JP2021111160A - Estimation device, estimation method and program concerning manufacturing setup for absorptive article - Google Patents

Abstract

To provide an estimation device concerning a manufacturing setup for an absorptive article that is adapted to improve the estimation accuracy of an abnormality in a manufacturing setup to produce an absorptive article.SOLUTION: An estimation device concerning a manufacturing setup for an absorptive article comprises an acquiring section that acquires processing data concerning manufacture processing by a manufacturing setup to produce an absorptive article and an estimating section that estimates an abnormality in the manufacturing setup based on processing data acquired by the acquiring section and log data concerning manufacture processing for an absorptive article by another manufacturing setup different from the manufacturing setup.SELECTED DRAWING: Figure 3

Description

The present invention relates to an estimation device for an apparatus for manufacturing an absorbent article, an estimation method for an apparatus for manufacturing an absorbent article, and a program for an apparatus for manufacturing an absorbent article.

Conventionally, in a manufacturing apparatus for manufacturing an absorbent article, product data and equipment data are associated with each other, and when an abnormality occurs in the product, at least one of the product data and the equipment data associated with the product determined to be abnormal is obtained. Techniques for identifying and further identifying the manufacturing process that caused the product abnormality are known.

JP-A-2018-129030

However, in the above-mentioned technique, there is room for improvement in improving the estimation accuracy of the abnormality generated in the manufacturing apparatus for manufacturing the absorbent article. For example, with the above-mentioned technique, it is not possible to estimate in advance an abnormality that may occur in a manufacturing apparatus for manufacturing an absorbent article.

The present application has been made in view of the above, and an object of the present application is to improve the estimation accuracy of an abnormality in a manufacturing apparatus for manufacturing an absorbent article.

The estimation device for the manufacturing device for the absorbent article according to the present application includes an acquisition unit that acquires processing data related to the manufacturing process by the manufacturing device that manufactures the absorbent article, the processing data acquired by the acquisition unit, and the manufacturing device. It is provided with an estimation unit that estimates an abnormality in the manufacturing apparatus based on log data relating to the manufacturing process of the absorbent article by another different manufacturing apparatus.

According to one aspect of the embodiment, it is possible to improve the estimation accuracy of the abnormality in the manufacturing apparatus for manufacturing the absorbent article.

FIG. 1 is a block diagram showing an example of the configuration of a manufacturing system including a manufacturing apparatus including the estimation apparatus according to the embodiment. FIG. 2 is a schematic side view showing a production line of the production apparatus according to the embodiment. FIG. 3 is a functional block diagram showing an example of the configuration of the estimation device according to the embodiment. FIG. 4 is a flowchart illustrating a learning process according to an embodiment. FIG. 5 is a schematic side view showing a part of the production line of the production apparatus according to the modified example. FIG. 6 is a diagram showing an example of a hardware configuration.

The description of this specification and the accompanying drawings will clarify at least the following matters.

The estimation device for the manufacturing device for the absorbent article is an acquisition unit that acquires processing data related to the manufacturing process by the manufacturing device that manufactures the absorbent article, the processing data acquired by the acquisition unit, and other manufacturing devices that are different from the manufacturing device. It is characterized by including an estimation unit for estimating an abnormality in the manufacturing apparatus based on the log data regarding the manufacturing process of the absorbent article according to the above.

According to such an estimation device, it is possible to estimate an abnormality in the manufacturing device by using log data related to the manufacturing process of the absorbent article by another manufacturing device. Therefore, the estimation device can improve the estimation accuracy of the abnormality in the manufacturing device even when the log data in the manufacturing device is small, for example. In addition, the estimation device can estimate an abnormality that has not occurred in the manufacturing device, and can improve the estimation accuracy of the abnormality in the manufacturing device.

Further, the estimation device for the absorbent article manufacturing apparatus may estimate the abnormality in the manufacturing apparatus by using a learning model learned based on the log data regarding the manufacturing process of the absorbent article by another manufacturing apparatus.

According to such an estimation device, an abnormality in a manufacturing device is estimated by using a learning model that is trained based on log data related to the manufacturing process of an absorbent article by another manufacturing device and has a high accuracy of estimating an abnormality. Can be done. Therefore, the estimation device can improve the estimation accuracy of the abnormality in the manufacturing device.

In addition, the estimation device for the absorbent article manufacturing device learns the relationship between the log data related to the manufacturing process of the absorbent article by the other manufacturing device and the information indicating whether the other manufacturing device is normal or not. The model may be used to estimate anomalies in the manufacturing equipment.

According to such an estimation device, it is possible to estimate that an abnormality similar to an abnormality that has occurred in another manufacturing apparatus occurs in the manufacturing apparatus. The estimation device can estimate that an abnormality caused by another manufacturing device occurs in the device included in the manufacturing device. Therefore, the estimation device can improve the estimation accuracy of the abnormality generated in the manufacturing device.

In addition, the estimation device for the absorbent article manufacturing device learns the relationship between the log data related to the manufacturing process of the absorbent article by the other manufacturing device and the information indicating whether the product in the other manufacturing device is normal or not. The learning model may be used to estimate anomalies in the manufacturing equipment.

According to such an estimation device, it is possible to estimate that an abnormality similar to an abnormality of an absorbent article caused by another manufacturing apparatus occurs in an absorbent article manufactured by the manufacturing apparatus.

In addition, the estimation device for the absorbent article manufacturing apparatus uses a learning model in which the relationship between the log data regarding the manufacturing process of the absorbent article by the manufacturing apparatus and the information indicating whether or not the manufacturing apparatus is normal is learned. , Anomalies in the manufacturing equipment may be estimated.

According to such an estimation device, it is possible to estimate that an abnormality similar to the abnormality generated in the manufacturing apparatus occurs in the manufacturing apparatus.

In addition, the estimation device for the absorbent article manufacturing device uses a learning model in which the relationship between the log data related to the manufacturing process of the absorbent article by the manufacturing device and the information indicating whether the product in the manufacturing device is normal or not is learned. It may be used to estimate anomalies in the manufacturing equipment.

According to such an estimation device, it is possible to estimate that an abnormality similar to the abnormality of the absorbent article generated in the manufacturing apparatus occurs in the manufacturing apparatus.

In addition, the estimation device for the absorbent article manufacturing apparatus obtains a learning model based on at least one of the log data regarding the manufacturing process of the absorbent article by the other manufacturing apparatus and the log data regarding the manufacturing process of the absorbent article by the manufacturing apparatus. A learning unit for learning may be provided.

According to such an estimation device, the learning model can be updated based on the new log data, and the estimation accuracy of the abnormality in the manufacturing device can be improved.

In addition, the estimation device related to the manufacturing device for the absorbent article acquires processing data related to a plurality of manufacturing processes related to each other with respect to the occurrence of an abnormality, and among the plurality of manufacturing processes related to each other, a process related to the first manufacturing process. Based on the data and the log data related to the manufacturing process of the absorbent article by another manufacturing apparatus, an abnormality in the second manufacturing process among a plurality of interrelated manufacturing processes may be estimated.

According to such an estimation device, it is possible to estimate an abnormality in another manufacturing process based on processing data related to a certain manufacturing process among a plurality of manufacturing processes.

Further, the log data relating to the manufacturing process of the absorbent article by the other manufacturing apparatus may be the sensor data acquired by the sensor provided in the other manufacturing apparatus. For example, sensors provided in other manufacturing equipment include a vibration sensor that detects vibration in the manufacturing process of an absorbent article, a temperature sensor that detects a temperature in the manufacturing process of an absorbent article, and a pressure in the manufacturing process of an absorbent article. May include at least one of the pressure sensors to detect.

According to such an estimation device, an abnormality in the manufacturing device is estimated using a learning model learned based on sensor data acquired by sensors provided in another manufacturing device. Therefore, the estimation device can estimate the abnormality in the manufacturing device based on the actually acquired sensor data, and can improve the estimation accuracy of the abnormality.

In addition, the estimation device for the device for manufacturing the absorbent article may acquire processing data related to a plurality of manufacturing processes for processing the continuous product, which is a continuum that is the processing source of the absorbent article, at different positions.

According to such an estimation device, it is possible to estimate an abnormality in another manufacturing process based on processing data related to a certain manufacturing process.

Hereinafter, a mode for carrying out an estimation device for an apparatus for manufacturing an absorbent article, an estimation method for an apparatus for manufacturing an absorbent article, and a program for an apparatus for manufacturing an absorbent article (hereinafter, referred to as "embodiment"). An example will be described in detail with reference to the drawings. The estimation device, estimation method, and program are not limited by this embodiment. Further, in the following embodiments, the same parts are designated by the same reference numerals, and duplicate description will be omitted.

[Embodiment]
[Example of manufacturing system configuration]
A manufacturing system 1 including a manufacturing apparatus 2 including the estimation apparatus 10 according to the embodiment will be described with reference to FIG. FIG. 1 is a block diagram showing an example of the configuration of a manufacturing system 1 including a manufacturing device 2 including the estimation device 10 according to the embodiment.

The manufacturing system 1 includes a plurality of manufacturing devices 2. The plurality of manufacturing devices 2 are devices for manufacturing absorbent articles. Absorbent articles are, for example, diapers, sanitary napkins, and urine absorbing pads.

The plurality of manufacturing devices 2 may manufacture the same type of absorbent article. Further, the plurality of manufacturing devices 2 may manufacture absorbent articles of the same type and different sizes. In addition, the plurality of manufacturing devices 2 may manufacture different types of absorbent articles. For example, one manufacturing device 2 may manufacture a diaper and another manufacturing device 2 may manufacture a sanitary napkin.

Further, the plurality of manufacturing devices 2 do not have to be installed in the same facility. That is, the manufacturing system 1 may include manufacturing equipment 2 installed in different facilities, for example, in different regions or in different countries.

Each of the plurality of manufacturing devices 2 includes an estimation device 10. Each estimation device 10 is connected to the network N by wire or wirelessly, and transmits / receives information to and from each other. The network N is, for example, a WAN (Wide Area Network) such as the Internet, but is not limited to this, and may be another communication network such as a carrier network. Each estimation device 10 may send and receive information to and from a server connected by the network N.

[Example of production line configuration]
The manufacturing apparatus 2 is provided with a manufacturing line PL for manufacturing absorbent articles. The manufacturing apparatus 2 performs a plurality of manufacturing processes for processing a continuous sheet (continuous product), which is a continuous product that is a processing source of an absorbent article, at different positions by a manufacturing line PL. That is, the production line PL is a series of processing steps (manufacturing steps) for manufacturing an absorbent article. As a result, an absorbent article is produced.

A production line PL for producing an absorbent article will be described with reference to FIG. FIG. 2 is a schematic side view showing a production line PL of the production apparatus 2 according to the embodiment. Here, a production line PL for producing disposable diapers will be described as an example.

In the following, the width direction of the production line PL (the direction penetrating the paper surface of FIG. 2) is referred to as the "CD direction", and of the two directions orthogonal to the CD direction, the vertical direction is referred to as the "vertical direction". The horizontal direction may be referred to as the "front-back direction".

The production line PL includes a core wrap transfer path R1, an absorber transfer path R2, a fastening tape transfer path R3, a top sheet transfer path R4, a target tape transfer path R5, a back sheet transfer path R6, and a base sheet. The transport path R7 is included.

A transport device is provided in each of the transport paths R1 to R7. The transfer device is composed of a belt conveyor, a transfer roller, and the like. The belt conveyor is, for example, a suction belt conveyor having a suction function on the outer peripheral surface of the endless belt. The transport device may include a belt conveyor that does not have a suction function.

In the core wrap transport path R1, the core wrap sheet Cs is unwound from the material coil 201 in which the core wrap sheet Cs is wound into a coil. That is, in the core wrap transport path R1, the core wrap sheet Cs, which is a continuous sheet, is transported. The core wrap sheet Cs is a liquid-permeable sheet member such as tissue paper or non-woven fabric.

In the absorber transport path R2, the absorber Ab is placed on the core wrap sheet Cs transported from the core wrap transport path R1. The absorber Ab is placed on the core wrap sheet Cs by a stacking fiber drum 202 that rotates about a rotation axis along the CD direction. Absorbent Ab is a liquid absorber material, for example, pulp fiber and superabsorbent polymer (SAP).

A plurality of recesses 202a are formed on the outer peripheral surface of the fiber stacking drum 202 along the rotation direction. The recess 202a is formed so that the shape of the absorber Ab placed on the core wrap sheet Cs is rectangular in a plan view. An intake hole (not shown) is formed on the bottom surface of the recess 202a. By being sucked through the intake hole, the pulp fibers sprayed from the spray duct and SAP are laminated in the recess 202a.

In the stacking fiber drum 202, when the recess 202a in which the pulp fiber and the SAP are laminated is located above the core wrap sheet Cs, the intake air through the intake hole is stopped. As a result, a plurality of absorbers Ab are placed side by side on the core wrap sheet Cs along the front-rear direction.

Further, a cutting device 203 is provided in the absorber transport path R2. The cutting device 203 cuts the core wrap sheet Cs on which the absorber Ab is placed. The cutting device 203 includes a cutter roll 203a and an anvil roll 203b.

The cutter roll 203a rotates about a rotation axis along the CD direction. The cutter roll 203a is provided with a cutter blade along the direction of rotation axis. The anvil roll 203b rotates about a rotation axis along the CD direction.

The cutting device 203 cuts the core wrap sheet Cs on which the absorber Ab is placed by sandwiching it with the cutter roll 203a and the anvil roll 203b. The cutting device 203 cuts the core wrap sheet Cs at a position between the adjacent absorbers Ab.

In the absorber transport path R2, the core wrap sheet Cs cut by the cutting device 203 is transported forward.

In the fastening tape transport path R3, the fastening tape Ft1 which is a continuous sheet is transported. In the fastening tape transport path R3, the adhesive is applied to the fastening tape Ft1 by the adhesive coating device 204. As the adhesive, for example, a hot melt adhesive is used.

In the top sheet transport path R4, the top sheet Ts is unwound from the material coil 205 in which the top sheet Ts is wound into a coil. That is, in the top sheet transport path R4, the top sheet Ts, which is a continuous sheet, is transported. The top sheet Ts is a liquid-permeable sheet member, and is, for example, a non-woven fabric containing thermoplastic resin fibers such as polyethylene and polypropylene.

Further, a slip cut device 206 is provided in the top sheet transport path R4. The slip cut device 206 cuts the fastening tape Ft1 transported along the fastening tape transport path R3. The slip cut device 206 includes a cutter roll 206a and an anvil roll 206b.

The cutter roll 206a rotates about a rotation axis along the CD direction. The cutter roll 206a is provided with a cutter blade (not shown) that cuts the continuous sheet fastening tape Ft1 into a single-cut fastening tape Ft2. A plurality of cutter blades are provided in the rotation direction.

The anvil roll 206b adsorbs and holds the fastening tape Ft1 of the continuous body coated with the adhesive. The anvil roll 206b rotates about a rotation axis along the CD direction. The anvil roll 206b is provided with a receiving blade (not shown) facing the cutter blade of the cutter roll 206a.

The slip cutting device 206 attracts the fastening tape Ft1 of the continuous sheet coated with the adhesive by the anvil roll 206b, cuts the fastening tape Ft1 of the continuous sheet by the cutter roll 206a, and generates the fastening tape Ft2 in the form of a single sheet. ..

The slip cut device 206 attracts the fastening tape Ft2 cut into a single sheet by the anvil roll 206b and conveys it to a position facing the top sheet Ts.

Further, in the top sheet transport path R4, a temporary press roll 207 is provided below the anvil roll 206b. The temporary press roll 207 is provided so as to face the anvil roll 206b with the top sheet Ts interposed therebetween.

The temporary press roll 207 rotates about a rotation axis along the CD direction. The temporary press roll 207 can move in the vertical direction, and presses the fastening tape Ft2 adsorbed on the anvil roll 206b toward the anvil roll 206b at the timing when it is conveyed above the top sheet Ts. As a result, the continuous top sheet Ts is pressed against the anvil roll 206b, and the fastening tape Ft2 is adhered to the top sheet Ts by the adhesive applied to the fastening tape Ft2. As a result, the fastening tape Ft2 is temporarily fixed to the top sheet Ts.

Further, the press device 208 is provided in the top sheet transport path R4. The press device 208 is provided on the downstream side of the temporary press roll 207 in the transport direction of the top sheet Ts in the top sheet transport path R4.

The press device 208 actually fixes the fastening tape Ft2 temporarily fixed to the top sheet Ts. The press device 208 sandwiches the top sheet Ts to which the fastening tape Ft2 is temporarily fixed by a pair of rolls, and finally fixes the fastening tape Ft2 to the top sheet Ts.

Each roll rotates about a rotation axis along the CD direction. Of the pair of rolls, one roll reciprocates toward the other roll. That is, the pair of rolls can change the interval between the pair of rolls.

Further, an adhesive coating device 209 is provided in the top sheet transport path R4. The adhesive application device 209 is provided on the downstream side of the press device 208 in the transport direction of the top sheet Ts. The adhesive application device 209 applies the adhesive to the top sheet Ts to which the fastening tape Ft2 is finally fixed. The adhesive application device 209 applies the adhesive to the non-skin side surface of the top sheet Ts. As the adhesive, for example, a hot melt adhesive is used.

The target tape Tt1 which is a continuous sheet is conveyed to the target tape conveying path R5. In the target tape transport path R5, the adhesive is applied to the target tape Tt1 by the adhesive coating device 210. As the adhesive, for example, a hot melt adhesive is used.

In the backseat transfer path R6, the backseat Bs is unwound from the material coil 211 in which the backseat Bs is wound into a coil. That is, in the backsheet transport path R6, the backsheet Bs, which is a continuous sheet, is transported. The back sheet Bs is a sheet member having liquid impermeability, and is, for example, a thermoplastic resin film such as polyethylene.

Further, a slip cut device 212 is provided in the back sheet transport path R6. The slip cut device 212 cuts the target tape Tt1 transported along the target tape transport path R5. The slip cut device 212 includes a cutter roll 212a and an anvil roll 212b.

The cutter roll 212a rotates about a rotation axis along the CD direction. The cutter roll 212a is provided with a cutter blade (not shown) that cuts the target tape Tt1 of a continuous sheet into a single-cut target tape Tt2. A plurality of cutter blades are provided in the rotation direction.

The anvil roll 212b adsorbs and holds the target tape Tt1 of the continuous body coated with the adhesive. The anvil roll 212b rotates about a rotation axis along the CD direction. The anvil roll 212b is provided with a receiving blade (not shown) facing the cutter blade of the cutter roll 212a.

The slip cutting device 212 attracts the target tape Tt1 of the continuous sheet coated with the adhesive by the anvil roll 212b, cuts the target tape Tt1 of the continuous sheet by the cutter roll 212a, and generates a single-cut target tape Tt2. ..

The slip cut device 212 attracts the target tape Tt2 cut into a single sheet by the anvil roll 212b and conveys the target tape Tt2 to a position facing the back sheet Bs.

Further, in the back sheet transport path R6, a temporary press roll 213 is provided below the anvil roll 212b. The temporary press roll 213 is provided so as to face the anvil roll 212b with the back sheet Bs interposed therebetween.

The temporary press roll 213 rotates about a rotation axis along the CD direction. The temporary press roll 213 is movable in the vertical direction, and presses the target tape Tt2 adsorbed on the anvil roll 212b toward the anvil roll 212b at the timing when the target tape Tt2 is conveyed above the backsheet Bs. As a result, the backsheet Bs, which is a continuous body, is pressed against the anvil roll 212b, and the target tape Tt2 is adhered to the backsheet Bs by the adhesive applied to the target tape Tt2. As a result, the target tape Tt2 is temporarily fixed to the backsheet Bs.

Further, the press device 214 is provided in the back sheet transport path R6. The press device 214 is provided on the downstream side of the temporary press roll 213 in the transfer direction of the back sheet Bs in the back sheet transfer path R6.

The press device 214 actually fixes the target tape Tt2 temporarily fixed to the back sheet Bs. The press device 214 sandwiches the backsheet Bs to which the target tape Tt2 is temporarily fixed by a pair of rolls, and finally fixes the target tape Tt2 to the backsheet Bs.

Each roll rotates about a rotation axis along the CD direction. Of the pair of rolls, one roll reciprocates toward the other roll. That is, the pair of rolls can change the interval between the pair of rolls.

Further, an adhesive coating device 215 is provided in the back sheet transport path R6. The adhesive application device 215 is provided on the downstream side of the press device 214 in the transport direction of the back sheet Bs. The adhesive application device 215 applies the adhesive to the back sheet Bs to which the target tape Tt2 is finally fixed. The adhesive application device 215 applies the adhesive to the skin side surface of the back sheet Bs. As the adhesive, for example, a hot melt adhesive is used.

The absorber Ab transported by the absorber transport path R2, the top sheet Ts transported by the top sheet transport path R4, and the back sheet Bs transported by the back sheet transport path R6 merge at the confluence position Mp.

Specifically, at the merging position Mp, the back sheet Bs of the continuous sheet merges from the non-skin side of the absorber Ab, and the top sheet Ts of the continuous sheet merges from the skin side of the absorber Ab. Since the top sheet Ts and the back sheet Bs are each coated with an adhesive, the top sheet Ts, the absorber Ab, and the back sheet Bs are joined and integrated by the adhesive to form a continuous sheet base sheet. BMs are generated. In the base sheet BMs, the absorbers Ab are continuously arranged in the front-rear direction at a product pitch corresponding to the length of one piece of the diaper D.

In FIG. 2, the base sheet BMs on the downstream side of the confluence position Mp in the transport direction of the base sheet BMs are shown in a state where the top sheet Ts, the absorber Ab, and the back sheet Bs are separated from each other. , In fact, they are joined together.

In the base sheet transfer path R7, the base sheet BMs are conveyed. A leg hole cutting device 216 is provided in the base sheet transport path R7. The leg hole cutting device 216 cuts a part of the base sheet BMs on both sides in the CD direction to form an opening around the leg of the diaper. The leg hole cutting device 216 includes a cutter roll 216a and an anvil roll 216b.

The cutter roll 216a rotates about a rotation axis along the CD direction. The cutter roll 216a is provided with a cutter blade (not shown) along the direction of rotation. The cutter blade is provided in a curved shape according to the shape of the leg circumference opening. The anvil roll 216b rotates about a rotation axis along the CD direction.

The rotation of each of the rolls 216a and 216b in the leg hole cutting device 216 is interlocked with the transport operation of the base sheet BMs so that the leg circumference opening is formed at a predetermined position in the base sheet BMs.

In the leg hole cutting device 216, the cutter roll 216a can move toward the anvil roll 216b, and the distance between the cutter roll 216a and the anvil roll 216b can be changed.

Further, an end cut device 217 is provided in the base sheet transport path R7. The end cut device 217 is provided on the downstream side of the leg hole cut device 216 in the transport direction of the base sheet BMs in the base sheet transfer path R7.

The end cutting device 217 cuts the base sheet BMs transported by the base sheet transport path R7. The end cutting device 217 includes a cutter roll 217a and an anvil roll 217b.

The cutter roll 217a rotates about a rotation axis along the CD direction. The cutter roll 217a is provided with a cutter blade (not shown) along the direction of rotation axis. The anvil roll 217b rotates about a rotation axis along the CD direction.

The end cutting device 217 cuts the downstream end of the base sheet BMs at a preset position on the base sheet BMs to generate a diaper D.

[Example of configuration of estimation device]
Next, the estimation device 10 according to the embodiment will be described with reference to FIG. FIG. 3 is a functional block diagram showing an example of the configuration of the estimation device 10 according to the embodiment. In the following, with respect to the manufacturing apparatus 2, the other manufacturing apparatus different from the manufacturing apparatus 2 and the configuration included in the other manufacturing apparatus are designated by the reference numeral "A", and the manufacturing apparatus 2 and the configuration included in the manufacturing apparatus 2 are defined. It will be explained separately.

The estimation device 10 includes a communication unit 11, a control unit 12, and a storage unit 13.

The communication unit 11 communicates with the estimation device 10A of the other manufacturing device 2A via the network N (see FIG. 1). The communication unit 11 is realized by, for example, a NIC (Network Interface Card) or the like.

The control unit 12 corresponds to an electronic circuit such as a CPU (Central Processing Unit). Then, the control unit 12 has an internal memory for storing programs and control data that define various processing procedures, and executes various processing by these. The control unit 12 includes an acquisition unit 20, an estimation unit 21, a notification unit 22, and a learning unit 23.

The storage unit 13 is, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk.

The storage unit 13 stores log data related to the manufacturing process of the diaper (absorbent article) by the manufacturing apparatus 2. Specifically, the log data relating to the diaper manufacturing process by the manufacturing device 2 is the sensor data detected by the sensor 30 provided in the manufacturing device 2. The sensor 30 is provided at a position where an abnormality is detected in the manufacturing apparatus 2. The sensor 30 is, for example, a vibration sensor, a temperature sensor, and a pressure sensor.

The vibration sensor is provided in, for example, the cutting device 203, the slip cutting devices 206 and 212, the leg hole cutting device 216, and the end cutting device 217. The vibration sensor detects the vibration of the cutter blade or the like. The vibration sensor detects, for example, the degree of change in acceleration of the cutting device 203 and the cutter blades of the slip cutting devices 206 and 212 in a predetermined direction (vertical direction). The vibration sensor may be provided in the transport device, the temporary press rolls 207 and 213, the present press devices 208 and 214, and the like.

Temperature sensors are provided, for example, in adhesive coating devices 204, 209, 210, 215. The temperature sensor detects the temperature of the heating unit that heats the adhesive, the heated adhesive, and the like in the adhesive coating devices 204, 209, 210, and 215. The temperature sensor may be provided in the transport device, the cutting device 203, the slip cutting device 206, 212, or the like. For example, the temperature sensor may detect the temperature of the transport device, the motor that operates the cut device 203, the slip cut devices 206, 212, and the like.

The pressure sensor is provided, for example, on the fiber drum 202, the suction conveyor of the transport device, the cutting device 203, and the anvil rolls 203b, 206b, 212b of the slip cutting devices 206, 212. The pressure sensor detects, for example, the negative pressure of the suction portion of the suction conveyor. Further, the pressure sensor may be provided on the cutter rolls 203a, 206a, 212a, etc. of the cutting device 203, the slip cutting device 206, 212. The pressure sensor detects, for example, the pressing pressure of the cutter blades of the cutting device 203, the slip cutting device 206, and 212.

Further, the storage unit 13 stores a learning model for estimating an abnormality in the manufacturing apparatus 2. A plurality of learning models are stored according to the location where the abnormality is estimated. The storage unit 13 stores, for example, a learning model corresponding to each of the cutting device 203, the slip cutting device 206, 212, the leg hole cutting device 216, the transport device, and the adhesive coating device 204, 209, 210, 215. ..

The learning model is a model learned based on the log data regarding the diaper manufacturing process by the manufacturing apparatus 2 and the log data regarding the diaper manufacturing process by the other manufacturing apparatus 2A. Specifically, the learning model is a model in which the relationship between the log data related to the manufacturing process of the diaper by the manufacturing device 2 and the processing state information of the manufacturing device 2 is learned, and the diaper by the other manufacturing device 2A. This is a model in which the relationship between the log data related to the manufacturing process and the processing state information of the other manufacturing apparatus 2A is learned. The learning model may be a model in which the relationship between the log data related to the diaper manufacturing process by the other manufacturing device 2A and the processing state information of the other manufacturing device 2A is learned. Further, the learning model may be a model learned based on the log data regarding the manufacturing process of the diaper by the plurality of other manufacturing devices 2A. Further, the learning model may be acquired via the network N.

The log data related to the diaper manufacturing process by the other manufacturing device 2A is the sensor data acquired by the sensor 30A provided in the other manufacturing device 2A. Similar to the sensor 30 described above, the sensor 30A is, for example, a vibration sensor, a temperature sensor, or a pressure sensor, and includes at least one of these sensors.

The processing state information is information indicating whether or not the manufacturing process of the manufacturing apparatus 2 or the manufacturing process of the other manufacturing apparatus 2A is normal, and is associated with the log data of each manufacturing process. For example, when the log data is the sensor data acquired by the vibration sensor provided in the cutting device 203, the processing state information is information indicating whether the cutting device 203 is "normal" or "abnormal". Is.

The diaper manufacturing process by the other manufacturing apparatus 2A includes a compatible manufacturing process in addition to the same manufacturing process as the diaper manufacturing process by the manufacturing apparatus 2.

The same manufacturing process is a manufacturing process of an apparatus that executes the same process in another manufacturing apparatus 2A that manufactures diapers of the same type and the same size. For example, the same manufacturing process is provided in the absorber transport path R2A of another manufacturing device 2A that manufactures a diaper of the same type and the same size as the diaper manufactured by the manufacturing device 2, and is the same as the cutting device 203. This is a manufacturing process in the cutting device 203A.

The compatible manufacturing process is a manufacturing process in another manufacturing device 2A capable of obtaining log data suitable for learning a learning model for estimating an abnormality in the manufacturing device 2. A compatible manufacturing process is, for example, a manufacturing process of an apparatus that executes the same process in another manufacturing apparatus 2A that manufactures diapers of the same type and different sizes. For example, a compatible manufacturing process is performed in the absorber transport path R2A of another manufacturing device 2A that manufactures diapers of the same type and different sizes with respect to the cutting device 203 provided in the absorber transport path R2. This is a manufacturing process in the provided cutting device 203A.

The diaper manufacturing process by the other manufacturing device 2A is not limited to the above, and may be any manufacturing process related to log data capable of generating a learning model for estimating an abnormality in the manufacturing device 2. Further, the learning model is learned and updated by the learning unit 23, which will be described later.

The acquisition unit 20 acquires the processing data. The processing data is data related to the manufacturing process by the manufacturing apparatus 2. Specifically, the processing data is data related to a plurality of manufacturing processes for processing the core wrap sheet Cs, the top sheet Ts, the back sheet Bs, etc., which are the continuums that are the processing sources of the diapers, at different positions. The processed data is sensor data detected by the sensor 30 provided in the manufacturing apparatus 2. The acquired processing data is stored in the storage unit 13 as log data in the manufacturing apparatus 2.

The estimation unit 21 takes the processing data in the manufacturing apparatus 2 acquired by the acquisition unit 20 as an input, and estimates the abnormality in the manufacturing apparatus 2 by using the learning model. The estimation unit 21 may estimate the abnormality in the manufacturing device 2 by using the learning model by inputting the log data of the manufacturing device 2 which is the processing data stored in the storage unit 13.

As described above, the learning model is a model in which the relationship between the log data related to the manufacturing process of the diaper by the manufacturing device 2 and the processing state information of the manufacturing device 2 is learned, and the diaper by the other manufacturing device 2A. This is a model in which the relationship between the log data related to the manufacturing process and the processing state information of the other manufacturing apparatus 2A is learned.

Therefore, the estimation unit 21 uses a learning model learned based on the log data related to the diaper manufacturing process by the manufacturing device 2 and the log data related to the diaper manufacturing process by the other manufacturing device 2A in the manufacturing device 2. Estimate the anomaly. That is, the estimation unit 21 can estimate the abnormality in the manufacturing apparatus 2 by using the learning model learned based on a lot of log data.

The estimation unit 21 estimates an abnormality that may occur in the manufacturing apparatus 2. That is, the estimation unit 21 predicts in advance an abnormality that may occur in the manufacturing apparatus 2. The estimation unit 21 estimates an abnormality of each device included in the manufacturing device 2.

For example, the estimation unit 21 receives the vibration of the cutter blade detected by the vibration sensor provided in the cutting device 203 as an input, and estimates the abnormality of the cutter blade of the cutting device 203 using the learning model.

For example, when the same cutting device 203 as the cutting device 203A of the other manufacturing device 2A is used in the manufacturing device 2, the estimation unit 21 logs the vibration sensor provided in the cutting device 203A of the other manufacturing device 2A. Using the learning model learned based on the data, the abnormality of the cutter blade of the cutting device 203 in the manufacturing device 2 is estimated.

As a result, when the cutting device 203 of the manufacturing device 2 has a vibration tendency similar to the vibration tendency detected when an abnormality occurs in the cutting device 203A of the other manufacturing device 2A, the abnormality of the cutting device 203 occurs. Is estimated. In this way, the estimation unit 21 detects the occurrence of an abnormality in the cutting device 203 before the abnormality actually occurs in the cutting device 203 of the manufacturing device 2 based on the log data of the cutting device 203A of the other manufacturing device 2A. can do.

Therefore, the estimation unit 21 can estimate the occurrence of an abnormality in the cutting device 203 even when the log data in the cutting device 203 of the manufacturing device 2 is small. In addition, the estimation unit 21 can estimate the occurrence of an abnormality that has not occurred in the past in the cutting device 203 of the manufacturing apparatus 2.

The notification unit 22 notifies the abnormality estimated by the estimation unit 21. When the estimation unit 21 estimates the occurrence of an abnormality, the notification unit 22 notifies that the abnormality will occur. For example, the notification unit 22 displays an estimated abnormality on a monitor (not shown) or turns on a warning light (not shown).

The learning unit 23 learns a learning model for estimating an abnormality in the manufacturing apparatus 2. The learning unit 23 learns a learning model according to a location where an abnormality is estimated. The learning unit 23 learns, for example, learning models for estimating abnormalities in the cutting device 203, the slip cutting devices 206, 212, the leg hole cutting device 216, the transport device, and the adhesive coating device 204, 209, 210, 215, respectively. ..

The learning unit 23 receives the input of the processing state information for the abnormality estimated by the estimation unit 21 and notified by the notification unit 22, and learns the learning model. In this way, the learning unit 23 learns the learning model based on the log data related to the diaper manufacturing process by the manufacturing apparatus 2.

The learning unit 23 may learn the learning model based on the log data regarding the manufacturing process of the diaper by the other manufacturing apparatus 2A. The learning unit 23 learns the learning model when the log data regarding the manufacturing process of the diaper by the other manufacturing device 2A and the processing state information in the other manufacturing device 2A are acquired.

The learning unit 23 may learn the learning model based on either the log data related to the diaper manufacturing process by the manufacturing device 2 or the log data related to the diaper manufacturing process by the other manufacturing device 2A. That is, the learning unit 23 learns the learning model based on at least one of the log data related to the diaper manufacturing process by the manufacturing device 2 and the log data related to the diaper manufacturing process by the other manufacturing device 2A.

[Estimation processing]
Next, the estimation process according to the embodiment will be described with reference to FIG. FIG. 4 is a flowchart illustrating a learning process according to an embodiment. The estimation device 10 estimates the abnormality at all the locations where the abnormality is estimated in the manufacturing device 2.

The estimation device 10 acquires processing data from the sensor 30 (S100). The estimation device 10 estimates an abnormality in the manufacturing device 2 based on the acquired processing data using the learning model (S101).

When an abnormality is estimated (S102: Yes), the estimation device 10 notifies that the abnormality has been estimated (S103). If no abnormality is estimated (S102: No), the estimation device 10 ends the current process.

[Modification example]
The estimation device 10 according to the modified example may generate a learning model. The estimation device 10 according to the modified example acquires log data related to the manufacturing process of the diaper by the other manufacturing device 2A from the other manufacturing device 2A, and the processing state information of the other manufacturing device 2A, and the acquired log data and the processing. A learning model may be generated from the relationship with the state information. Further, the estimation device 10 according to the modified example may generate a learning model from the relationship between the log data related to the manufacturing process of the diaper by the manufacturing device 2 and the processing state information of the manufacturing device 2. Further, in the estimation device 10 according to the modified example, the relationship between the log data regarding the manufacturing process of the diaper by the other manufacturing device 2A from the other manufacturing device 2A and the processing state information of the other manufacturing device 2A, and the manufacturing device 2 A learning model may be generated based on the relationship between the log data related to the manufacturing process of the diaper and the processing state information of the manufacturing apparatus 2. That is, even if the estimation device 10 according to the modified example generates a learning model based on at least one of the log data related to the diaper manufacturing process by the other manufacturing device 2A and the log data related to the diaper manufacturing process by the manufacturing device 2. good.

Further, in the estimation device 10 according to the modified example, the relationship between the log data regarding the manufacturing process of the diaper by the other manufacturing device 2A and the product state information indicating whether or not the diaper in the other manufacturing device 2A is normal has a relationship. The learned learning model may be used to estimate the anomaly in the manufacturing apparatus 2. Further, the estimation device 10 according to the modified example learns the relationship between the log data related to the manufacturing process of the diaper by the manufacturing device 2 and the product state information indicating whether or not the diaper in the manufacturing device 2 is normal. An abnormality in the manufacturing apparatus 2 may be estimated using the model. The product status information may be information on a finished diaper or information on a diaper in the process of being manufactured. The estimation device 10 according to the modified example estimates the abnormality of the diaper generated by the manufacturing apparatus 2 as an abnormality in the manufacturing apparatus 2.

As a result, the estimation device 10 according to the modified example can estimate the abnormality of the diaper generated by the manufacturing device 2 by using the learning model. Therefore, the estimation device 10 according to the modified example can suppress the generation of defective diapers.

Even if the estimation device 10 according to the modification determines the log data suitable for learning the learning model with respect to the log data of the manufacturing process in the other manufacturing device 2A and acquires the log data suitable for learning the learning model. good. For example, an ID is assigned to each of the devices included in the manufacturing device 2 and the other manufacturing device 2A, and log data suitable for learning the learning model is determined based on the ID.

For example, the estimation device 10 according to the modification acquires the log data of the cutting device 203A of another manufacturing device 2A having an ID capable of learning the learning model with respect to the ID of the cutting device 203 of the manufacturing device 2. The ID that can learn the learning model is set in advance. Then, the estimation device 10 according to the modified example learns the learning model based on the acquired log data. Further, the estimation device 10 according to the modified example may generate a learning model based on the acquired log data.

As a result, the estimation device 10 according to the modified example can automatically acquire the log data of the manufacturing process in the other manufacturing device 2A, and automatically learn the learning model based on the acquired log data. Further, the estimation device 10 according to the modified example can automatically acquire the log data of the manufacturing process in the other manufacturing device 2A, and automatically generate the learning model based on the acquired log data.

Further, in the manufacturing apparatus 2 according to the above embodiment, the absorbent body Ab having a rectangular shape in a plan view is formed by the stacking fiber drum 202, but the present invention is not limited to this. As shown in FIG. 5, the manufacturing apparatus 2 according to the modified example may use the fiber stacking drum 300 to form a continuously laminated absorber Ab along the transport direction, and may cut the absorber Ab by the cutting apparatus 301. FIG. 5 is a schematic side view showing a part of the production line PL of the production apparatus 2 according to the modified example.

In the manufacturing apparatus 2 according to the modified example, the absorber Ab formed by the fiber stacking drum 300 is sandwiched and compressed by the compression roll 302, and then cut by the cutting apparatus 301. The compression roll 302 sandwiches the absorber Ab by a pair of rolls. For a pair of rolls, the interval between the pair of rolls can be changed.

When an abnormality occurs in the cutting device 301 in the manufacturing apparatus 2 according to such a modification, an abnormality in the compression roll 302 may be considered in addition to the abnormality in the cutting apparatus 301. For example, when an abnormality occurs in the compression roll 302 and the compression of the absorber Ab by the compression roll 302 is large, the load on the cutter blade or the like that cuts the absorber Ab in the cutting device 301 becomes large, and the vibration in the cutting device 301 increases. Becomes larger. That is, in the manufacturing apparatus 2, there are a plurality of manufacturing processes that are mutually related to the occurrence of an abnormality.

The estimation device 10 according to the modified example acquires processing data related to a plurality of manufacturing processes that are related to each other with respect to the occurrence of an abnormality. Then, the estimation device 10 according to the modified example mutually is based on the processing data related to the first manufacturing process among the plurality of manufacturing processes related to each other and the log data related to the manufacturing process of the diaper by the other manufacturing device 2A. Of the plurality of related manufacturing processes, an abnormality in the second manufacturing process is estimated. In other words, the estimation device 10 according to the modified example estimates an abnormality in the second manufacturing process based on the processing data related to the first manufacturing process in a plurality of manufacturing processes in which the absorber Ab, which is a continuous sheet, is processed at different positions. do.

Specifically, the estimation device 10 according to the modified example estimates the abnormality in the second manufacturing process by using the above-mentioned learning model as input of the processing data related to the first manufacturing process.

For example, the estimation device 10 according to the modified example uses the learning model as an input of the vibration log data of the cutting device 301 to estimate the abnormality in the compression roll 302. In the learning model, the relationship between the log data showing the characteristics of the vibration generated by cutting the absorber Ab, which has a large compression, and the vibration generated by the deterioration of the cutter blade, and the manufacturing process that causes the vibration was learned. It is a model. The estimation device 10 according to the modified example estimates the abnormality of the compression roll 302 according to the characteristics of the vibration in the cutting device 301. The estimation device 10 according to the modified example may identify the manufacturing process that causes the vibration according to the characteristics of the vibration in the cutting device 301, and estimate the abnormality of the compression roll 302 or the abnormality of the cutting device 301. good.

Further, in the manufacturing apparatus 2 according to the modified example, a plurality of compression rolls 302 may be provided along the conveying direction of the absorber Ab, and the absorber Ab may be compressed stepwise by the plurality of compression rolls 302.

In the manufacturing apparatus 2 according to such a modification, when there is a lump of pulp fiber in a part of the absorber Ab, for example, when the lump of pulp fiber passes through the compression roll 302, each compression roll 302 The vibration becomes large. That is, in the manufacturing apparatus 2 according to the modified example, there are a plurality of manufacturing processes that are mutually related to the occurrence of an abnormality.

For example, the estimation device 10 according to the modified example uses the learning model to estimate the abnormality in each compression roll 302 by inputting the vibration log data in each compression roll 302. Then, the estimation device 10 according to the modified example estimates the abnormality in the absorber Ab when the abnormality is estimated by the plurality of compression rolls 302. Further, the estimation device 10 according to the modified example estimates an abnormality in a part of the compression rolls 302 when an abnormality is estimated in a part of the plurality of compression rolls 302.

In this way, the estimation device 10 according to the modified example estimates the abnormality in the manufacturing device 2 by using the learning model by inputting the processing data related to the plurality of manufacturing processes that are mutually related to the occurrence of the abnormality. , The location where the abnormality occurs in the manufacturing apparatus 2 can be estimated.

[Hardware configuration]
Further, the estimation device 10 according to the above-described embodiment is realized by, for example, a computer 1000 having a configuration as shown in FIG. FIG. 6 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to the output device 1010 and the input device 1020, and the arithmetic unit 1030, the cache 1040, the memory 1050, the output IF (Interface) 1060, the input IF 1070, and the network IF 1080 are connected by the bus 1090.

The arithmetic unit 1030 operates based on a program stored in the cache 1040 or the memory 1050, a program read from the input device 1020, or the like, and executes various processes. The cache 1040 is a cache that temporarily stores data used by the arithmetic unit 1030 for various operations such as RAM. Further, the memory 1050 is a storage device in which data used by the arithmetic unit 1030 for various calculations and various databases are registered, and is realized by a ROM (Read Only Memory), an HDD (Hard Disk Drive), a flash memory, or the like. Memory.

The output IF 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various information such as a monitor and a printer. For example, USB (Universal Serial Bus), DVI (Digital Visual Interface), and the like. It may be realized by a connector of a standard such as HDMI (registered trademark) (High Definition Multimedia Interface). On the other hand, the input IF 1070 is an interface for receiving information from various input devices 1020 such as a mouse, a keyboard, and a scanner, and is realized by, for example, USB.

For example, the input device 1020 includes an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), and a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), and a tape medium. It may be realized by a device that reads information from a magnetic recording medium, a semiconductor memory, or the like. Further, the input device 1020 may be realized by an external storage medium such as a USB memory.

The network IF 1080 has a function of receiving data from another device via the network N and sending the data to the arithmetic unit 1030, and transmitting the data generated by the arithmetic unit 1030 to the other device via the network N.

Here, the arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output IF 1060 and the input IF 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the memory 1050 onto the cache 1040, and executes the loaded program. For example, when the computer 1000 functions as the estimation device 10, the arithmetic unit 1030 of the computer 1000 realizes the function of the control unit 12 by executing the program loaded on the cache 1040.

[effect]
The estimation device 10 acquires processing data related to the manufacturing process by the manufacturing device 2 that manufactures diapers. The estimation device 10 estimates an abnormality in the manufacturing device 2 based on the acquired processing data and log data related to the manufacturing process of the diaper by another manufacturing device 2A different from the manufacturing device 2.

As a result, the estimation device 10 can estimate the abnormality in the manufacturing device 2 by using the log data related to the manufacturing process of the diaper by the other manufacturing device 2A. Therefore, the estimation device 10 can improve the estimation accuracy of the abnormality in the manufacturing device 2, for example, even when the log data in the manufacturing device 2 is small. Further, the estimation device 10 can estimate an abnormality that has not occurred in the manufacturing device 2, and can improve the estimation accuracy of the abnormality in the manufacturing device 2.

Further, the estimation device 10 estimates an abnormality in the manufacturing device 2 by using a learning model learned based on log data related to the manufacturing process of the diaper by the other manufacturing device 2A.

As a result, the estimation device 10 is learned based on the log data related to the manufacturing process of the diaper by the other manufacturing device 2A, and the abnormality in the manufacturing device 2 can be estimated by using a learning model with high accuracy for estimating the abnormality. can. Therefore, the estimation device 10 can improve the estimation accuracy of the abnormality in the manufacturing device 2.

Further, the estimation device 10 is manufactured by using a learning model in which the relationship between the log data related to the manufacturing process of the diaper by the other manufacturing device 2A and the information indicating whether or not the other manufacturing device 2A is normal is learned. Estimate the abnormality in the device 2.

As a result, the estimation device 10 can estimate that an abnormality similar to the abnormality generated in the other manufacturing device 2A occurs in the manufacturing device 2. The estimation device 10 can estimate that the abnormality generated in the other manufacturing device 2A occurs in the device included in the manufacturing device 2, specifically, the cutting device 203 or the like. Therefore, the estimation device 10 can improve the estimation accuracy of the abnormality generated in the manufacturing device 2.

Further, the estimation device 10 uses a learning model in which the relationship between the log data related to the diaper manufacturing process by the other manufacturing device 2A and the information indicating whether or not the diaper is normal in the other manufacturing device 2A is learned. , Estimate the abnormality in the manufacturing apparatus 2.

As a result, the estimation device 10 can estimate that the same abnormality as the diaper abnormality caused by the other manufacturing device 2A occurs in the diaper manufactured by the manufacturing device 2.

Further, the estimation device 10 uses a learning model in which the relationship between the log data related to the diaper manufacturing process by the manufacturing device 2 and the information indicating whether or not the manufacturing device 2 is normal is learned, and the abnormality in the manufacturing device 2 is used. To estimate.

As a result, the estimation device 10 can estimate that an abnormality similar to the abnormality generated in the manufacturing device 2 occurs in the manufacturing device 2.

Further, the estimation device 10 uses a learning model in which the relationship between the log data related to the manufacturing process of the diaper by the manufacturing device 2 and the information indicating whether or not the product in the manufacturing device 2 is normal is learned, and the manufacturing device 2 is used. Estimate the anomaly in.

As a result, the estimation device 10 can estimate that an abnormality similar to the diaper abnormality that occurred in the manufacturing device 2 occurs in the manufacturing device 2.

Further, the estimation device 10 learns the learning model based on at least one of the log data regarding the diaper manufacturing process by the other manufacturing device 2A and the log data regarding the diaper manufacturing process by the manufacturing device 2.

As a result, the estimation device 10 can update the learning model based on the new log data, and can improve the estimation accuracy of the abnormality in the manufacturing device 2.

In addition, the estimation device 10 acquires processing data related to a plurality of manufacturing processes that are related to each other with respect to the occurrence of an abnormality. Then, the estimation device 10 has a plurality of interrelated manufacturing processes based on the processing data related to the first manufacturing process and the log data related to the diaper manufacturing process by the other manufacturing device 2A. Estimate an abnormality in the second manufacturing process of the manufacturing process.

Thereby, the estimation device 10 can estimate the abnormality in the other manufacturing process based on the processing data related to the certain manufacturing process among the plurality of manufacturing processes.

Further, the log data relating to the diaper manufacturing process by the other manufacturing device 2A is the sensor data acquired by the sensor 30A provided in the other manufacturing device 2A. Specifically, the sensor 30A provided in the other manufacturing apparatus 2A is a vibration sensor that detects vibration in the diaper manufacturing process, a temperature sensor that detects the temperature in the diaper manufacturing process, and a pressure in the diaper manufacturing process. Includes at least one of the pressure sensors to detect.

As a result, the estimation device 10 estimates the abnormality in the manufacturing device 2 by using the learning model learned based on the sensor data acquired by the sensor 30A provided in the other manufacturing device 2A. Therefore, the estimation device 10 can estimate the abnormality in the manufacturing device 2 based on the actually acquired sensor data, and can improve the estimation accuracy of the abnormality.

In addition, the estimation device 10 acquires processing data related to a plurality of manufacturing processes for processing continuous sheets, which are continuums that are the processing sources of diapers, at different positions.

Thereby, the estimation device 10 can estimate the abnormality in another manufacturing process based on the processing data related to one manufacturing process.

The embodiments of the present application have been described in detail with reference to the drawings. However, these are examples, and the embodiments of the present application shall be implemented in other embodiments that have been modified or improved in various ways based on the knowledge of those skilled in the art, including the embodiments described in the disclosure column of the invention. Is possible. Further, the above-mentioned "section, module, unit" can be read as "means" or "circuit".

1 Manufacturing system 2 Manufacturing equipment 10 Estimating device 12 Control unit 13 Storage unit 20 Acquisition unit 21 Estimating unit 23 Learning unit

Claims (13)

An acquisition unit that acquires processing data related to manufacturing processing by a manufacturing device that manufactures absorbent articles,
It is provided with an estimation unit for estimating an abnormality in the manufacturing apparatus based on the processing data acquired by the acquisition unit and log data relating to the manufacturing process of the absorbent article by another manufacturing apparatus different from the manufacturing apparatus. An estimation device for a featured absorbent article manufacturing apparatus. The estimation unit
Anomalies in the manufacturing apparatus are estimated using a learning model learned based on log data related to the manufacturing process of the absorbent article by the other manufacturing apparatus.
The estimation device for the device for manufacturing an absorbent article according to claim 1. The estimation unit
Abnormality in the manufacturing apparatus using the learning model in which the relationship between the log data regarding the manufacturing process of the absorbent article by the other manufacturing apparatus and the information indicating whether or not the other manufacturing apparatus is normal is learned. To estimate,
The estimation device for the device for manufacturing an absorbent article according to claim 2. The estimation unit
Using the learning model in which the relationship between the log data related to the manufacturing process of the absorbent article by the other manufacturing apparatus and the information indicating whether or not the product in the other manufacturing apparatus is normal is learned, the manufacturing apparatus is used. Estimate the anomaly in
The estimation device for the device for manufacturing an absorbent article according to claim 2 or 3. The estimation unit
An abnormality in the manufacturing apparatus is estimated using the learning model in which the relationship between the log data regarding the manufacturing process of the absorbent article by the manufacturing apparatus and the information indicating whether or not the manufacturing apparatus is normal is learned.
The estimation device for the device for manufacturing an absorbent article according to any one of claims 2 to 4. The estimation unit
An abnormality in the manufacturing apparatus is estimated using the learning model in which the relationship between the log data related to the manufacturing process of the absorbent article by the manufacturing apparatus and the information indicating whether or not the product in the manufacturing apparatus is normal is learned. do,
The estimation device for the device for manufacturing an absorbent article according to any one of claims 2 to 5. It is further provided with a learning unit that learns the learning model based on at least one of the log data regarding the manufacturing process of the absorbent article by the other manufacturing apparatus and the log data regarding the manufacturing process of the absorbent article by the manufacturing apparatus. The estimation device for the device for manufacturing an absorbent article according to any one of claims 2 to 6. The acquisition unit
Acquire processing data related to multiple manufacturing processes that are related to each other in response to the occurrence of an abnormality.
The estimation unit
Based on the processing data relating to the first manufacturing process among the plurality of interrelated manufacturing processes and the log data relating to the manufacturing process of the absorbent article by the other manufacturing apparatus, the plurality of interrelated manufacturing processes Of these, the abnormality in the second manufacturing process is estimated.
The estimation device for the device for manufacturing an absorbent article according to any one of claims 1 to 7. The log data relating to the manufacturing process of the absorbent article by the other manufacturing apparatus is the sensor data acquired by the sensor provided in the other manufacturing apparatus.
The estimation device for the device for manufacturing an absorbent article according to any one of claims 1 to 8. The sensors provided in the other manufacturing apparatus are a vibration sensor that detects vibration in the manufacturing process of the absorbent article, a temperature sensor that detects the temperature in the manufacturing process of the absorbent article, and a pressure in the manufacturing process of the absorbent article. Includes at least one of the pressure sensors to detect,
The estimation device for the device for manufacturing an absorbent article according to claim 9. The acquisition unit
The absorption according to any one of claims 1 to 10, wherein the processing data relating to a plurality of manufacturing processes for processing a continuous product, which is a continuous product that is a processing source of the absorbent article, at different positions is acquired. An estimation device for manufacturing equipment for sex goods. The acquisition process for acquiring processing data related to the manufacturing process by the manufacturing equipment that manufactures absorbent articles, and
Including the estimation step of estimating the abnormality in the manufacturing apparatus based on the processing data acquired by the acquisition step and the log data regarding the manufacturing process of the absorbent article by another manufacturing apparatus different from the manufacturing apparatus. An estimation method for a characteristic absorbent article manufacturing apparatus. The acquisition procedure for acquiring processing data related to the manufacturing process by the manufacturing equipment that manufactures absorbent articles, and
Based on the processing data acquired by the acquisition procedure and the log data related to the manufacturing process of the absorbent article by another manufacturing device different from the manufacturing device, the computer executes the estimation procedure for estimating the abnormality in the manufacturing device. A program for manufacturing equipment for absorbent articles, characterized in that it is made to.

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