Estimation method, computer program, estimation device, and estimation system

Abstract

To provide an estimation method or the like, capable of estimating the state of a film molding machine.SOLUTION: An estimation method comprises: acquiring detection data pertaining to the state of a film molding machine, the data being detected by a detector; and inputting the acquired detection data into an estimation model that estimates, upon input of the detection data pertaining to the state of the film molding machine, a malfunction or service life of the film molding machine, thereby estimating the malfunction or service life of the film molding machine.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an estimation method, a computer program, an estimation device, and an estimation system. [Background technology]

[0002] 2. Description of the Related Art Film forming machines that form a film by solidifying molten resin extruded from a die outlet are widely used, and techniques relating to such forming machines have been proposed.

[0003] For example, Patent Document 1 discloses a film forming device that can determine whether or not there is an abnormality in the winding shape of a film roll body based on an axial change in the radius of the film roll body. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2019-177508 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology described in Patent Document 1 is a technology related to determining the state of a molded body, and is not a technology for estimating the state of a film forming machine. A technology that can estimate the state of a film forming machine is desired.

[0006] An object of the present disclosure is to provide an estimation method etc. capable of estimating the state of a film forming machine. [Means for solving the problem]

[0007] An estimation method according to one aspect of the present disclosure acquires detection data regarding the state of a film forming machine detected by a detector, and estimates an abnormality or lifespan of the film forming machine by inputting the acquired detection data into an estimation model that estimates an abnormality or lifespan of the film forming machine when detection data regarding the state of the film forming machine is input.

[0008] A computer program according to one embodiment of the present disclosure acquires detection data regarding the condition of a film forming machine detected by a detector, and inputs the acquired detection data into an estimation model that estimates an abnormality or lifespan of the film forming machine when detection data regarding the condition of the film forming machine is input, thereby causing a computer to execute a process of estimating an abnormality or lifespan of the film forming machine.

[0009] An estimation device according to one aspect of the present disclosure includes an acquisition unit that acquires detection data regarding the state of a film forming machine detected by a detector, and an estimation unit that estimates an abnormality or lifespan of the film forming machine by inputting the acquired detection data into an estimation model that estimates an abnormality or lifespan of the film forming machine when detection data regarding the state of the film forming machine is input.

[0010] An estimation system according to one aspect of the present disclosure includes a film forming machine, a detector, and an estimation device. The estimation device includes an acquisition unit that acquires detection data regarding the state of the film forming machine detected by the detector, and an estimation unit that estimates an abnormality or lifespan of the film forming machine by inputting the acquired detection data into an estimation model that estimates an abnormality or lifespan of the film forming machine when detection data regarding the state of the film forming machine is input. Effect of the Invention

[0011] According to the present disclosure, the state of a film forming machine can be estimated. [Brief description of the drawings]

[0012] [Figure 1]FIG. 1 is a schematic diagram of an estimation system according to a first embodiment. [Diagram 2] FIG. 2 is a block diagram showing an example of the configuration of a data collection device. [Diagram 3] FIG. 2 is a block diagram showing a configuration example of an estimation device. [Figure 4] 11 is a diagram showing an example of the contents of information stored in a molding information DB. FIG. [Diagram 5] FIG. 2 is an explanatory diagram showing an overview of an estimation model. [Figure 6] 13 is a flowchart illustrating an example of a processing procedure executed by the estimation device. [Figure 7] 13 is a schematic diagram showing an example of a screen showing the estimation results of an abnormality and a life span displayed on a terminal device; FIG. [Figure 8] 10 is a flowchart illustrating an example of a processing procedure executed by the estimation device of the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] The present disclosure will be specifically described with reference to the drawings showing the embodiments thereof. (First embodiment) 1 is a schematic diagram of an estimation system 100 according to a first embodiment. The estimation system 100 mainly includes a film forming machine 1, a plurality of detectors 2, and an estimation device 4. The estimation device 4 is communicatively connected to a data collecting device 3 and terminal devices 5a and 5b via a network N1 such as the Internet.

[0014] 1 shows one film forming machine 1 and one data collecting device 3, but a plurality of data collecting devices 3 (not shown) are connected to the estimation device 4 via a network N1. One or more film forming machines 1 are connected to the data collecting device 3. Each data collecting device 3 and the one or more film forming machines 1 connected to the data collecting device 3 are installed, for example, in the same factory.

[0015] The estimation device 4 is a device capable of various information processing and information transmission and reception, such as a server computer, a personal computer, a quantum computer, etc. The estimation device 4 collects molding information of each of the multiple film forming machines 1, and estimates an abnormality or lifespan of each film forming machine 1 based on the collected molding information. The molding information includes operation data and detection data, which will be described later. The estimation device 4 transmits the estimation result of the abnormality or lifespan to the terminal devices 5a, 5b.

[0016] The terminal devices 5a and 5b are information processing terminals having communication and display functions, such as a personal computer, a tablet terminal, or a smartphone. The terminal device 5a is a terminal used by a user of the film forming machine 1. The user of the film forming machine 1 includes an organization such as a corporation that owns the film forming machine 1, and an operator belonging to the organization. The terminal device 5b is a terminal used by a service provider such as a sales representative or maintenance manager related to the user's film forming machine 1. The number of the terminal devices 5a and 5b may be two or more.

[0017] <Film forming machine 1> The film forming machine 1 forms a thin resin film by extrusion molding. The film forming machine 1 includes an extruder 11, a casting device 12, an MD stretching device 13, a TD stretching device 14, a winder 15, and a control device 16.

[0018] The extruder 11 is, for example, a single-screw extruder or a twin-screw extruder, and includes a raw material supply section 111 into which a resin raw material is introduced, a cylinder 112, a screw 113, and a die 114. The raw material supply section 111 includes a hopper for storing the resin raw material and a feeder for introducing the resin raw material, and discharges the resin raw material introduced into the hopper from the feeder into the cylinder 112 at a constant flow rate. The screw 113 is rotatably inserted into the hole of the cylinder 112, and conveys the resin raw material discharged from the raw material supply section 111 in the extrusion direction (rightward in FIG. 1), melting and kneading the resin raw material. The screw 113 rotates at a predetermined rotation speed by a driving force output from a driving device 115 including, for example, a motor and a reducer (not shown). The extruder 11 extrudes the molten resin raw material into a film shape from a narrow gap (lip) at the tip of the die 114. The die 114 is, for example, a T-die.

[0019] The casting device 12 includes multiple casting rolls 121 for cooling and shaping the high-temperature molten material extruded from the die 114. The casting rolls 121 rotate at a predetermined rotation speed by a driving force output from a driving device 122 including, for example, a motor and a reducer (not shown). The multiple casting rolls 121 include a first roll 121a and a second roll 121b.

[0020] The first roll 121a is, for example, a metal roll having a temperature adjustment unit (not shown) for cooling the molten material, and is supported below the die 114. The first roll 121a sandwiches the film-like molten material extruded from the die 114 between itself and the second roll 121b, and forms the film-like molten material into a film (sheet) shape while cooling it in a short time together with the second roll 121b. The casting device 12 controls the film thickness within a predetermined range. This results in an unstretched film. The temperature adjustment method for the first roll 121a is not particularly limited, but may include, for example, a method using a heat medium such as air, water, or oil, or a method using an electric heater or dielectric heating. In the example shown in FIG. 1, the multiple casting rolls 121 further include a roll for cooling or transporting the molten material.

[0021] The MD stretching device 13 includes multiple tension rolls 131, and the unstretched film transported from the casting device 12 is inserted between the tension rolls 131 to stretch it in the MD direction. The MD direction is the direction along the film transport direction, and is also called the longitudinal direction. The tension rolls 131 rotate at a predetermined number of revolutions by a driving force output from a driving device 132 including, for example, a motor and a reducer (not shown).

[0022] The tension rolls 131 include a heating roll having a temperature adjustment unit for heating the film, and a cooling roll having a temperature adjustment unit for cooling the film. The temperature of the tension rolls 131 can be adjusted in the same manner as the above-mentioned cast roll 121. The film is heated to a predetermined temperature range in which it can be stretched while in contact with the heating roll, and then stretched in the MD direction by utilizing the difference in rotation speed of each cooling roll. The stretch ratio in the MD direction can be adjusted by the speed ratio of each tension roll 131. Note that FIG. 1 is merely an example, and the number of tension rolls 131 is not limited to the example shown in FIG. 1.

[0023] The TD stretching device 14 stretches in the TD direction the film that has been longitudinally stretched by the MD stretching device 13. The TD direction is a direction intersecting the film transport direction (the width direction of the film), and is also called the transverse direction.

[0024] The TD stretching device 14 is, for example, a tenter stretching device, which has a heating device such as a hot air blower (not shown) and heats the film to a predetermined temperature range at which the film can be stretched, and stretches the film laterally.

[0025] The TD stretching device 14 includes an oven 141 having a heating device such as a hot air blower, and a traveling mechanism including a pair of left and right rails 142 and a plurality of links 143 arranged on the rails 142 is provided inside the oven 141. The rails 142 are arranged so as to expand in the TD direction toward the downstream direction in the MD direction. The plurality of links 143 form an endless chain. The endless chain formed by the plurality of links 143 travels on the rails 142 at a predetermined speed by a rotational driving force output from a link driving device 145 arranged outside the oven 141.

[0026] A clip 144, which is a film gripper, is attached to each of the links 143. The number of links 143 and the number of clips 144 provided on each of the left and right rails 142 are the same. That is, the TD stretching device 14 includes a pair of left and right links 143 and a pair of left and right clips 144. The pair of links 143 and the pair of clips 144 are arranged on the rails 142 so as to be located at both the left and right ends of the film, that is, so as to be aligned in a row along the TD direction. The pair of left and right clips 144 are adjusted in advance by synchronizing the driving devices so that no phase difference occurs between the clips 144, that is, so that the conveying periods are the same. The TD stretching device 14 may be of a bearing clip type or a slide clip type.

[0027] At the entrance of the TD stretching device 14, the film is gripped at both ends by clips 144. The clips 144 grip the film and are transported together with the links 143 in the film feed direction, and transport the film in the film feed direction by traveling on the rails 142. Both sides of the film in the oven are heated by air blown from hot air blowers provided above and below the traveling mechanism, and the film is stretched in the width direction as the clips 144 gripping both ends of the film move. The stretch ratio in the TD direction can be adjusted by the width of the rails 142 in the TD direction. The oven 141 is divided into a plurality of zones in the transport direction, for example, and the set temperature or the amount of air can be adjusted for each zone. The above zones include a preheating zone, a stretching zone, and a heat setting zone.

[0028] The clip 144 releases the film at the exit of the TD stretching device 14. The film stretched in the width direction is taken up by the winder 15 via a take-up machine (not shown). The TD stretching device 14 is not limited to a clip tenter, and may be, for example, a pin tenter having pins as film gripping devices. The above description of the structure of each part of the TD stretching device 14 is an example, and the configurations of temperature adjustment, air volume adjustment, link 143 of rail 142, etc. are not particularly limited.

[0029] The film forming machine 1 normally operates continuously without stopping, except at specific times such as when an apparatus abnormality occurs or during a preset stop period.

[0030] The control device 16 is a computer that controls the operation of the film forming machine 1. The control device 16 includes a control unit such as a central processing unit (CPU) (not shown), a storage unit such as a read only memory (ROM), a random access memory (RAM), or a hard disk, a communication unit that transmits and receives information to and from an external device via wired or wireless communication, a display unit such as a liquid crystal display or an organic electroluminescence (EL) display that displays various images to the user, and an operation unit that accepts operations from the user.

[0031] The control device 16 is a computer that controls the operation of the film forming machine 1. The control device 16 accepts settings of molding condition parameters for the film forming machine 1 through an operation unit or a communication unit. The control device 16 also receives detection data detected by the detector 2 from the data collecting device 3 through the communication unit. The control device 16 controls the operation of the film forming machine 1 based on the accepted molding condition parameters and detection data. The control device 16 also transmits operation data indicating the operating state of the film forming machine 1 to the data collecting device 3 through the communication unit. The data collecting device 3 transmits the accepted operation data to the estimation device 4. Note that the operation data may be transmitted to the estimation device 4 directly from devices such as a motor and an inverter in the film forming machine 1 without going through the collection device 3.

[0032] In the film forming machine 1, forming condition parameters that define forming conditions are set, and the film forming machine 1 operates in accordance with the forming condition parameters. Examples of the molding condition parameters include the feeder supply amount in the extruder 11, the number of revolutions or the rotation speed of the screw 113, the number of revolutions or the rotation speed of the motor of the drive device 115, and the lip opening of the die 114, the number of revolutions or the rotation speed of the cast roll 121 in the casting device 12, the number of revolutions or the rotation speed of the motor of the drive device 122, and the discharge amount of the film, the number of revolutions or the rotation speed of the tension roll 131 in the MD stretching device 13, the number of revolutions or the rotation speed of the motor of the drive device 132, the stretching ratio, and the discharge amount of the film, and the line speed of the film conveying line in the TD stretching device 14, the temperature in the oven 141 (particularly for each zone), the pressure in the oven 141 (particularly for each zone), the air volume or wind speed at the entrance and exit of the oven 141 (particularly the air blowing nozzle of the hot air blowing device near the entrance and exit), the stretching angle, the stretching ratio, and the discharge amount of the film. Note that instead of the discharge amount of the film, the film speed, the film width, and the film thickness may be set. The optimal molding condition parameters change from time to time during operation depending on the environment of the film molding machine 1 and the state of the molded product.

[0033] Examples of the operation data of the film forming machine 1 include the feeder supply amount in the extruder 11, the temperature of the cylinder 112, the rotation speed of the screw 113, the rotation speed of the motor, the motor torque, and the motor current, the rotation speed of the casting roll 121 in the casting device 12, the rotation speed of the motor, the motor torque, and the motor current, the rotation speed of the tension roll 131 in the MD stretching device 13, the motor rotation speed, the motor torque, and the motor current, and the line speed and line tension in the TD stretching device 14. The operation data corresponds to the set value of each item, i.e., the control value by the control device 16.

[0034] <Detector 2> The detector 2 is a sensor that chronologically detects physical quantities that indicate the state of the film forming machine 1. The state of the film forming machine 1 detected by the detector 2 includes the state of the film forming machine 1 itself as well as the state of the film (resin) formed by the film forming machine 1. The detector 2 is connected to the data collecting device 3 by wire or wirelessly, and outputs detection data obtained by detection to the data collecting device 3 directly or indirectly.

[0035] The estimation system 100 includes a plurality of detectors 2 that detect various detection data. Some of the plurality of detectors 2 are connected to a data collecting device 3, and the data collecting device 3 acquires the detection data from the detectors 2. Some of the plurality of detectors 2 are connected to a control device 16, and the data collecting device 3 acquires the detection data from the detectors 2 via the control device 16. Note that in FIG. 1, connection lines between some of the detectors 2 and the data collecting device 3 or the control device 16 are omitted from illustration.

[0036] The detectors 2 include those provided for the purpose of detecting sensor values ​​necessary for controlling the operation of the film forming machine 1, and those provided for the purpose of detecting sensor values ​​necessary for carrying out the estimation of an abnormality or lifespan of the film forming machine 1 by the present estimation method. The detectors 2 may be configured separately from the film forming machine 1, or may be configured to be incorporated into the film forming machine 1.

[0037] Examples of detector 2 include an acceleration sensor, a speed sensor, a displacement sensor, an AE (Acoustic Emission) sensor, a viscometer, a gel counter, a strain sensor, a crystal orientation sensor, anemometer, anemometer, a thermometer, a surface potential sensor, a laser sensor, an infrared sensor, an X-ray sensor, a position sensor, a tension sensor, a load cell, a torque sensor, an encoder, a current sensor, a tachometer, an angle sensor, a pressure gauge, a weight gauge, a length measuring sensor, a photosensor (camera), a flow meter, a voltmeter, a timer, a wattmeter, etc.

[0038] Examples of detection data detected by detector 2 include acceleration, speed, displacement, viscosity, number of gels, strain, degree of crystal orientation, wind speed, air volume, temperature, charge amount, position, phase difference, tension, torque, rotation speed, angle, pressure, weight, length, thickness, image, flow rate, current, voltage, time, force, power consumption, etc. In this specification, detection data detected by detector 2 is not limited to sensor values ​​directly detected by detector 2, but also includes calculated values ​​indirectly calculated from the sensor values.

[0039] Examples of detection data related to the extruder 11 and the detector 2 for the detection data include an acceleration sensor that detects the acceleration (vibration) of the reducer, a tachometer that detects the rotation speed of the screw 113 or the motor, a torque sensor that detects the torque of the motor, a current sensor that detects the current of the motor, a viscometer that detects the resin viscosity (particularly the resin viscosity immediately after discharge from the die 114), a contact thermometer or a non-contact thermographic camera that detects the temperature inside the cylinder 112 or the resin (particularly the resin immediately after discharge from the die 114), a laser displacement sensor that detects the lip opening of the die 114, and a weight scale that detects the feeder supply amount.

[0040] Examples of detection data related to the casting device 12 and the detector 2 for the detection data include, for example, an acceleration sensor that detects the acceleration (vibration) of the bearing of the casting roll 121, a speed sensor that detects the rotational speed of the casting roll 121 or the motor, a tachometer that detects the rotation speed of the casting roll 121 or the motor, a torque sensor that detects the torque of the motor, a current sensor that detects the current of the motor, a thermometer or thermographic camera that detects the temperature of the casting roll 121 or the film, a laser sensor that detects the width of the film, a laser thickness sensor that detects the thickness of the film, and a gel counter that detects the number of gels in the film (particularly the film downstream of the casting roll 121).

[0041] Examples of detection data related to the MD stretching device 13 and the detector 2 for the detection data include an acceleration sensor that detects the acceleration (vibration) of the bearing of the tension roll 131, a tachometer that detects the rotation speed of the tension roll 131 or the motor, a speed sensor that detects the rotation speed of the tension roll 131 or the motor, a torque sensor that detects the torque of the motor, a current sensor that detects the current of the motor, a thermometer or thermographic camera that detects the temperature of the tension roll 131 or the film, a laser sensor that detects the width of the film, and a laser sensor that detects the thickness of the film.

[0042] Examples of the detection data related to the TD stretching device 14 and the detector 2 for the detection data include an acceleration sensor that detects the acceleration (vibration) of the TD stretching device 14 (particularly the rail 142 near the start and end points of the stretching zone in the oven 141), a velocity sensor that detects the velocity (vibration), a displacement sensor that detects the displacement (vibration), an AE sensor that detects elastic waves, a strain sensor that detects the distortion of the clip 144, a laser sensor that detects the phase difference (shift in the passing time of the reference position) between a pair of left and right clips 144, a photo sensor or a photoelectric sensor, a thermometer or a thermography camera that detects the temperature in the oven 141 (particularly for each zone), a pressure gauge that detects the pressure in the oven 141 (particularly for each zone), and a sensor that detects the air volume at the entrance and exit of the oven 141 (particularly the air blowing nozzle of the hot air blowing device near the entrance and exit). Examples of such sensors include an air flow meter that detects the wind speed at the entrance and exit of oven 141 (particularly the air blowing nozzle of the hot air blowing device near the entrance and exit), a pressure gauge that detects the pressure at the entrance and exit of oven 141 (particularly the air blowing nozzle of the hot air blowing device near the entrance and exit), a tachometer that detects the fan rotation speed in the hot air blowing device, a speed sensor that detects the line speed of the film transport line, a tension sensor that detects the tension of the film transport line or clip 144, a crystal orientation sensor that detects the degree of crystal orientation of the film (particularly the film at the most downstream of TD stretching device 14), a gel counter that detects the number of gels in the film (particularly the film at the most downstream of TD stretching device 14), a laser sensor that detects the width of the film, and a laser sensor that detects the thickness of the film.

[0043] An example of the detection data related to the winder 15 and the detector 2 for the detection data is a surface potential sensor that detects the amount of charge on the film.

[0044] Detector 2 is provided in an appropriate location on film forming machine 1 or in the vicinity of film forming machine 1 so as to detect the state of the member and film at a desired position in film forming machine 1. Detector 2 preferably has heat resistance, water resistance, oil resistance, etc., depending on the installation position. Note that detector 2 and the contents of the detection data are not limited to the above-mentioned example. In addition, the above-mentioned detection data is not limited to those detected by detector 2 exemplified in combination, and detector 2 is not limited to those for detecting the detection data exemplified in combination.

[0045] <Data Collection Device 3> 2 is a block diagram showing an example of the configuration of the data collection device 3. The data collection device 3 is a computer, and includes a control unit 31, a storage unit 32, a communication unit 33, and a data input unit 34. The storage unit 32, the communication unit 33, and the data input unit 34 are connected to the control unit 31. The data collection device 3 is, for example, a PLC (Programmable Logic Controller).

[0046] The control unit 31 includes an arithmetic processing circuit such as a CPU (Central Processing Unit), a multi-core CPU, an ASIC (Application Specific Integrated Circuit), and an FPGA (Field-Programmable Gate Array), an internal storage device such as a ROM (Read Only Memory) and a RAM (Random Access Memory), and an I / O terminal. The control unit 31 executes a control program stored in a storage unit 32 (described later) to collect molding information including detection data and operation data, and transmits the information to the estimation device 4. Each functional unit of the data collection device 3 may be realized by software, hardware, or a combination of these.

[0047] The storage unit 32 includes a non-volatile memory such as a hard disk, an EEPROM (Electrically Erasable Programmable ROM), a flash memory, etc. The storage unit 32 stores a control program for causing a computer to execute a process of collecting molding information.

[0048] The communication unit 33 includes a communication device that realizes communication via the networks N1 and N2. The communication unit 33 is connected to the control device 16 via the network N2, such as a LAN (Local Area Network). The control unit 31 can transmit and receive various information to and from the control device 16 via the communication unit 33. The communication unit 33 is also connected to the estimation device 4 on the cloud, which is the network N1, via a relay device (not shown). The relay device is, for example, a router, a gateway, etc. The control unit 31 can transmit and receive various information to and from the estimation device 4 via the relay device and the communication unit 33.

[0049] The data input unit 34 is an input interface that receives the signal output from the detector 2. The detector 2 is connected to the data input unit 34. The control unit 31 acquires the detection data output from the detector 2 via the data input unit 34 at any time.

[0050] The data collection device 3 and the control device 16 are not limited to being separate devices, and may be configured as one common device.

[0051] <Estimation device 4> 3 is a block diagram showing an example of the configuration of the estimation device 4. The estimation device 4 includes a control unit 41, a storage unit 42, and a communication unit 43. The storage unit 42 and the communication unit 43 are connected to the control unit 41. The estimation device 4 may be configured to perform distributed processing using a plurality of computers, may be realized by a plurality of virtual machines provided in one server, or may be realized by using a cloud server.

[0052] The control unit 41 has an arithmetic processing circuit such as a CPU, a multi-core CPU, an ASIC, an FPGA, etc., an internal storage device such as a ROM, a RAM, etc., an I / O terminal, etc. Each functional unit of the estimation device 4 may be realized by software, hardware, or a combination thereof.

[0053] The storage unit 42 includes a non-volatile memory such as a hard disk, a flash memory, or an SSD (Solid State Drive). The storage unit 42 may be an external storage device connected to the estimation device 4. The storage unit 42 stores various computer programs and data referenced by the control unit 41. The storage unit 42 of this embodiment stores a program 4P for causing a computer to execute processing related to the estimation of an abnormality or lifespan of the film forming machine 1, and a molding information DB (Data Base) 421 and an estimation model 422 required for executing the program 4P. The molding information DB 421 is a database that stores molding information received from each data collection device 3. The estimation model 422 is a learning model generated by machine learning. It is assumed that the estimation model 422 is used as a program module constituting a part of artificial intelligence software.

[0054] A computer program (computer program product) including the program 4P may be provided by a non-transitory recording medium 4A on which the computer program is readably recorded. The storage unit 42 stores the computer program read from the recording medium 4A by a reading device (not shown). The recording medium 4A is, for example, a magnetic disk, an optical disk, a semiconductor memory, or the like. The computer program may also be downloaded from an external server connected to a communication network and stored in the storage unit 42. The program 4P may be a single computer program or may be composed of multiple computer programs, and may be executed on a single computer or multiple computers interconnected by a communication network.

[0055] The communication unit 43 includes a communication device that realizes communication via the network N1. The control unit 41 can transmit and receive various information between the data collection device 3 and the terminal devices 5a and 5b via the communication unit 43.

[0056] <Molding information DB421> FIG. 4 is a diagram showing an example of the contents of information stored in the molding information DB 421. The molding information DB 421 stores records in which information such as an equipment ID, date and time, operation data, and detection data is linked to, for example, a data ID as a key. The equipment ID represents an equipment identifier of the film molding machine 1. The date and time represent the year, month, date and time when the operation data or detection data stored as a record was obtained. The operation data includes the above-mentioned multiple types of operation data. The detection data includes the above-mentioned multiple types of detection data. Every time the estimation device 4 receives operation data and detection data from the data collection device 3, it stores them in chronological order in the molding information DB 421. The contents of the molding information DB 421 are updated as needed.

[0057] <Estimated Model 422> FIG. 5 is an explanatory diagram showing an outline of the estimation model 422. FIG. 5 shows an estimation model 422 composed of a first estimation model 422a and a second estimation model 422b as an example. The first estimation model 422a is a learning model that receives molding information including detection data and operation data of the film forming machine 1 as input, and outputs abnormality presence / absence data indicating the presence / absence of an abnormality in the film forming machine 1. The second estimation model 422b is a learning model that receives molding information including detection data and operation data of the film forming machine 1 as input, and outputs failure probability data indicating the failure probability of the film forming machine 1 at a predetermined time point. Since the first estimation model 422a and the second estimation model 422b have the same configuration, the configuration will be described below by taking the first estimation model 422a as an example.

[0058] The first estimation model 422a is, for example, a neural network. The first estimation model 422a includes an input layer to which shaping information is input, an output layer to which anomaly presence / absence data or failure probability data is output, and an intermediate layer (hidden layer) to which features are extracted. The intermediate layer may include a convolutional layer, a pooling layer, a fully connected layer, and the like. The intermediate layer has a plurality of nodes to extract features of the input data, and transfers the features extracted using various parameters to the output layer. When shaping information is input to the input layer, a calculation is performed in the intermediate layer using the learned parameters, and output information indicating anomaly presence / absence data or failure probability data is output from the output layer.

[0059] The above-mentioned multiple types of detection data and operation data are input to the input layer of the first estimation model 422a. The detection data includes detection data detected by the detector 2 for state estimation. The detection data may further include detection data detected by the detector 2 for operation control.

[0060] In this embodiment, various feature quantities calculated from the detection data and operation data are input as input information to the first estimation model 422a. The detection data and operation data used to calculate the feature quantities include the various detection data and operation data described above. The detection data and operation data input to the first estimation model 422a may be a combination of appropriate detection data and operation data selected from the detection data and operation data exemplified above. The detection data and operation data are, for example, time-series data for a part of the entire period from the start of operation to the time of data acquisition or for a predetermined number of detections. The number of pieces of detection data and operation data may be one cycle of the film forming machine 1. By using a group of data at multiple points in time, it is expected that noise can be reduced and estimation accuracy can be improved.

[0061] Incidentally, by inputting the features of the detection data and driving data into the first estimation model 422a, the features of each data are reflected and the estimation accuracy of the model is expected to be improved, which is preferable, but the detection data and driving data themselves may also be input into the first estimation model 422a.

[0062] Examples of the feature values ​​obtained from the detection data and the operation data include a feature value representing a time series feature such as RMS, which is the root mean square of various data, RMS, which is the root mean square of the time-synchronous average of various data, an energy ratio of the time-synchronous average of various data, a feature value representing a feature of a frequency component such as amplitude, intensity, frequency, image data, etc. obtained by frequency analysis such as Fourier transform, fast Fourier transform, and Hilbert spectrum analysis of various data, and a value based on the feature values ​​such as a time derivative and difference of the feature values. Different feature values ​​may be calculated for each of the detection data and the operation data. The estimation device 4 stores in advance in the storage unit 42, for example, a table (not shown) in which a formula used to calculate a feature value and a data item used to calculate the feature value are associated with each other for each feature value.

[0063] The output layer of the first estimation model 422a has a node that outputs anomaly presence / absence data indicating the presence or absence of anomaly. Note that the estimation of anomaly is not limited to being indicated by the presence or absence of anomaly. The estimation model 422 may be configured to output, for example, an anomaly degree that indicates the degree of anomaly as a numerical value, or an anomaly level that indicates the degree of anomaly at multiple levels.

[0064] The second estimation model 422b has the same configuration as the first estimation model 422a, and various feature quantities calculated from the detection data and driving data are input. The feature quantity items input to the first estimation model 422a may be the same as or different from the feature quantity items input to the second estimation model 422b. The detection data and driving data items used to calculate the feature quantities input to the first estimation model 422a may be the same as or different from the detection data and driving data items input to the second estimation model 422b. The second estimation model 422b may be configured to further use anomaly presence / absence data estimated by the first estimation model 422a as an input element.

[0065] The output layer of the second estimation model 422b has nodes that output failure probability data. The failure probability data represents the failure probability of the film forming machine 1 at a future point in time a predetermined period from the present, such as the probability that the film forming machine 1 will break down in three months. The failure probability represents the lifespan of the film forming machine 1. Note that the estimation model 422 may output a numerical value indicating the remaining lifespan of the film forming machine 1 (how long it will take for the film forming machine 1 to break down) as an estimate of the lifespan.

[0066] The first estimation model 422a can be generated by preparing training data in which data indicating the presence or absence of an abnormality is labeled for feature quantities obtained from the detection data and operation data, and by machine learning an untrained neural network using the training data. The estimation device 4 acquires training data including a data set in which detection data and operation data during past operation of the film forming machine 1 are associated with an identification result that identifies the actual presence or absence of an abnormality. It is preferable that the estimation device 4 collects a large amount of detection data and operation data related to multiple film forming machines 1 as the training data.

[0067] The estimation device 4 inputs the feature quantity included in the training data to the input layer of the first estimation model 422a, and obtains the identification result of the presence or absence of an abnormality output from the output layer through arithmetic processing in the intermediate layer. The estimation device 4 compares the identification result of the presence or absence of an abnormality output from the output layer with the identification result included in the training data, and optimizes parameters such as weights (coupling coefficients) between neurons using, for example, an error backpropagation method so that the identification result output from the output layer approaches a correct answer value. Before learning is started, the definition information describing the first estimation model 422a is assumed to have initial settings. When learning is completed by the error, the number of learning times, etc. satisfying a predetermined standard, optimized parameters are obtained. When learning is completed, the estimation device 4 stores the definition information regarding the trained first estimation model 422a in the storage unit 42 as the trained first estimation model 422a.

[0068] The second estimation model 422b can be generated in a similar manner. The above-mentioned process constructs the first estimation model 422a, which has been trained to be able to appropriately identify the presence or absence of an abnormality for the feature amount of the molding information, and the second estimation model 422b, which has been trained to be able to appropriately identify the failure probability for the feature amount of the molding information. Note that the configurations of the first estimation model 422a and the second estimation model 422b are not limited to being the same, and for example, the first estimation model 422a may be a binary classification model, and the second estimation model 422b may be a regression model that outputs a continuous value.

[0069] The estimation model 422 is not limited to being generated and learned by the estimation device 4. The estimation model 422 may be a model that has been learned by an external server and transmitted to the estimation device 4 and stored in the storage unit 42. The estimation model 422 may be generated by an external server and learned by the estimation device 4.

[0070] The configuration of the first estimation model 422a and the second estimation model 422b is not limited as long as they can estimate an abnormality or a lifespan for the detection data and the driving data. The first estimation model 422a and the second estimation model 422b may be models based on other learning algorithms, such as a recurrent neural network (RNN), a convolution neural network (CNN), a graph neural network (GNN), a transformer, a support vector machine, a decision tree, an eXtreme Gradient Boosting (XGBoost), a logistic regression, a random forest, etc. The first estimation model 422a and the second estimation model 422b may be models learned by unsupervised learning. The first estimation model 422a and the second estimation model 422b may include a plurality of models and be constructed by applying ensemble learning such as bagging, boosting, or stacking. The first estimation model 422a and the second estimation model 422b may estimate an abnormality or a lifespan by a rule-based method.

[0071] In this embodiment, the first estimation model 422a for estimating the presence or absence of an abnormality and the second estimation model 422b for estimating the failure probability are configured separately, but a configuration may be adopted in which the presence or absence of an abnormality and the failure probability are estimated using one learning model. Furthermore, the estimation device 4 may include only one of the first estimation model 422a and the second estimation model 422b and estimate only one of the presence or absence of an abnormality and the failure probability.

[0072] The estimation model 422 can accurately estimate anomalies or lifespans by taking into account various characteristics by using a wide variety of detection data and operation data as input elements. For example, it is considered that the deterioration state of the equipment components can be taken into account by inputting mechanical vibrations indicated by acceleration. It is considered that the change in the properties of the resin can be taken into account by inputting the resin viscosity. It is considered that the quality abnormality due to the presence or absence of unmixed parts of the resin raw material and the presence or absence of foreign matter can be taken into account by inputting the number of gels. It is considered that the deterioration state of the equipment can be taken into account by inputting the clip distortion. It is considered that the quality abnormality can be taken into account by inputting the degree of crystal orientation. It is considered that the environment inside the oven 141 can be taken into account by inputting the wind speed, air volume, or pressure in the oven 141. It is considered that various temperature environments can be taken into account by inputting the temperature of each detection point and the detection target. It is considered that the film charge amount can be taken into account film winding failure and quality abnormality around the winder 15 can be taken into account. It is considered that the sliding load and quality change due to the equipment load and deterioration can be taken into account by inputting the synchronization error indicated by the phase difference between the left and right clips. It is believed that the wear state of the rail can be taken into account by inputting the line tension. It is believed that the deterioration state of the drive unit can be taken into account by inputting the motor torque, rotation number, and rotation speed of each detection point and detection target.

[0073] 6 is a flowchart showing an example of a processing procedure executed by the estimation device 4. The control unit 41 of the estimation device 4 executes the following processing in accordance with a program 4P stored in the storage unit 42. The control unit 41 starts the following processing at predetermined or appropriate intervals.

[0074] The control unit 41 of the estimation device 4, by functioning as an acquisition unit, acquires molding information including detection data and operation data during continuous operation through the data collection device 3, and stores the acquired molding information in the molding information DB 421 (step S10). The molding information is associated with the device ID of the film forming machine 1. The control unit 41 may acquire a plurality of detection data and operation data collectively, or may acquire each detection data and operation data individually at the timing when it is detected. The timing to receive the molding information may be a time period when the traffic volume in the network N2 is relatively low.

[0075] The control unit 41 determines whether or not to execute the estimation process (step S11). When it is determined that the estimation process is not to be executed because a preset start condition is not satisfied (S11: NO), the control unit 41 returns the process to step S11 and waits until the start condition is satisfied. The start condition may be that the detection data and the driving data are acquired a preset number of times, that the estimation timing is a preset timing, or the like.

[0076] When it is determined that the estimation process is to be executed by satisfying a preset start condition (S11: YES), the control unit 41 calculates various feature amounts based on the acquired time-series detection data and driving data (step S12). The control unit 41, for example, refers to a table in which the formulas used to calculate the feature amounts are associated with data items, and identifies the detection data and driving data to be used for calculating each feature amount.

[0077] The control unit 41, functioning as an estimation unit, inputs the calculated feature amounts of the detection data and the operation data to the first estimation model 422a (step S13), and obtains abnormality presence / absence data output from the first estimation model 422a (step S14). An abnormality in the film forming machine 1 is estimated by the processes of steps S13 and S14.

[0078] The control unit 41, functioning as an estimation unit, inputs the calculated feature amounts of the detection data and the operation data to the second estimation model 422b (step S15), and obtains failure probability data output from the second estimation model 422b (step S16). The life of the film forming machine 1 is estimated by the processes of steps S15 and S16.

[0079] The control unit 41 determines whether to output an alert (step S17). For example, if the estimation result corresponds to at least one of the presence of an abnormality and the failure probability being equal to or greater than a preset threshold, the control unit 41 determines to output an alert. If the estimation result corresponds to no abnormality and the failure probability is less than a preset threshold, the control unit 41 determines not to output an alert.

[0080] When it is determined that an alert should not be output (S17: NO), the control unit 41 ends the process. When it is determined that an alert should be output (S17: YES), the control unit 41 generates a screen showing the estimation results of the abnormality and lifespan of the film forming machine 1 (step S18). The screen showing the estimation results is an example of alert information that notifies of an abnormality and lifespan of the film forming machine 1. The control unit 41 transmits the generated screen showing the estimation results to the user's terminal device 5a and the service provider's terminal device 5b corresponding to the device ID of the film forming machine 1 (step S19). The control unit 41 may transmit the estimation results to the control device 16. The control unit 41 ends the series of processes.

[0081] The control unit 41 repeatedly executes the above-mentioned process during continuous operation of the film forming machine 1. By estimating abnormalities and lifespan while continuing the continuous operation based on the detection data and operation data detected during the continuous operation, the estimation results can be fed back to the operation of the film forming machine 1.

[0082] Although an example in which the estimation device 4 executes a series of processes has been described above, the processing entity of each process is not limited. Each process in the flowchart may be executed by, for example, the control device 16, the data collection device 3, or the terminal device 5. The estimation model 422 may be deployed in the control device 16 or the data collection device 3, and the anomaly and lifespan estimation process may be executed on the control device 16 or the data collection device 3 side.

[0083] In the above process, the order of the abnormality estimation process in steps S13 and S14 and the life estimation process in steps S15 and S16 may be interchanged or may be processed in parallel. Either the abnormality estimation process or the life estimation process may be omitted.

[0084] The estimation device 4 may re-learn the estimation model 422 based on the molding information obtained during continuous operation. The estimation device 4 receives, for example, from a service provider, the results of identifying an abnormality or lifespan for the molding information obtained during a predetermined period. The estimation device 4 performs re-learning using information obtained by labeling the obtained results of identifying an abnormality or lifespan for the molding information as training data, and updates the estimation model 422. The training data used for re-learning may include molding information obtained from multiple film molding machines 1.

[0085] The estimation device 4 may evaluate the accuracy of the estimation model 422 by an evaluation method such as cross-validation, and when the evaluation index satisfies a predetermined standard, update the estimation model 422. The estimation device 4 may receive a request for updating the estimation model 422 from a service provider. This makes it possible to improve the estimation accuracy of anomalies or life spans through the operation of this system.

[0086] 7 is a schematic diagram showing an example of a screen 70 showing the estimation results of anomalies and life spans displayed on the terminal devices 5a and 5b. The screen 70 has a result display section 701 that displays the estimation results of the presence or absence of anomalies and the failure probability.

[0087] The estimation device 4 acquires the estimation result by the estimation model 422, and displays the acquired presence or absence of anomaly and failure probability on the result display unit 701. The estimation device 4 also outputs alert information via the screen 70 by displaying on the screen 70 an alert display unit 702 that displays text or the like indicating that the estimation result of the anomaly or lifespan is abnormal.

[0088] The screen 70 also has a trend display section 703 that displays the trend of the feature quantity. The trend display section 703 includes a reception section 704 that receives a period and a type of feature quantity for displaying the trend, and a trend graph display section 705 that displays a trend graph. The reception section 704 has, for example, a pull-down menu. The user or the service provider can select an arbitrary period and type of feature quantity by opening the pull-down menu. The list of feature quantity types in the pull-down includes each feature quantity used in the input information of the estimation model 422.

[0089] When the estimation device 4 receives the selection of the period and the type of the feature through the terminal devices 5a and 5b, it reads out the detection data and the operation data for the selected period from the molding information D421. The estimation device 4 generates a time series graph showing the trend of the selected feature based on the read out detection data and operation data, and displays it on the trend display unit 703. Note that the trend display unit 703 may display the trends of the detection data and the operation data instead of or in addition to the feature.

[0090] Although the above describes an example in which alert information is output together with the estimation result, the estimation device 4 may output only the alert information notifying the terminal devices 5a and 5b of an abnormality in the film forming machine 1. The alert information may be in the form of, for example, a message, a voice, an alert sound, or the like.

[0091] Regardless of the presence or absence of an abnormality, the estimation device 4 may provide a screen 70 shown in Fig. 7 in response to a request from the terminal devices 5a, 5b. Each user or service provider can view and check the status of the film forming machine 1 owned by the user at any time by accessing the service provider site using a browser, for example.

[0092] According to this embodiment, the state of the film forming machine 1, such as an abnormality or lifespan of the film forming machine 1, can be accurately estimated based on various detection data obtained by the detector 2 that detects the state of the film forming machine 1. The estimation result makes it possible to accurately grasp the state of the film forming machine 1. In conventional film forming machines, the detection data by the detector 2 is mainly used for operation control, and in estimating the abnormality or lifespan, detection of the state of the film forming machine 1 during operation was not taken into consideration. In this embodiment, by installing the detector 2 for the purpose of maintaining the film forming machine 1, it is possible to collect more detection data that could not be obtained with conventional film forming machines 1 and use it for state analysis.

[0093] By collecting detection data during continuous operation of the film forming machine 1 and estimating abnormalities or lifespan based on the collected data, the condition of the film forming machine 1 can be grasped early, leading to smooth response. Since it is possible to respond before the condition deteriorates, it is possible to facilitate equipment maintenance and reduce production downtime.

[0094] By using the estimation model 422, it is possible to easily and accurately estimate an abnormality or life span of the film forming machine 1. By using a plurality of detection data representing the state of the film forming machine 1 as input information for the estimation model 422, it is possible to improve the accuracy of estimating an abnormality or life span. By performing pre-processing for calculating feature amounts on the detection data and operation data, it is possible to provide the estimation model 422 with information suitable for estimating an abnormality or life span, thereby improving the estimation accuracy.

[0095] The estimation device 4 can accumulate a huge amount of data on the cloud and provide trends of the accumulated huge amount of data and estimated results of abnormalities or lifespan based on the data to the terminal device 5. A user or a service provider can visually and clearly recognize the state of the film forming machine 1 at any time.

[0096] Second embodiment In the second embodiment, a configuration will be described that uses an estimation model 422 or feature amounts corresponding to the film forming machine 1. In the following embodiment, differences from the first embodiment will be mainly described, and configurations common to the first embodiment will be denoted by the same reference numerals and detailed descriptions thereof will be omitted.

[0097] The estimation device 4 of the second embodiment stores a plurality of types of estimation models 422 generated by different algorithms in the storage unit 42. Prior to estimating an abnormality or lifespan according to the molding information, the estimation device 4 determines the estimation model 422 to be used for estimating an abnormality or lifespan based on the type of film forming machine 1 owned by the user and the molding information collected from the film forming machine 1. The estimation model 422 can be determined based on the estimation accuracy of each estimation model 422. For example, the accuracy of each estimation model 422 is evaluated by an evaluation method such as cross-validation based on the estimation result of each estimation model 422 for the test data of the molding information in the film forming machine 1. The estimation model 422 is determined by selecting the estimation model 422 with the highest estimation accuracy from all the prepared estimation models 422.

[0098] The estimation device 4 may select multiple estimation models 422 with high estimation accuracy, and estimate the abnormality or lifespan using the selected multiple estimation models 422. The estimation device 4 estimates the abnormality or lifespan by, for example, combining the estimation results of the multiple types of estimation models 422 and integrating them by majority vote or weighting.

[0099] The estimation device 4 may generate an estimation model 422 specialized for estimating an abnormality or life span of the film forming machine 1 owned by the user, by fine-tuning a trained estimation model 422 generated in advance.

[0100] The estimation device 4 may also select a feature to be used as input information to the estimation model 422 from among a plurality of preset feature values ​​according to the type of the film forming machine 1 owned by the user. The feature to be used as input information can be determined, for example, based on the contribution of the feature value. For example, estimation is performed by the estimation model 422 using a plurality of preset feature values, and the contribution of the input data corresponding to each feature value is calculated using SHAP (SHapley Additive exPlanation), LIME (Local Interpretable Model-Agnostic Explanations), feature importance in a decision tree algorithm model, or the like. The estimation device 4 determines the feature to be used as input information by, for example, selecting a feature value whose calculated contribution value is equal to or greater than a predetermined value, or a predetermined number of feature values ​​in descending order of contribution value.

[0101] The estimation device 4 stores in the storage unit 42 the device ID of the determined estimation model 422 and the type of feature used in the input information in association with the device ID of the film forming machine 1 to which the estimation model 422 and the feature are applied.

[0102] FIG. 8 is a flowchart showing an example of a processing procedure executed by the estimation device 4 of the second embodiment.

[0103] The control unit 41 of the estimation device 4 acquires molding information including detection data and operation data during continuous operation in association with the device ID of the film forming machine 1, and stores the acquired molding information in the molding information DB 421 (step S21).

[0104] The control unit 41 determines whether or not to execute the estimation process (step S22). When it is determined that the estimation process is not to be executed because a preset start condition is not satisfied (S22: NO), the control unit 41 returns the process to step S22 and waits until the start condition is satisfied.

[0105] When it is determined that the estimation process is to be executed by satisfying the preset start condition (S22: YES), the control unit 41 selects the estimation model 422 associated with the acquired device ID of the film forming machine 1 (step S23). In step S23, the control unit 41 specifies the estimation model 422 corresponding to the acquired device ID based on the correspondence relationship between the film forming machine 1 and the estimation model 422 to be applied, which is stored in advance.

[0106] The control unit 41 calculates various characteristic amounts based on the acquired time-series detection data and operation data (step S24). When the characteristic amounts to be used are set for each type of film forming machine 1, the control unit 41 identifies the characteristic amount associated with the acquired device ID of the film forming machine 1 based on the correspondence between the film forming machine 1 and the characteristic amount to be applied that is stored in advance, and calculates the identified characteristic amount.

[0107] Thereafter, the control unit 41 executes the same process as in the first embodiment to estimate an abnormality and a life span using the selected estimation model 422 and the feature amount.

[0108] According to this embodiment, the estimation model 422 and feature values ​​according to the type of film forming machine 1 owned by the user are used, thereby making it possible to improve the accuracy of estimating an abnormality or lifespan. The configuration of each film forming machine 1 often differs significantly. By setting the type of estimation model 422 and the feature values ​​to be used according to the type of film forming machine 1, it is possible to accurately estimate an abnormality or lifespan according to the state of the film forming machine 1.

[0109] The embodiments disclosed herein are illustrative in all respects and should not be considered as limiting. The technical features described in each embodiment can be combined with each other, and the scope of the present invention is intended to include all modifications within the scope of the claims and the scope equivalent to the claims. The sequences shown in each embodiment are not limited, and the order of each process may be changed and multiple processes may be executed in parallel, as long as there is no contradiction. The subject of each process is not limited, and the process of each device may be executed by another device, as long as there is no contradiction.

[0110] The matters described in each embodiment can be combined with each other. In addition, the independent claims and dependent claims described in the claims can be combined with each other in any and all combinations regardless of the citation format. Furthermore, the claims use a format in which a claim cites two or more other claims (multiple claim format), but this is not limited to this. A multiple claim (multi-multi claim) that cites at least one multiple claim may also be used.

[0111] Regarding the above embodiment, the following supplementary notes are further disclosed. (Appendix 1) Obtaining detection data regarding the state of the film forming machine detected by the detector; The abnormality or lifespan of the film forming machine is estimated by inputting the acquired detection data into an estimation model that estimates the abnormality or lifespan of the film forming machine when the detection data regarding the state of the film forming machine is input. Estimation method. (Appendix 2) acquiring the detection data during continuous operation of the film forming machine; While the continuous operation is continued, an abnormality or a life span of the film forming machine is estimated based on the detection data. Estimation method described in Appendix 1. (Appendix 3) Deriving a feature amount based on the detection data; The abnormality or lifespan of the film forming machine is estimated by inputting the derived feature amount into the estimation model, which estimates the abnormality or lifespan of the film forming machine when the feature amount based on the detection data regarding the state of the film forming machine is input. The estimation method described in Appendix 1 or Appendix 2. (Appendix 4) Transmitting information indicating the estimated abnormality or lifespan of the film forming machine to a terminal device. 1. The estimation method according to any one of appendix 1 to appendix 3. (Appendix 5) generating information indicating a trend of the detection data based on the time series of the detection data; Transmitting the generated information indicating the trend of the detection data to a terminal device. 1. The estimation method according to any one of appendix 1 to appendix 4. (Appendix 6) The film forming machine includes an extruder, a casting device, an MD stretching device, a TD stretching device, and a winding device, The detection data includes a physical quantity related to the extruder, a physical quantity related to the casting device, a physical quantity related to the MD stretching device, a physical quantity related to the TD stretching device, or a physical quantity related to the winding device. 10. The estimation method according to any one of claims 1 to 5. (Appendix 7) The film forming machine includes an extruder, a casting device, an MD stretching device, a TD stretching device, and a winding device, The detection data includes at least one of the acceleration in the extruder, the resin viscosity in the extruder, the acceleration in the casting device, the number of gels in the film in the casting device, the acceleration in the MD stretching device, the acceleration in the TD stretching device, the wind speed in the TD stretching device, the air volume in the TD stretching device, the distortion of the film gripping tools in the TD stretching device, the phase difference between a pair of left and right film gripping tools in the TD stretching device, the degree of crystal orientation in the TD stretching device, the number of gels in the film in the TD stretching device, and the amount of electrostatic charge of the film in the winder. 13. The estimation method according to any one of claims 1 to 6. (Appendix 8) Detection data selected from a plurality of types of detection data set in advance is input to the estimation model. 13. The estimation method according to any one of claims 1 to 7. (Appendix 9) Prepare multiple types of estimation models generated by different algorithms, Select the estimation model to be used depending on the type of the film forming machine. 10. The estimation method according to any one of appendix 1 to appendix 8. (Appendix 10) The estimation model includes a plurality of models, and an abnormality or a lifespan of the film forming machine is estimated based on an output of each of the plurality of models. 10. The estimation method according to any one of claims 1 to 9. [Explanation of symbols]

[0112] 100 Estimation System 1 Film forming machine 11 Extruder 12 Casting equipment 13 MD stretching device 14 TD drawing device 15 Winder 16 Control device 2. Detector 3 Data collection equipment 31 Control Unit 32 Storage section 33 Communications Department 34 Data Entry Section 4 Estimation device 41 Control section 42 Storage section 43 Communications Department 4A Recording Media 4P Program 421 Molding information DB 422 Estimation Model 5a, 5b Terminal device

Claims

1. Obtaining detection data regarding the state of the film forming machine detected by the detector; The abnormality or lifespan of the film forming machine is estimated by inputting the acquired detection data into an estimation model that estimates the abnormality or lifespan of the film forming machine when the detection data regarding the state of the film forming machine is input. Estimation method.

2. acquiring the detection data during continuous operation of the film forming machine; While the continuous operation is continued, an abnormality or a life span of the film forming machine is estimated based on the detection data. The estimation method according to claim 1 .

3. Deriving a feature amount based on the detection data; The abnormality or lifespan of the film forming machine is estimated by inputting the derived feature amount into the estimation model, which estimates the abnormality or lifespan of the film forming machine when the feature amount based on the detection data regarding the state of the film forming machine is input. The estimation method according to claim 1 or 2.

4. Transmitting information indicating the estimated abnormality or lifespan of the film forming machine to a terminal device. The estimation method according to claim 1 or 2.

5. generating information indicating a trend of the detection data based on the time series of the detection data; Transmitting the generated information indicating the trend of the detection data to a terminal device. The estimation method according to claim 1 or 2.

6. The film forming machine includes an extruder, a cast device, an MD stretching device, a TD stretching device, and a winding device, The detection data includes a physical quantity related to the extruder, a physical quantity related to the casting device, a physical quantity related to the MD stretching device, a physical quantity related to the TD stretching device, or a physical quantity related to the winding device. The estimation method according to claim 1 or 2.

7. The film forming machine includes an extruder, a cast device, an MD stretching device, a TD stretching device, and a winding device, The detection data includes at least one of acceleration in the extruder, resin viscosity in the extruder, acceleration in the casting device, number of gels in the film in the casting device, acceleration in the MD stretching device, acceleration in the TD stretching device, wind speed in the TD stretching device, air volume in the TD stretching device, distortion of film grippers in the TD stretching device, phase difference between a pair of left and right film grippers in the TD stretching device, degree of crystal orientation in the TD stretching device, number of gels in the film in the TD stretching device, and amount of electrostatic charge of the film in the winder. The estimation method according to claim 1 or 2.

8. Detection data selected from a plurality of types of detection data set in advance is input to the estimation model. The estimation method according to claim 1 or 2.

9. Prepare multiple types of estimation models generated by different algorithms, Select the estimation model to be used depending on the type of the film forming machine. The estimation method according to claim 1 or 2.

10. The estimation model includes a plurality of models, and an abnormality or a lifespan of the film forming machine is estimated based on an output of each of the plurality of models. The estimation method according to claim 1 or 2.

11. Obtaining detection data regarding the state of the film forming machine detected by the detector; The abnormality or lifespan of the film forming machine is estimated by inputting the acquired detection data into an estimation model that estimates the abnormality or lifespan of the film forming machine when the detection data regarding the state of the film forming machine is input. A computer program for causing a computer to execute a process.

12. an acquisition unit that acquires detection data related to a state of the film forming machine detected by the detector; and an estimation unit that estimates an abnormality or a service life of the film forming machine by inputting the acquired detection data into an estimation model that estimates an abnormality or a service life of the film forming machine when the detection data relating to the state of the film forming machine is input. Estimation device.

13. The present invention includes a film forming machine, a detector, and an estimation device, The estimation device includes: an acquisition unit that acquires detection data related to a state of the film forming machine detected by the detector; and an estimation unit that estimates an abnormality or a service life of the film forming machine by inputting the acquired detection data into an estimation model that estimates an abnormality or a service life of the film forming machine when the detection data relating to the state of the film forming machine is input. Estimation system.

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