JP7460399B2 - State estimation device, control valve, state estimation program, and state estimation method - Google Patents

Description

The present invention relates to a state estimation device, a control valve, a state estimation program, and a state estimation method for estimating the state of a control valve.

Control valves such as hydraulic servo valves are used to control engines installed on moving objects such as ships. By using electrically controllable control valves to precisely control the supply of fuel to the engine and the exhaust from the engine, the thermal efficiency of the engine can be improved and fuel consumption can be reduced.

Patent No. 5465365

Because ships sail on the ocean, it is not always possible to immediately deal with a malfunction or other problem that occurs in the onboard control valves. Conventionally, when the ship is anchored in port, the control valve with the malfunction or problem would be removed and brought to a factory or other facility for inspection, and any necessary repairs or replacements would be carried out. However, in order to prevent the ship from becoming unable to sail and causing significant damage, it is extremely important to accurately grasp the condition of the control valve and take appropriate measures before it becomes inoperable due to a malfunction or problem.

The present invention has been made in view of these problems, and its purpose is to provide a technique for more accurately estimating the state of a control valve.

In order to solve the above problems, a state estimation device of one embodiment of the present invention includes an acquisition unit that acquires detection information of a dataset including a target position and an actual position of a first movable part in a control valve that controls the flow rate of a working fluid depending on the position of the first movable part and a value of a current supplied to a drive unit that drives the first movable part, and an estimation unit that estimates an abnormality in which the first movable part deviates from a neutral position for minimizing the flow rate of the working fluid based on the detection information acquired by the acquisition unit.

Yet another aspect of the present invention is a control valve. This control valve includes a first movable part whose position changes in response to a control signal for specifying a position, and whose flow rate of a working fluid is controlled in response to the position, an acquisition unit that acquires detection information of a data set including a target position and an actual position of the first movable part and a value of a current supplied to a drive unit that drives the first movable part, and an estimation unit that estimates an abnormality in which the first movable part deviates from a neutral position for minimizing the flow rate of the working fluid based on the detection information acquired by the acquisition unit.

Yet another aspect of the present invention is a state estimation program. This state estimation program supplies the computer with the target position and actual position of the first movable part in the control valve that controls the flow rate of the working fluid according to the position of the first movable part, and the drive part that drives the first movable part. an abnormality in which the first movable part deviates from a neutral position for minimizing the flow rate of the working fluid based on the detection information acquired by the acquisition unit; It functions as an estimator that estimates.

Yet another aspect of the present invention is a state estimation method. This state estimation method supplies a computer with the target position and actual position of a first movable part in a control valve that controls the flow rate of working fluid according to the position of the first movable part, and a drive part that drives the first movable part. the first movable part is shifted from a neutral position for minimizing the flow rate of the working fluid based on the detected information acquired in the acquiring step; and a step of estimating an abnormality.

Yet another aspect of the present invention is a state estimation device. This state estimation device includes an acquisition unit that acquires detection information of a data set including a target position or actual position of a first movable part in a control valve that controls the flow rate of a working fluid according to the position of the first movable part and an actual position of a second movable part that changes its position according to the flow rate of the working fluid, and an estimation unit that estimates a position shift of a detection unit for detecting the position of the second movable part based on the detection information acquired by the acquisition unit.

Yet another aspect of the invention is a control valve. This control valve includes a first movable part whose position changes according to a control signal for specifying the position, and whose flow rate of working fluid is controlled according to the position, and a target position or actual position of the first movable part. an acquisition unit that acquires detection information of a data set including an actual position of a second movable part whose position changes according to the flow rate of the working fluid; and a position of the second movable part based on the detection information acquired by the acquisition unit. and an estimation section that estimates a positional shift of the detection section for detecting.

Yet another aspect of the present invention is a state estimation program. This state estimation program causes the computer to change the position according to the target position or actual position of the first movable part and the flow rate of the working fluid in a control valve that controls the flow rate of the working fluid according to the position of the first movable part. an acquisition unit that acquires detection information of a dataset including the actual position of the second movable part, and estimates a positional shift of the detection unit for detecting the position of the second movable part based on the detection information acquired by the acquisition unit; function as an estimator.

Yet another aspect of the present invention is a state estimation method. This state estimation method causes a computer to execute a step of acquiring detection information of a data set including a target position or actual position of a first movable part in a control valve that controls the flow rate of a working fluid according to the position of the first movable part and an actual position of a second movable part that changes its position according to the flow rate of the working fluid, and a step of estimating a positional deviation of a detection part for detecting the position of the second movable part based on the detection information acquired in the acquiring step.

Furthermore, any combination of the above components, or mutual substitution of the components or expressions of the present invention among methods, devices, programs, temporary or non-temporary storage media recording programs, systems, etc. are also effective as embodiments of the present invention.

The present invention allows the state of the control valve to be estimated more accurately.

1 is a diagram showing a configuration of a management system according to an embodiment of the present invention; FIG. 2 is a diagram schematically showing the configuration around a hydraulic servo valve mounted on a ship. FIG. 2 is a diagram schematically showing the configuration of a hydraulic servo valve. FIG. 3 is a schematic diagram schematically showing the position of the valve body of the pilot valve in the first direction and the open/closed state of the port. 1 is a diagram showing the configuration of a servo valve control device. FIG. 2 is a diagram showing the configuration of a learning device and a state estimation device. 3 is a flowchart showing the procedure of a state estimation method according to the present embodiment. 2 is a diagram showing the configuration of a learning device and a state estimation device according to Example 1-1. FIG. FIG. 1 is a diagram illustrating a configuration of a learning device and a state estimation device according to an embodiment 1-2. 3 is a diagram showing the configuration of a learning device and a state estimation device according to Example 1-3. FIG. 3 is a diagram showing the configuration of a learning device and a state estimation device according to Example 1-4. FIG. FIG. 13 is a diagram illustrating the configuration of a learning device and a state estimation device according to Examples 1-5. FIG. 11 is a diagram illustrating a configuration of a learning device and a state estimation device according to a second embodiment. FIG. 3 is a diagram schematically showing the operation of the spool of the pilot valve and the spool of the main valve. FIG. 11 is a diagram illustrating a configuration of a learning device and a state estimation device according to a third embodiment. FIG. 13 is a diagram illustrating the configuration of a learning device and a state estimation device according to a fourth embodiment. FIG. 3 is a diagram schematically showing the operation of the spool of the pilot valve and the spool of the main valve. FIG. 7 is a diagram showing the configuration of a learning device and a state estimation device according to a fifth embodiment. FIG. 11 is a diagram showing the state of a spool position sensor of the main valve. FIG. 7 is a diagram showing the configuration of a learning device and a state estimation device according to a sixth embodiment. FIG. 7 is a diagram showing the configuration of a learning device and a state estimation device according to a seventh embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below based on preferred embodiments with reference to the drawings. In the embodiments and modified examples, the same or equivalent components and members are given the same reference numerals, and redundant explanations will be omitted as appropriate. Further, the dimensions of members in each drawing are shown enlarged or reduced as appropriate to facilitate understanding. Further, in each drawing, some members that are not important for explaining the embodiments are omitted.

In addition, terms including ordinal numbers such as first and second are used to describe various components, but these terms are used only for the purpose of distinguishing one component from another and do not limit the components.

Figure 1 shows the configuration of a management system according to an embodiment of the present invention. The management system 1 manages a control valve based on information related to the operation of the control valve. Although the management system 1 can be used to manage any control valve, this embodiment will mainly describe an example of managing a hydraulic servo valve for controlling an engine mounted on a ship 2. The management system 1 includes a learning device 300 and a state estimation device 400. The learning device 300 learns an estimation model for estimating the state of the hydraulic servo valve based on information related to the hydraulic servo valve. The state estimation device 400 estimates the state of the hydraulic servo valve using the estimation model learned by the learning device 300.

The learning device 300 learns the estimation model using learning data for learning the estimation model. The learning data may be log data recorded when the hydraulic servo valve or another hydraulic servo valve of the same type as the hydraulic servo valve is actually used in the ship 2, or test data recorded when the hydraulic servo valve or another hydraulic servo valve of the same type as the hydraulic servo valve is used in a test environment other than the ship 2, or simulation data generated by a simulator for simulating the operation of the hydraulic servo valve, or a combination of two or more of them. Hereinafter, unless otherwise specified, the log data or test data may be log data or test data recorded when the hydraulic servo valve itself, the state of which is to be estimated, is used or tested, or log data or test data recorded when another hydraulic servo valve of the same type as the hydraulic servo valve is used or tested, or a combination of them.

When log data is used as learning data, the estimation model can be learned based on log data collected when the hydraulic servo valve is actually used in an environment similar to that of the hydraulic servo valve whose state is to be estimated, thereby improving the accuracy of the estimation model. In this case, the ship 2 may be equipped with a log data storage device for storing the log data, and the log data may be read from the log data storage device when the ship 2 is docked, for example, and supplied to the learning device 300. In addition, the ship 2 may be equipped with a communication device for ship-to-shore communication, and the log data may be transmitted from the ship 2 to the learning device 300 via the communication network 3.

When test data or simulation data is used as learning data, a large amount of data can be generated when the hydraulic servo valve is used in various conditions or environments, thereby improving the accuracy and versatility of the estimation model. For example, even if it is difficult to obtain log data when a fault that occurs very rarely actually occurs, an estimation model that can accurately estimate such faults can be generated by training the estimation model using test data when the fault is experimentally generated or simulation data when the fault is simulated.

The learning device 300 may further learn and update the learned estimation model used in the state estimation device 400.

The state estimating device 400 acquires information related to the hydraulic servo valve mounted on the ship 2, and estimates the state of the hydraulic servo valve using an estimation model. The state estimating device 400 may read log data from a log data storage device mounted on the ship 2 and estimate the state of the hydraulic servo valve during past navigation of the ship 2 based on the read log data. Further, the state estimation device 400 receives information related to the hydraulic servo valve via the communication network 3 from a communication device for ship-to-shore communication mounted on the ship 2, and based on the received information, the state estimating device 400 The state of the hydraulic servo valve in the ship 2 may be estimated. Further, the state estimating device 400 may be mounted on the ship 2, acquire information related to the hydraulic servo valve in real time, and estimate the state of the hydraulic servo valve. In this case, the estimation result by the state estimating device 400 may be transmitted to the owner, management entity, maintenance entity, etc. of the ship 2 through ship-to-land communication.

According to the technology of this embodiment, it is possible to accurately estimate the past or present state of the hydraulic servo valve, so even if a failure or malfunction occurs in the hydraulic servo valve, measures can be taken quickly and appropriately. be able to. In addition, even if the hydraulic servo valve does not have any failure or malfunction, it is possible to evaluate the possibility of failure or malfunction of the hydraulic servo valve in future voyages, the lifespan of the hydraulic servo valve, etc. of the current or past hydraulic servo valve. It can be accurately predicted based on the state. In this way, the technology of this embodiment has very important significance, especially for improving the safety and efficiency of moving bodies such as the ship 2.

Figure 2 shows a schematic diagram of the configuration around the hydraulic servo valve 100 mounted on the ship 2. The engine 80 mounted on the ship 2 includes a plurality of cylinders 81 and a sensor 82. The sensor 82 detects the engine 80's speed, load, pressure, exhaust temperature, and the like. The hydraulic servo valve 100 is provided corresponding to each of the plurality of cylinders 81, and controls the fuel injection and exhaust of each cylinder 81. In this embodiment, the hydraulic servo valve 100 includes a pilot valve that controls the flow rate of hydraulic oil supplied to the actuator by electrically controlling the position of a spool, and a main valve, which is an example of an actuator controlled by the pilot valve. The hydraulic servo valve 100 controls the flow rate of hydraulic oil supplied to another actuator provided to drive an injection valve, an exhaust valve, and the like, according to the position of the spool of the main valve. In another example, the hydraulic servo valve 100 may directly drive an injection valve, an exhaust valve, and the like by moving the spool of the main valve.

The engine control device 91 determines the rotation speed of the engine 80 and the like in response to instructions input from a control panel (not shown) for controlling navigation of the ship 2, and inputs the instructions to the servo valve control device 110. The servo valve control device 110 calculates target positions of the spools of the main valves of the plurality of hydraulic servo valves 100 in response to instructions from the engine control device 91, and sets the spool positions of the respective main valves to the calculated target positions. The position of each pilot valve spool is controlled so that The servo valve control device 110 acquires information indicating the actual position of the spool of the main valve from each of the plurality of hydraulic servo valves 100 via the junction box 92, and based on the target position and the actual position of the spool of the main valve, By calculating the target position of the pilot valve spool and outputting it to the hydraulic servo valve 100, the position of the main valve spool is feedback-controlled. The servovalve control device 110 may feedback-control the position of the spool of the main valve using any method such as P control, PI control, or PID control.

The servo valve control device 110 records input data from the engine control device 91, output data to the hydraulic servo valve 100, and various detection data indicating the status of the hydraulic servo valve 100 and the engine 80 obtained via the junction box 92 in the log data storage device 90.

In this figure, an example in which the log data storage device 90 is connected to the servo valve control device 110 is shown, but the log data storage device 90 may be connected to the junction box 92, may be connected between the junction box 92 and the hydraulic servo valve 100, or may be mounted within the hydraulic servo valve 100. When the log data storage device 90 is connected to the servo valve control device 110, the log data storage device 90 can be installed at a position relatively far from the engine 80, so that the effects of vibrations and heat generated in the engine 80 can be suppressed. In addition, the amount of wiring and other changes required when installing the log data storage device 90 in an existing ship 2 can be reduced. When the log data storage device 90 is connected to the junction box 92, even if the data indicating the actual position of the spool of the pilot valve of the hydraulic servo valve 100 is configured not to be transmitted to the servo valve control device 110, the data indicating the actual position of the spool of the pilot valve can be acquired and recorded, so that the state of the hydraulic servo valve 100 can be more accurately estimated. When the log data storage device 90 is connected between the hydraulic servo valve 100 and the junction box 92, in addition to the data indicating the actual position of the spool of the pilot valve of the hydraulic servo valve 100, the value of the voltage or current supplied to the hydraulic servo valve 100 can be obtained, so that the state of the hydraulic servo valve 100 can be estimated more accurately. The same applies when the log data storage device 90 is installed inside the hydraulic servo valve 100.

When the state estimating device 400 is mounted on the ship 2, the state estimating device 400 may acquire data from the log data storage device 90, or may be installed in place of the log data storage device 90. In this case, as described above, the state estimating device 400 may be connected to the servo valve control device 110, the junction box 92, or between the junction box 92 and the hydraulic servo valve 100. It may be connected or mounted within the hydraulic servo valve 100.

Figure 3 shows a schematic configuration of the hydraulic servo valve 100. The hydraulic servo valve 100 includes a pilot valve 10 and a main valve 20. The pilot valve 10 controls the position of the spool 28 of the main valve 20 by changing the state of delivery of hydraulic oil 48 to the main valve 20, which is a controlled device, based on a command from the servo valve control device 110.

The pilot valve 10 is connected to the main valve 20 by a number of bolts B1. The pilot valve 10 is provided with a number of through holes 10h for passing the bolts B1 through. The main valve 20 is provided with a number of female threads 20h for screwing the bolts B1 into. The number of through holes 10h are arranged at positions corresponding to the positions of the number of female threads 20h. The pilot valve 10 is connected to the main valve 20 by screwing the bolts B1 into the female threads 20h through the through holes 10h. The pilot valve 10 is separated from the main valve 20 by removing the bolts B1.

The hydraulic system of the main valve 20 in FIG. 3 includes a drain tank 44 that stores hydraulic oil 48, and a hydraulic pump 42 that pressurizes and delivers the hydraulic oil 48 in the drain tank 44. The hydraulic oil 48 sent out from the hydraulic pump 42 is supplied to the inside of the main valve 20 and the pilot valve 10 through the pump-side piping section 22p inside the main valve 20. The hydraulic oil 48 discharged from inside the pilot valve 10 and the main valve 20 is returned to the drain tank 44 through the tank-side piping section 22t inside the main valve 20. When the pump side piping section 22p and the tank side piping section 22t are collectively referred to as a main valve piping section.

The pilot valve 10 mainly includes a main body 10b, a spool 12, a port 16, and a spool drive unit 18. The spool 12 functions as a first movable unit and has a shaft 12s and a number of valve bodies 14 that move integrally with the shaft 12s. The spool 12 is driven by the spool drive unit 18 to move forward and backward in a first direction. Hereinafter, for convenience, the direction in which the spool 12 extends from the spool drive unit 18 along the first direction (downward in FIG. 1) will be referred to as the "extension direction" and "extension side", and the direction opposite to the extension direction will be referred to as the "counter-extension direction" and "counter-extension side".

A biasing member 12h is provided on the extending side of the spool 12 to bias the spool 12 in the opposite direction. The biasing member 12h may be, for example, a coil spring that expands and contracts in the first direction. The spool drive section 18 includes an electromagnetic actuator (not shown) such as a coil that moves the shaft 12s back and forth in the first direction. The spool drive unit 18 moves the shaft 12s forward and backward based on a command from the servo valve control device 110, and controls the position of the valve body 14 by balancing with the urging force of the urging member 12h.

The valve body 14 includes a first valve body 14a, a second valve body 14b, and a third valve body 14c that are spaced apart in the first direction. The second valve body 14b is arranged on the opposite side of the first valve body 14a, and the third valve body 14c is arranged on the extending side of the first valve body 14a. The first valve body 14a changes the communication state of the A port 16a (described later) depending on its position in the first direction. The main body 10b has a cylindrical space 10s that extends in the first direction and houses the spool 12. The cylindrical space 10s functions as a cylinder that surrounds the valve body 14 via a narrow gap.

Ports 16 are provided in the main body 10b. In this embodiment, the ports 16 include a P port 16p, an A port 16a, and a T port 16t. The P port 16p is connected to the pump side piping 22p, and is supplied with pressurized hydraulic oil 48 from the hydraulic pump 42. The A port 16a is connected to the hydraulic oil receiving portion 22a of the main valve 20. The spool 28 of the main valve 20 is moved by the pressure of the hydraulic oil 48 supplied to the hydraulic oil receiving portion 22a. The T port 16t is connected to the tank side piping 22t, and discharges the hydraulic oil 48 that has flowed through the main body 10b to the drain tank 44 through the tank side piping 22t.

Main valve 20 includes a spool 28 that functions as a second movable part. Although details of the main valve 20 are omitted in this figure, the main valve 20 may have the same structure as the pilot valve 10.

A position sensor 19 for detecting the position of the spool 12 is provided at the tip of the spool 12 of the pilot valve 10 . A position sensor 29 for detecting the position of the spool 28 is provided at the tip of the spool 28 of the main valve 20. Data indicating the actual position of the spool 12 of the pilot valve 10 detected by the position sensor 19 and data indicating the actual position of the spool 28 of the main valve 20 detected by the position sensor 29 are sent to the junction box 92 via wiring. Sent.

Figure 4 is a schematic diagram showing the position of the valve body 14 of the pilot valve 10 in the first direction and the open/closed state of the port. Elements that are not important for the explanation are omitted in this figure. Figure 4 (a) shows the state in which the valve body 14 is located in the first region that connects the A port 16a and the P port 16p. In this state, the A port 16a supplies hydraulic oil 48 from the P port 16p to the hydraulic oil receiving portion 22a (hereinafter referred to as the "supply mode"). In the supply mode, the hydraulic oil 48 from the P port 16p is supplied to the hydraulic oil receiving portion 22a of the main valve 20. This operation causes, for example, the spool 28 of the main valve 20 to move in a direction that increases the amount of fuel supplied to the engine 80.

Figure 4 (b) shows the state where the valve body 14 is located in the neutral region where the A port 16a is blocked and the P port 16p and the T port 16t are not connected (hereinafter, the position in the neutral region is also referred to as the "neutral position"). In this state, the A port 16a is blocked and hydraulic oil is neither supplied to nor collected from the hydraulic oil receiving portion 22a (hereinafter, referred to as the "neutral mode"). In the neutral mode, the hydraulic oil pressure in the hydraulic oil receiving portion 22a of the main valve 20 is maintained in the state just before the valve body 14 was located in the neutral region. This operation, for example, stops the spool 28 of the main valve 20 in the previous position, and the amount of fuel supplied to the engine 80 is maintained in the previous state.

Figure 4 (c) shows the state where the valve body 14 is located in the second region that connects the A port 16a and the T port 16t. In this state, the A port 16a recovers hydraulic oil 48 from the hydraulic oil receiving section 22a and returns it to the pump side piping section 22p (hereinafter referred to as the "recovery mode"). In the recovery mode, the hydraulic oil 48 in the hydraulic oil receiving section 22a of the main valve 20 is recovered to the drain tank 44 through the A port 16a, the T port 16t, and the tank side piping section 22t. This action, for example, causes the spool 28 of the main valve 20 to move in a direction that reduces the amount of fuel supplied to the engine 80.

Figure 5 shows the configuration of the servo valve control device 110. The servo valve control device 110 includes a power supply circuit 120, a servo valve control circuit 130, a servo valve drive circuit 140, a voltage detection unit 150, and a data collection circuit 160.

The power supply circuit 120 supplies power supplied from an external power supply to the servo valve control circuit 130 and the servo valve drive circuit 140. The voltage detection unit 150 detects the voltage input to the power supply circuit 120 or the voltage output from the power supply circuit 120. Instead of or in addition to the voltage detection unit 150, a current detection unit may be provided that detects the current input to the power supply circuit 120 or the current output from the power supply circuit 120.

The servo valve control circuit 130 calculates the target position of the spool 28 of the main valve 20 based on a command from the engine control device 91. The servo valve control circuit 130 calculates a target position of the spool 12 of the pilot valve 10 for moving the spool 28 of the main valve 20 to the calculated target position. The servo valve control circuit 130 inputs the calculated target position of the spool 12 of the pilot valve 10 to the servo valve drive circuit 140.

The servo valve drive circuit 140 supplies power to the coil of the spool drive unit 18 of the pilot valve 10 to move the spool 12 according to the target position of the spool 12 of the pilot valve 10 input from the servo valve control circuit 130. . Servovalve drive circuit 140 may be provided within hydraulic servovalve 100.

When the spool 12 of the pilot valve 10 moves to change the open/closed state of the port 16, the spool 28 of the main valve 20 moves toward the target position by supplying or withdrawing hydraulic oil 48. When the actual position of the spool 28 of the main valve 20 matches the target position, the servo valve control circuit 130 returns the spool 12 of the pilot valve 10 to the neutral position. This causes the spool 28 of the main valve 20 to stop at the target position. This series of controls is repeated while the engine 80 is operating.

The data collection circuit 160 records data such as the voltage value detected by the voltage detection unit 150 and the target position of the spool 12 of the pilot valve 10 input from the servo valve control circuit 130 to the servo valve drive circuit 140 in the log data storage device 90. The data collection circuit 160 acquires data indicating the state of the engine 80 detected by the sensor 82, the actual position of the spool 12 of the pilot valve 10 detected by the position sensor 19, the actual position of the spool 28 of the main valve 20 detected by the position sensor 29, etc. via the junction box 92, and records them in the log data storage device 90.

FIG. 6 shows the configurations of the learning device 300 and the state estimation device 400. The learning device 300 includes a learning data acquisition section 301, an estimated model generation section 302, and an estimated model providing section 303. The state estimation device 400 includes a detection information acquisition section 401, a state estimation section 402, and an estimation result output section 403.

The learning data acquisition unit 301 acquires learning data used to learn an estimation model for estimating the state of the hydraulic servo valve 100. The learning data includes a set of data that can be obtained related to the operation of the hydraulic servo valve 100 and data that indicates the state of the hydraulic servo valve 100. The learning data acquisition unit 301 may acquire log data stored in the log data storage device 90 or test data recorded when the hydraulic servo valve 100 is used in a test environment other than the ship 2. Alternatively, simulation data generated by a simulator for simulating the operation of the hydraulic servo valve 100 may be obtained.

The learning data acquisition unit 301 may acquire data recorded or generated under a specific situation as learning data. The learning data acquisition unit 301 may select data recorded or generated under a specific situation as the learning data from among the acquired data. For example, the learning data acquisition unit 301 collects log data when a specific state that should be estimated by an estimation model occurs, test data when a specific state occurs in a test environment, or simulates a specific state using a simulator. You may also obtain or select simulation data when the The learning data acquisition unit 301 may acquire, as learning data, data recorded or generated when the hydraulic servo valve 100 is operated under a specific environment. For example, the learning data acquisition unit 301 classifies data according to the type of ship 2, route, sailing period, type of engine 80, number of cylinders, cumulative operating time, etc., and learns a separate estimation model for each environment. You may.

The learning data acquisition unit 301 may preprocess the acquired data to generate learning data. For example, the learning data acquisition unit 301 may calculate, from the acquired data, a feature amount that is correlated with the state to be estimated by the estimation model, and use the calculated feature amount as the learning data. In addition, in order to associate the target position or actual position of the spool 12 of the pilot valve 10 and the target position or actual position of the spool 28 of the main valve 20 with other data, the target position is input and then the spool 12 or the spool The offset time until the actual position of No. 28 reaches the target position may be adjusted.

The estimation model generating unit 302 uses the learning data acquired by the learning data acquiring unit 301 to generate an estimation criterion used to estimate the state of the hydraulic servo valve 100 in the state estimation device 400. The estimation criterion may be a table or a program that associates data that can be acquired in relation to the operation of the hydraulic servo valve 100 with data indicating the state of the hydraulic servo valve 100. The estimation criterion may be an estimation model that models the correspondence between data that can be acquired in relation to the operation of the hydraulic servo valve 100 and data indicating the state of the hydraulic servo valve 100. The estimation model may be a mathematical formula for calculating data indicating the state of the hydraulic servo valve 100 using data that can be acquired in relation to the operation of the hydraulic servo valve 100 as input variables. In this case, the estimation model generating unit 302 may generate the estimation model by a statistical method such as multivariate analysis, multiple regression analysis, or principal component analysis. The estimation model may be a neural network that inputs data indicating the state of the hydraulic servo valve 100 to an input layer and outputs data indicating the state of the hydraulic servo valve 100 from an output layer. In this case, the estimation model generating unit 302 learns the estimation model by adjusting the intermediate layer of the neural network so that when data acquired in relation to the operation of the hydraulic servo valve 100 included in the learning data is input to the input layer, a value approximating the data indicating the state of the hydraulic servo valve 100 corresponding to the data is output from the output layer. The estimation model may be a rule-based estimation algorithm or any form of artificial intelligence.

The estimated model providing unit 303 provides the estimated model generated by the estimated model generating unit 302 to the state estimation device 400. The estimated model providing unit 303 may be provided to the state estimation device 400 when the state estimation device 400 is manufactured. Furthermore, if the learning device 300 learns the estimation model and updates the estimation model even after the state estimation device 400 is manufactured, the updated estimation model may be provided to the state estimation device 400 at a predetermined timing. .

The detected information acquisition unit 401 acquires information detected in relation to the operation of the hydraulic servo valve 100. The detection information acquisition unit 401 may acquire data stored in the log data storage device 90, directly from the data collection circuit 160, or acquire data from the ship 2 through ship-to-land communication. You may obtain it. The detection information acquisition unit 401 may perform the same preprocessing on the acquired data as the learning data used when the estimation model was generated.

The state estimation unit 402 estimates the state of the hydraulic servo valve 100 using the estimation model generated by the learning device 300. The state estimation unit 402 inputs the data acquired by the detection information acquisition unit 401 into an estimation model, and acquires the state of the hydraulic servo valve 100 output from the estimation model as an estimation result.

The estimation result output unit 403 outputs the estimation result by the state estimation unit 402. When the state estimation unit 402 estimates that the hydraulic servo valve 100 is in an abnormal state, the estimation result output unit 403 may notify that fact. Whether or not the hydraulic servo valve 100 is in an abnormal state may be determined based on whether or not the state estimation data indicating the state of the hydraulic servo valve 100 output by the state estimation unit 402 is equal to or greater than a predetermined threshold value or less than a predetermined threshold value, or whether or not it is within a predetermined range, or based on whether or not the amount of change from the initial value of the state estimation data is equal to or greater than a predetermined threshold value. In addition, the estimation result output unit 403 may determine whether or not the hydraulic servo valve 100 is in an abnormal state by comparing the state estimation results of multiple hydraulic servo valves 100 provided corresponding to multiple cylinders 81 of the same engine 80. For example, when the value of the state estimation result of a specific hydraulic servo valve 100 is significantly different from the average value of the state estimation results of the multiple hydraulic servo valves 100, the hydraulic servo valve 100 may be determined to be in an abnormal state. The deviation value of the state estimation results for each of the multiple hydraulic servo valves 100 may be calculated, and the hydraulic servo valves 100 whose deviation value is equal to or greater than a predetermined threshold value, for example, 80 or less than 30, may be determined to be in an abnormal state.

Figure 7 is a flowchart showing the steps of the state estimation method of this embodiment. First, the steps of the stage of generating an estimation model will be described. When the ship 2 equipped with the hydraulic servo valve 100 is sailing, data related to the operation of the hydraulic servo valve 100 is recorded in the log data storage device 90 (step S10). The learning data acquisition unit 301 of the learning device 300 acquires the log data recorded in the log data storage device 90 as learning data (step S12). The learning data acquisition unit 301 also acquires test data and simulation data as learning data. The estimation model generation unit 302 generates an estimation model based on the acquired learning data (step S14). The estimation model provision unit 303 provides the generated estimation model to the state estimation device 400 (step S16).

Next, the procedure for estimating the state of the hydraulic servo valve 100 using the estimation model will be described. When the ship 2 equipped with the hydraulic servo valve 100 is sailing, data related to the operation of the hydraulic servo valve 100 is recorded in the log data storage device 90 (step S20). The detection information acquisition unit 401 of the state estimation device 400 acquires the detection information recorded in the log data storage device 90 (step S22). The state estimation unit 402 estimates the state of the hydraulic servo valve 100 using the estimation model based on the acquired detection information (step S24). The estimation result output unit 403 outputs the estimation result (step S26).

A specific example of the management system of this embodiment will be described.

[Example 1: Estimation of Internal Oil Leakage Amount]
When the pilot valve 10 is used for a long time, the valve body 14 wears and the moderation of the valve decreases, and even in the neutral mode, the A port 16a, the P port 16p, and the T port 16t communicate with each other slightly. When the hydraulic oil 48 leaks from the P port 16p to the A port 16a in the neutral mode, the hydraulic pressure of the hydraulic oil receiving section 22a gradually increases, the amount of fuel supplied to the engine 80 increases, and the fuel efficiency of the engine 80 deteriorates. Also, when the hydraulic oil 48 leaks from the A port 16a to the T port 16t in the neutral mode, the hydraulic pressure of the hydraulic oil receiving section 22a gradually decreases, the amount of fuel supplied to the engine 80 decreases, and the output of the engine 80 decreases. When the amount of leakage of the hydraulic oil 48 exceeds the allowable amount, the hydraulic servo valve 100 does not function normally and breaks down. If the amount of leakage can be estimated with high accuracy, the hydraulic servo valve 100 can be replaced or repaired before the amount of leakage exceeds the allowable amount, thereby avoiding unexpected breakdowns.

As described above, when the hydraulic servo valve 100 is operating normally, the spool 28 of the main valve 20 remains stationary when the spool 12 of the pilot valve 10 is in the neutral position, but the amount of leakage of the hydraulic oil 48 increases. Then, even when the spool 12 of the pilot valve 10 is in the neutral position, the spool 28 of the main valve 20 moves. Through experiments by the present inventors, it has been found that there is a strong correlation between the moving speed of the spool 28 of the main valve 20 and the amount of leakage of the hydraulic oil 48 when the spool 12 of the pilot valve 10 is in the neutral position. There is. Therefore, the amount of internal oil leakage in the pilot valve 10 can be estimated from the moving speed of the spool 28 of the main valve 20 when the spool 12 of the pilot valve 10 is in the neutral position.

[Example 1-1]
8 shows the configuration of a learning device and a state estimation device according to Example 1-1. In a learning device 300 of this example, a learning data acquisition unit 301 acquires an actual pilot valve spool position 310, an actual main valve spool position 311, and an actual measured internal oil leakage amount value 312 as learning data.

The pilot valve actual spool position 310 and the main valve actual spool position 311 are time series data when the hydraulic servo valve 100 was used within a predetermined period before or after the actual measurement of the amount of internal oil leakage. This time series data may be time series data for one to several cycles of the rotational operation of the engine 80. Since the position of the spool 12 of the pilot valve 10 is at the neutral position at least several times during one cycle of rotational operation of the engine 80, time series data of the pilot valve actual spool position 310 and the main valve actual spool position 311 for one cycle are provided. contains information indicating the moving speed of the spool 28 of the main valve 20 when the spool 12 of the pilot valve 10 is in the neutral position.

The internal oil leakage amount actual value 312 is the value of the internal oil leakage amount in the hydraulic servo valve 100. When using log data or test data as learning data, the actual internal oil leakage amount value 312 is the value of the internal oil leakage amount actually measured by a flow meter or the like. Since it is considered that the amount of internal oil leakage does not change rapidly during use of the hydraulic servo valve 100, the amount of hydraulic oil 48 that was actually measured will be reduced during a predetermined period before or after the actual measurement of the amount of internal oil leakage. It is assumed that the hydraulic servo valve 100 is used in a state where the oil is leaking internally, and the pilot valve actual spool position 310 and the main valve actual spool position 311 recorded during that predetermined period are associated with the actual internal oil leakage amount actual value 312. and use it as learning data. The predetermined period may be any period during which the amount of internal oil leakage remains the same while the hydraulic servo valve 100 is in use, and may vary depending on the usage time of the hydraulic servo valve 100, the operating time of the engine 80, the rotation speed of the engine 80, etc. may be specified. When using simulation data as learning data, the actual internal oil leakage amount value 312 is the value of the internal oil leakage amount input into the simulator as a simulation condition.

The internal oil leakage amount estimation model generation unit 313 uses the learning data acquired by the learning data acquisition unit 301 to generate an internal oil leakage amount used for estimating the internal oil leakage amount of the hydraulic servo valve 100 in the state estimation device 400. Generate an oil leak amount estimation model. The internal oil leak amount estimation model inputs time-series data for a predetermined period of the pilot valve actual spool position 310 and the main valve actual spool position 311 into the input layer, and outputs the internal oil leak amount of the hydraulic servo valve 100 from the output layer. It may also be a neural network or the like. In this case, when the internal oil leakage amount estimation model generation unit 313 inputs time series data for a predetermined period of the pilot valve actual spool position 310 and the main valve actual spool position 311 to the input layer, the internal oil leakage amount actual measurement value 312 is generated. The internal oil leak amount estimation model is learned by adjusting the intermediate layer of the neural network so that the output layer outputs a value that approximates .

The internal oil leakage amount estimation model providing unit 314 provides the internal oil leakage amount estimation model generated by the internal oil leakage amount estimation model generation unit 313 to the state estimation device 400.

The detection information acquisition unit 401 acquires the pilot valve actual spool position 410 and the main valve actual spool position 411 as detection information. This detection information is time series data for the same predetermined period as the learning data used to generate the internal oil leak amount estimation model.

The internal oil leakage amount estimation unit 412 estimates the internal oil leakage amount of the hydraulic servo valve 100 using the internal oil leakage amount estimation model generated by the learning device 300. The internal oil leakage amount estimation unit 412 inputs the time series data of the pilot valve actual spool position 410 and the main valve actual spool position 411 acquired by the detection information acquisition unit 401 into the internal oil leakage amount estimation model, and acquires the internal oil leakage amount output from the internal oil leakage amount estimation model as an estimation result.

The internal oil leakage amount estimated value output unit 413 outputs an estimated value of the internal oil leakage amount estimated by the internal oil leakage amount estimation unit 412. When it is estimated that the hydraulic servo valve 100 is in an abnormal state, the internal oil leakage amount estimated value output unit 413 may notify that fact. Whether or not the hydraulic servo valve 100 is in an abnormal state may be determined based on whether or not the value of the internal oil leakage amount output by the internal oil leakage amount estimation unit 412 is equal to or greater than a predetermined threshold value, or whether or not the change amount from the initial value of the internal oil leakage amount is equal to or greater than a predetermined threshold value. In addition, the internal oil leakage amount estimated value output unit 413 may determine whether or not the hydraulic servo valve 100 is in an abnormal state by comparing the values of the internal oil leakage amounts of multiple hydraulic servo valves 100 provided corresponding to multiple cylinders 81 of the same engine 80. For example, when the value of the internal oil leakage amount of a specific hydraulic servo valve 100 is significantly different from the average value of the internal oil leakage amounts of the multiple hydraulic servo valves 100, the hydraulic servo valve 100 may be determined to be in an abnormal state. The deviation value of the internal oil leakage amount of each of the multiple hydraulic servo valves 100 may be calculated, and the hydraulic servo valves 100 whose deviation value is equal to or greater than a predetermined threshold value, for example, 80 or less than 30, may be determined to be in an abnormal state.

[Example 1-2]
Fig. 9 shows the configurations of a learning device and a state estimation device according to Example 1-2. The learning device 300 of Example 1-2 includes a feature calculation unit 315 in addition to the configuration of the learning device 300 of Example 1-1 shown in Fig. 8. Moreover, the state estimation device 400 of Example 1-2 includes a feature calculation unit 414 in addition to the configuration of the state estimation device 400 of Example 1-1 shown in Fig. 8. The following mainly describes the differences from Example 1-1. Other points are the same as those of Example 1-1.

The feature calculation unit 315 of the learning device 300 calculates a feature correlated with the amount of internal oil leakage from the pilot valve actual spool position 310 or the main valve actual spool position 311 acquired by the learning data acquisition unit 301. As described above, the inventors' experiments have revealed that the amount of internal oil leakage is correlated with the moving speed of the spool 28 of the main valve 20 when the spool 12 of the pilot valve 10 is in the neutral position. Therefore, the feature calculation unit 315 calculates the moving speed of the spool 28 of the main valve 20 from the main valve actual spool position 311 during the period when the pilot valve actual spool position 310 is in the neutral position. If there is a time lag between when the spool 12 of the pilot valve 10 moves to the neutral position and when the supply of hydraulic oil to the main valve 20 stops and the position of the spool 28 of the main valve 20 stops, the feature calculation unit 315 may adjust the time lag before calculating the moving speed of the spool 28 of the main valve 20. The amount of time lag adjustment may be determined in advance through experiments or the like depending on the type of hydraulic servo valve 100 and the environment in which the hydraulic servo valve 100 is used, such as the temperature, pressure of the hydraulic oil 48, the engine 80 RPM, load, and exhaust temperature.

The internal oil leakage amount estimation model generation unit 313 generates an internal oil leakage amount estimation model using the learning data acquired by the learning data acquisition unit 301 and the feature amount calculated by the feature amount calculation unit 315. The internal oil leakage amount estimation model generation unit 313 may generate an internal oil leakage amount estimation model using the internal oil leakage amount actual measurement value 312 and the feature amount calculated by the feature amount calculation unit 315. For example, the internal oil leakage amount estimation model may be a formula that calculates the internal oil leakage amount using the feature amount as an input variable. In this case, the internal oil leakage amount estimation model generation unit 313 may generate a formula by a statistical method such as regression analysis. The internal oil leakage amount estimation model generation unit 313 may generate an internal oil leakage amount estimation model using the pilot valve actual spool position 310, the main valve actual spool position 311, and the feature amount. For example, the internal oil leakage amount estimation model may be a neural network that inputs time series data of the pilot valve actual spool position 310 and the main valve actual spool position 311 for a predetermined period and feature values for the predetermined period into an input layer and outputs the internal oil leakage amount from an output layer. In this case, the internal oil leakage amount estimation model generation unit 313 learns the internal oil leakage amount estimation model by adjusting the intermediate layer of the neural network so that when the time series data of the pilot valve actual spool position 310 and the main valve actual spool position 311 for a predetermined period and feature values for the predetermined period are input into the input layer, a value that is close to the actual internal oil leakage amount value 312 is output from the output layer.

The feature calculation unit 414 of the state estimation device 400 calculates the feature from the pilot valve actual spool position 410 or the main valve actual spool position 411 acquired by the detection information acquisition unit 401 in the same manner as the feature calculation unit 315 calculated the feature when the internal oil leakage amount estimation model used by the internal oil leakage amount estimation unit 412 was generated. If the pilot valve actual spool position 410 and the main valve actual spool position 411 of the hydraulic servo valve 100 used in an environment different from the learning data used when the internal oil leakage amount estimation model was generated are acquired by the detection information acquisition unit 401, the feature calculation unit 414 may calculate the feature after adjusting for the difference in the environment. For example, the adjustment amount of the time lag may be changed depending on the environment. This can improve the robustness of the estimation of the internal oil leakage amount.

[Examples 1-3]
FIG. 10 shows the configuration of the learning device and state estimation device according to Example 1-3. In the learning device 300 of Example 1-2 shown in FIG. 9, the learning data acquisition unit 301 acquires the pilot valve actual spool position 310, but in the learning device 300 of Example 1-3, the learning data acquisition unit 301 acquires the pilot valve target spool position 316 instead of the pilot valve actual spool position 310. Also, in the state estimation device 400 of Example 1-2 shown in FIG. 9, the detection information acquisition unit 401 acquires the pilot valve actual spool position 410, but in the state estimation device 400 of Example 1-3, the detection information acquisition unit 401 acquires the pilot valve target spool position 415 instead of the pilot valve actual spool position 410. The following mainly describes the differences from Example 1-2. Other points are the same as those of Example 1-1 or 1-2.

As described above, depending on the connection position of the log data storage device 90, it may not be possible to record the actual pilot valve spool position when the hydraulic servo valve 100 is used. In this case, the pilot valve target spool position is used instead of the pilot valve actual spool position as learning data and detection information. As a result, it is possible to generate an internal oil leakage estimation model using data that can be obtained regardless of the connection position of the log data storage device 90, and also use the internal oil leakage estimation model to generate an internal oil leakage estimation model. The amount of oil leakage can be estimated. In the example of FIG. 10, the feature amount calculation section 315 and the feature amount calculation section 414 are provided as in Example 1-2, but the feature amount calculation section may not be provided as in Example 1-1. good.

[Examples 1 to 4]
Fig. 11 shows the configuration of a learning device and a state estimation device according to Example 1-4. The learning device 300 of Example 1-4 includes an offset time calculation unit 319 in addition to the configuration of the learning device 300 of Example 1-3 shown in Fig. 10. Furthermore, in the learning device 300 of Example 1-4, the learning data acquisition unit 301 further acquires a coil drive voltage 317 and a pilot valve physical parameter 318. Furthermore, in the state estimation device 400 of Example 1-4, the detection information acquisition unit 401 further acquires a coil drive voltage 416. The following mainly describes the differences from Example 1-3. Other points are the same as those of Examples 1-1 to 1-3.

There is a time lag between when a command signal for the target position of the spool 12 is input from the servo valve control device 110 to the pilot valve 10, when current is supplied to the coil of the spool drive unit 18, and until the spool 12 actually reaches the target position. There is. Therefore, when using the pilot valve target spool position 316 as learning data instead of the pilot valve actual spool position 310, the offset time required for the actual position of the spool 12 of the pilot valve 10 to follow the pilot valve target spool position 316 is By adjusting, it is possible to generate an internal oil leak amount estimation model based on the position of the spool 28 of the main valve 20 when the spool 12 of the pilot valve 10 actually follows the neutral position. The accuracy of the estimation model can be improved.

The offset time calculation unit 319 calculates the offset time based on the coil drive voltage 317 and the pilot valve physical parameters 318. The pilot valve physical parameters 318 include physical quantities such as the resistance value of the elements constituting the drive circuit of the spool drive unit 18, the inductance of the coil, the mass of the spool 12, and the friction coefficient between the main body 10b and the spool 12. The offset time calculation unit 319 calculates the offset time from an equation of motion including these physical parameters and the coil drive voltage 317. If the offset time may vary depending on the position, movement direction, movement speed, etc. of the spool 12 of the pilot valve 10, these data may also be used to calculate the offset time.

When the learning device 300 generates an internal oil leak amount estimation model for estimating the internal oil leak amount of a specific type of hydraulic servo valve 100, the pilot valve physical parameter 318 may be treated as a constant. Thereby, log data in which the pilot valve physical parameter 318 is not recorded can also be used as learning data. As the pilot valve physical parameter 318, a predetermined constant depending on the type of hydraulic servo valve 100 may be used. As the pilot valve physical parameter 318, a physical parameter measured at the time of shipment of the hydraulic servo valve 100 may be used. As a result, it is possible to suppress the influence of variations in the physical parameters of the pilot valves due to individual differences in the hydraulic servo valves 100, and to calculate the offset time more accurately. As the pilot valve physical parameter 318, a physical parameter measured during maintenance such as overhaul of the hydraulic servo valve 100 may be used. Thereby, the influence of aging of the pilot valve 10 can be suppressed, and the offset time can be calculated more accurately.

The internal oil leakage amount estimation model generation unit 313 inputs the pilot valve actual spool position 410 and the main valve actual spool position 411 and outputs the internal oil leakage amount, as in the embodiment 1-1 or 1-2. A leakage amount estimation model may be generated, or an internal oil leakage amount estimation model that inputs the pilot valve target spool position 415 and the main valve actual spool position 411 and outputs the internal oil leakage amount as in Example 1-3. You may generate a model, or you may generate an internal oil leak amount estimation model that inputs the pilot valve target spool position 415, main valve actual spool position 411, and coil drive voltage 416 and outputs the internal oil leak amount. Alternatively, an internal oil leakage amount estimation model that outputs the internal oil leakage amount may be generated by further inputting the pilot valve physical parameters or offset time in addition to these. In the example shown in this figure, the internal oil leak amount estimation model inputs the pilot valve target spool position 415, the main valve actual spool position 411, and the coil drive voltage 416, and outputs the internal oil leak amount. Since the coil drive voltage 416 supplied to the coil from the power supply circuit 120 of the servo valve control device 110 may vary depending on the rotation speed of the engine 80, etc., the coil drive voltage 416 may vary depending on the rotation speed of the engine 80, etc. By generating an internal oil leak amount estimation model that inputs the drive voltage 416 and outputs the internal oil leak amount, it is possible to suppress the influence of offset time fluctuations and improve estimation accuracy.

The detection information acquisition unit 401 of the state estimation device 400 acquires detection information that needs to be input into the internal oil leak amount estimation model used by the internal oil leak amount estimation unit 412. In the example shown in the figure, the detection information acquisition unit 401 acquires a pilot valve target spool position 415, a main valve actual spool position 411, and a coil drive voltage 416. If it is necessary to input the pilot valve physical parameters into the internal oil leak amount estimation model, the detection information acquisition unit 401 may further acquire the pilot valve physical parameters. Alternatively, the internal oil leakage amount estimating unit 412 may previously hold physical parameters corresponding to the type of the hydraulic servo valve 100 for which the internal oil leakage amount is to be estimated. Furthermore, if it is necessary to input an offset time into the internal oil leak amount estimation model, the detection information acquisition unit 401 may further acquire the offset time. Alternatively, the state estimating device 400 may further include an offset time calculation unit that calculates the offset time based on the coil drive voltage 416, the pilot valve physical parameters, and the like.

[Examples 1 to 5]
Fig. 12 shows the configurations of a learning device and a state estimation device according to Example 1-5. The learning device 300 of Example 1-5 includes a data selection unit 320 in addition to the configuration of the learning device 300 of Example 1-1 shown in Fig. 8. Furthermore, the learning device 300 of Example 1-5 includes a data selection unit 417 in addition to the configuration of the state estimation device 400 of Example 1-1 shown in Fig. 8. The following mainly describes the differences from Example 1-1. Other points are the same as those of Examples 1-1 to 1-4.

In this embodiment, in order to improve the estimation accuracy using the internal oil leak amount estimation model, the condition estimation device 400 estimates the internal oil leak amount under the same environment as the environment in which the hydraulic servo valve 100 is used. An internal oil leak amount estimation model is generated using log data when the servo valve 100 is used. This makes it possible to suppress the influence of the environment and improve the robustness of estimating the amount of internal oil leakage.

The data selection unit 320 selects the learning data recorded when the hydraulic servo valve 100 is used in a specific environment from the learning data acquired by the learning data acquisition unit 301. The data selection unit 320 may select the learning data recorded when the hydraulic servo valve 100 is used in a specific environment by referring to data indicating the usage environment of the hydraulic servo valve 100 included in the log data, test data, simulation data, etc., such as the exhaust temperature and pressure of the cylinder 81 of the engine 80. The data selection unit 320 may classify the learning data by environment. In this case, an internal oil leakage amount estimation model may be generated for each environment. The learning data such as the log data, test data, and simulation data may be recorded or generated in a specific environment, or may be classified by usage environment when recorded or generated. In this case, the learning data acquisition unit 301 only needs to acquire the learning data recorded, generated, or classified in a specific environment, so the data selection unit 320 may not be provided.

The data selection unit 417 selects detection information recorded when the hydraulic servo valve 100 was used in a specific environment from among the detection information acquired by the detection information acquisition unit 401. The data selection unit 417 selects detection information recorded in an environment similar to the environment in which the learning data used when the internal oil leakage estimation model used by the internal oil leakage estimation unit 412 was generated. You may choose. If a plurality of internal oil leak amount estimation models generated for each use environment are held in the state estimation device 400, the internal oil leak amount estimation unit 412 estimates the use environment of the hydraulic servo valve 100 included in the detection information. The internal oil leak amount estimation model to be used may be selected by referring to the data shown. In this case, the data selection section 417 may not be provided.

The internal oil leak amount estimation model may be generated using learning data recorded when the hydraulic servo valve 100 is operated in a test mode in which the spool 12 of the pilot valve 10 is operated in a specific pattern. In this case, the state estimating device 400 estimates the amount of internal oil leakage using the detection information recorded when the hydraulic servo valve 100 was operated in the test mode. Thereby, the robustness of estimating the amount of internal oil leakage can be improved.

In order to further suppress the influence of the usage environment, log data, test data, simulation data, etc. recorded while the engine 80 is stopped may be used as learning data. While the engine 80 is stopped, a stop command is issued from the engine control device 91 to the servo valve control device 110, but if the hydraulic oil 48 is pressurized by the hydraulic pump 42, the servo valve control device 110 repeatedly moves the spool 12 of the pilot valve 10 in a small amplitude pattern to the extent that fuel is not supplied to the engine 80 in order to prevent the spool 12 of the pilot valve 10 and the spool 28 of the main valve 20 from sticking. The learning data recorded during this sticking prevention operation is used to generate an internal oil leak amount estimation model, and when the state estimation device 400 estimates the internal oil leak amount of the hydraulic servo valve 100, the sticking prevention By using log data recorded during the operation, it is possible to suppress the influence of disturbances such as vibrations accompanying the operation of the engine 80 and improve estimation accuracy. The pattern of the anti-sticking operation may be a sine wave pattern, a sawtooth pattern, a dither pattern, etc. When the anti-sticking operation is performed using a plurality of different patterns, learning data and detection information recorded when the anti-sticking operation is performed using a specific pattern may be used.

[Modification of the first embodiment]
In addition to the data used as the learning data and detection information in Examples 1-1 to 1-5, any of other data such as engine speed, engine load, hydraulic oil pressure, hydraulic oil temperature, or any combination thereof may be used as the learning data and detection information. Of these data, data that may affect data such as the pilot valve actual spool position, the pilot valve target spool position, and the main valve actual spool position as an environmental factor may be selected and used. By generating an internal oil leakage amount estimation model that reflects the influence of these data, the robustness of the estimation of the internal oil leakage amount can be further improved.

The features described in Examples 1-1 to 1-5 above may be applied in any combination.

[Example 2: Power supply failure determination]
In the second embodiment, a technique for determining a failure of the power supply circuit 120 and the power supply that supplies power to the power supply circuit 120 will be described. If the power supply or power supply circuit 120 fails, the hydraulic servo valve 100 will no longer operate normally, causing abnormalities such as a decrease in the output of the engine 80. If failures in the power supply or power supply circuit 120 can be predicted with high accuracy, the power supply or power supply circuit 120 can be replaced or repaired before the power supply or power supply circuit 120 fails, thereby avoiding unexpected abnormalities.

The hydraulic servo valve 100 for controlling fuel supply and exhaust to the engine 80 repeats a certain operation while the engine 80 is running, so the operating waveform of the spool 12 of the pilot valve 10 or the spool 28 of the main valve 20 and the power A certain pattern can be seen between the voltage waveforms. Therefore, by modeling the difference between the operating waveform and power supply voltage waveform pattern during normal times and the operating waveform and power supply voltage waveform pattern when signs of failure are appearing, failures in the power supply or power supply circuit 120 can be detected. can be determined.

FIG. 13 shows the configuration of a learning device and a state estimation device according to the second embodiment. In the learning device 300 of this embodiment, the learning data acquisition unit 301 acquires the power supply voltage 321, the pilot valve actual spool position 310, and the power failure symptom index value 322 as learning data.

The pilot valve actual spool position 310 is time series data when the hydraulic servo valve 100 is used within a specified period before or after the power supply failure symptom index value 322 is actually measured. The power supply voltage 321 is time series data of the power supply voltage detected by the voltage detection unit 150 when the pilot valve actual spool position 310 is recorded. The power supply voltage 321 may be the input voltage to the power supply circuit 120, the output voltage from the power supply circuit 120, or both. These time series data may be time series data for one to several cycles of the rotational operation of the engine 80.

The power supply failure symptom index value 322 is calculated based on the cumulative operating time of the power supply circuit 120, the deterioration state of elements such as the capacitors that make up the power supply circuit 120, and the actual measured value of the emission noise emitted from the power supply circuit 120. The method of calculating the power supply failure symptom index value 322 may be determined by experiments, field tests, etc.

When log data or test data is used as the learning data, the power supply failure symptom index value 322 is calculated based on the actual measured values of the above variables. Since the power supply failure symptom index value 322 is not considered to change rapidly during use of the hydraulic servo valve 100, it is assumed that the power supply failure symptom index value 322 does not change during a predetermined period before or after the time when the variables for calculating the power supply failure symptom index value 322 are actually measured, and the power supply failure symptom index value 322 is associated with the power supply voltage 321 and the pilot valve actual spool position 310 recorded during the predetermined period and used as learning data. The predetermined period may be a period during which the power supply failure symptom index value 322 remains roughly the same during use of the hydraulic servo valve 100, and may be determined by the use time of the hydraulic servo valve 100, the operation time of the engine 80, the rotation speed of the engine 80, etc. When simulation data is used as the learning data, the power supply failure symptom index value 322 is calculated based on the values of the variables input to the simulator as simulation conditions.

The power supply failure determination model generation unit 323 uses the learning data acquired by the learning data acquisition unit 301 to generate a power supply failure determination model that is used in the state estimation device 400 to determine a failure of the power supply or power supply circuit 120. generate. The power supply failure determination model may be a neural network or the like that inputs time series data of the power supply voltage 321 and the pilot valve actual spool position 310 for a predetermined period into the input layer, and outputs the power supply failure symptom index value from the output layer. In this case, when the power supply failure determination model generation unit 323 inputs the time series data of the power supply voltage 321 and the pilot valve actual spool position 310 for a predetermined period to the input layer, the power supply failure determination model generation unit 323 outputs a value that approximates the power supply failure symptom index value 322. A power failure determination model is learned by adjusting the intermediate layer of the neural network so that the output from the layer is output from the neural network.

The power failure determination model providing unit 324 provides the power failure determination model generated by the power failure determination model generation unit 323 to the state estimation device 400.

The detection information acquisition unit 401 acquires the power supply voltage 420 and the pilot valve actual spool position 410 as detection information. This detection information is time series data for the same predetermined period as the learning data used to generate the power failure determination model.

The power supply failure determination unit 421 uses the power supply failure determination model generated by the learning device 300 to determine a failure in the power supply of the hydraulic servo valve 100 or the power supply circuit 120 . The power supply failure determination unit 421 inputs the time-series data of the power supply voltage 420 and the pilot valve actual spool position 410 acquired by the detection information acquisition unit 401 into a power supply failure determination model, and calculates power failure signs output from the power supply failure determination model. Obtain the index value as the judgment result. The power supply failure determining unit 421 may determine whether there is a failure in the power supply or the power supply circuit 120 based on the output power failure symptom index value. For example, the power supply failure determination unit 421 may determine that the power supply or the power supply circuit 120 is malfunctioning when the output power supply failure symptom index value is equal to or greater than a predetermined threshold, or may determine that the power supply failure symptom index value It may be determined that the power supply or power supply circuit 120 is out of order when the amount of change from the initial value becomes equal to or greater than a predetermined threshold. The power failure determination unit 421 may estimate the period until the power supply or power supply circuit 120 fails, that is, the life span, based on the output power failure symptom index value. For example, the power supply failure determining unit 421 may estimate the lifespan using a mathematical formula using the outputted power supply failure symptom index value as a variable. The power supply failure determination model may be configured to output the presence or absence of a failure in the power supply or power supply circuit 120, or the lifespan of the power supply or power supply circuit 120, instead of or in addition to the power supply failure symptom index value.

When a power supply circuit 120 is provided for each of a plurality of hydraulic servo valves 100 provided corresponding to a plurality of cylinders 81 of the same engine 80, the power supply failure determination unit 421 detects a power failure of each hydraulic servo valve 100. By comparing the symptom index values, it may be determined whether the power supply circuit 120 is out of order. For example, if the power supply failure symptom index value of a specific hydraulic servo valve 100 deviates significantly from the average value of the power supply failure symptom index values of a plurality of hydraulic servo valves 100, the power supply circuit 120 of that hydraulic servo valve 100 has failed. It may be determined that the The deviation value of the power failure symptom index value of each of the plurality of hydraulic servo valves 100 is calculated, and if the power circuit 120 of the hydraulic servo valve 100 for which the deviation value is a predetermined threshold value, for example, 80 or more or less than 30 is malfunctioning. You may judge.

The power failure determination result output unit 422 outputs the result determined by the power failure determination unit 421. The power supply failure determination result output unit 422 may notify that when the power supply failure determination unit 421 determines that the power supply or power supply circuit 120 of the hydraulic servo valve 100 is malfunctioning. The power failure determination result output unit 422 may output the lifespan of the power supply or power supply circuit 120 determined by the power failure determination unit 421.

The inventors' experiments have shown that the amount of drop in the power supply voltage relative to the peak value of the load current is correlated with a failure of the power supply or power supply circuit 120. Therefore, if an ammeter is provided to detect the value of the current supplied to the hydraulic servo valve 100, the current value measured by the ammeter may be used as learning data and detection information instead of or in addition to the pilot valve actual spool position 310. This can improve the accuracy of failure determination.

The amount of drop in the power supply voltage with respect to the peak value of the load current may be calculated from the power supply voltage, the actual pilot valve spool position, and the like. In this case, as in Example 1-2, the learning device 300 may further include a feature amount calculation section 315, and the state estimation device 400 may further include a feature amount calculation section 414.

The feature calculation unit 315 of the learning device 300 calculates the amount of drop in the power supply voltage relative to the peak value of the load current as a feature correlated with a failure of the power supply or the power supply circuit 120 from the power supply voltage 321 and the pilot valve actual spool position 310 acquired by the learning data acquisition unit 301. The feature calculation unit 315 may calculate the value of the current supplied to the hydraulic servo valve 100 based on the acceleration or speed of the spool 12 of the pilot valve 10 calculated from the fluctuation of the pilot valve actual spool position 310. The feature calculation unit 315 may calculate the amount of drop in the power supply voltage based on the value of the power supply voltage 321 when the calculated current value peaks. If there is a time lag between when power is supplied from the power supply circuit 120 to the coil of the spool drive unit 18 of the pilot valve 10 and when the spool 12 of the pilot valve 10 moves, the feature calculation unit 315 may adjust the time lag and then calculate the acceleration or speed of the spool 12 of the pilot valve 10. The amount of time lag adjustment may be determined in advance through experiments or the like depending on the type of hydraulic servo valve 100 and the environment in which the hydraulic servo valve 100 is used, such as the temperature, pressure of the hydraulic oil 48, the engine 80 RPM, load, and exhaust temperature.

The power supply failure judgment model generation unit 323 generates a power supply failure judgment estimation model using the learning data acquired by the learning data acquisition unit 301 and the feature amount calculated by the feature amount calculation unit 315. The power supply failure judgment model generation unit 323 may generate a power supply failure judgment model using the power supply failure symptom index value 322 and the feature amount calculated by the feature amount calculation unit 315. For example, the power supply failure judgment model may be a formula that calculates the power supply failure symptom index value using the feature amount as an input variable. In this case, the power supply failure judgment model generation unit 323 may generate the formula by a statistical method such as regression analysis. The power supply failure judgment model generation unit 323 may generate a power supply failure judgment model using the power supply voltage 321, the pilot valve actual spool position 310, and the feature amount. For example, the power supply failure judgment model may be a neural network that inputs time series data of the power supply voltage 321 and the pilot valve actual spool position 310 for a predetermined period and the feature amount for the predetermined period into an input layer and outputs the power supply failure symptom index value from an output layer. In this case, the power supply failure judgment model generation unit 323 learns the power supply failure judgment model by adjusting the intermediate layer of the neural network so that when the time series data for a specified period of time of the power supply voltage 321 and the pilot valve actual spool position 310 and the feature values for that specified period are input to the input layer, a value approximating the power supply failure symptom index value 322 is output from the output layer.

The feature amount calculation unit 414 of the state estimation device 400 calculates the feature amount from the power supply voltage 420 and the pilot valve actual spool position 410 acquired by the detection information acquisition unit 401 in the same manner as the feature amount calculation unit 315 calculated the feature amount when the power supply failure judgment model used by the power supply failure judgment unit 421 was generated. If the power supply voltage 420 and the pilot valve actual spool position 410 of the hydraulic servo valve 100 used in an environment different from the learning data used when the power supply failure judgment model was generated are acquired by the detection information acquisition unit 401, the feature amount calculation unit 414 may calculate the feature amount after adjusting for the difference in the environment. For example, the adjustment amount of the time lag may be changed depending on the environment. This can improve the robustness of the power supply failure judgment. In addition, since the amount of drop in the power supply voltage relative to the peak value of the load current can be calculated as a feature amount without implementing an ammeter, it is possible to improve the efficiency of the circuit and reduce costs.

As the characteristic quantity, a physical quantity representing the characteristic of the operation of the spool 12 of the pilot valve 10 or the like may be calculated. For example, the acceleration, speed, overshoot amount, delay amount, etc. of the spool 12 of the pilot valve 10 may be calculated as the feature amount.

Instead of or in addition to the pilot valve actual spool position, the pilot valve target spool position or the main valve actual spool position may be used as learning data and sensing information. In this case, similarly to Embodiment 1-4, the learning device 300 operates the offset time calculation unit 319 in order to adjust the offset time required until the actual spool position of the pilot valve 10 follows the target spool position of the pilot valve. You may prepare. The offset time calculation unit 319 calculates the amount of drop in the power supply voltage when the value of the current supplied to the hydraulic servo valve 100 reaches its peak. An offset time between the fluctuations may be calculated. The method for calculating the offset time may be the same as in Example 1-2.

As the learning data and the detection information, data recorded when the hydraulic servo valve 100 is used in a specific environment may be used. In this case, similar to the first to fifth embodiments, the learning device 300 may further include a data selection unit 320, and the state estimation device 400 may further include a data selection unit 417. The data selection unit 320 and the data selection unit 417 may select data recorded when the hydraulic servo valve 100 is operated in a test mode in which the spool 12 of the pilot valve 10 is operated in a specific pattern. This can improve the robustness of the fault determination. The data selection unit 320 and the data selection unit 417 may select data recorded when the engine 80 is stopped and the spool is prevented from sticking. This can reduce the influence of disturbances and improve the accuracy of the fault determination.

[Example 3: Detection of foreign object biting]
In the third embodiment, a technique for detecting that a foreign object is caught between the main body portion 10b of the pilot valve 10 and the spool 12 will be described. If a foreign object gets caught between the main body part 10b and the spool 12, the spool 12 of the pilot valve 10 will not operate normally, so the actual spool position of the main valve 20 will deviate from the target spool position and an alarm will be issued. . Previously, it was not possible to determine the cause of this alarm, but if it were possible to detect with high accuracy that a foreign object had become lodged, it would be possible to identify the cause of the alarm when it was triggered and take appropriate measures quickly. can be taken.

FIG. 14 schematically shows the operations of the spool 12 of the pilot valve 10 and the spool 28 of the main valve 20. FIG. 14(a) shows an example of the operation when the hydraulic servo valve 100 is normally controlled. When the target spool position of the main valve 20 is input at time t1, a positive value is input as the target spool position of the pilot valve 10 at time t2 in order to move the spool 28 of the main valve 20 in the positive direction. When the spool 12 of the pilot valve 10 follows the target spool position, hydraulic oil is supplied to the main valve 20, and the spool 28 of the main valve 20 begins to move in the forward direction at time t3. At time t4, when the target spool position of the main valve 20 is fixed to a constant value and the actual spool position of the main valve 20 follows the target spool position, the neutral position is input as the target spool position of the pilot valve 10, and at time t5, the main spool position is fixed to a constant value. The spool 28 of the valve 20 comes to rest at the target spool position.

FIG. 14(b) shows an example of the operation when a foreign object is caught in the pilot valve 10. The operation up to time t4 is the same as that shown in FIG. 14(a), but it is assumed that a foreign object is caught in the pilot valve 10 at time t4. The neutral position has been input as the target spool position for the pilot valve 10, but since the spool 12 cannot be moved due to foreign objects being caught, hydraulic oil continues to be supplied to the main valve 20, and the spool 28 of the main valve 20 is moved in the forward direction. keep moving to. Since the feedback signal of the main valve actual spool position deviates from the target position, a negative value continues to be input as the target spool position of the pilot valve 10 in order to return the spool 28 of the main valve 20 to the target position. When the spool 28 of the main valve 20 reaches the limit position in the forward direction, it does not move any further and remains stationary at the limit position.

As described above, the operation patterns of the spool 12 of the pilot valve 10 and the spool 28 of the main valve 20 are significantly different between normal operation and when foreign matter is caught. Therefore, by modeling the difference between the normal operation pattern and the operation pattern when a foreign object is trapped, it is possible to detect the foreign object being trapped. Since the movement pattern can vary depending on the type, size, amount, etc. of foreign objects, it is possible to detect not only whether the foreign object is bitten, but also the type, size, amount, etc. of the bitten foreign object. Even if the foreign object is removed naturally after being bitten, the history of the foreign object being bitten can be detected from log data, which can be used as evidence of the cause of the malfunction. can. In the ship 2 and the like, hydraulic oil is circulated not only to the hydraulic servo valve 100 for controlling the engine 80 but also to the hydraulic servo valve for controlling another device, etc. Even if the trapped foreign object is removed naturally, it may become trapped in another hydraulic servo valve. According to the technology of this embodiment, it is possible to detect foreign objects caught in the hydraulic servo valve 100 and perform appropriate maintenance such as replacing the hydraulic oil, thereby preventing foreign objects from getting caught in other devices. Can be done.

Figure 15 shows the configuration of a learning device and a state estimation device according to Example 3. In the learning device 300 of this example, the learning data acquisition unit 301 acquires the main valve actual spool position 311, the main valve target spool position 330, the pilot valve target spool position 316, and foreign object data 331 as learning data.

The main valve actual spool position 311, the main valve target spool position 330, and the pilot valve target spool position 316 are determined when the hydraulic servo valve 100 is used within a predetermined period before or after the time when the foreign object data 331 is actually measured. This is time series data. These time series data may be time series data for one to several cycles of the rotational operation of the engine 80.

The foreign object data 331 is data representing the presence or absence, type, size, amount, etc. of foreign objects caught in the pilot valve 10. When using log data as learning data, the foreign object data 331 is an actual measurement value of the foreign object caught in the pilot valve 10. When using test data as learning data, the foreign object data 331 is an actual measurement value of a foreign object caught in the pilot valve 10. When using simulation data as learning data, the foreign object data 331 is foreign object data input to the simulator as simulation conditions.

The foreign object detection model generation section 333 uses the learning data acquired by the learning data acquisition section 301 to generate a foreign object detection model used in the state estimation device 400 to detect biting of a foreign object. The foreign object detection model is a neural network that inputs time series data for a predetermined period of main valve actual spool position 311, main valve target spool position 330, and pilot valve target spool position 316 to an input layer, and outputs foreign object data from an output layer. etc. In this case, when the time-series data of the main valve actual spool position 311, the main valve target spool position 330, and the pilot valve target spool position 316 for a predetermined period are input to the input layer, the foreign object detection model generation unit 333 generates foreign object data. The foreign object detection model is learned by adjusting the intermediate layer of the neural network so that a value approximating 331 is output from the output layer.

The foreign object detection model providing unit 334 provides the foreign object detection model generated by the foreign object detection model generating unit 333 to the state estimation device 400.

The detection information acquisition unit 401 acquires the main valve actual spool position 411, the main valve target spool position 430, and the pilot valve target spool position 415 as detection information. This detection information is time series data for the same predetermined period as the learning data used to generate the foreign object detection model.

The foreign object detection unit 431 detects the presence of a foreign object in the pilot valve 10 using the foreign object detection model generated by the learning device 300. The foreign object detection unit 431 inputs the time series data of the main valve actual spool position 411, the main valve target spool position 430, and the pilot valve target spool position 415 acquired by the detection information acquisition unit 401 into the foreign object detection model, and acquires the foreign object data output from the foreign object detection model as a judgment result. The foreign object detection unit 431 may estimate the period until a foreign object is caught in the pilot valve 10 based on the output foreign object data. In this case, the period until a foreign object is caught may be estimated by comparing the foreign object data of multiple hydraulic servo valves 100 provided corresponding to multiple cylinders 81 of the same engine 80.

The foreign object detection result output section 432 outputs the result determined by the foreign object detection section 431. The foreign object detection result output section 432 may notify when the foreign object detection section 431 detects that a foreign object is stuck in the pilot valve 10 . The foreign object detection result output unit 432 may output the period estimated by the foreign object detection unit 431 until the foreign object becomes bitten.

A feature value correlated with the presence or absence of a foreign object trapped, or the type, size, or amount of trapped foreign object may be calculated from the actual main valve spool position, the target main valve spool position, or the target pilot valve spool position. In this case, as in the first and second embodiments, the learning device 300 may further include a feature value calculation unit 315, and the state estimation device 400 may further include a feature value calculation unit 414. If there is a time lag between when the power supply circuit 120 supplies power to the coil of the spool drive unit 18 of the pilot valve 10 and when the spool 12 of the pilot valve 10 moves and when the actual main valve spool position follows the target main valve spool position, the feature value calculation unit 315 may adjust the time lag before calculating the feature value. The amount of adjustment of the time lag may be determined in advance by experiments or the like depending on the type of hydraulic servo valve 100, the temperature, the pressure of the hydraulic oil 48, the engine 80 rotation speed, the load, the exhaust temperature, and other environmental factors in which the hydraulic servo valve 100 is used.

The pilot valve actual spool position may be used instead of or in addition to the pilot valve target spool position as learning data and sensing information. A combination of any two or more of the pilot valve target spool position, the pilot valve actual spool position, the main valve target spool position, and the main valve actual spool position may be used as the learning data and the detection information.

As the learning data and the detection information, data recorded when the hydraulic servo valve 100 is used in a specific environment may be used. In this case, similar to the first to fifth embodiments, the learning device 300 may further include a data selection unit 320, and the state estimation device 400 may further include a data selection unit 417. The data selection unit 320 and the data selection unit 417 may select data recorded when the hydraulic servo valve 100 is operated in a test mode in which the spool 12 of the pilot valve 10 is operated in a specific pattern. This can improve the robustness of the detection of foreign object entrapment. The data selection unit 320 and the data selection unit 417 may select data recorded when the engine 80 is stopped and the spool is being prevented from sticking. This can reduce the influence of disturbances and improve the detection accuracy of foreign object entrapment.

Example 4: Estimation of hydraulic oil cleanliness
In the fourth embodiment, a technique for estimating the cleanliness of hydraulic oil will be described. If foreign matter is mixed into the hydraulic oil during operation of the hydraulic servo valve 100 and the cleanliness of the hydraulic oil is reduced, the mixed foreign matter may hinder the operation of the spool 12 of the pilot valve 10 or reduce the pressure of the hydraulic oil supplied to the main valve 20, causing the engine 80 or other controlled objects to malfunction. In addition, the possibility of foreign matter getting caught in the spool 12 of the pilot valve 10 increases. By accurately estimating the cleanliness of the hydraulic oil and quickly taking appropriate measures when the cleanliness of the hydraulic oil is reduced, the hydraulic servo valve 100 and the controlled objects can be maintained in good condition and the ingestion of foreign matter can be prevented.

When the cleanliness of the hydraulic oil decreases due to contamination, the frictional force generated between the spool 12 and the main body 10b increases when the spool 12 of the pilot valve 10 slides. 12 behavior changes. Therefore, the difference between the waveform of the current supplied to the coil of the spool drive unit 18 of the pilot valve 10 and the operation waveform pattern of the target spool position and the actual spool position of the pilot valve 10 due to the cleanliness of the hydraulic oil is modeled. By this, the cleanliness of the hydraulic oil can be estimated.

FIG. 16 shows the configuration of a learning device and a state estimation device according to the fourth embodiment. In the learning device 300 of this embodiment, the learning data acquisition unit 301 acquires the coil applied current 341, the pilot valve target spool position 316, the pilot valve actual spool position 310, and the actual measured value of hydraulic oil cleanliness 342 as learning data.

The coil applied current 341, the pilot valve target spool position 316, and the pilot valve actual spool position 310 are time series data when the hydraulic servo valve 100 is used within a specified period before or after the actual measurement of the hydraulic oil cleanliness value 342. These time series data may be time series data for one to several cycles of the rotational operation of the engine 80.

The measured value of hydraulic oil cleanliness 342 is data representing the number, amount, size, etc. of foreign matter mixed into the hydraulic oil. When log data or test data is used as learning data, the measured value of hydraulic oil cleanliness 342 is an actual measurement value obtained by measuring the cleanliness of the hydraulic oil used in the hydraulic servo valve 100 using a general liquid-borne particle measuring device. The cleanliness of the hydraulic oil may be measured, for example, by a gravimetric method, a microscopic method, a light scattering method, a light blocking method, an electrical resistance method, an acoustic method, a dynamic light scattering method, etc. Since the cleanliness of the hydraulic oil is not considered to change rapidly during the use of the hydraulic servo valve 100, it is assumed that the hydraulic oil of the measured cleanliness was used during a predetermined period before or after the time when the cleanliness of the hydraulic oil was actually measured, and the coil applied current 341, the pilot valve target spool position 316, and the pilot valve actual spool position 310 recorded during that predetermined period are associated with the measured value of hydraulic oil cleanliness 342 to form learning data. The specified period may be any period during which the cleanliness of the hydraulic oil remains the same while the hydraulic servo valve 100 is in use, and may be determined by the usage time of the hydraulic servo valve 100, the operating time of the engine 80, the rotation speed of the engine 80, etc. When simulation data is used as the learning data, the measured value of cleanliness of the hydraulic oil 342 is the value of cleanliness of the hydraulic oil input to the simulator as a simulation condition.

The hydraulic oil cleanliness estimation model generating unit 343 uses the learning data acquired by the learning data acquiring unit 301 to generate a hydraulic oil cleanliness estimation model used to estimate the cleanliness of the hydraulic oil used in the state estimation device 400. The hydraulic oil cleanliness estimation model may be a neural network that inputs time series data of the coil applied current 341, the pilot valve target spool position 316, and the pilot valve actual spool position 310 for a predetermined period into the input layer and outputs the hydraulic oil cleanliness from the output layer. In this case, the hydraulic oil cleanliness estimation model generating unit 343 learns the hydraulic oil cleanliness estimation model by adjusting the intermediate layer of the neural network so that a value approximating the hydraulic oil cleanliness actual measurement value 342 is output from the output layer when the time series data of the coil applied current 341, the pilot valve target spool position 316, and the pilot valve actual spool position 310 for a predetermined period are input into the input layer.

The hydraulic oil cleanliness estimation model providing unit 344 provides the hydraulic oil cleanliness estimation model generated by the hydraulic oil cleanliness estimation model generating unit 343 to the state estimation device 400.

The detection information acquisition unit 401 acquires the coil applied current 441, the pilot valve target spool position 415, and the pilot valve actual spool position 410 as detection information. This detection information is time series data for a predetermined period of time that is the same as the learning data used to generate the hydraulic oil cleanliness estimation model.

The hydraulic oil cleanliness estimation unit 442 estimates the cleanliness of the hydraulic oil using the hydraulic oil cleanliness estimation model generated by the learning device 300. The hydraulic oil cleanliness estimation unit 442 inputs the time series data of the coil applied current 441, the pilot valve target spool position 415, and the pilot valve actual spool position 410 acquired by the detection information acquisition unit 401 into the hydraulic oil cleanliness estimation model, and acquires the hydraulic oil cleanliness output from the hydraulic oil cleanliness estimation model as an estimation result.

The hydraulic oil cleanliness estimation result output unit 443 outputs the result estimated by the hydraulic oil cleanliness estimation unit 442. The hydraulic oil cleanliness estimation result output unit 443 determines whether maintenance such as cleaning or replacement of the hydraulic oil is necessary based on the output hydraulic oil cleanliness, and it is determined that maintenance of the hydraulic oil is necessary. In such cases, notification to that effect may be made. For example, the hydraulic oil cleanliness estimation result output unit 443 may determine that maintenance of the hydraulic oil is necessary when the output cleanliness of the hydraulic oil is less than a predetermined threshold; If the amount of change in cleanliness from the initial value exceeds a predetermined threshold value, it may be determined that maintenance of the hydraulic fluid is necessary. The hydraulic oil cleanliness estimation result output unit 443 may estimate and output the period until maintenance of the hydraulic oil becomes necessary. For example, the hydraulic oil cleanliness estimation result output unit 443 may estimate the period until maintenance of the hydraulic oil becomes necessary using a mathematical formula or the like using the output hydraulic oil cleanliness as a variable. The hydraulic oil cleanliness estimation model may be configured to output whether hydraulic oil maintenance is required or the period until hydraulic oil maintenance is required instead of or in addition to the hydraulic oil cleanliness. good.

When a plurality of hydraulic servo valves 100 are provided corresponding to a plurality of cylinders 81 of the same engine 80, the hydraulic oil cleanliness estimating unit 442 calculates the hydraulic oil cleanliness based on the hydraulic oil cleanliness estimated in each hydraulic servo valve 100. It may also be determined whether or not maintenance of the hydraulic oil is necessary. When a common hydraulic fluid is circulated and used in a plurality of hydraulic servo valves 100, if the estimated cleanliness of the hydraulic fluid in any of the hydraulic servo valves 100 is less than a predetermined threshold, another hydraulic fluid is used. Even if the estimated cleanliness of the hydraulic oil in the servo valve 100 is equal to or higher than the threshold value, the less clean hydraulic oil may be circulated and affect the operation of another hydraulic servo valve 100. Therefore, when the estimated hydraulic oil cleanliness of at least one hydraulic servo valve 100 is less than a predetermined threshold, the hydraulic oil cleanliness estimation result output unit 443 determines that maintenance of the hydraulic oil is necessary. It's okay. The hydraulic oil cleanliness estimation result output unit 443 may determine whether maintenance of the hydraulic oil is necessary based further on the order in which the hydraulic oil circulates. For example, the threshold value for determining whether hydraulic oil maintenance is necessary in the upstream hydraulic servo valve 100 is set higher than the threshold value for determining whether hydraulic oil maintenance is necessary in the downstream hydraulic servo valve 100. It's okay. The hydraulic oil cleanliness estimation result output unit 443 calculates statistical values such as the average value of the hydraulic oil cleanliness estimated in the plurality of hydraulic servo valves 100, and performs hydraulic oil maintenance based on the calculated statistical values. You may decide whether or not it is necessary.

Feature quantities correlated with the cleanliness of the hydraulic oil may be calculated from the coil applied current, the pilot valve target spool position, the pilot valve actual spool position, and the like. In this case, similar to Example 1-2, the learning device 300 may further include a feature quantity calculation unit 315, and the state estimation device 400 may further include a feature quantity calculation unit 414. When the cleanliness of the hydraulic oil decreases and the frictional force between the spool 12 of the pilot valve 10 and the main body portion 10b increases, it is considered that the speed or acceleration of the spool 12 decreases. Therefore, the feature quantity calculation unit may calculate the speed or acceleration of the spool 12 of the pilot valve 10 by calculating the rate of change or the rate of change of the rate of change of the pilot valve actual spool position.

The hydraulic oil cleanliness estimation model generation unit 343 generates a hydraulic oil cleanliness estimation model using the learning data acquired by the learning data acquisition unit 301 and the feature amount calculated by the feature amount calculation unit 315. The hydraulic oil cleanliness estimation model generation unit 343 may generate a hydraulic oil cleanliness estimation model using the hydraulic oil cleanliness actual measurement value 342 and the feature amount calculated by the feature amount calculation unit 315. For example, the hydraulic oil cleanliness estimation model may be a formula that calculates the hydraulic oil cleanliness using the feature amount as an input variable. In this case, the hydraulic oil cleanliness estimation model generation unit 343 may generate a formula using a statistical method such as regression analysis. The hydraulic oil cleanliness estimation model may be a formula that calculates the hydraulic oil cleanliness using the speed or acceleration of the spool 12 when the coil applied current or the pilot valve target spool position satisfies a predetermined condition as an input variable. For example, the speed or acceleration of the spool 12 at the peak value of the coil applied current may be used as the input variable, or the speed or acceleration of the spool 12 when the spool 12 is moved from the neutral position may be used as the input variable. The hydraulic oil cleanliness estimation model generating unit 343 may generate the hydraulic oil cleanliness estimation model using the coil applied current 341, the pilot valve target spool position 316, the pilot valve actual spool position 310, and the feature amount. For example, the hydraulic oil cleanliness estimation model may be a neural network that inputs the time series data of the coil applied current 341, the pilot valve target spool position 316, and the pilot valve actual spool position 310 for a predetermined period and the feature amount for the predetermined period into an input layer and outputs the hydraulic oil cleanliness from an output layer. In this case, the hydraulic oil cleanliness estimation model generation unit 343 learns the hydraulic oil cleanliness estimation model by adjusting the intermediate layer of the neural network so that when the time series data for a specified period of time of the coil applied current 341, the pilot valve target spool position 316, and the pilot valve actual spool position 310 and the feature values for that specified period are input to the input layer, a value approximating the actual measured hydraulic oil cleanliness value 342 is output from the output layer.

The feature calculation unit 414 of the state estimation device 400 calculates the feature from the coil applied current 441, the pilot valve target spool position 415, or the pilot valve actual spool position 410 acquired by the detection information acquisition unit 401 in the same manner as the feature calculation unit 315 calculated the feature when the hydraulic oil cleanliness estimation model used by the hydraulic oil cleanliness estimation unit 442 was generated. If the coil applied current 441, the pilot valve target spool position 415, or the pilot valve actual spool position 410 of the hydraulic servo valve 100 used in an environment different from the learning data used when the hydraulic oil cleanliness estimation model was generated is acquired by the detection information acquisition unit 401, the feature calculation unit 414 may calculate the feature after adjusting for the difference in the environment. This can improve the robustness of the hydraulic oil cleanliness estimation.

As the characteristic quantity, another physical quantity that represents the characteristics of the operation of the spool 12 of the pilot valve 10 may be calculated. For example, the amount of overshoot or delay of the spool 12 of the pilot valve 10 may be calculated as the characteristic quantity.

Data recorded when the hydraulic servo valve 100 is used in a specific environment may be used as the learning data and detection information. In this case, the learning device 300 may further include a data selection section 320, and the state estimation device 400 may further include a data selection section 417, as in Example 1-5. The data selection unit 320 and the data selection unit 417 may select data recorded when the hydraulic servo valve 100 is operated in a test mode in which the spool 12 of the pilot valve 10 is operated in a specific pattern. Thereby, the robustness of the estimation of hydraulic oil cleanliness can be improved. The data selection unit 320 and the data selection unit 417 may select data recorded when the engine 80 is stopped and the spool sticking prevention operation is being performed. Thereby, the influence of disturbance can be suppressed and the accuracy of estimating the hydraulic oil cleanliness can be improved.

In addition to the data used as learning data and detection information, engine speed, engine load, pressure of the hydraulic oil pressurized by the hydraulic pump 42, and operation before and after the filter for removing foreign matter mixed in the hydraulic oil. Any of the other data, such as oil pressure or flow rate, hydraulic oil temperature, power supply voltage, power supply current, etc., or any combination thereof, may be used as learning data and sensed information. Among these data, data that can influence data such as coil applied current, pilot valve target spool position, pilot valve actual spool position, etc. as an environmental factor may be selected and used. The frictional force when the spool 12 of the pilot valve 10 slides may change not only due to the cleanliness of the hydraulic oil but also due to wear of the spool 12 of the pilot valve 10 and the inner wall of the main body 10b. Factors that can affect wear on the inner wall of the spool 12 and the main body portion 10b, such as the cumulative usage time of the pilot valve 10 and the cumulative travel distance of the spool 12, may be used as learning data and detection information. By generating a hydraulic oil cleanliness estimation model that reflects the influence of these data, the robustness of the hydraulic oil cleanliness estimation can be further improved.

[Example 5: Position sensor disconnection detection]
In the fifth embodiment, a technique for detecting disconnection of the position sensor 29 for detecting the actual position of the spool 28 of the main valve 20 will be described. If the position sensor 29 is disconnected, a signal indicating the correct actual position of the spool 28 cannot be received from the position sensor 29, so the actual spool position of the main valve 20 deviates from the target spool position. As explained in the third embodiment, conventionally, an alarm was issued when the actual spool position of the main valve 20 deviated from the target spool position, but the cause of the alarm could not be identified. If the disconnection of the position sensor 29 can be detected with high accuracy, when an alarm is issued, the cause of the alarm can be identified and appropriate measures can be taken quickly.

FIG. 17 schematically shows the operations of the spool 12 of the pilot valve 10 and the spool 28 of the main valve 20. FIG. 17(a) shows an example of the operation when the hydraulic servo valve 100 is normally controlled. The operation example shown in FIG. 17(a) is the same as the operation example shown in FIG. 14(a).

FIG. 17(b) shows an example of the operation when the position sensor 29 of the spool 28 of the main valve 20 is disconnected. The operation up to time t3 is the same as that shown in FIG. 17(a), but assume that the position sensor 29 of the spool 28 of the main valve 20 is disconnected at time t6. The feedback signal of the actual spool position of the main valve 20 suddenly changes to an abnormal value (for example, zero) immediately after the disconnection, and thereafter remains fixed at the abnormal value. In the example shown in this figure, the actual spool position of the main valve 20 is fixed while being deviated from the target spool position in the positive direction, so in order to return the spool 28 of the main valve 20 to the target spool position, the target spool position of the pilot valve 10 must be The maximum negative value continues to be entered as the position. Therefore, in reality, as shown by the broken line, the spool 28 of the main valve 20 continues to move in the negative direction, and when it reaches the limit position in the negative direction, it does not move any further and stops at the limit position.

In this way, the operation patterns of the spool 12 of the pilot valve 10 and the spool 28 of the main valve 20 are significantly different between normal times and when the position sensor 29 is disconnected. Therefore, by modeling the difference between the normal operation pattern and the operation pattern when the position sensor 29 is disconnected, it is possible to detect the disconnection of the position sensor 29. Even if time has passed since an alarm indicating that the actual spool position of the main valve 20 has deviated from the target spool position, a history of disconnection of the position sensor 29 is detected from log data. Therefore, it can be used as evidence of the cause of the malfunction. According to the technology of this embodiment, when an alarm is issued to notify that the actual spool position of the main valve 20 has deviated from the target spool position, it is possible to determine whether the cause is due to the foreign object caught in the third embodiment. , it is possible to accurately determine whether the cause is the disconnection of the position sensor 29 described in this embodiment, and to perform appropriate maintenance. Furthermore, the occurrence of chattering due to poor contact of the position sensor 29 can be accurately detected by modeling the operation pattern of the actual spool position of the main valve 20 and the target spool position of the pilot valve 10. This makes it possible to detect an abnormality and perform appropriate maintenance before the position sensor 29 is disconnected, thereby preventing malfunctions caused by the disconnection of the position sensor 29.

Figure 18 shows the configuration of a learning device and a state estimation device according to Example 5. In the learning device 300 of this example, the learning data acquisition unit 301 acquires the main valve actual spool position 311, the pilot valve target spool position 316, and the position sensor disconnection data 351 as learning data.

The main valve actual spool position 311 and the pilot valve target spool position 316 are time series data when the hydraulic servo valve 100 is used within a specified period before or after the point in time when the position sensor disconnection data 351 is actually measured. These time series data may be time series data for one to several cycles of the rotational operation of the engine 80.

The position sensor breakage data 351 is data that indicates the presence or absence, timing, manner, cause, etc. of a breakage in the position sensor 29 of the spool 28 of the main valve 20. When log data is used as the learning data, the position sensor breakage data 351 is the actual measurement value when the position sensor 29 actually breaks. When test data is used as the learning data, the position sensor breakage data 351 is the actual measurement value when the position sensor 29 is broken. When simulation data is used as the learning data, the position sensor breakage data 351 is data input to the simulator as a simulation condition.

The position sensor disconnection detection model generation unit 353 uses the learning data acquired by the learning data acquisition unit 301 to generate a position sensor disconnection detection model used to detect a disconnection of the position sensor 29 in the state estimation device 400. The position sensor disconnection detection model may be a neural network that inputs time series data of the main valve actual spool position 311 and the pilot valve target spool position 316 for a predetermined period to an input layer and outputs position sensor disconnection data from an output layer. In this case, the position sensor disconnection detection model generation unit 353 learns the position sensor disconnection detection model by adjusting the intermediate layer of the neural network so that a value approximating the position sensor disconnection data 351 is output from the output layer when the time series data of the main valve actual spool position 311 and the pilot valve target spool position 316 for a predetermined period is input to the input layer.

The position sensor disconnection detection model providing unit 354 provides the position sensor disconnection detection model generated by the position sensor disconnection detection model generation unit 353 to the state estimation device 400.

The detection information acquisition unit 401 acquires the main valve actual spool position 411 and the pilot valve target spool position 415 as detection information. This detection information is time series data for the same predetermined period as the learning data used to generate the position sensor disconnection detection model.

The position sensor disconnection detection unit 451 detects a disconnection of the position sensor 29 of the spool 28 of the main valve 20 using the position sensor disconnection detection model generated by the learning device 300. The position sensor disconnection detection unit 451 inputs the time series data of the main valve actual spool position 411 and the pilot valve target spool position 415 acquired by the detection information acquisition unit 401 to the position sensor disconnection detection model, and acquires the position sensor disconnection data output from the position sensor disconnection detection model as a judgment result. The position sensor disconnection detection unit 451 may estimate the period until the position sensor 29 disconnects based on the output position sensor disconnection data. In this case, the period until the position sensor 29 disconnects may be estimated by comparing the position sensor disconnection data of multiple hydraulic servo valves 100 provided corresponding to multiple cylinders 81 of the same engine 80.

The position sensor disconnection detection result output unit 452 outputs the result determined by the position sensor disconnection detection unit 451. When the position sensor disconnection detection unit 451 detects that the position sensor 29 is disconnected, the position sensor disconnection detection result output unit 452 may report this. The position sensor disconnection detection result output unit 452 may output the period until the position sensor 29 is disconnected, which is estimated by the position sensor disconnection detection unit 451.

The feature amount correlated with the disconnection of the position sensor 29 may be calculated from the main valve actual spool position, the pilot valve target spool position, or the like. In this case, as in Example 1-2, the learning device 300 may further include a feature amount calculation section 315, and the state estimation device 400 may further include a feature amount calculation section 414. When the position sensor 29 of the spool 28 of the main valve 20 is disconnected, the main valve actual spool position rapidly changes to an abnormal value. By calculating, the speed or acceleration of the spool 28 of the main valve 20 may be calculated.

The position sensor disconnection detection model generation unit 353 generates a position sensor disconnection detection model using the learning data acquired by the learning data acquisition unit 301 and the feature amount calculated by the feature amount calculation unit 315. The position sensor disconnection detection model generation unit 353 may generate a position sensor disconnection detection model using the position sensor disconnection data 351 and the feature amount calculated by the feature amount calculation unit 315. The position sensor disconnection detection model generation unit 353 may generate a position sensor disconnection detection model using the main valve actual spool position 311, the pilot valve target spool position 316, and the feature amount. For example, the position sensor disconnection detection model may be a neural network that inputs time series data of the main valve actual spool position 311 and the pilot valve target spool position 316 for a predetermined period and the feature amount for the predetermined period to an input layer and outputs the position sensor disconnection data from an output layer. In this case, the position sensor disconnection detection model generation unit 353 learns the position sensor disconnection detection model by adjusting the intermediate layer of the neural network so that when the time series data for a specified period of time of the main valve actual spool position 311 and the pilot valve target spool position 316 and the feature values for that specified period are input to the input layer, a value approximating the position sensor disconnection data 351 is output from the output layer.

The feature amount calculation unit 414 of the state estimation device 400 calculates the feature amount from the main valve actual spool position 411 or the pilot valve target spool position 415 acquired by the detection information acquisition unit 401 in the same manner as the feature amount calculation unit 315 calculated the feature amount when the position sensor breakage detection model used by the position sensor breakage detection unit 451 was generated. When the main valve actual spool position 411 and the pilot valve target spool position 415 of the hydraulic servo valve 100 used in an environment different from the learning data used when the position sensor breakage detection model was generated are acquired by the detection information acquisition unit 401, the feature amount calculation unit 414 may calculate the feature amount after adjusting for the difference in the environment. This can improve the robustness of the position sensor breakage detection.

As the characteristic quantity, another physical quantity that represents the characteristics of the operation of the spool 28 of the main valve 20 may be calculated. For example, the amount of overshoot or delay of the spool 28 of the main valve 20 may be calculated as the characteristic quantity.

The pilot valve actual spool position may be used instead of or in addition to the pilot valve target spool position as learning data and sensing information. In this case, similarly to Embodiment 1-4, the learning device 300 operates the offset time calculation unit 319 in order to adjust the offset time required until the actual spool position of the pilot valve 10 follows the target spool position of the pilot valve. You may prepare. The method for calculating the offset time may be the same as in Example 1-2. A combination of any two or more of the pilot valve target spool position, the pilot valve actual spool position, the main valve target spool position, and the main valve actual spool position may be used as the learning data and the detection information.

Data recorded when the hydraulic servo valve 100 is used in a specific environment may be used as the learning data and detection information. In this case, the learning device 300 may further include a data selection section 320, and the state estimation device 400 may further include a data selection section 417, as in Example 1-5. The data selection unit 320 and the data selection unit 417 may select data recorded when the hydraulic servo valve 100 is operated in a test mode in which the spool 12 of the pilot valve 10 is operated in a specific pattern. Thereby, the robustness of the disconnection detection of the position sensor 29 can be improved. The data selection unit 320 and the data selection unit 417 may select data recorded when the engine 80 is stopped and the spool sticking prevention operation is being performed. Thereby, the influence of disturbance can be suppressed and the accuracy of detecting disconnection of the position sensor 29 can be improved.

[Example 6: Detection of loose position sensor]
In the sixth embodiment, a technique for detecting a deviation in the position of the position sensor 29 for detecting the actual position of the spool 28 of the main valve 20 due to, for example, loosening of the screws for fixing the position sensor 29 will be described. If the position of the position sensor 29 is deviated, a deviation occurs between the feedback signal of the main valve actual spool position and the actual position of the spool 28 of the main valve 20, making it impossible to control the spool 28 of the main valve 20 normally, which may cause a malfunction of a controlled object such as the engine 80. If it is possible to detect a deviation in the position of the position sensor 29 with high accuracy, appropriate measures such as tightening the screws or replacing the position sensor 29 can be quickly taken.

Figure 19 shows the state of the position sensor 29 of the spool 28 of the main valve 20. Figure 19(a) shows the state in which the position sensor 29 is normally attached. The position sensor 29 is screwed into the female thread 30 cut at the upper end of the spool 28. When this thread loosens, as shown in Figure 19(b), a gap is generated between the position sensor 29 and the spool 28, and the position of the spool 28 is offset downward by the amount of this gap d. However, since the position of the spool 28 is detected by the position sensor 29, the same position is detected in both the case of Figure 19(a) and the case of Figure 19(b), and the actual position of the spool 28 offset downward cannot be detected. In the state of Figure 19(b), even if the main valve actual spool position appears to correctly follow the main valve target spool position, the actual position of the spool 28 is shifted downward from the main valve target spool position, causing a malfunction in the operation of the controlled object. In this embodiment, the amount of control oil supplied to the fuel injection actuator for supplying fuel to cylinder 81 of engine 80 is reduced, and the amount of fuel supplied to cylinder 81 is reduced, so the pressure inside cylinder 81 and the temperature of the exhaust gas from cylinder 81 drop, causing an alarm to be issued.

In this way, when the position sensor 29 is displaced due to looseness, the actual main valve spool position follows the main valve target spool position, but the pressure and exhaust temperature of the cylinder 81 drop. Therefore, when an alarm is issued due to an abnormality in the pressure or exhaust temperature of the cylinder 81, it is possible to identify whether the abnormality in the position sensor 29 is the cause by checking whether the actual main valve spool position followed the main valve target spool position. Even if time has passed since an alarm was issued to notify the engine 80 of an abnormality, it is possible to detect from the log data that the position sensor 29 has loosened, and this can be used as evidence of the cause of the malfunction. This allows the looseness of the position sensor 29 to be accurately detected and appropriate maintenance to be performed, thereby reducing the maintenance costs and labor required to improve the malfunction of the engine 80.

FIG. 20 shows the configuration of a learning device and a state estimation device according to the sixth embodiment. In the learning device 300 of this embodiment, the learning data acquisition unit 301 acquires the main valve actual spool position 311, the main valve target spool position 330, the cylinder exhaust temperature 361, and the position sensor slack data 362 as learning data.

The main valve actual spool position 311, the main valve target spool position 330, and the cylinder exhaust temperature 361 are determined when the hydraulic servo valve 100 is used within a predetermined period before or after the time when the position sensor looseness data 362 is actually measured. This is time series data. These time series data may be time series data for one to several cycles of the rotational operation of the engine 80.

The position sensor looseness data 362 is data that indicates the presence or absence, degree, manner, cause, etc. of position misalignment of the position sensor 29 of the spool 28 of the main valve 20. When log data is used as the learning data, the position sensor looseness data 362 is the actual measurement value when the position sensor 29 is actually misaligned. When test data is used as the learning data, the position sensor looseness data 362 is the actual measurement value when the position sensor 29 is misaligned. When simulation data is used as the learning data, the position sensor looseness data 362 is data input to the simulator as a simulation condition.

The position sensor looseness detection model generation unit 363 uses the learning data acquired by the learning data acquisition unit 301 to generate a position sensor looseness detection model used for detecting the looseness of the position sensor 29 in the state estimation device 400. generate. The position sensor looseness detection model inputs time-series data for a predetermined period of main valve actual spool position 311, main valve target spool position 330, and cylinder exhaust temperature 361 into the input layer, and outputs position sensor looseness data from the output layer. It may also be a neural network or the like. In this case, the position sensor loosening detection model generation unit 363 generates a position sensor when time series data for a predetermined period of the main valve actual spool position 311, the main valve target spool position 330, and the cylinder exhaust temperature 361 are input to the input layer. The position sensor looseness detection model is learned by adjusting the intermediate layer of the neural network so that a value that approximates the looseness data 362 is output from the output layer.

The position sensor looseness detection model providing unit 364 provides the position sensor looseness detection model generated by the position sensor looseness detection model generation unit 363 to the state estimation device 400.

The detection information acquisition unit 401 acquires the main valve actual spool position 411, the main valve target spool position 430, and the cylinder exhaust temperature 461 as detection information. This detection information is time series data for the same predetermined period as the learning data used to generate the position sensor looseness detection model.

The position sensor loosening detection unit 462 detects the loosening of the position sensor 29 of the spool 28 of the main valve 20 using the position sensor loosening detection model generated by the learning device 300. The position sensor loosening detection unit 462 inputs the time series data of the main valve actual spool position 411, the main valve target spool position 430, and the cylinder exhaust temperature 461 acquired by the detection information acquisition unit 401 into the position sensor loosening detection model, and acquires the position sensor loosening data output from the position sensor loosening detection model as a judgment result. The position sensor loosening detection unit 462 may estimate the period until the position sensor 29 deviates by a predetermined value or more based on the output position sensor loosening data. In this case, the position sensor loosening data of multiple hydraulic servo valves 100 provided corresponding to multiple cylinders 81 of the same engine 80 may be compared to estimate the period until the position sensor 29 deviates by a predetermined value or more.

When multiple hydraulic servo valves 100 are provided corresponding to multiple cylinders 81 of the same engine 80, the position sensor looseness detection unit 462 may detect looseness of the position sensor 29 based on position sensor looseness data estimated in each hydraulic servo valve 100. For example, the position sensor looseness detection unit 462 may detect looseness of the position sensor 29 by comparing the exhaust temperatures of each cylinder 81. When the exhaust temperature of a certain cylinder 81 is lower than the average exhaust temperature of the other cylinders 81 by a predetermined value or more, the position sensor looseness detection unit 462 may determine that the position sensor 29 of the hydraulic servo valve 100 provided in that cylinder 81 is loose.

The position sensor looseness detection result output section 463 outputs the result determined by the position sensor looseness detection section 462. The position sensor looseness detection result output unit 463 may notify that when the position sensor 29 is detected to be loose by the position sensor looseness detection unit 462. The position sensor looseness detection result output unit 463 may output a period until the position sensor 29 estimated by the position sensor looseness detector 462 deviates by a predetermined value or more.

A feature quantity correlated with the looseness of the position sensor 29 may be calculated from the main valve actual spool position, the main valve target spool position, the cylinder exhaust temperature, or the like. In this case, as in the first and second embodiments, the learning device 300 may further include a feature quantity calculation unit 315, and the state estimation device 400 may further include a feature quantity calculation unit 414. The greater the degree of looseness of the position sensor 29 of the spool 28 of the main valve 20, the greater the deviation between the actual main valve spool position and the actual position of the spool 28, and therefore the greater the amount of decrease in the exhaust temperature or pressure of the cylinder from its normal value. Therefore, the feature quantity calculation unit may calculate the amount of decrease, the rate of change, the rate of change of the rate of change, or the like of the exhaust temperature or pressure of the cylinder from its normal value as a feature quantity.

The position sensor looseness detection model generation unit 363 generates a position sensor looseness detection model using the learning data acquired by the learning data acquisition unit 301 and the feature amount calculated by the feature amount calculation unit 315. For example, the position sensor looseness detection model may be a mathematical formula that calculates the position sensor looseness data using the feature amount as an input variable. In this case, the position sensor looseness detection model generation unit 363 may generate the formula using a statistical method such as regression analysis. The position sensor looseness detection model generation unit 363 may generate a position sensor looseness detection model using the position sensor looseness data 362 and the feature amount calculated by the feature amount calculation unit 315. The position sensor looseness detection model generation unit 363 may generate a position sensor looseness detection model using the main valve actual spool position 311, the main valve target spool position 330, and the feature amount. For example, the position sensor looseness detection model inputs the time series data of the main valve actual spool position 311 and the main valve target spool position 330 for a predetermined period, and the feature amounts for the predetermined period into the input layer, and outputs the position sensor loosening data. It may also be a neural network that outputs from a layer. In this case, when the position sensor looseness detection model generation unit 363 inputs the time series data of the main valve actual spool position 311 and the main valve target spool position 330 for a predetermined period and the feature amount for the predetermined period into the input layer, The position sensor looseness detection model is learned by adjusting the intermediate layer of the neural network so that a value that approximates the position sensor looseness data 362 is output from the output layer.

The feature calculation unit 414 of the state estimation device 400 calculates the feature from the main valve actual spool position 411, the main valve target spool position 430, or the cylinder exhaust temperature 461 acquired by the detection information acquisition unit 401 in the same manner as the feature calculation unit 315 calculated the feature when the position sensor looseness detection model used by the position sensor looseness detection unit 462 was generated. If the main valve actual spool position 411, the main valve target spool position 430, or the cylinder exhaust temperature 461 of the hydraulic servo valve 100 used in an environment different from the learning data used when the position sensor looseness detection model was generated is acquired by the detection information acquisition unit 401, the feature calculation unit 414 may calculate the feature after adjusting for the difference in the environment. This can improve the robustness of the position sensor looseness detection.

As the characteristic quantity, another physical quantity representing the characteristic of the operation of the spool 28 of the main valve 20 may be calculated. For example, the overshoot amount, delay amount, etc. of the spool 28 of the main valve 20 may be calculated as the feature amount.

As learning data and detection information, the pressure of the cylinder 81 or the maximum value of the pressure may be used instead of or in addition to the cylinder exhaust temperature. Furthermore, various types of data indicating the state of the controlled object controlled by the hydraulic servo valve 100, the quality of the operation, etc. may be used as the learning data and the detection information.

Data recorded when the hydraulic servo valve 100 is used in a specific environment may be used as the learning data and detection information. In this case, the learning device 300 may further include a data selection section 320, and the state estimation device 400 may further include a data selection section 417, as in Example 1-5. The data selection unit 320 and the data selection unit 417 may select data recorded when the hydraulic servo valve 100 is operated in a test mode in which the spool 12 of the pilot valve 10 is operated in a specific pattern. Thereby, the robustness of position sensor loosening detection can be improved. The data selection unit 320 and the data selection unit 417 may select data recorded when the engine 80 is stopped and the spool sticking prevention operation is being performed. This makes it possible to suppress the influence of disturbances and improve the accuracy of detecting loosening of the position sensor.

[Example 7: Detection of deviation of pilot valve from neutral position]
In the seventh embodiment, a technique for detecting the shift of the neutral position of the spool 12 of the pilot valve 10 will be described. When the neutral position is commanded as the pilot valve target spool position, if the pilot valve 10 is operating normally, the spool 12 is moved to the neutral position, the supply and recovery of hydraulic oil to the main valve 20 is stopped, and the spool 28 of the main valve 20 stops at the main valve target spool position. However, if the neutral position of the spool 12 is shifted due to some cause, the supply and recovery of hydraulic oil to the main valve 20 cannot be controlled normally, and the controlled object such as the engine 80 may malfunction. If the shift of the neutral position of the spool 12 of the pilot valve 10 can be detected with high accuracy, appropriate measures such as maintaining the control system of the pilot valve 10 or replacing the pilot valve 10 can be quickly taken.

There are two main reasons why the neutral position of the spool 12 of the pilot valve 10 shifts. One is that the spool 12 cannot be accurately moved to the neutral position due to an abnormality in the position sensor 19 for detecting the position of the spool 12 of the pilot valve 10 or an abnormality in the electrical system for driving the spool 12. This is an electrical cause. The other reason is that the spool 12 or the main body portion 10b wears unevenly, so that even when the spool 12 is in the neutral position, the valve body 14 blocks the A port 16a and prevents it from communicating with both the P port 16p and the T port 16t. This is a mechanical cause of not being able to do so. In either case, when the neutral position is specified as the pilot valve target spool position, the spool 28 of the main valve 20 moves without stopping, so the feedback signal of the main valve actual spool position is the main valve target. The pilot valve 10 is controlled to correct the deviation from the spool position. As described above, the operation patterns of the spool 12 of the pilot valve 10 and the spool 28 of the main valve 20 are significantly different between normal times and when the neutral position of the spool 12 of the pilot valve 10 is shifted. Therefore, by modeling the difference between the normal operating pattern and the operating pattern when the spool 12 shifts from its neutral position, it is possible to detect the shift in the neutral position of the spool 12. Since the operation pattern can vary depending on the degree and cause of deviation of the spool 12 from the neutral position, it is possible to detect not only the presence or absence of deviation of the spool 12 from the neutral position, but also the cause of the deviation and the magnitude of the deviation. .

When the spool 12 wears unevenly, the valve body 14 cannot shut off the A port 16a even if the spool 12 is in the correct neutral position, so hydraulic oil leaks from the A port 16a. By actually measuring the amount of internal oil leakage, it is possible to determine whether the shift in the neutral position of the spool 12 is due to uneven wear or an electrical cause. The amount of internal oil leakage may be measured by providing a flowmeter in the pilot valve 10, may be measured with an external flowmeter when maintaining the pilot valve 10, or may be estimated using the technique of Example 1. You may.

When the neutral position of the spool 12 of the pilot valve 10 is shifted, an abnormality occurs in the pressure or exhaust temperature of the cylinder 81 in the engine 80 to be controlled, and an alarm is issued. When an alarm is issued, it is possible to determine whether the cause is an abnormality in the position sensor 29 described in the sixth embodiment or a neutral position shift of the spool 12 of the pilot valve 10 by checking the operation patterns of the spool 12 of the pilot valve 10 and the spool 28 of the main valve 20. In addition, if the cause is a neutral position shift of the spool 12 of the pilot valve 10, it is possible to determine whether the neutral position is shifted due to an electrical cause or a mechanical cause by checking the amount of internal oil leakage. This allows the cause of the malfunction of the engine 80 to be accurately determined and appropriate maintenance to be performed according to the cause, thereby reducing the maintenance costs and labor required to improve the malfunction of the engine 80. When PID control is performed in the feedback control of the hydraulic servo valve 100, the deviation is corrected to a certain extent by the integral component (I component), but the technology of this embodiment can accurately detect the neutral position deviation of the spool 12 of the pilot valve 10 even when the neutral position deviation is corrected by PID control. Also, when P control or PD control is performed, the deviation is not corrected, so the technology of this embodiment is particularly effective.

Figure 21 shows the configuration of a learning device and a state estimation device according to Example 7. In the learning device 300 of this example, the learning data acquisition unit 301 acquires the main valve actual spool position 311, the main valve target spool position 330, the pilot valve actual spool position 310, the cylinder exhaust temperature 361, and the neutral position deviation data 371 as learning data.

The main valve actual spool position 311, the main valve target spool position 330, the pilot valve actual spool position 310, and the cylinder exhaust temperature 361 are time series data when the hydraulic servo valve 100 is used within a specified period before or after the neutral position deviation data 371 is actually measured. These time series data may be time series data for one to several cycles of the rotational operation of the engine 80.

The neutral position deviation data 371 is data that indicates the presence or absence, the degree, and the cause of neutral position deviation of the spool 12 of the pilot valve 10. When log data is used as the learning data, the neutral position deviation data 371 is the actual measurement value when the neutral position deviation of the spool 12 actually occurs. When test data is used as the learning data, the neutral position deviation data 371 is the actual measurement value when the neutral position deviation of the spool 12 occurs. When simulation data is used as the learning data, the neutral position deviation data 371 is data that is input to the simulator as a simulation condition.

The neutral position deviation detection model generation unit 373 uses the learning data acquired by the learning data acquisition unit 301 to generate a neutral position deviation detection model used to detect a neutral position deviation of the spool 12 of the pilot valve 10 in the state estimation device 400. The neutral position deviation detection model may be a neural network that inputs time series data of the main valve actual spool position 311, the main valve target spool position 330, the pilot valve actual spool position 310, and the cylinder exhaust temperature 361 for a predetermined period to an input layer and outputs neutral position deviation data from an output layer. In this case, the neutral position deviation detection model generation unit 373 learns the neutral position deviation detection model by adjusting the intermediate layer of the neural network so that a value approximating the neutral position deviation data 371 is output from the output layer when the time series data of the main valve actual spool position 311, the main valve target spool position 330, the pilot valve actual spool position 310, and the cylinder exhaust temperature 361 for a predetermined period is input to the input layer.

The neutral position deviation detection model providing unit 374 provides the neutral position deviation detection model generated by the neutral position deviation detection model generating unit 373 to the state estimation device 400.

The detection information acquisition unit 401 acquires the main valve actual spool position 411, the main valve target spool position 430, the pilot valve actual spool position 410, and the cylinder exhaust temperature 461 as detection information. This detection information is time series data for a predetermined period of time that is the same as the learning data used to generate the neutral position deviation detection model.

The neutral position deviation detection unit 472 detects the neutral position deviation of the spool 12 of the pilot valve 10 using the neutral position deviation detection model generated by the learning device 300. The neutral position deviation detection unit 472 inputs the time series data of the main valve actual spool position 411, the main valve target spool position 430, the pilot valve actual spool position 410, and the cylinder exhaust temperature 461 acquired by the detection information acquisition unit 401 into the neutral position deviation detection model, and acquires the neutral position deviation data output from the neutral position deviation detection model as a judgment result. The neutral position deviation detection unit 472 may estimate the period until the neutral position of the spool 12 of the pilot valve 10 deviates from the actual neutral position by a predetermined value or more based on the output neutral position deviation data. In this case, the neutral position deviation data of multiple hydraulic servo valves 100 provided corresponding to multiple cylinders 81 of the same engine 80 may be compared to estimate the period until the neutral position of the spool 12 of the pilot valve 10 deviates from the actual neutral position by a predetermined value or more.

When a plurality of hydraulic servo valves 100 are provided corresponding to a plurality of cylinders 81 of the same engine 80, the neutral position deviation detection unit 472 detects the pilot position based on the neutral position deviation data estimated for each hydraulic servo valve 100. A shift in the neutral position of the spool 12 of the valve 10 may be detected. For example, the neutral position deviation detection unit 472 may detect the neutral position deviation of the spool 12 of the pilot valve 10 by comparing the exhaust gas temperatures of the respective cylinders 81. When the exhaust gas temperature of a certain cylinder 81 is lower than the average value of the exhaust gas temperatures of other cylinders 81 by a predetermined value or more, the neutral position deviation detection unit 472 detects that the pilot valve 10 of the hydraulic servo valve 100 provided in that cylinder 81 is It may be determined that the neutral position of the spool 12 has shifted.

The neutral position deviation detection result output unit 473 outputs the result determined by the neutral position deviation detection unit 472. The neutral position deviation detection result output unit 473 may notify that when the neutral position deviation detection unit 472 detects that the neutral position of the spool 12 of the pilot valve 10 has deviated. The neutral position deviation detection result output unit 473 may output a period until the neutral position of the spool 12 of the pilot valve 10 estimated by the neutral position deviation detection unit 472 deviates by a predetermined value or more.

A feature quantity correlated with the neutral position deviation of the spool 12 of the pilot valve 10 may be calculated from the actual main valve spool position, the target main valve spool position, the actual pilot valve spool position, or the cylinder exhaust temperature. In this case, as in the first and second embodiments, the learning device 300 may further include a feature quantity calculation unit 315, and the state estimation device 400 may further include a feature quantity calculation unit 414. The greater the degree of neutral position deviation of the spool 12 of the pilot valve 10, the greater the degree to which the actual main valve spool position deviates from the target main valve spool position, and therefore the greater the deviation of the exhaust temperature or pressure of the cylinder from its normal value. Therefore, the feature quantity calculation unit may calculate the deviation, rate of change, or rate of change of the rate of change of the exhaust temperature or pressure of the cylinder from its normal value as a feature quantity.

The neutral position deviation detection model generation unit 373 generates a neutral position deviation detection model using the learning data acquired by the learning data acquisition unit 301 and the feature amount calculated by the feature amount calculation unit 315. For example, the neutral position deviation detection model may be a mathematical formula that calculates neutral position deviation data using feature amounts as input variables. In this case, the neutral position shift detection model generation unit 373 may generate the formula using a statistical method such as regression analysis. The neutral position deviation detection model generation unit 373 may generate a neutral position deviation detection model using the neutral position deviation data 371 and the feature amount calculated by the feature amount calculation unit 315. The neutral position deviation detection model generation unit 373 may generate a neutral position deviation detection model using the main valve actual spool position 311, the main valve target spool position 330, the pilot valve actual spool position 310, and the feature amount. For example, in the neutral position deviation detection model, time series data of a main valve actual spool position 311, a main valve target spool position 330, and a pilot valve actual spool position 310 for a predetermined period and feature amounts for the predetermined period are input to the input layer. However, it may also be a neural network that outputs neutral positional deviation data from the output layer. In this case, the neutral position deviation detection model generation unit 373 generates time series data of the main valve actual spool position 311, the main valve target spool position 330, and the pilot valve actual spool position 310 for a predetermined period, and the feature amount for the predetermined period. A neutral positional deviation detection model is learned by adjusting the intermediate layer of the neural network so that a value that approximates the neutral positional deviation data 371 is output from the output layer when input to the input layer.

The feature calculation unit 414 of the state estimation device 400 calculates the feature from the main valve actual spool position 411, the main valve target spool position 430, the pilot valve actual spool position 410, or the cylinder exhaust temperature 461 acquired by the detection information acquisition unit 401 in the same manner as the feature calculation unit 315 calculated the feature when the neutral position deviation detection model used by the neutral position deviation detection unit 472 was generated. If the main valve actual spool position 411, the main valve target spool position 430, the pilot valve actual spool position 410, or the cylinder exhaust temperature 461 of the hydraulic servo valve 100 used in an environment different from the learning data used when the neutral position deviation detection model was generated is acquired by the detection information acquisition unit 401, the feature calculation unit 414 may calculate the feature after adjusting for the difference in the environment. This can improve the robustness of the neutral position deviation detection.

As the characteristic quantity, another physical quantity representing the characteristic of the operation of the spool 12 of the pilot valve 10 or the spool 28 of the main valve 20 may be calculated. For example, the acceleration, speed, overshoot amount, delay amount, etc. of the spool 12 of the pilot valve 10 or the spool 28 of the main valve 20 may be calculated as the feature amount.

As learning data and detection information, the pressure of the cylinder 81 or the maximum value of the pressure may be used instead of or in addition to the cylinder exhaust temperature. Furthermore, various types of data indicating the state of the controlled object controlled by the hydraulic servo valve 100, the quality of the operation, etc. may be used as the learning data and the detection information.

As the learning data and detection information, the pilot valve target spool position may be used instead of or in addition to the pilot valve actual spool position. In this case, similar to Example 1-4, the learning device 300 may be provided with an offset time calculation unit 319 to adjust the offset time required for the actual spool position of the pilot valve 10 to follow the target spool position of the pilot valve. The method of calculating the offset time may be similar to Example 1-2. As the learning data and detection information, a combination of any two or more of the pilot valve target spool position, the pilot valve actual spool position, the main valve target spool position, and the main valve actual spool position may be used.

As the learning data and the detection information, data recorded when the hydraulic servo valve 100 is used in a specific environment may be used. In this case, similar to the first to fifth embodiments, the learning device 300 may further include a data selection unit 320, and the state estimation device 400 may further include a data selection unit 417. The data selection unit 320 and the data selection unit 417 may select data recorded when the hydraulic servo valve 100 is operated in a test mode in which the spool 12 of the pilot valve 10 is operated in a specific pattern. This can improve the robustness of the detection of the neutral position deviation. The data selection unit 320 and the data selection unit 417 may select data recorded when the engine 80 is stopped and the spool is prevented from sticking. This can suppress the influence of disturbances and improve the detection accuracy of the neutral position deviation.

The features described in Examples 1 to 7 above may be applied in any combination.

Examples of embodiments of the present invention have been described above in detail. The embodiments described above are merely specific examples for carrying out the present invention. The contents of the embodiments do not limit the technical scope of the present invention, and many design changes such as changes, additions, and deletions of constituent elements may be made without departing from the spirit of the invention defined in the claims. It is possible. In the above-mentioned embodiment, contents that allow such design changes are explained with the notations such as "in the embodiment" and "in the embodiment", but the design does not include such notations. This does not mean that changes are not allowed.

[Modification]
The following describes the modified examples. In the drawings and description of the modified examples, the same or equivalent components and members as those in the embodiment are denoted by the same reference numerals. Descriptions that overlap with the embodiment will be omitted as appropriate, and the description will focus on configurations that differ from the embodiment.

In the description of the embodiment, an example is shown in which the pilot valve 10 of the hydraulic servo valve 100 is a three-port valve, but the present invention is not limited to this. Hydraulic servovalve 100 may be other types of valves.

Although the embodiment has been described as an example in which the pilot valve 10 of the hydraulic servo valve 100 controls the main valve 20, the present invention is not limited thereto. The control target of the pilot valve 10 may be any other actuator.

In the description of the embodiment, an example is shown in which the hydraulic servo valve 100 is used to control the engine 80 of the ship 2, but the present invention is not limited thereto. Hydraulic servo valve 100 may be used to control any controlled object.

The above-mentioned modified example provides the same effects and advantages as the embodiment.

Any combination of the embodiments and variations described above are also useful as embodiments of the invention. A new embodiment resulting from a combination has the effects of each of the combined embodiments and modified examples.

[Aspects of the present invention]
One aspect of the present invention is a state estimation device. The state estimation device includes an acquisition unit that acquires detection information of a data set including a target position and an actual position of a first movable part in a control valve that controls a flow rate of a working fluid depending on a position of the first movable part and a value of a current supplied to a drive unit that drives the first movable part, and an estimation unit that estimates an abnormality in which the first movable part deviates from a neutral position for minimizing the flow rate of the working fluid based on the detection information acquired by the acquisition unit. According to this aspect, since it is possible to estimate the neutral position deviation of the first movable part in the control valve, it is possible to take an appropriate measure depending on the state of the control valve.

The acquisition unit may further acquire the amount of leakage of the working fluid, and the estimation unit may estimate the cause of the neutral position shift of the first movable part by referring to the amount of leakage of the working fluid. According to this aspect, it is possible to estimate the cause of the neutral position shift of the first movable part in the control valve, and therefore it is possible to take appropriate measures according to the state of the control valve.

The estimating unit may estimate that the cause of the neutral position shift of the first movable portion is uneven wear of the first movable portion when the amount of leakage of the working fluid is equal to or greater than a predetermined value. According to this aspect, the cause of the shift in the neutral position of the first movable part in the control valve can be estimated, so that appropriate measures can be taken depending on the state of the control valve.

If the amount of leakage of the working fluid is less than a predetermined value, the estimation unit may estimate that the cause of the neutral position shift of the first movable part is an abnormality in the detection unit for detecting the position of the first movable part or in the electrical system for driving the first movable part. According to this aspect, since it is possible to estimate the cause of the neutral position shift of the first movable part in the control valve, it is possible to take appropriate measures according to the state of the control valve.

The estimation unit may estimate the cleanliness of the working fluid using an estimation standard for estimating the neutral position deviation of the first movable part based on the sensed information of the data set. According to this aspect, the accuracy of estimating the neutral position shift of the first movable part can be improved.

The estimation criterion may be generated based on the relationship between the values of the data set when the control valve or a control valve of the same type as the control valve is used within a specified period of time and information on the neutral position deviation of the first movable part actually measured within the specified period of time. According to this aspect, it is possible to improve the accuracy of estimating the neutral position deviation of the first movable part.

The estimation standard is generated based on the relationship between the feature quantity calculated from the data set value and the information regarding the neutral position deviation of the first movable part, and the estimation unit calculates the feature value from the detection information acquired by the acquisition unit. The neutral position shift of the first movable part may be estimated based on the calculated feature amount. According to this aspect, the accuracy of estimating the neutral position shift of the first movable part can be improved.

The acquisition unit may acquire detection information of the data set in a usage environment similar to the usage environment of the control valve or a control valve of the same type as the control valve when the value of the data set used to generate the estimation criterion was recorded. According to this aspect, it is possible to improve the robustness of the estimation of the neutral position deviation of the first movable part.

The usage environment may be a situation in which the control valve or a control object of the same type as the control valve is stopped and an operation to prevent the first movable part from sticking is being performed. According to this aspect, it is possible to improve the robustness of the estimation of the neutral position deviation of the first movable part.

The state estimating device may further include a notification section that notifies when the estimation section estimates that there is a shift in the neutral position of the first movable section. According to this aspect, it is possible to accurately grasp the shift in the neutral position of the first movable part of the control valve, and it is possible to take appropriate measures depending on the state of the control valve.

The notification unit may estimate the time until the first movable part shifts from its neutral position and notify the user. According to this aspect, it is possible to accurately predict the shift of the first movable part of the control valve from its neutral position, and to take appropriate measures according to the state of the control valve.

The control valve may be a control valve for regulating the amount of fuel supplied to the engine. According to this aspect, abnormalities in the control valve for controlling the engine can be accurately grasped, and appropriate measures can be taken depending on the state of the control valve.

The control valve is provided in each cylinder in order to adjust the amount of fuel supplied to each cylinder of an engine having a plurality of cylinders, and the estimation unit calculates information regarding each of the plurality of control valves provided in the plurality of cylinders. The neutral position shift of the first movable part may be estimated by comparing the values. According to this aspect, abnormalities in the control valves that control the engine can be accurately grasped, and appropriate measures can be taken depending on the state of the control valves.

Another aspect of the present invention is a control valve. This control valve includes a first movable part whose position changes in response to a control signal for specifying a position, and whose flow rate of a working fluid is controlled in response to the position; an acquisition unit that acquires detection information of a data set including a target position and an actual position of the first movable part and a value of a current supplied to a drive unit that drives the first movable part; and an estimation unit that estimates an abnormality in which the first movable part deviates from a neutral position for minimizing the flow rate of the working fluid based on the detection information acquired by the acquisition unit. According to this aspect, since it is possible to estimate the neutral position deviation of the first movable part in the control valve, it is possible to take appropriate measures according to the state of the control valve.

Yet another aspect of the present invention is a state estimation program. This state estimation program causes a computer to function as an acquisition unit that acquires detection information of a data set including a target position and an actual position of a first movable part in a control valve that controls the flow rate of a working fluid depending on the position of the first movable part and a value of a current supplied to a drive unit that drives the first movable part, and an estimation unit that estimates an abnormality in which the first movable part deviates from a neutral position for minimizing the flow rate of the working fluid based on the detection information acquired by the acquisition unit. According to this aspect, since it is possible to estimate the neutral position deviation of the first movable part in the control valve, it is possible to take appropriate measures depending on the state of the control valve.

Yet another aspect of the present invention is a state estimation method. This state estimation method supplies a computer with the target position and actual position of a first movable part in a control valve that controls the flow rate of working fluid according to the position of the first movable part, and a drive part that drives the first movable part. the first movable part is shifted from a neutral position for minimizing the flow rate of the working fluid based on the detected information acquired in the acquiring step; and a step of estimating an abnormality. According to this aspect, it is possible to estimate the neutral position shift of the first movable part in the control valve, so that appropriate measures can be taken depending on the state of the control valve.

An aspect of the present invention is a state estimation device. This state estimating device includes a target position or an actual position of a first movable part in a control valve that controls the flow rate of working fluid according to the position of the first movable part, and a second movable part that changes the position according to the flow rate of the working fluid. an acquisition unit that acquires detection information of a data set including the actual position of the second movable unit; and an estimation that estimates a positional deviation of the detection unit for detecting the position of the second movable unit based on the detection information acquired by the acquisition unit. It is equipped with a section. According to this aspect, it is possible to estimate the positional deviation of the detection part for detecting the position of the second movable part in the control valve, so that appropriate measures can be taken depending on the state of the control valve.

The estimation unit may estimate an abnormality in the detection unit using an estimation standard for estimating a positional shift of the detection unit based on the detection information of the data set. According to this aspect, the accuracy of estimating the positional deviation of the detection unit can be improved.

The estimation standard may be generated based on the relationship between the value of the data set and the information regarding the positional deviation of the detection unit when the control valve or the same type of control valve as the control valve is used within a predetermined period. . According to this aspect, the accuracy of estimating the positional deviation of the detection unit can be improved.

The information regarding the positional deviation of the detection unit may be the presence or absence, degree, manner, or cause of a disconnection in the detection unit. According to this embodiment, the accuracy of estimating the positional deviation of the detection unit can be improved.

The estimation criterion is generated based on the relationship between the feature amount calculated from the values of the data set and the information related to the positional deviation of the detection unit, and the estimation unit may calculate the feature amount from the detection information of the data set acquired by the acquisition unit, and estimate the positional deviation of the detection unit based on the calculated feature amount. According to this aspect, it is possible to improve the estimation accuracy of the positional deviation of the detection unit.

The acquisition unit acquires detection information of the dataset in a usage environment similar to the usage environment of the control valve or a control valve of the same type as the control valve when the value of the dataset used to generate the estimation standard was recorded. It's okay. According to this aspect, the robustness of estimating the positional deviation of the detection unit can be improved.

The usage environment may be a situation in which the control valve or a control object of the same type as the control valve is stopped and an anti-sticking operation of the first movable part and the second movable part is being performed. According to this aspect, it is possible to improve the robustness of the estimation of the positional deviation of the detection unit.

The state estimation device may further include a notification unit that notifies the user when the estimation unit estimates that the position of the detection unit is misaligned. According to this aspect, the positional misalignment of the detection unit of the control valve can be accurately grasped, and appropriate measures can be taken depending on the state of the control valve.

The notification unit may estimate the time it will take for the position of the detection unit to shift by a certain amount or more and notify the user. According to this embodiment, it is possible to accurately predict the position shift of the detection unit of the control valve, and appropriate measures can be taken depending on the state of the control valve.

The control valve may be a control valve for regulating the amount of fuel supplied to the engine. According to this aspect, it is possible to accurately grasp the positional deviation of the control valve for controlling the engine, and it is possible to take appropriate measures depending on the state of the control valve.

The control valve is provided in each cylinder in order to adjust the amount of fuel supplied to each cylinder of an engine having a plurality of cylinders, and the estimation unit calculates information regarding each of the plurality of control valves provided in the plurality of cylinders. The positional deviation of the second movable part may be estimated by comparing the values. According to this aspect, the robustness of estimating the positional shift of the detection unit can be improved.

Yet another aspect of the invention is a control valve. This control valve includes a first movable part whose position changes according to a control signal for specifying the position, and whose flow rate of working fluid is controlled according to the position, and a target position or actual position of the first movable part. an acquisition unit that acquires detection information of a data set including an actual position of a second movable part whose position changes according to the flow rate of the working fluid; and a position of the second movable part based on the detection information acquired by the acquisition unit. and an estimation section that estimates a positional shift of the detection section for detecting. According to this aspect, it is possible to estimate the positional deviation of the detection part for detecting the position of the second movable part in the control valve, so that appropriate measures can be taken depending on the state of the control valve.

Yet another aspect of the present invention is a state estimation program. This state estimation program causes a computer to function as an acquisition unit that acquires detection information of a data set including a target position or actual position of a first movable part in a control valve that controls the flow rate of a working fluid according to the position of the first movable part and an actual position of a second movable part that changes its position according to the flow rate of the working fluid, and an estimation unit that estimates a positional deviation of a detection unit for detecting the position of the second movable part based on the detection information acquired by the acquisition unit. According to this aspect, since it is possible to estimate a positional deviation of a detection unit for detecting the position of the second movable part in the control valve, it is possible to take appropriate measures according to the state of the control valve.

Yet another aspect of the present invention is a state estimation method. This state estimation method causes a computer to execute a step of acquiring detection information of a data set including a target position or actual position of a first movable part in a control valve that controls the flow rate of a working fluid according to the position of the first movable part and an actual position of a second movable part that changes its position according to the flow rate of the working fluid, and a step of estimating a positional deviation of a detection part for detecting the position of the second movable part based on the detection information acquired in the acquiring step. According to this aspect, since it is possible to estimate a positional deviation of a detection part for detecting the position of the second movable part in the control valve, it is possible to take an appropriate response according to the state of the control valve.

1. Management system, 2. Ship, 10. Pilot valve, 12. Spool, 16. Port, 18. Spool drive unit, 19. Position sensor, 20. Main valve, 28. Spool. , 29... Position sensor, 48... Hydraulic oil, 80... Engine, 81... Cylinder, 82... Sensor, 90... Log data storage device, 91... Engine control device, 92... Junction box, 100 ... Hydraulic servo valve, 110 ... Servo valve control device, 120 ... Power supply circuit, 130 ... Servo valve control circuit, 140 ... Servo valve drive circuit, 150 ... Voltage detection section, 160 ... Data collection circuit, 300... Learning device, 301... Learning data acquisition unit, 302... Estimated model generation unit, 303... Estimated model providing unit, 310... Pilot valve actual spool position, 311... Main valve actual spool position, 315...・Feature quantity calculation unit, 320... Data selection unit, 330... Main valve target spool position, 361... Cylinder exhaust temperature, 362... Position sensor looseness data, 363... Position sensor looseness detection model generation unit, 364... - Position sensor looseness detection model providing unit, 371... Neutral position deviation data, 373... Neutral position deviation detection model generation unit, 374... Neutral position deviation detection model providing unit, 400... State estimation device, 401... Detection Information acquisition unit, 402... State estimation unit, 403... Estimation result output unit, 410... Pilot valve actual spool position, 411... Main valve actual spool position, 414... Feature value calculation unit, 417... Data selection. Part, 430... Main valve target spool position, 461... Cylinder exhaust temperature, 462... Position sensor looseness detection section, 463... Position sensor looseness detection result output section, 472... Neutral position deviation detection section, 473... Neutral position deviation detection result output section.

Claims (30)

an acquisition unit that acquires detection information of a data set including a target position or an actual position of a first movable part in a control valve that controls a flow rate of a working fluid in accordance with a position of the first movable part, a target position and an actual position of a second movable part that changes its position in accordance with the flow rate of the working fluid, and an exhaust temperature or a cylinder pressure of an engine controlled by the control valve;
an estimation unit that estimates an abnormality in which the first movable part deviates from a neutral position for minimizing a flow rate of the working fluid based on the detection information acquired by the acquisition unit. The acquisition unit further acquires the leakage amount of the working fluid,
The state estimating device according to claim 1, wherein the estimating section estimates the cause of the neutral position shift of the first movable section by referring to the amount of leakage of the working fluid. The state estimation device according to claim 2, wherein the estimation unit estimates that the cause of the neutral position shift of the first movable part is uneven wear of the first movable part when the leakage amount of the working fluid is equal to or greater than a predetermined value. The estimation unit includes a detection unit or a detection unit for detecting the position of the first movable unit as the cause of the neutral position shift of the first movable unit when the leakage amount of the working fluid is less than a predetermined value. The state estimating device according to claim 2 or 3, wherein the state estimating device estimates that there is an abnormality in the electrical system for driving the. A state estimation device according to any one of claims 1 to 4, wherein the estimation unit estimates the neutral position deviation of the first movable part using an estimation criterion for estimating the neutral position deviation of the first movable part based on the detection information of the data set. The estimation standard is generated based on the relationship between the value of the data set and information regarding the neutral position deviation of the first movable part when the control valve or a control valve of the same type as the control valve is used within a predetermined period. The state estimating device according to claim 5, wherein the state estimating device is the estimation criterion is generated based on a relationship between a feature amount calculated from values of the data set and information regarding a neutral position deviation of the first movable part,
The state estimation device according to claim 6 , wherein the estimation unit calculates the feature amount from the detection information acquired by the acquisition unit, and estimates the neutral position deviation of the first movable part based on the calculated feature amount. The acquisition unit acquires the data set in a usage environment similar to the usage environment of the control valve or a control valve of the same type as the control valve when the value of the data set used to generate the estimation standard was recorded. The state estimation device according to any one of claims 5 to 7, which acquires detection information. The state estimation according to claim 8, wherein the usage environment is a situation in which the control target of the control valve or a control valve of the same type as the control valve is stopped, and an operation to prevent the first movable part from sticking is being performed. Device. The state estimating device according to any one of claims 1 to 9, further comprising a notifying unit that notifies when the estimating unit estimates that there is a shift in the neutral position of the first movable part. The state estimation device according to claim 10, wherein the notification unit estimates and notifies the user of the time until the first movable part is displaced from its neutral position. The state estimating device according to any one of claims 1 to 11, wherein the control valve is a control valve for adjusting the amount of fuel supplied to the engine. the control valve is provided for each cylinder of a multi-cylinder engine to adjust the amount of fuel supplied to each cylinder,
The state estimating device according to claim 12 , wherein the estimating unit estimates the neutral position deviation of the first movable part by comparing information relating to each of a plurality of control valves provided in the plurality of cylinders. a first movable part in a control valve whose position changes according to a control signal for specifying the position, and whose flow rate of working fluid is controlled according to the position;
The target position or actual position of the first movable part, the target position and actual position of the second movable part whose position changes according to the flow rate of the working fluid, and the engine exhaust temperature or cylinder pressure controlled by the control valve. an acquisition unit that acquires detection information of a dataset including;
and an estimating section that estimates an abnormality in which the first movable section deviates from a neutral position for minimizing the flow rate of the working fluid based on the detection information acquired by the acquiring section. Computer,
an acquisition unit that acquires detection information of a data set including a target position or an actual position of a first movable part in a control valve that controls a flow rate of a working fluid in accordance with a position of the first movable part, a target position and an actual position of a second movable part that changes its position in accordance with the flow rate of the working fluid, and an exhaust temperature or a cylinder pressure of an engine controlled by the control valve;
A state estimation program for causing the device to function as an estimation unit that estimates an abnormality in which the first movable part deviates from a neutral position for minimizing the flow rate of the working fluid based on the detection information acquired by the acquisition unit. On the computer,
A step of acquiring detection information of a data set including a target position or an actual position of a first movable part in a control valve that controls a flow rate of a working fluid depending on a position of the first movable part, a target position and an actual position of a second movable part that changes its position depending on the flow rate of the working fluid, and an exhaust temperature or a cylinder pressure of an engine controlled by the control valve;
and a step of estimating an abnormality in which the first movable part deviates from a neutral position for minimizing the flow rate of the working fluid based on the detection information acquired in the acquiring step. an acquisition unit that acquires detection information of a data set including an actual position and a target position of a second movable part that changes its position according to the flow rate of the working fluid in a control valve that controls the flow rate of the working fluid according to a position of a first movable part, and an exhaust temperature or a cylinder pressure or a maximum pressure value of an engine controlled by the control valve;
and an estimation unit that estimates a positional deviation of a detection unit for detecting the position of the second movable part based on the detection information acquired by the acquisition unit. The state estimating device according to claim 17, wherein the estimating unit estimates the positional deviation of the sensing unit using an estimation standard for estimating the positional deviation of the sensing unit based on the detection information of the data set. The state estimation device according to claim 18, wherein the estimation criterion is generated based on the relationship between the value of the data set and information regarding the positional deviation of the detector when the control valve or a control valve of the same type as the control valve is used within a predetermined period of time. 20. The state estimating device according to claim 19, wherein the information regarding the positional deviation of the detection unit is the presence, degree, mode, or cause of the positional deviation of the detection unit. the estimation criterion is generated based on a relationship between a feature amount calculated from values of the data set and information regarding the positional deviation of the detection unit,
21. The state estimation device according to claim 18, wherein the estimation unit calculates the feature amount from the detection information of the data set acquired by the acquisition unit, and estimates a positional deviation of the detection unit based on the calculated feature amount. The acquisition unit acquires the data set in a usage environment similar to the usage environment of the control valve or a control valve of the same type as the control valve when the value of the data set used to generate the estimation standard was recorded. The state estimation device according to any one of claims 18 to 21, which acquires detection information. The state estimation device according to claim 22, wherein the usage environment is a situation in which the control valve or a control target of the same type as the control valve is stopped, and an operation to prevent sticking of the first movable part and the second movable part is being performed. The state estimating device according to any one of claims 17 to 23, further comprising a notifying unit that notifies when the estimating unit estimates that the position of the detecting unit has shifted. 25. The state estimating device according to claim 24, wherein the notifying unit estimates and notifies the time until the position of the detecting unit deviates by a certain amount or more. A state estimation device according to any one of claims 17 to 25, wherein the control valve is a control valve for adjusting the amount of fuel supplied to the engine. The control valve is provided in each cylinder in order to adjust the amount of fuel supplied to each cylinder of an engine having a plurality of cylinders,
27. The state estimating device according to claim 26, wherein the estimating section estimates the positional deviation of the second movable section by comparing information regarding each of the plurality of control valves provided in the plurality of cylinders. a first movable part whose position changes according to a control signal for specifying the position, and whose flow rate of working fluid is controlled according to the position;
a data set including an actual position and a target position of a second movable part whose position changes according to the flow rate of the working fluid, and an exhaust temperature of the engine, a cylinder pressure, or a maximum value of the pressure controlled by the second movable part; an acquisition unit that acquires detection information of the
A control valve comprising: an estimation section that estimates a positional shift of a detection section for detecting the position of the second movable section based on the detection information acquired by the acquisition section. Computer,
an acquisition unit that acquires detection information of a data set including an actual position and a target position of a second movable part that changes its position depending on the flow rate of the working fluid in a control valve that controls the flow rate of the working fluid depending on a position of a first movable part, and an exhaust temperature or a cylinder pressure or a maximum pressure value of an engine controlled by the control valve;
A state estimation program for causing the device to function as an estimation unit that estimates a positional deviation of a detection unit for detecting the position of the second movable part based on detection information acquired by the acquisition unit. On the computer,
A step of acquiring detection information of a data set including an actual position and a target position of a second movable part, the position of which changes according to the flow rate of the working fluid, in a control valve that controls the flow rate of the working fluid according to a position of a first movable part, and an exhaust temperature or a cylinder pressure or a maximum pressure value of an engine controlled by the control valve;
a step of estimating a positional deviation of a detection unit for detecting the position of the second movable part based on the detection information acquired in the acquiring step.

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