3 and the temperature of the solenoid unit 3 of the solenoid valve 1. In the example shown in FIG. 9, all the sensors 4 provided in the solenoid valve 1 are connected so that all the input data that can be used for inference described later can be acquired, but which one is connected to the input data acquisition unit 301. Whether or not to connect the sensor 4 can be appropriately selected according to the trained model or the like used in the inference unit 302 described later. Further, the inference result of the inference unit 302 is preferably stored in a storage means (not shown), and the stored past inference result is, for example, the learned model storage unit 30.
It can be used as a learning data set used for online learning to further improve the inference accuracy of the trained model in 3.

The inference unit 302 is for inferring whether or not an abnormality has occurred in the solenoid valve 1 from various data of the solenoid unit 3 acquired by the input data acquisition unit 301. For this inference, for example, a trained model trained using the machine learning device 200 and the machine learning method described above is used, and the trained model is a trained model storage unit 303 composed of an arbitrary storage medium. It is stored in. The inference unit 302 not only has a function of performing inference processing using the trained model, but also adjusts the input data acquired by the input data acquisition unit 301 to a desired format or the like as a preprocessing of the inference processing. To the output value output by the trained model as a pre-processing function to be input to the trained model and post-processing of inference processing
For example, by applying a predetermined threshold value, a post-processing function that finally determines whether or not an abnormality (including an ex post facto abnormality and a sign of an abnormality) has occurred (no abnormality (normal) or an abnormality (abnormality)) is provided. Also includes.

As described above, the trained model storage unit 303 is a storage medium for storing the trained model used in the inference unit 302. This trained model storage unit 3
The number of trained models stored in 03 is not limited to one. For example, a plurality of trained models with different numbers of input data or different learning methods (for example, supervised learning and unsupervised learning performed by the machine learning device 200 or the like described above) are stored and selectively. Can be made available to.

The notification unit 304 is for notifying an operator or the like of the inference result of the inference unit 302. Various specific means of notification can be adopted. For example, the inference result is transmitted to the external device 15 via the communication unit 8 and displayed on the GUI of the external device 15, or a light emitting member, a speaker, etc. are previously displayed on the solenoid unit 3. By providing and operating them, it is possible to notify the operator or the like of the presence or absence of an abnormality.

The data processing process by the data processing system having the above configuration will be described below with reference to FIGS. 5 (inference phase), 6 (inference phase), and FIG. 10. FIG. 10 shows
It is a flowchart which shows the example of the data processing process by the data processing system 300 which concerns on one Embodiment of this invention.

When the supply of electric power from the external power source 16 is started to the solenoid valve 1 of the fluid pressure drive valve 10 and the diagnosis of the abnormality of the solenoid unit 3 is started accordingly, the input data acquisition unit 301 is operated by the plurality of sensors 4. Various data indicating the state of each part of the acquired solenoid valve 1 are acquired (step S21). When the input data acquisition unit 301 can acquire the desired input data (control parameters of the solenoid unit 3 and the temperature of the solenoid unit 3 of the solenoid valve 1 (see FIGS. 5 and 6)), the input data is the input data. The reasoning by the reasoning unit 302 based on the above is carried out (step S22). At this time, it is preferable that the trained model used for inference is specified in advance. Further, when the trained model specified in advance requires predetermined time-series data as its input data, for example, in step S22 after the required amount of data is acquired by the input data acquisition unit 301. The inference is carried out.

Specifically, the inference unit 302 is an inference result by performing preprocessing on the input data and inputting it to the training modeled model, and post-processing the output value from the learning modeled model. Judge the presence or absence of abnormalities (including ex post facto abnormalities and signs of abnormalities). In the post-processing in supervised learning (see “Inference Phase” in FIG. 5), the inference unit 302 sets the output value of the learning modeled model (the number between 0 and 1 in the case of binary classification) and a predetermined value. Compare with the threshold value, for example, if the output value of the trained model has been equal to or greater than the predetermined threshold value,
By judging that "there is an abnormality (abnormal)" and "no abnormality (normal)" if it is less than the predetermined threshold value,
The judgment result is output as an inference result. In addition, unsupervised learning (“inference phase” in Fig. 6”
reference. In the post-processing in), the inference unit 302 obtains the difference (distance) between the output value (feature amount) of the trained modeled model and the feature amount based on the input data, and the difference (distance) is equal to or greater than a predetermined threshold value. If there is, it is judged as "abnormal (abnormal)", and if it is less than a predetermined threshold value, it is judged as "no abnormality (normal)", and the judgment result is output as an inference result.

Then, as a result of inference by the inference unit 302 in step S22,
When the inference result indicates "no abnormality (normal)" (No in step S23), the process returns to step S21 so as to continue the series of inferences. On the other hand, the inference results are shown in FIGS. 5 and 6.
As shown in the above, when "abnormal (abnormal)" is indicated (Yes in step S23), the inference result by the notification unit 304 is "abnormal (abnormal)", that is, the solenoid valve 1
Notify workers, etc. that an abnormality (including an ex post facto abnormality and a sign of an abnormality) has occurred in
Step S24). Then, after notifying the occurrence of the abnormality in step S24, the process returns to step S21 so as to continue a series of inferences. Depending on the intended use of the solenoid valve 1 and the content of the detected abnormality, the solenoid valve 1 may be stopped at the stage when the abnormality is detected.

(Inference device)
The present invention can be provided not only by the mode of the data processing system 300 described above, but also by the mode of an inference device for performing inference. In that case, the inference device may include a memory and at least one processor, of which the processor may execute a series of processes. The series of processes is the control parameter of the solenoid unit 3.
It also includes a process of acquiring input data including the temperature of the solenoid valve 1 and a process of inferring diagnostic information in the solenoid unit 3 when the input data is input. By providing the present invention in the form of the inference device described above, it is possible to easily apply the present invention to the solenoid valve 1 including various solenoid units 3 as compared with the case where the data processing system 300 is mounted. At this time, when the inference device performs the process of inferring the diagnostic information, the inference of the data processing system is performed by using the trained model learned by the machine learning device and the machine learning method in the present invention described in this document. It is of course understood by those skilled in the art that the inference method performed by unit 302 may be applied.

The present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the gist of the present invention. And all of them are included in the technical idea of the present invention.

1 ... Solenoid valve, 3 ... Solenoid part, 4 ... Sensor, 10 ... Fluid pressure drive valve, 11 ... Main valve, 12 ...
(Fluid pressure type) drive device, 14 ... air supply source, 15 ... external device, 26 ... input side flow path, 27 ...
Output side flow path, 28 ... Exhaust flow path, 30 ... Solenoid case, 31 ... Solenoid coil, 32
... Movable iron core, 40 ... 1st pressure sensor, 41 ... 2nd pressure sensor, 42 ... Main valve opening sensor, 43 ... Voltage sensor, 44 ... Current / resistance sensor, 45 ... Temperature sensor, 46 ... Magnetic sensor ,
47 ... Operating time meter (timer), 48 ... Operation counter (counter), 70 ... Microcontroller, 100 ... Piping, 200 ... Machine learning device, 201 ... Learning data set acquisition unit, 202 ... Learning data set storage unit, 203 ... Learning unit, 204 ... Trained model storage unit, 300 ... Data processing system, 301 ... Input data acquisition unit,
302 ... Inference unit, 303 ... Trained model storage unit, 304 ... Notification unit, A
… Air (driving fluid), PC1… Working computer

Claims (10)

A machine learning device applied to solenoid valves equipped with a solenoid section.
A plurality of sets of learning data sets composed of input data including control parameters of the solenoid unit, temperature of the electromagnetic valve, and output data consisting of diagnostic information of the solenoid unit associated with the input data. With a learning data set storage unit to store;
With a learning unit that learns a learning model that infers the correlation between the input data and the output data by inputting a plurality of sets of the training data sets;
A learned model storage unit for storing said learning model that is learned by the learning unit; equipped with,
The diagnostic information of the solenoid unit is
The solenoid part is normal
Or
The solenoid part is abnormal within the preset remaining life period.
A machine learning device composed of information representing any of the above . A plurality of the remaining lifespan are set in advance.
The diagnostic information of the solenoid unit is
The solenoid part is normal
Or
It is an abnormality that the solenoid part corresponds to any of a plurality of preset remaining life periods.
The machine learning device according to claim 1 , which comprises information representing any of the above. The solenoid unit includes a solenoid coil.
The machine learning device according to claim 1 or 2 , wherein the diagnostic information is information about the solenoid coil. Control parameter of the solenoid portion constituting the input data is less supply voltage of the solenoid portion is also a current value during energization of the solenoid portion, or includes one operating time of the solenoid portion,
The machine learning device according to any one of claims 1 to 3 . Main valve and
The drive device that drives the main valve and
The solenoid valve including the solenoid unit that controls the supply and discharge of the driving fluid to the driving device, and the solenoid valve.
The machine learning apparatus according to any one of claims 1 to 4 , which is applied to a fluid pressure driven valve comprising at least. A data processing system used for solenoid valves including solenoids.
An input data acquisition unit that acquires input data including the control parameters of the solenoid unit and the temperature of the solenoid valve;
The input data acquired by the input data acquisition unit is input to the trained model generated by the machine learning device according to any one of claims 1 to 5 , and the diagnostic information of the solenoid unit is input. With an inference unit to infer;
Data processing system. An inference device used for solenoid valves including solenoids.
The inference device comprises a memory and at least one processor.
The at least one processor
The process of acquiring the control parameters of the solenoid section and the input data including the temperature of the solenoid valve;
When the input data is input, the process of inferring the diagnostic information of the solenoid unit;
Are configured to run,
The diagnostic information of the solenoid unit is
The solenoid part is normal
Or
The solenoid part is abnormal within the preset remaining life period.
An inference device composed of information representing any of the above . A plurality of the remaining lifespans are set in advance.
The diagnostic information of the solenoid unit is
The solenoid part is normal
Or
It is an abnormality that the solenoid part corresponds to any of a plurality of preset remaining life periods.
The inference device according to claim 7 , which comprises information representing any of the above. It is a machine learning method using a machine learning device applied to a solenoid valve including a solenoid part.
A plurality of sets of learning data sets composed of input data including the control parameters of the solenoid unit and the temperature of the solenoid valve, and output data consisting of diagnostic information of the solenoid unit associated with the input data. Steps to remember;
A step of learning a learning model for inferring a correlation between the input data and the output data by inputting a plurality of sets of the training data sets;
A step of storing the learned learning model;
The diagnostic information of the solenoid unit is
The solenoid part is normal
Or
The solenoid part is abnormal within the preset remaining life period.
A machine learning method consisting of information representing any of the above . A plurality of the remaining lifespans are set in advance.
The diagnostic information of the solenoid unit is
The solenoid part is normal
Or
It is an abnormality that the solenoid part corresponds to any of a plurality of preset remaining life periods.
The machine learning method according to claim 9 , which comprises information representing any of the above.

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