Patent Publication Number: US-11658346-B2

Title: Information processing method, learned model generation method, and apparatus

Description:
BACKGROUND 
     1. Technical Field 
     The present disclosure relates to an information processing method, a learned model generation method, and an apparatus, used to display information regarding maintenance of a production facility. 
     2. Description of the Related Art 
     It is common practice to provide a maintenance system in a certain facility in order to prevent deterioration and failure and to maintain a normal operation. Japanese Patent Unexamined Publication No. 2017-167708 discloses a maintenance system that monitors the occurrence of abnormalities such as a drainage pump failure and a switchboard ground fault in a substation, and, in a case where an abnormality occurs, notifies a facility related person of the abnormality, and stores information regarding maintenance work for abnormalities performed by the facility related person who receives the notification. 
     SUMMARY 
     According to an aspect of the present disclosure, there is provided an information processing method of outputting information for displaying, on a display apparatus, information regarding a winding apparatus including a first supply mechanism that supplies a first electrode sheet, a second supply mechanism that supplies a second electrode sheet, a first bonding roller that is provided on a first electrode sheet side, a second bonding roller that is provided on a second electrode sheet side, and is paired with the first bonding roller to bond the first electrode sheet and the second electrode sheet to each other, a first winding core, a drive mechanism that moves the first winding core to a predetermined winding position and winds the first electrode sheet and the second electrode sheet in an overlapping manner on the first winding core, and a sensor that reads a first end surface of the first electrode sheet and a second end surface of the second electrode sheet along a radial direction of a first winding body in which the first electrode sheet and the second electrode sheet are wound in an overlapping manner by a plurality of turns on the first winding core, the information processing method including acquiring, from the sensor, first group data indicating a position of the first end surface read along the radial direction of the first winding body, and second group data indicating a position of the second end surface read along the radial direction of the first winding body; determining that detection of the first winding body in the sensor is not normally performed in a case where continuous positions of the first end surface and the second end surface indicated by the first group data and the second group data are separated from reference positions by a predetermined distance or more; and outputting information indicating that the detection of the first winding body in the sensor is not normally performed to the display apparatus. 
     According to another aspect of the present disclosure, there is provided a learned model generation method of generating a learned model for maintenance of a winding apparatus including a first supply mechanism that supplies a first electrode sheet, a second supply mechanism that supplies a second electrode sheet, a first bonding roller that is provided on a first electrode sheet side, a second bonding roller that is provided on a second electrode sheet side, and is paired with the first bonding roller to bond the first electrode sheet and the second electrode sheet to each other, a first winding core, a drive mechanism that moves the first winding core to a predetermined winding position and winds the first electrode sheet and the second electrode sheet in an overlapping manner on the first winding core, and a sensor that reads a first end surface of the first electrode sheet and a second end surface of the second electrode sheet along a radial direction of a first winding body in which the first electrode sheet and the second electrode sheet are wound in an overlapping manner by a plurality of turns on the first winding core, the learned model generation method including acquiring, from the sensor, first group data indicating a position of the first end surface read along the radial direction of the first winding body, and second group data indicating a position of the second end surface read along the radial direction of the first winding body; determining that detection of the first winding body in the sensor is not normally performed in a case where continuous positions of the first end surface and the second end surface indicated by the first group data and the second group data are separated from reference positions by a predetermined distance or more; outputting information indicating that detection of the first winding body in the sensor is not normally performed to the display apparatus; and determining whether or not the first group data and the second group data read before the maintenance of the winding apparatus are used for generating the learned model on the basis of a first occurrence degree of a defect of the first winding body before the maintenance of the winding apparatus and a second occurrence degree of a defect of the first winding body after the maintenance of the winding apparatus, and, in a case where it is determined that the first group data and the second group data are used, generating a learned model for outputting information indicating that the detection of the first winding body is not normally performed by using the first group data and the second group data read before the maintenance of the winding apparatus. 
     According to still another aspect of the present disclosure, there is provided an apparatus outputting information for displaying information regarding maintenance of a winding apparatus including a first supply mechanism that supplies a first electrode sheet, a second supply mechanism that supplies a second electrode sheet, a first bonding roller that is provided on a first electrode sheet side, a second bonding roller that is provided on a second electrode sheet side, and is paired with the first bonding roller to bond the first electrode sheet and the second electrode sheet to each other, a first winding core, a drive mechanism that moves the first winding core to a predetermined winding position and winds the first electrode sheet and the second electrode sheet in an overlapping manner on the first winding core, and a sensor that reads a first end surface of the first electrode sheet and a second end surface of the second electrode sheet along a radial direction of a first winding body in which the first electrode sheet and the second electrode sheet are wound in an overlapping manner by a plurality of turns on the first winding core, the apparatus including an acquirer that acquires, from the sensor, first group data indicating a position of the first end surface read along the radial direction of the first winding body, and second group data indicating a position of the second end surface read along the radial direction of the first winding body; and a notification determinator that determines that detection of the first winding body in the sensor is not normally performed in a case where continuous positions of the first end surface and the second end surface indicated by the first group data and the second group data are separated from reference positions by a predetermined distance or more, and outputs information indicating that the detection of the first winding body in the sensor is not normally performed to a display apparatus. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a network diagram including a maintenance display apparatus and a winding apparatus to which the maintenance display apparatus is applied; 
         FIG.  2    is a flowchart for describing all process steps in the maintenance display apparatus; 
         FIG.  3 A  is a diagram exemplifying a configuration of a winder that produces a winding body in the winding apparatus; 
         FIG.  3 B  is a perspective view exemplifying a winding body produced in the winder; 
         FIG.  4 A  is a diagram illustrating in detail a scene in which a winding body is moved after being pulled out from a winding core by a take-out chuck; 
         FIG.  4 B  is a diagram illustrating in detail a scene in which the winding body is moved after being pulled out from the winding core by the take-out chuck; 
         FIG.  4 C  is a diagram illustrating in detail a scene in which the winding body is moved after being pulled out from the winding core by the take-out chuck; 
         FIG.  5 A  is a schematic diagram exemplifying a scene in which an inspection machine inspects a winding body; 
         FIG.  5 B  is a schematic diagram exemplifying a sectional shape of the winding body along a radial direction; 
         FIG.  5 C  is a diagram exemplifying an image generated by the inspection machine scanning a section of the winding body illustrated in  FIG.  5 B ; 
         FIG.  6 A  is a schematic diagram illustrating examples of sectional shapes and shape data of winding bodies in a case where a winding body wound on a certain winding core has a defect; 
         FIG.  6 B  is a schematic diagram illustrating examples of sectional shapes and shape data of winding bodies in a case where a winding body wound on a certain winding core has a defect; 
         FIG.  7    is a block diagram exemplifying a functional configuration of a maintenance display apparatus according to a first exemplary embodiment; 
         FIG.  8 A  is a diagram exemplifying production result data; 
         FIG.  8 B  is a diagram exemplifying production result data; 
         FIG.  9    is a diagram exemplifying maintenance result data; 
         FIG.  10    is a sequence diagram for schematically describing the overall flow of processes in the maintenance display apparatus; 
         FIG.  11    is a sequence diagram for schematically describing the overall flow of processes in the maintenance display apparatus; 
         FIG.  12    is a flowchart for describing a process executed by a maintenance effect determinator in a learning process; 
         FIG.  13 A  is a conceptual diagram for describing a scene in which an effect of maintenance work in the learning process is determined; 
         FIG.  13 B  is a conceptual diagram for describing a scene in which an effect of maintenance work in the learning process is determined; 
         FIG.  14    is a flowchart for describing a process executed by a facility state diagnosis model generator in the learning process; 
         FIG.  15    is a flowchart for describing a process executed by a facility state diagnoser in an identification process; 
         FIG.  16    is a flowchart for describing a process executed by a notification determinator in the identification process; 
         FIG.  17 A  is a diagram illustrating a specific example of maintenance group information; 
         FIG.  17 B  is a diagram illustrating a specific example of a maintenance plan list; 
         FIG.  18    is a flowchart for describing a process executed by the maintenance effect determinator in an update process; 
         FIG.  19 A  is a conceptual diagram for describing a scene in which an effect of maintenance work is determined in the update process; 
         FIG.  19 B  is a conceptual diagram for describing a scene in which an effect of maintenance work is determined in the update process; 
         FIG.  20    is a flowchart for describing a process executed by the facility state diagnosis model generator in the update process; 
         FIG.  21    is a diagram exemplifying a configuration of a maintenance display apparatus according to a second exemplary embodiment; 
         FIG.  22    is a flowchart for describing a process executed by a maintenance effect determinator in the second exemplary embodiment; 
         FIG.  23    is a diagram exemplifying a configuration of a maintenance display apparatus according to a third exemplary embodiment; 
         FIG.  24    is a flowchart for describing a process performed by a facility state diagnosis model generator in the third exemplary embodiment; 
         FIG.  25    is a flowchart for describing a process performed by a notification determinator in the third exemplary embodiment; 
         FIG.  26 A  is a diagram for describing a modification example of a method of determining whether or not maintenance work is effective in the maintenance effect determinator in a learning process; 
         FIG.  26 B  is a diagram for describing a modification example of a method of determining whether or not maintenance work is effective in the maintenance effect determinator in the learning process; 
         FIG.  27 A  is a diagram for describing a modification example of a method of determining whether or not maintenance work is effective in the maintenance effect determinator in an update process; and 
         FIG.  27 B  is a diagram for describing a modification example of a method of determining whether or not maintenance work is effective in the maintenance effect determinator in the update process. 
     
    
    
     DETAILED DESCRIPTIONS 
     In the technique disclosed in Japanese Patent Unexamined Publication No. 2017-167708, the facility related person is notified after an abnormality occurs in the facility. Thus, the maintenance is performed by the facility related person after an abnormality occurs. In a case where the maintenance is performed after an abnormality occurs, it is necessary to stop an operation of the facility. Therefore, it is desirable that a notification is performed at the time at which the maintenance is determined as being necessary before the occurrence of the abnormality. Thus, it is required to detect a sign of an abnormality occurring in a facility. 
     An object of the present disclosure is to provide an information processing method, a learned model generation method, and an apparatus for detecting a sign of an abnormality. 
     Hereinafter, each exemplary embodiment of the present disclosure will be described in detail with reference to the drawings. However, detailed description more than necessary, for example, detailed description of well-known matters and repeated description of substantially the same configuration may be omitted. 
     The following description and referenced drawings are provided for those skilled in the art to understand the present disclosure and are not intended to limit the scope of the claims of the present disclosure. 
     First Exemplary Embodiment 
     Maintenance Display Apparatus  100  and Winding Apparatus  200   
       FIG.  1    is a network diagram including maintenance display apparatus  100  according to a first exemplary embodiment of the present disclosure and winding apparatus  200  to which maintenance display apparatus  100  is applied. Maintenance display apparatus  100  described in the present exemplary embodiment is an apparatus that performs maintenance display for winding apparatus  200  producing a lithium ion secondary battery. In the example illustrated in  FIG.  1   , maintenance display apparatus  100  is applied to single winding apparatus  200 , but the present disclosure is not limited thereto, and a single maintenance display apparatus may be applied to a plurality of winding apparatuses. In the present exemplary embodiment, maintenance display apparatus  100  is described as an apparatus, but the present disclosure is not limited thereto, and a maintenance display system in which individual constituents are connected to each other via a network may be used. 
     Maintenance display apparatus  100  includes server  10  having storage  110  and controller  120 , and notifier  130 . Server  10  is communicably connected to winding apparatus  200  via network NT. Network NT is, for example, a public network such as the Internet, or a local network such as an in-company local area network (LAN). 
     Server  10  is, for example, a general-purpose computer, and has storage  110  and controller  120  as illustrated in  FIG.  1   . 
     Storage  110  is a main storage apparatus (not illustrated) such as a read only memory (ROM) or a random access memory (RAM), and/or an auxiliary storage apparatus (not illustrated) such as a hard disk drive (HDD), a solid state drive (SSD), or a flash memory. 
     Controller  120  is, for example, a hardware processor (not illustrated) such as a central processing unit (CPU), and controls the entire maintenance display apparatus  100  by loading and executing a program stored in storage  110 . 
     Storage  110  and controller  120  may not be configured as an integrated computer. In other words, storage  110  and controller  120  may be configured separately from each other and disposed at distant positions as long as the storage and the controller are configured to be able to communicate with each other. Maintenance display apparatus  100  may have an operator (not illustrated in  FIG.  1   ) and receive an operation input from the outside. Details of storage  110  and controller  120  will be described later. 
     In the example illustrated in  FIG.  1   , notifier  130  is included in winding apparatus  200 , and is connected to server  10  via network NT. Notifier  130  performs a notification on a user of maintenance display apparatus  100  under the control of controller  120 . In the present exemplary embodiment, the user of maintenance display apparatus  100  includes an administrator of maintenance display apparatus  100  or a worker who performs production of a winding body (refer to  FIG.  3 B  described later) by using winding apparatus  200 . 
     As illustrated in  FIG.  1   , notifier  130  has alarm  131  and display  132 . Alarm  131  is configured to issue an alarm to the user with sound, light, or the like by using a buzzer or a lamp. Display  132  is a display device such as a liquid crystal display or an organic EL display, and has a configuration of displaying a warning content. In addition to alarm  131  and display  132 , notifier  130  may include, for example, a transmitter that transmits a mail including a warning content to a pre-registered user&#39;s mail address. 
     In the present exemplary embodiment, winding apparatus  200  is an apparatus winding a positive electrode sheet and a negative electrode sheet to produce a lithium ion secondary battery. As illustrated in  FIG.  1   , winding apparatus  200  has winder  201  and inspection machine  207 . As details will be described later, winder  201  winds a positive electrode sheet and a negative electrode sheet to produce a winding body. Inspection machine  207  inspects the winding body produced by winder  201 . 
     In the example illustrated in  FIG.  1   , notifier  130  is included in winding apparatus  200 , but the present disclosure is not limited thereto, and notifier  130  may be installed outside winding apparatus  200 . In the example illustrated in  FIG.  1   , notifier  130  is connected to server  10  via network NT, but the present disclosure is not limited thereto, and server  10  and notifier  130  may be directly connected to each other without using network NT. 
     In the present exemplary embodiment, a case where winding apparatus  200  is a winding apparatus winding a positive electrode sheet and a negative electrode sheet of a lithium ion secondary battery will be described, but the present disclosure is not limited thereto. The maintenance display apparatus of the present disclosure may be applied to production facilities other than the winding apparatus for a lithium ion secondary battery. The maintenance display apparatus of the present disclosure may be applied to various facilities other than the production facility. 
       FIG.  2    is a flowchart for describing all process steps in maintenance display apparatus  100 . 
     In step S 1 , controller  120  causes winder  201  of winding apparatus  200  to produce a winding body. 
     In step S 2 , controller  120  causes inspection machine  207  to inspect the produced winding body. Details of the inspection of the winding body in inspection machine  207  will be described later. 
     In step S 3 , controller  120  stores the inspection result from inspection machine  207  into storage  110 . Simultaneously, in step S 4 , controller  120  determines whether or not the inspection result from inspection machine  207  indicates defective. In a case where it is determined that the inspection result does not indicate defective (step S 4 : NO), controller  120  causes the process to proceed to step S 5 . In a case where it is determined that the inspection result indicates defective (step S 4 : YES), controller  120  causes the process to proceed to step S 6 . 
     In a case where it is determined that the inspection result does not indicate defective, in step S 5 , controller  120  causes winding apparatus  200  to supply the winding body to the next step. 
     In a case where it is determined that the inspection result indicates defective, in step S 6 , controller  120  causes notifier  130  to perform a notification that an abnormality has occurred in the inspection. Details of the notification performed by notifier  130  will be described later. 
     In step S 5  of the flowchart illustrated in  FIG.  2   , controller  120  causes winding apparatus  200  to supply the winding body to the next step, but the present disclosure is not limited thereto. For example, a user of maintenance display apparatus  100  may be caused to supply the winding body to the next step by being notified via notifier  130  such that the winding body is to be supplied to the next step. 
     Next, winder  201  and inspection machine  207  of winding apparatus  200  will be described in detail. 
     Winder  201   
       FIG.  3 A  is a diagram exemplifying a configuration of winder  201 . 
     As illustrated in  FIG.  3 A , winder  201  includes first supply reel  50 , second supply reel  51 , first bonding roller  205 A, second bonding roller  205 B, winding core  206  ( 206 α,  206 β, and  206 γ), winding core rotation driver  206 M, index table  208 , cutters  209 , presser  210 , tab welder  211 , tape paster  212 , and cylinder  213 . Winder  201  is a device that bonds first sheet material  202  supplied from first supply reel  50  to second sheet material  203  supplied from second supply reel  51  with first bonding roller  205 A and second bonding roller  205 B, and produces winding body  204  ( 204 α,  204 β, and  204 γ) by winding the sheet materials on winding core  206 . Winding core rotation driver  206 M drives winding core  206  at a desired rotation speed. Any one of winding bodies  204 α,  204 β, and  204 γ is an example of a first winding body of the present disclosure, and the others are examples of second winding bodies of the present disclosure. 
     First sheet material  202  is, for example, a sheet-shaped member (positive electrode sheet) coated with a positive electrode material, and second sheet material  203  is, for example, a sheet-shaped member (negative electrode sheet) coated with a negative electrode material. First sheet material  202  is an example of a first electrode sheet of the present disclosure, and second sheet material  203  is an example of a second electrode sheet of the present disclosure. In the above-described example, first sheet material  202  is a positive electrode sheet material and second sheet material  203  is a negative electrode sheet material, but the present disclosure is not limited thereto, and first sheet material  202  may be a negative electrode sheet material, and second sheet material  203  may be a positive electrode sheet material. 
     In the example illustrated in  FIG.  3 A , index table  208  holds three winding cores  206 α,  206 β, and  206 γ. Any one of these three winding cores  206 α,  206 β, and  206 γ is an example of a first winding core of the present disclosure, and the others are examples of second winding cores of the present disclosure. In the following description, three winding cores  206 α,  206 β, and  206 γ will be collectively referred to as winding core  206  in some cases. 
     Index table  208  rotates each of the winding cores  206  along a circular orbit while rotating the winding cores stepwise at predetermined angles. Consequently, one of three winding cores  206  is disposed at a winding position. The winding position is a position where winding core  206  can be rotated by winding core rotation driver  206 M. In the example illustrated in  FIG.  3 A , winding core  206 α is disposed at the winding position. When the winding for one winding core  206  is completed, index table  208  sequentially switches the winding core to next winding core  206 . In the following description, a winding body wound on winding core  206 α will be referred to as winding body  204 α, a winding body wound on winding core  2068  will be referred to as winding body  204 β, and a winding body wound on winding core  206 γ will be referred to as winding body  204 γ. In the following description, three winding bodies  204 α,  204 β, and  204 γ will be collectively referred to as winding body  204  in some cases. 
     In the example illustrated in  FIG.  3 A , the configuration in which index table  208  sequentially switches the three winding cores is described, but the present disclosure is not limited thereto, and the number of winding cores  206  held by index table  208  may be two or more. 
     Cutters  209  cut first sheet material  202  and second sheet material  203  when the winding on one winding core  206  is completed. In this case, presser  210  presses winding body  204  wound on winding core  206 , and thus suppresses the fluttering of ends of the cut first sheet material  202  and second sheet material  203 . In the example illustrated in  FIG.  3 A , cutters  209  are disposed at positions where first sheet material  202  and second sheet material  203  are cut before being bonded to each other, but may be disposed at positions where first sheet material  202  and second sheet material  203  are cut after being bonded to each other. 
     Tab welder  211  welds a current collecting tab to first sheet material  202 . Tape paster  212  fixes winding body  204  with a tape such that winding body  204  is not separated when being cut by cutters  209  after the winding on winding core  206  is completed. Cylinder  213  adjusts a tension applied to first sheet material  202  and second sheet material  203  via second bonding roller  205 B. 
       FIG.  3 B  is a perspective view exemplifying winding body  204  produced in winder  201 .  FIG.  3 B  illustrates a scene in which ends (ends cut by cutters  209 ) of first sheet material  202  and second sheet material  203  forming winding body  204  are not wound. As illustrated in  FIG.  3 B , a width (a length of winding body  204  along an axial direction) of second sheet material  203  is larger than that of first sheet material  202 . In  FIG.  3 B , the aspect ratio of winding body  204  is exaggerated for the sake of explanation. 
     Inspection Machine  207   
     Inspection machine  207  inspects produced winding body  204 . Inspection machine  207  is, for example, a swept source-optical coherence tomography (SS-OCT) device. Inspection machine  207  is an example of a sensor of the present disclosure. 
     In any of the winding cores  206 , winding body  204  produced after winding is completed is pulled out from winding core  206  by take-out chuck  214  and held to be moved to inspection position P by inspection machine  207  as illustrated in  FIG.  3 A . In  FIG.  3 A , a scene in which winding body  20413  wound on winding core  20613  is moved to inspection position P by take-out chuck  214  is exemplified by a dotted arrow. Take-out chuck  214  is an example of a movement member of the present disclosure. 
       FIGS.  4 A to  4 C  are diagrams illustrating in more detail a scene in which winding body  204  is moved after being pulled out from winding core  206  by take-out chuck  214 .  FIG.  4 A  is a diagram illustrating a scene in which winding body  204  is wound on winding core  206 . When the winding of winding body  204  on winding core  206  is completed, winding body  204  is sandwiched and held by take-out chuck  214 . 
     As illustrated in  FIG.  4 B , take-out chuck  214  moves to one end side of winding core  206  in a state in which take-out chuck  214  sandwiches and holds winding body  204 , and thus winding body  204  is pulled out from winding core  206 . Then, as illustrated in  FIG.  4 C , take-out chuck  214  moves while holding winding body  204 . Consequently, winding body  204  is moved to inspection position P. 
       FIG.  5 A  is a schematic diagram exemplifying a scene in which inspection machine  207  inspects winding body  204 . The position of winding body  204  in  FIG.  5 A  is inspection position P of inspection machine  207  described above. As illustrated in  FIG.  5 A , inspection machine  207  scans inspection target winding body  204  with light L by moving light L from the inside to the outside of winding body  204  in the radial direction, and generates an image indicating a shape of an internal structure of winding body  204  by using the interference of light L. Also in  FIG.  5 A , the aspect ratio of winding body  204  is exaggerated for the sake of explanation. 
       FIG.  5 B  is a schematic diagram exemplifying a sectional shape of winding body  204  along the radial direction.  FIG.  5 C  is a diagram exemplifying image I generated by inspection machine  207  scanning the section of winding body  204  illustrated in  FIG.  5 B . In  FIGS.  5 B and  5 C , an upward-downward direction corresponds to the axial direction of winding body  204 , and a leftward-rightward direction corresponds to the radial direction of winding body  204 . 
     As illustrated in  FIG.  5 B , in the section of winding body  204  along the radial direction, first sheet material  202  and second sheet material  203  having a width larger than that of first sheet material  202  are alternately stacked. Inspection machine  207  extracts and images positions of both ends of first sheet material  202  along the axial direction and both ends of second sheet material  203  along the axial direction, in the radial direction of winding body  204 . In the example (image I) illustrated in  FIG.  5 C , rhombus a corresponds to a first sheet material end position data group (an example of first group data or third group data of the present disclosure) indicating the positions of both ends of first sheet material  202 , and black circle  8  corresponds to a second sheet material end position data group (an example of second group data or fourth group data) indicating the positions of both ends of second sheet material  203 . 
     As described above, inspection machine  207  generates an image indicating a sectional shape of winding body  204  along the radial direction, and stores the image as shape data into storage  110 . An inspection result is determined on the basis of the shape data. The determination of the inspection result is an act of determining whether or not shape data of produced winding body  204  is normal. The determination of the inspection result may be performed by controller  120  illustrated in  FIG.  1   , may be performed by inspection machine  207 , and may be performed by other constituents that are not illustrated in  FIG.  1  or  3 A . Hereinafter, a case where controller  120  determines an inspection result will be described. 
       FIGS.  6 A and  6 B  are diagrams for describing a case where an inspection result is normal and a case where the inspection result indicates defective.  FIG.  6 A  describes a case where an inspection result is normal, and  FIG.  6 B  describes a case where the inspection result indicates defective. 
     In each of  FIGS.  6 A and  6 B , a scene in which winding body  204  is sandwiched and held by take-out chuck  214  is illustrated on the left side of the figure, a scene in which winding body  204  held by take-out chuck  214  is moved to inspection position P is illustrated on the center of the figure, and an example of shape data indicating a sectional shape of winding body  204  generated by inspection machine  207  is illustrated on the right side of the figure. 
     A position where take-out chuck  214  holds winding body  204  is determined in advance. As illustrated on the left side of  FIG.  6 A , in a case where winding body  204  is held at a position determined in advance by take-out chuck  214 , winding body  204  is moved between, for example, two inspection machines  207  (corresponding to inspection position P illustrated in  FIG.  3 A ) arranged to face each other vertically, and is disposed normally, as illustrated on the center of  FIG.  6 A . When inspection machine  207  scans winding body  204  in this state, normal image I normal  as illustrated on the right side of  FIG.  6 A  is generated. When such image I normal  is generated, controller  120  determines that an inspection result of winding body  204  indicates “good”. In the following description, positions of both end surfaces of first sheet material  202  and second sheet material  203  in normal image I normal  will be referred to as reference positions. Although the reference positions are indicated by lines in  FIGS.  6 A and  6 B , the present disclosure is not limited thereto, and the reference positions may be positions included in a predetermined range having a width in the vertical direction. 
     On the other hand, as illustrated on the left side of  FIG.  6 B , a position where take-out chuck  214  holds winding body  204  may be deviated relative to the normal position. On the left side and the center of  FIG.  6 B , a dotted line indicates the normal position of winding body  204 , and a solid line indicates the position of winding body  204  of which a holding position is deviated. Such deviation occurs, for example, in a case where take-out chuck  214  has a weak force to hold winding body  204 , or a case where winding body  204  is tightly wound on winding core  206  and thus cannot be successfully pulled out from winding core  206  in a state in which take-out chuck  214  holds winding body  204 . In other words, the cause of such deviation is take-out chuck  214  or winding core  206 . 
     Since a positional relationship between take-out chuck  214  and inspection machine  207  when winding body  204  is moved to the inspection position is fixed, in a case where a position of winding body  204  held by take-out chuck  214  is deviated, as illustrated in the center of  FIG.  6 B , a position of winding body  204  at inspection position P is deviated relative to the normal position. 
     When winding body  204  is scanned by inspection machine  207  in such a state, an image I error  in which positions of both end surfaces of first sheet material  202  and second sheet material  203  are vertically deviated relative to the reference positions is generated as illustrated on the right side of  FIG.  6 B . In a case where such image I error  is generated, controller  120  determines that an inspection result of winding body  204  indicates “defective”. 
     As described above, in a case where a position of winding body  204  held by take-out chuck  214  is deviated when produced winding body  204  is pulled out from winding core  206  and is moved to the inspection position by inspection machine  207 , the image generated from winding body  204  is an image in which positions of both end surfaces of first sheet material  202  and second sheet material  203  are vertically deviated relative to the reference positions. In a case where such an image is generated, controller  120  determines that an inspection result of the corresponding winding body indicates “defective”. 
     Maintenance Display Apparatus  100   
     Next, a functional configuration and an operation of maintenance display apparatus  100  that displays information regarding maintenance work to be performed on winding apparatus  200  described above will be described in detail. The maintenance work in the present exemplary embodiment is work of appropriately performing adjustment of each constituent or component replacement on winding apparatus  200  such that an inspection result of winding body  204  produced by winding apparatus  200  does not indicate defective. In the present disclosure, the maintenance work is work particularly for maintaining a defect of inspection machine  207  described above. The maintenance work is performed by a worker or the like who actually handles winding apparatus  200 . 
     Storage  110   
       FIG.  7    is a block diagram exemplifying a functional configuration of maintenance display apparatus  100  according to a first exemplary embodiment. As described above, maintenance display apparatus  100  includes storage  110 , controller  120 , and notifier  130  (refer to  FIG.  1   ). 
     As illustrated in  FIG.  7   , storage  110  has production result database  111 , facility state diagnosis model database  112 , and maintenance result database  113 . 
     Production result database  111  is a database in which production result data regarding a production result of winding apparatus  200  is registered. The production result data includes the production date and time of produced winding body  204  and shape data of winding body  204 . 
       FIGS.  8 A and  8 B  are diagrams exemplifying production result data PD.  FIG.  8 A  illustrates part of production result data PD in a table form. As illustrated in  FIG.  8 A , production result data PD includes respective pieces of data such as the “production date and time”, a “facility”, an “inspection result”, a “first sheet material”, a “second sheet material”, and a “shape data ID”. 
     The “production date and time” data is data regarding the production date and time at which winding body  204  was produced. The “facility” data is data for identifying a facility that has achieved production results in a case where there are plurality of winding apparatuses  200 . In  FIG.  8 A , as an example, identifiers “A”, “B”, and “C” of different winding apparatuses  200  are illustrated. 
     The “inspection result” data is data indicating an inspection result (refer to  FIGS.  6 A and  6 B ) of winding body  204  produced in winding apparatus  200 . In  FIG.  8 A , “good” or “defective” is illustrated as an inspection result as an example. 
     The “first sheet material” data and the “second sheet material” data are data regarding materials used to produce winding body  204 . An identifier for identifying each material is stored as the “first sheet material” data and the “second sheet material” data. 
     The “shape data ID” is an identification number correlated with shape data indicating a sectional shape of winding body  204 .  FIG.  8 B  exemplifies a correspondence relationship between the shape data ID and the shape data. 
     Among the pieces of production result data PD, each piece of data other than the shape data is registered in production result database  111 , for example, automatically or by a worker manually inputting every time winding body  204  is produced in winding apparatus  200 . The shape data is generated when produced winding body  204  is inspected by inspection machine  207  (refer to  FIG.  1    or  FIG.  5 A ), and is registered in correlation with the shape data ID. In other words, production result data PD substantially includes the shape data of winding body  204 . Consequently, every time winding body  204  is produced, production result data PD of the produced winding body  204  is registered in production result database  111 . 
     Facility state diagnosis model database  112  is a database in which plurality of facility state diagnosis models M are registered. Facility state diagnosis model M is a learned model that serves as a diagnosis reference and is used for diagnosing whether or not maintenance work is required for winding apparatus  200 . Facility state diagnosis model M is a learned model in which corresponding maintenance work that is effective to a certain defect has been learned in a case where winding apparatus  200  in which an inspection result indicates defective is improved through maintenance work (a ratio of defective products is reduced in inspection results). More specifically, facility state diagnosis model M is an aggregate of data that includes shape data of a plurality of winding bodies determined as being defective as an inspection result and contents of maintenance work performed to improve defects of the winding bodies. Facility state diagnosis model M is generated by facility state diagnosis model generator  124  described later. 
     Facility state diagnosis model M is generated for each piece of maintenance work in which a ratio of defects is reduced in an inspection result during the subsequent production of the winding body due to the maintenance work. In other words, for example, facility state diagnosis model M related to maintenance work performed yesterday and facility state diagnosis model M related to maintenance work performed today are independently generated. 
     A format of facility state diagnosis model M is not particularly limited, but it is desirable that a machine learning model such as a neural network model is employed in order to further improve the diagnosis accuracy. Selection of a model employed in facility state diagnosis model M may be performed by a user of maintenance display apparatus  100  via an operator or the like (not illustrated), and may be performed by facility state diagnosis model generator  124 . 
     Maintenance result database  113  is a database in which maintenance result data MD regarding maintenance work actually performed on winding apparatus  200  is registered. Maintenance result data MD includes, for example, facility data for identifying winding apparatus  200 , data regarding the date and time at which the maintenance work was performed (maintenance date and time), and data indicating a content of performed maintenance work. For example, in a case of maintenance work that is finished in a short time of several minutes, the maintenance date and time may be the start time or the end time of the maintenance work. In a case where the maintenance work takes a long time, for example, several hours, the maintenance date and time is preferably the central time of the maintenance work. 
       FIG.  9    is a diagram exemplifying maintenance result data MD. Maintenance result data MD is manually input to maintenance display apparatus  100  by a worker or the like who has actually performed maintenance work for winding apparatus  200  via, for example, an operator that is not illustrated in  FIG.  1    immediately after the maintenance work is executed. In  FIG.  9   , “take-out chuck replacement”, “first winding core replacement”, and “third winding core replacement” are exemplified as contents of the maintenance work. 
     The “take-out chuck replacement” as the content of the maintenance work is work for eliminating a situation in which a position of winding body  204  held by take-out chuck  214  is deviated as illustrated in  FIG.  6 B . Specifically, as the “take-out chuck replacement”, for example, take-out chuck  214  of which force for gripping winding body  204  is weakened due to wear is replaced. 
     On the other hand, the “first winding core replacement” or the “third winding core replacement” as the content of the maintenance work is work such as replacing winding core  206  from which it is difficult for winding body  204  to be pulled off due to wear, for example. 
     Controller  120   
     As illustrated in  FIG.  7   , controller  120  includes facility state diagnoser  121 , notification determinator  122 , maintenance effect determinator  123 , and facility state diagnosis model generator  124 . 
     Facility state diagnoser  121  diagnoses a state of winding apparatus  200  by using shape data of new winding body  204  that is produced in winding apparatus  200  and facility state diagnosis model M. The diagnosis result is calculated as coincidence C indicating the degree of coincidence between the shape data of produced new winding body  204  and the past shape data included in facility state diagnosis model M. Here, facility state diagnosis model M includes a content of maintenance work and shape data before the time at which the maintenance work is performed. This means that, in a case where winding body  204  having shape data included in facility state diagnosis model M was determined as being defective as an inspection result in the past, a ratio in which winding body  204  produced thereafter is determined as being defective as an inspection result is reduced by performing maintenance work included in facility state diagnosis model M. In other words, coincidence C between the shape data of produced new winding body  204  and the shape data included in facility state diagnosis model M indicates a probability of improving a situation in which winding body  204  is determined as being defective as an inspection result by performing maintenance included in facility state diagnosis model M. 
     As a method of calculating coincidence C by comparing a plurality of pieces (m) of shape data of produced new winding body  204  with a plurality of pieces (n) of past shape data included in facility state diagnosis model M, pattern matching, or deep learning using feature amounts of a plurality of pieces of dimensionally compressed shape data may be used as appropriate. The coincidence may be calculated on the basis of a distance between vectors obtained from respective pieces of shape data. 
     Notification determinator  122  determines whether or not to perform a notification of maintenance work for winding apparatus  200  on the basis of coincidence C. Notification determinator  122  determines that a notification that the maintenance work is to be performed will be performed in a case where coincidence C is greater than or equal to a predetermined threshold value, and determines that the notification will not be performed in a case where coincidence C is less than the predetermined threshold value. The notification of the maintenance work includes an alarm for attracting the user&#39;s attention, display for performing a notification of a content of a maintenance work that can be expected to be effective by performing the maintenance work, and the like. 
     Maintenance effect determinator  123  determines whether or not the maintenance work for winding apparatus  200  is effective. Maintenance effect determinator  123  determines whether or not the maintenance work is effective on the basis of, for example, defect ratios before and after maintenance work (a ratio of defective products to a total number of products) or shape data of winding body  204  before and after the maintenance work (refer to  FIG.  5 C ). 
     Facility state diagnosis model generator  124  generates facility state diagnosis model M on the basis of maintenance result data MD determined as being effective and shape data of a winding body of which an inspection result indicates defective, produced before the maintenance work is performed. Facility state diagnosis model M generated by facility state diagnosis model generator  124  is registered in facility state diagnosis model database  112  described above. 
     Overall Flow of Processes in Maintenance Display Apparatus  100   
     Next, with reference to  FIGS.  10  and  11   , a description will be made of the overall flow of processes in maintenance display apparatus  100  having the functional configuration illustrated in  FIG.  7   .  FIGS.  10  and  11    are sequence diagrams for schematically describing the overall flow of processes in maintenance display apparatus  100 . 
       FIG.  10    schematically illustrates a learning process in maintenance display apparatus  100  and an identification process using a learned model generated through the learning process. 
     Learning Process 
     A learning process in maintenance display apparatus  100  is a process for generating a learned model (facility state diagnosis model M) including shape data of a winding body and a content of maintenance work in a case where a situation in which an inspection result of the winding body indicates defective is eliminated by performing the maintenance work on winding apparatus  200  after the winding body produced in the past is determined as being defective as an inspection result. More specifically, the learning process is a process for generating a learned model in which corresponding maintenance work that improves a situation of producing a winding body related to certain shape data has been learned. Therefore, the learning process presupposes the maintenance work being performed before the start of the learning process. 
     In step S 11 , maintenance effect determinator  123  acquires shape data (refer to  FIG.  5 C ) included in production result data PD (refer to  FIG.  8 A ) regarding plurality of winding bodies  204  produced before maintenance work performed before the start of the learning process, and calculates defect ratio Nf before  before the maintenance work on the basis of the shape data. Defect ratio Nf before  is calculated by dividing, for example, the number of winding bodies  204  determined as being defective as an inspection result among winding bodies  204  produced before the maintenance work by a total number of winding bodies produced before the maintenance work. 
     In step S 12 , maintenance effect determinator  123  acquires shape data included in production result data PD regarding plurality of winding bodies  204  produced after the maintenance work, and calculates defect ratio Nf after  after the maintenance work on the basis of the shape data. Defect ratio Nf after  is calculated by dividing, for example, the number of winding bodies  204  determined as being defective as an inspection result among winding bodies  204  produced after the maintenance work by a total number of winding bodies manufactured after the maintenance work. 
     In step S 13 , maintenance effect determinator  123  compares defect ratios Nf before  and Nf after  before and after the maintenance work with each other, and determines whether or not the maintenance work is effective. Details of the process of determining an effect of the maintenance work in maintenance effect determinator  123  in the learning process will be described later. 
     In a case where it is determined in step S 13  that the maintenance work is effective, maintenance effect determinator  123  outputs maintenance result data MD (refer to  FIG.  9   ) indicating a content of the maintenance work performed before the start of the learning process, to facility state diagnosis model generator  124  in step S 14 . 
     In step S 15 , facility state diagnosis model generator  124  generates facility state diagnosis model M by using maintenance result data MD determined as being effective. Details of facility state diagnosis model M will be described later. 
     In step S 16 , facility state diagnosis model generator  124  registers generated facility state diagnosis model M into facility state diagnosis model database  112  (refer to  FIG.  7   ). 
     The processes from step S 11  to step S 16  described above correspond to the learning process in maintenance display apparatus  100 . 
     Identification Process 
     In the identification process described below, whether or not an abnormality or a sign of an abnormality has occurred in winding apparatus  200  is identified by using facility state diagnosis model M generated in the learning process on the basis of shape data indicating sectional shapes of plurality of produced new winding bodies  204 . 
     In step S 17 , facility state diagnoser  121  acquires shape data (hereinafter, new shape data) regarding the plurality of produced new winding bodies. 
     In step S 18 , facility state diagnoser  121  calculates coincidence C by using the shape data of the new winding body and facility state diagnosis model M. Coincidence C is a value indicating the degree of coincidence between the new shape data of the winding body and the past shape data of the winding body included in facility state diagnosis model M. In other words, the larger coincidence C, the higher the probability that an abnormality or a sign of an abnormality may have occurred in winding apparatus  200 , and thus produced new winding body  204  may be determined as being defective as an inspection result. 
     In step S 19 , notification determinator  122  determines that a notification is necessary for a user of maintenance display apparatus  100  in a case where coincidence C is greater than or equal to a predetermined threshold value. The case where coincidence C is greater than or equal to a predetermined threshold value is a case where an abnormality or a sign of an abnormality has occurred in winding apparatus  200  and maintenance work is required again. 
     In step S 110 , notification determinator  122  outputs a content of the maintenance work of which a notification is necessary for the user to notifier  130 . The content of the maintenance work of which a notification is necessary for the user is determined on the basis of facility state diagnosis model M in which coincidence C is greater than or equal to a predetermined threshold value. 
     In steps S 111  and S 112 , notifier  130  notifies the user that the maintenance work is required to be performed. In step S 111 , alarm  131  issues an alarm. In step S 112 , display  132  displays the content of the maintenance work of which a notification is necessary for the user.  FIG.  10    illustrates an example in which both the alarm in step S 111  and the content display of the maintenance work in step S 112  are performed, but, for example, the alarm may not be issued and only the content display of the maintenance work may be performed. 
     As described above, a worker who has received the notification in steps S 111  and S 112  executes the maintenance work for winding apparatus  200  through the notification on the basis of the content of the maintenance work of which the notification has been performed. 
     The processes from step S 17  to step S 112  described above correspond to the identification process in maintenance display apparatus  100  using the learned model generated in the learning process. 
       FIG.  11    schematically illustrates an update process in maintenance display apparatus  100  and an identification process using a learned model updated in the update process. 
     Update Process 
     In an update process in maintenance display apparatus  100 , in a case where new maintenance work is performed after the above-described learning process, the learned model (facility state diagnosis model M) is updated on the basis of a maintenance work result based on the maintenance work. In other words, the update process presupposes the maintenance work being performed before the start of the update process. 
     In step S 21 , maintenance effect determinator  123  calculates coincidence C before  before the maintenance work by using shape data (refer to  FIG.  5 C ) included in production result data PD (refer to  FIG.  8 A ) regarding plurality of winding bodies  204  produced before the maintenance work, and facility state diagnosis model M registered in facility state diagnosis model database  112 . 
     In step S 22 , maintenance effect determinator  123  calculates coincidence C after  after the maintenance work by using the shape data included in the production result data regarding plurality of winding bodies  204  produced after the maintenance work, and the past shape data included in facility state diagnosis model M registered in facility state diagnosis model database  112 . 
     In step S 23 , maintenance effect determinator  123  compares coincidences C before  and C after  before and after the maintenance work with each other, and thus determines whether or not the maintenance work is effective. Details of the process of determining an effect of the maintenance work in maintenance effect determinator  123  in the update process will be described later. 
     In a case where it is determined in step S 23  that the maintenance work is effective, maintenance effect determinator  123  outputs maintenance result data MD indicating a content of the maintenance work performed before the start of the update process, to facility state diagnosis model generator  124  in step S 24 . 
     In step S 25 , facility state diagnosis model generator  124  updates facility state diagnosis model M by using maintenance result data MD determined as being effective. Details of the process of updating facility state diagnosis model M will be described later. 
     In step S 26 , facility state diagnosis model generator  124  updates facility state diagnosis model database  112  (refer to  FIG.  7   ) by using generated facility state diagnosis model M. 
     The processes from step S 21  to step S 26  described above correspond to the update process in maintenance display apparatus  100 . 
     Identification Process 
     In the identification process described below, whether or not an abnormality or a sign of an abnormality has occurred in winding apparatus  200  is identified by using facility state diagnosis model M updated in the update process on the basis of shape data indicating sectional shapes of plurality of produced new winding bodies  204 . 
     In step S 27 , facility state diagnoser  121  acquires shape data (hereinafter, new shape data) of the plurality of produced new winding bodies. 
     In step S 28 , facility state diagnoser  121  calculates coincidence C by using the new shape data and facility state diagnosis model M. Coincidence C is a value indicating the degree of coincidence between the new shape data and the past shape data included in facility state diagnosis model M. 
     In step S 29 , notification determinator  122  determines that a notification is necessary for a user of maintenance display apparatus  100  in a case where coincidence C is greater than or equal to a predetermined threshold value. The case where coincidence C is greater than or equal to a predetermined threshold value is a case where an abnormality or a sign of an abnormality has occurred in winding apparatus  200  and maintenance work is required again. 
     In step S 210 , notification determinator  122  outputs a content of the maintenance work of which a notification is necessary for the user to notifier  130 . The content of the maintenance work of which a notification is necessary for the user is determined on the basis of facility state diagnosis model M in which coincidence C is greater than or equal to a predetermined threshold value. 
     In steps S 211  and S 212 , notifier  130  notifies the user that the maintenance work is required to be performed. In step S 211 , alarm  131  issues an alarm. In step S 212 , display  132  displays the content of the maintenance work of which a notification is necessary for the user.  FIG.  11    illustrates an example in which both the alarm in step S 211  and the content display of the maintenance work in step S 212  are performed, but, for example, the alarm may not be issued and only the content display of the maintenance work may be performed. 
     A worker who has received the notification in steps S 211  and S 212  executes the maintenance work for winding apparatus  200  on the basis of the content of the maintenance work of which the notification has been performed. 
     The processes from step S 27  to step S 212  described above correspond to the identification process in maintenance display apparatus  100 . The identification process from step S 27  to step S 212  illustrated in  FIG.  11    is substantially the same as the identification process from step S 17  to step S 112  illustrated in  FIG.  10   . 
     Details of Each Process 
     Hereinafter, each of the learning process, the identification process, and the update process illustrated in  FIGS.  10  and  11    will be described in detail. 
     Learning Process 
     First, the learning process in maintenance effect determinator  123  and facility state diagnosis model generator  124  will be described. 
     Process in Maintenance Effect Determinator  123   
     Hereinafter, a description will be made of processes (processes from steps S 11  to S 14  in  FIG.  10   ) executed by maintenance effect determinator  123  in the learning process.  FIG.  12    is a flowchart for describing the processes executed by maintenance effect determinator  123  in the learning process. 
     In step S 31 , maintenance effect determinator  123  reads, from production result database  111 , production result data list PL before  including all production result data regarding winding bodies  204  produced within a predetermined time from the time point at which the maintenance work is performed before the learning process among pieces of production result data registered in production result database  111 . The predetermined time is a preset length of time and is a time required for manufacturing a certain number or more of winding bodies  204 . 
     In step S 32 , maintenance effect determinator  123  calculates pre-maintenance defect ratio Nf before  on the basis of production result data included in production result data list PL before . As described above, pre-maintenance defect ratio Nf before  is calculated by dividing the number of winding bodies  204  determined as being defective as an inspection result by a total number of winding bodies produced before the maintenance work on the basis of the shape data and inspection results in the production result data included in production result data list PL before . 
     In step S 33 , maintenance effect determinator  123  reads, from production result database  111 , production result data list PL after  including all production result data regarding winding bodies  204  produced until a predetermined time elapses from the time point at which the maintenance is performed. 
     In step S 34 , maintenance effect determinator  123  calculates post-maintenance defect ratio Nf after  on the basis of the production result data included in production result data list PL after . As described above, post-maintenance defect ratio Nf after  is calculated by dividing the number of winding bodies  204  determined as being defective as an inspection result by a total number of winding bodies produced after the maintenance work on the basis of the shape data and inspection results in the production result data included in production result data list PL after . 
     In step S 35 , maintenance effect determinator  123  takes a difference between pre-maintenance defect ratio Nf before  and post-maintenance defect ratio Nf after , and determines whether or not the difference is greater than predetermined threshold value Th N . Maintenance effect determinator  123  causes the process to proceed to step S 36  in a case where the difference is greater than threshold value Th N  (step S 35 : YES), and causes the process to proceed to step S 37  in other cases (step S 35 : NO). 
     In step S 36 , maintenance effect determinator  123  determines that the maintenance work is effective since post-maintenance defect ratio Nf after  is lower than pre-maintenance defect ratio Nf before . The maintenance work mentioned here is maintenance work performed before the learning process, that is, before step S 11  in  FIG.  10   . 
     On the other hand, in step S 37 , maintenance effect determinator  123  determines that the maintenance work is not effective or the effect is very small since post-maintenance defect ratio Nf after  is not lower than pre-maintenance defect ratio Nf before . 
     In the above-described way, maintenance effect determinator  123  determines whether or not the maintenance work performed before the learning process is effective in the learning process. 
       FIGS.  13 A and  13 B  are conceptual diagrams for describing a scene in which an effect of the maintenance work in the learning process is determined.  FIG.  13 A  illustrates an example of a case where it is determined that the maintenance work is effective, and  FIG.  13 B  illustrates an example of a case where it is determined that the maintenance work is not effective.  FIGS.  13 A and  13 B  illustrate five winding bodies wound on one winding core  206  among plurality of winding cores  206 . 
     In the examples illustrated in  FIGS.  13 A and  13 B , two of the five winding bodies wound on one winding core  206  are determined as being defective as an inspection result before maintenance. In other words, pre-maintenance defect ratio Nf before  is 40%. In the example illustrated in  FIG.  13 A , among the five winding bodies wound on one certain winding core  206  after maintenance, the number of winding bodies determined as being defective as an inspection result is 0 (post-maintenance defect ratio Nf after =0). On the other hand, in the example illustrated in  FIG.  13 B , among the five winding bodies wound on one winding core  206  after maintenance, the number of winding bodies determined as being defective as an inspection result is two that is the same as before maintenance (post-maintenance defect ratio Nf after =40%). 
     In the example illustrated in  FIG.  13 A , a difference between pre-maintenance defect ratio Nf before  and post-maintenance defect ratio Nf after  is 40%. On the other hand, in the example illustrated in  FIG.  13 B , a difference between pre-maintenance defect ratio Nf before  and post-maintenance defect ratio Nf after  is 0. Therefore, for example, in a case where threshold value Th N  for determining the presence or absence of the maintenance effect is 20%, it is determined that the maintenance work is effective in the example illustrated in  FIG.  13 A , and it is determined that the maintenance work is not effective in the example illustrated in  FIG.  13 B . 
     Processes in Facility State Diagnosis Model Generator  124   
     Next, processes (the processes in steps S 15  and S 16  in  FIG.  10   ) executed by facility state diagnosis model generator  124  in the learning process will be described.  FIG.  14    is a flowchart for describing the processes executed by facility state diagnosis model generator  124  in the learning process. 
     In step S 41 , facility state diagnosis model generator  124  reads maintenance result data MD of the maintenance work determined as being effective by maintenance effect determinator  123 . 
     In step S 42 , facility state diagnosis model generator  124  reads pre-maintenance production result data list PL before  from production result database  111 . Here, pre-maintenance production result data list PL before  read by facility state diagnosis model generator  124  is the same as pre-maintenance production result data list PL before  read in the process performed by maintenance effect determinator  123  (refer to step S 31  in  FIG.  12   ). 
     In step S 43 , facility state diagnosis model generator  124  generates facility state diagnosis model M new  by using read maintenance result data MD and production result data PD included in production result data list PL before . 
     In step S 44 , facility state diagnosis model generator  124  registers generated new facility state diagnosis model M new  into facility state diagnosis model database  112 . 
     As mentioned above, in the learning process, in a case where a situation of producing a winding body of which an inspection result indicates defective is improved, new facility state diagnosis model M new  in which a correspondence relationship between shape data of the winding body of which the inspection result indicates defective and performed maintenance work has been learned is generated, and is registered into facility state diagnosis model database  112 . 
     Identification Process 
     Next, the identification process performed by facility state diagnoser  121  and notification determinator  122  will be described. 
     Processes in Facility State Diagnoser  121   
     Hereinafter, the processes (processes in step S 17  and step S 18  in  FIG.  10   ) executed by facility state diagnoser  121  in the identification process will be described.  FIG.  15    is a flowchart for describing the processes executed by facility state diagnoser  121  in the identification process. 
     In step S 51 , facility state diagnoser  121  determines whether or not new production result data PD new  is registered in production result database  111 . In a case where new production result data PD new  is not registered (step S 51 : NO), facility state diagnoser  121  repeatedly executes step S 51 . In a case where new production result data PD new  is registered (step S 51 : YES), facility state diagnoser  121  causes the process to proceed to step S 52 . 
     In step S 52 , facility state diagnoser  121  extracts production result data list PL from production result database  111  on the basis of registered new production result data PD new . Production result data list PL is a list obtained by extracting production result data PD of winding bodies  204  produced within a predetermined time from the production date and time of registered new production result data PD new  among pieces of production result data PD registered in production result database  111 . In other words, at least registered new production result data PD new  is included in production result data list PL. 
     In step S 53 , facility state diagnoser  121  generates coincidence C by using the shape data included in production result data list PL and the past shape data included in facility state diagnosis model M read from facility state diagnosis model database  112 . 
     More specifically, facility state diagnoser  121  extracts respective pieces of shape data (refer to  FIG.  5 C ) from one or more pieces of production result data included in production result data list PL. On the other hand, facility state diagnoser  121  extracts plurality of facility state diagnosis models M registered in facility state diagnosis model database  112 . Plurality of facility state diagnosis models M respectively correspond to different pieces of maintenance work. 
     Facility state diagnoser  121  calculates plurality of coincidences C in all combinations of shape data extracted from the one or more pieces of production result data and plurality of facility state diagnosis models M. 
     Processes in Notification Determinator  122   
     Hereinafter, a description will be made of processes (processes from step S 19  to step S 112  in  FIG.  10   ) executed by notification determinator  122  in the identification process.  FIG.  16    is a flowchart for describing the processes executed by notification determinator  122  in the identification process. 
     In step S 61 , notification determinator  122  aggregates coincidence C for each maintenance group on the basis of plurality of coincidences C generated by facility state diagnoser  121 . The maintenance group is a group corresponding to a content of maintenance work. For example, notification determinator  122  groups the performed maintenance work on the basis of the maintenance group information illustrated in  FIG.  17 A . As illustrated in  FIG.  17 A , the maintenance group information is information in which a maintenance group is correlated with performed maintenance work included in the maintenance group. As illustrated in  FIG.  17 A , the maintenance group information may further include a maintenance plan to be performed corresponding to a maintenance group. In the present exemplary embodiment, a group divided for each member that is a maintenance work target is described as a maintenance group, but the present disclosure is not limited thereto, and the maintenance group may be, for example, a group divided for each content of maintenance work or for each component number of a component to be replaced in maintenance work. 
     In the following description, a result of aggregating coincidence C for each maintenance group will be referred to as aggregation value A. A method of generating aggregation value A may be determined as appropriate from a plurality of types of aggregation methods. Specific examples of the plurality of types of aggregation methods include, for example, a method of simply summing coincidences C, a method of averaging coincidences C, a method of selecting the maximum value from coincidences C, and a method of extracting and averaging a predetermined number of coincidences C in an upper rank. 
     In step S 62 , notification determinator  122  generates maintenance plan list ML. Maintenance plan list ML is a list of maintenance groups. For example, the maintenance groups are arranged in a descending order of aggregation value A.  FIG.  17 B  is a diagram illustrating a specific example of maintenance plan list ML. 
     As illustrated in  FIG.  17 B , maintenance plan list ML includes data such as a “maintenance plan ID”, a “facility”, a “maintenance plan”, and an “aggregation value”. The “maintenance plan ID” data is an identifier given for each maintenance group sorted according to the magnitude of the aggregation value. As the “maintenance plan ID” data, for example, a smaller number is given as the aggregation value becomes greater. The “maintenance plan” data is data indicating a content of maintenance to be performed corresponding to a maintenance group. Notification determinator  122  specifies a maintenance plan corresponding to a maintenance group with reference to the maintenance group information illustrated in  FIG.  17 A . The “aggregation value” data is data indicating a value of aggregation value A aggregated for each maintenance group. 
     In the example illustrated in  FIG.  17 B , in winding facility “A”, maintenance of take-out chuck  214  of inspection machine  207  is registered as maintenance group 1, and maintenance of the first winding core of winder  201  is registered as maintenance group 2. Maintenance of the third winding core of winder  201  is registered in maintenance plan list ML as maintenance group 3. 
     In the maintenance plans illustrated in  FIGS.  17 A and  17 B , “maintenance of the take-out chuck” indicates that at least one piece of maintenance work for take-out chuck  214  such as adjustment of take-out chuck  214 , cleaning of take-out chuck  214 , and replacement of take-out chuck  214 . The same applies to “maintenance of the first winding core” and “maintenance of the third winding core”. 
     As described with reference to  FIGS.  6 A and  6 B , in the shape data of winding body  204 , in a case where the continuous positions of both end surfaces of first sheet material  202  and second sheet material  203  are vertically deviated relative to the reference positions, winding body  204  is determined as being defective as an inspection result. It is known that the cause of an inspection result of winding body  204  for which such shape data is generated indicating “defective” is take-out chuck  214  of inspection machine  207  or winding core  206  of winder  201 . 
     Aggregation value A is a value obtained by aggregating coincidences C, and thus has the same property as coincidence C. Thus, as aggregation value A becomes greater, the need for a maintenance content of the maintenance group to be performed on target winding apparatus  200  becomes higher. Since maintenance plan list ML is a list of maintenance groups arranged in a descending order of aggregation value A, a maintenance group in an upper rank in maintenance plan list ML is highly required to be applied to maintenance of target winding apparatus  200 . 
     In step S 63 , notification determinator  122  determines whether or not aggregation value A is greater than predetermined sign threshold value Th f  for each maintenance group. Predetermined sign threshold value Th f  is the minimum value of aggregation values in which it is supposed that a sign that an abnormality occurs in winding apparatus  200  has occurred. In the present exemplary embodiment, an abnormality in winding apparatus  200  indicates that, for example, a ratio in which winding apparatus  200  produces winding body  204  of which an inspection result indicates defective is a predetermined ratio or higher. The sign of abnormality in winding apparatus  200  indicates that, for example, a ratio in which winding apparatus  200  produces winding body  204  of which an inspection result indicates “fair” is a predetermined ratio or higher. Therefore, when aggregation value A is smaller than sign threshold value Th f , it is expected that a ratio in which an inspection result of winding body  204  produced thereafter indicates “good” is a predetermined ratio or higher. Predetermined sign threshold value Th f  may be empirically determined on the basis of, for example, past maintenance result data MD. 
     In a case where at least one maintenance group for which aggregation value A is greater than sign threshold value Th f  is included in maintenance plan list ML (step S 63 : YES), notification determinator  122  causes the process to proceed to step S 64 . In a case where no maintenance group for which aggregation value A is greater than sign threshold value Th f  is included in maintenance plan list ML (step S 63 : NO), notification determinator  122  finishes the process since it is not necessary to perform a notification that maintenance is to be performed. 
     In step S 64 , notification determinator  122  determines whether or not there is a maintenance group for which aggregation value A is greater than predetermined abnormality threshold value Th a  among the maintenance groups included in maintenance plan list ML. Predetermined abnormality threshold value Th a  is the minimum value of aggregation values in which it is supposed that an abnormality has occurred in winding apparatus  200  beyond the sign stage. Thus, abnormality threshold value Th a  is empirically determined to a value greater than sign threshold value Th f  on the basis of, for example, past maintenance result data MD. In a case where a maintenance group for which aggregation value A is greater than abnormality threshold value Th a  is included in maintenance plan list ML (step S 64 : YES), notification determinator  122  causes the process to proceed to step S 66 . In a case where a maintenance group for which aggregation value A is greater than abnormality threshold value Th a  is not included in maintenance plan list ML (step S 64 : NO), notification determinator  122  causes the process to proceed to step S 65 . 
     In step S 65 , notification determinator  122  causes display  132  of notifier  130  to perform a notification of a maintenance content corresponding to the maintenance group for which aggregation value A is determined to be greater than sign threshold value Th f  in step S 63 . More specifically, notification determinator  122  causes display  132  to display, for example, a content of maintenance work recommended to be executed along with a message such as “Please execute the following maintenance contents”. The content of the maintenance work recommended to be executed is a content corresponding to the “maintenance plan” data included in maintenance plan list ML illustrated in  FIG.  17 B . 
     Here, in a case where there are a plurality of maintenance groups for which aggregation value A is greater than sign threshold value Th f , notification determinator  122  may display contents of a plurality of pieces of maintenance work by ranking the contents with an aggregation value. In this case, more specifically, notification determinator  122  may display contents of a plurality of pieces of maintenance work recommended to be executed in an order from an upper rank along with a message such as “Please execute the following maintenance contents. In a case where the abnormality is not improved despite a maintenance content in the upper rank being executed, the abnormality may be improved if a maintenance work in the lower rank is executed”. 
     Notification determinator  122  performs a notification of a maintenance plan ID correlated with a maintenance group having a maintenance content along with the maintenance work content. In a case where a worker who has performed maintenance work inputs maintenance result data MD, the worker inputs maintenance result data MD and a maintenance plan ID triggering the maintenance in correlation with each other, and thus it is possible to easily determine whether or not input maintenance result data MD is data corresponding to maintenance work executed with a notification from maintenance display apparatus  100  as a trigger. 
     In step S 66 , in the same manner as in step S 65 , notification determinator  122  displays the content of the maintenance work on display  132 , and causes alarm  131  to issue an alarm for a notification that an abnormality has occurred in target winding apparatus  200 . In a case where a sign of an abnormality has not occurred but the abnormality has occurred in target winding apparatus  200 , urgent maintenance work is required. Thus, notification determinator  122  not only displays the content of the maintenance work on display  132  but also causes alarm  131  to issue an alarm, and thus promptly notifies a user of maintenance display apparatus  100  of the occurrence of the abnormality. 
     As described above, in the identification process, it is determined whether or not an abnormality (a situation in which a ratio of an inspection result of produced winding body  204  indicating “defective” is a predetermined ratio or higher) or a sign of the abnormality has occurred in winding apparatus  200  by using production result data PD (particularly shape data) of produced new winding body  204  and facility state diagnosis model M. In a case where it is determined that an abnormality or a sign of an abnormality has occurred, a notification is sent to a user. Consequently, in a case where an abnormality has occurred in winding apparatus  200 , the user can promptly know the abnormality and can know the content of the maintenance work to be performed in order to improve the abnormality. 
     In the present disclosure, as described above, it is assumed that the cause of a situation in which an inspection result of winding body  204  indicates defective is take-out chuck  214  of inspection machine  207  or winding core  206  of winder  201 . Whether the cause of the situation in which an inspection result of winding body  204  indicates defective is take-out chuck  214  or winding core  206  may be determined on the basis of whether or not a winding body determined as being defective as an inspection result depends on winding core  206 . 
     Specifically, in a case where take-out chuck  214  is the cause of the situation, whether or not an inspection result of certain winding body  204  indicates defective does not depend on winding core  206  on which winding body  204  is wound. On the other hand, in a case where winding core  206  is the cause of the situation, inspection results of winding bodies  204  wound on winding cores  206  other than winding core  206  which is the cause among plurality of winding cores  206  do not indicate defective. Therefore, in a case where inspection results of winding bodies wound on all winding cores  206  indicate defective, it may be considered that the cause is take-out chuck  214 , and, in a case where winding body  204  of which an inspection result indicates defective is wound on specific winding core  206 , the cause may be considered to be winding core  206 . 
     In maintenance display apparatus  100 , as described above, take-out chuck  214  or any of plurality of winding cores  206  is set as a maintenance work target, an aggregation value for each maintenance group is calculated, and it is determined which maintenance work is to be performed depending on a magnitude of the aggregation value. Through the determination, maintenance work is performed on take-out chuck  214  or any of plurality of winding cores  206 , and thus maintenance work with high probability that production of winding body  204  determined as being defective as an inspection result may be reduced is preferably extracted and displayed. 
     Update Process 
     Next, the update process in maintenance effect determinator  123  and facility state diagnosis model generator  124  will be described. 
     Process in Maintenance Effect Determinator  123   
     Hereinafter, a description will be made of processes (processes from step S 21  to step S 24  in  FIG.  11   ) executed by maintenance effect determinator  123  in the update process.  FIG.  18    is a flowchart for describing the processes executed by maintenance effect determinator  123  in the update process. 
     In step S 71 , maintenance effect determinator  123  determines whether or not new maintenance result data MD new  is registered in maintenance result database  113  of storage  110 . In a case where it is determined that new maintenance result data MD new  is not registered (step S 71 : NO), maintenance effect determinator  123  repeatedly executes step S 71 . In a case where it is determined that new maintenance result data MD new  is registered (step S 71 : YES), maintenance effect determinator  123  causes the process to proceed to step S 72 . 
     In step S 72 , maintenance effect determinator  123  determines whether or not a predetermined time has elapsed from execution of maintenance corresponding to registered new maintenance result data MD new  on the basis of the “maintenance date and time” data (refer to  FIG.  9   ) included in registered new maintenance result data MD new . 
     In a case where it is determined that the predetermined time has elapsed from the execution time of the maintenance work (step S 72 : YES), maintenance effect determinator  123  causes the process to proceed to step S 73 . In a case where it is determined that the predetermined time has not elapsed from the execution time of the maintenance work (step S 72 : NO), maintenance effect determinator  123  repeatedly executes the process in step S 72 . 
     In step S 73 , maintenance effect determinator  123  reads pre-maintenance production result data list PL before  including all production result data PD of winding bodies  204  produced in a period a predetermined time before the maintenance work from production result database  111 . 
     In step S 74 , maintenance effect determinator  123  reads facility state diagnosis model M included in a maintenance group having a maintenance content corresponding to new maintenance result data MD new  from facility state diagnosis model database  112 , and generates pre-maintenance coincidence C before  on the basis of read facility state diagnosis model M and production result data list PL before . A method of generating pre-maintenance coincidence C before  is the same as the method of generating coincidence C in facility state diagnoser  121  in step S 53  in  FIG.  15   . 
     In step S 75 , maintenance effect determinator  123  reads production result data list PL after  including all production result data PD of winding bodies  204  produced within a predetermined time after the maintenance work from production result database  111 . 
     In step S 76 , maintenance effect determinator  123  reads facility state diagnosis model M included in the maintenance group having the maintenance content corresponding to maintenance result data MD new  from facility state diagnosis model database  112 , and generates post-maintenance coincidence C after  on the basis of read facility state diagnosis model M and production result data list PL after . A method of generating coincidence C after  is the same as the method of generating coincidence C in facility state diagnoser  121  in step S 53  of  FIG.  15   . 
     In step S 77 , maintenance effect determinator  123  takes a difference between pre-maintenance coincidence C before  and post-maintenance coincidence C after , and determines whether or not the difference is greater than predetermined threshold value Th D . Maintenance effect determinator  123  causes the process to proceed to step S 78  in a case where the difference is greater than the threshold Th D  (step S 77 : YES), and causes the process to proceed to step S 79  in other cases (step S 77 : NO). Predetermined threshold value Th D  may be determined as appropriate on the basis of the past maintenance work results and the like. 
     In step S 78 , since post-maintenance coincidence C after  is less than pre-maintenance coincidence C before , maintenance effect determinator  123  determines that the maintenance work performed on the basis of the maintenance content of which the notification has been performed by notification determinator  122  is effective. 
     In step S 79 , since post-maintenance coincidence C after  is not less than pre-maintenance coincidence C before , maintenance effect determinator  123  determines that the maintenance work performed on the basis of the maintenance content of which the notification has been performed by notification determinator  122  is not effective or the effect is very small. 
       FIGS.  19 A and  19 B  are conceptual diagrams for describing a scene in which an effect of maintenance work in the update process is determined.  FIG.  19 A  illustrates an example of a case where it is determined that the maintenance work is effective, and  FIG.  19 B  illustrates an example of a case where it is determined that the maintenance work is not effective. 
     In the examples illustrated in  FIGS.  19 A and  19 B , pre-maintenance coincidence C before =0.90 is calculated on the basis of the shape data of winding bodies  204  produced before maintenance and facility state diagnosis model M. 
     In the example illustrated in  FIG.  19 A , post-maintenance coincidence C after =0.20 is calculated on the basis of the shape data of winding body  204  produced after maintenance and facility state diagnosis model M. On the other hand, in the example illustrated in  FIG.  19 B , post-maintenance coincidence C after =0.90 is calculated on the basis of the shape data of winding body  204  produced after maintenance and facility state diagnosis model M. 
     Therefore, in the example illustrated in  FIG.  19 A , a difference between pre-maintenance coincidence C before  and post-maintenance coincidence C after  is 0.70. On the other hand, in the example illustrated in  FIG.  19 B , a difference between pre-maintenance coincidence C before  and post-maintenance coincidence C after  is 0. Therefore, for example, in a case where threshold value Th D  for determining the presence or absence of the maintenance effect is 0.30, it is determined that the maintenance work is effective in the example illustrated in  FIG.  19 A , and it is determined that the maintenance work is not effective in the example illustrated in  FIG.  19 B . 
     Processes in Facility State Diagnosis Model Generator  124   
     Next, processes (the processes in steps S 25  and S 26  in  FIG.  11   ) executed by facility state diagnosis model generator  124  in the update process will be described.  FIG.  20    is a flowchart for describing the processes executed by facility state diagnosis model generator  124  in the update process. 
     In step S 81 , facility state diagnosis model generator  124  reads maintenance result data MD new  of the maintenance work determined as being effective by maintenance effect determinator  123 . 
     In step S 82 , facility state diagnosis model generator  124  reads pre-maintenance production result data list PL before  from production result database  111 . Here, pre-maintenance production result data list PL before  read by facility state diagnosis model generator  124  is the same as pre-maintenance production result data list PL before  read in the process performed by maintenance effect determinator  123  (refer to step S 31  in  FIG.  12   ). 
     In step S 83 , facility state diagnosis model generator  124  generates new facility state diagnosis model M new  by using read maintenance result data MD and production result data PD included in production result data list PL before . 
     In step S 84 , facility state diagnosis model generator  124  adds new facility state diagnosis model M new  to facility state diagnosis model M already registered in facility state diagnosis model database  112 , and thus updates facility state diagnosis model M. 
     As mentioned above, in the update process, new facility state diagnosis model M new  is generated by using facility state diagnosis model M generated in the learning process, and facility state diagnosis model M already registered in facility state diagnosis model database  112  is updated by using new facility state diagnosis model M new . As described above, facility state diagnosis model M in facility state diagnosis model database  112  is updated by using new facility state diagnosis model M new  on the basis of the effective maintenance work, and thus diagnosis accuracy of a facility state of winding apparatus  200  in facility state diagnoser  121  is gradually improved. 
     According to a display method of displaying information for maintenance of a production apparatus related to the present disclosure, first group data indicating a position of a first end surface of a first electrode sheet along a radial direction of a first winding body in which the first electrode sheet and a second electrode sheet are wound in an overlapping manner by a plurality of turns on a first winding core and second group data indicating a position of a second end surface of the second electrode sheet along the radial direction of the first winding body are input to a learned model that is created according to a learned model generation method related to the present disclosure. In a case where information indicating that detection of the first winding body is not performed normally is output from the learned model, the information indicating that the detection of the first winding body is not performed normally is output to a display apparatus. 
     Operation and Effect of Maintenance Display Apparatus  100  of First Exemplary Embodiment 
     As described above, maintenance display apparatus  100  includes notification determinator  122 , and facility state diagnosis model generator  124  that is an example of a model generator. Notification determinator  122  acquires the first group data indicating a position of the first end surface and the second group data indicating a position of the second end surface read along the radial direction of winding body  204  from inspection machine  207  as a sensor. Notification determinator  122  determines whether or not an inspection result of winding body  204  indicates defective on the basis of whether or not continuous positions of the first end surface and the second end surface indicated by the first group data and the second group data are separated from reference positions by a predetermined distance or more. In a case where the inspection result of winding body  204  indicates defective, notification determinator  122  performs a notification that inspection in inspection machine  207  is not performed normally, and outputs information indicating that a cause of the defect is take-out chuck  214  of inspection machine  207  or any of plurality of winding cores  206  to display  132  for maintenance. 
     With this configuration, even though there is no abnormality in the produced winding body  204 , in a case where inspection machine  207  does not normally perform inspection, this can be accurately detected and a notification thereof can be performed. As a result, maintenance of take-out chuck  214  of inspection machine  207  or any of plurality of winding cores  206  can be promoted, and thus it is possible to appropriately reduce a ratio in which an inspection result of winding body  204  produced by winding apparatus  200  indicates defective. 
     Facility state diagnosis model generator  124  calculates a first difference between a first defect ratio of winding body  204  before maintenance of take-out chuck  214  or any of plurality of winding cores  206  and a second defect ratio of winding body  204  after the maintenance of take-out chuck  214  or any of plurality of winding cores  206  on the basis of first data and second data before and after the maintenance of take-out chuck  214  or any of plurality of winding cores  206 . In a case where it is determined that the first difference is less than a predetermined value, facility state diagnosis model generator  124  does not use the first data and the second data read before the maintenance of take-out chuck  214  or any of plurality of winding cores  206 , for generating or updating the learned model. On the other hand, in a case where it is determined that the first difference is greater than or equal to the predetermined value, the learned model (facility state diagnosis model M) is generated or updated by using the first data and the second data read before the maintenance of take-out chuck  214  or any of plurality of winding cores  206 . 
     On the other hand, facility state diagnosis model generator  124  calculates a third probability that a defect of winding body  204  may be improved, obtained by inputting the first data and the second data before the maintenance of take-out chuck  214  or any of plurality of winding cores  206  to the learned model. Facility state diagnosis model generator  124  calculates a fourth probability that a defect of winding body  204  may be improved, obtained by inputting the first data and the second data after the maintenance of take-out chuck  214  or any of plurality of winding cores  206  to the learned model. Facility state diagnosis model generator  124  calculates a second difference between the third probability and the fourth probability. In a case where it is determined that the second difference is less than a predetermined value, the first data and the second data read before the maintenance of take-out chuck  214  or any of plurality of winding cores  206  are not used for creating or updating the learned model. On the other hand, in a case where it is determined that the second difference is greater than or equal to the predetermined value, the learned model is created or updated by using the first data and the second data before the maintenance of take-out chuck  214  or any of plurality of winding cores  206 . 
     As described above, according to maintenance display apparatus  100  related to the first exemplary embodiment, it is possible to execute the learning process of generating facility state diagnosis model M for diagnosing a facility state of winding apparatus  200  through learning, the identification process of identifying whether or not an abnormality or a sign of the abnormality has occurred in winding apparatus  200  by using facility state diagnosis model M, and performing a notification of an abnormality or a sign of the abnormality in a case where the abnormality or the sign of the abnormality has occurred, and the update process of updating facility state diagnosis model M on the basis of maintenance result data MD corresponding to maintenance work that is performed on the basis of the notification. 
     More specifically, in the learning process, maintenance display apparatus  100  determines whether or not maintenance work is effective on the basis of registered new maintenance result data MD and shape data included in production result data PD of winding bodies  204  produced before and after the maintenance work, and generates facility state diagnosis model M by using maintenance result data MD corresponding to the maintenance work determined as being effective and the shape data. 
     In the identification process, maintenance display apparatus  100  calculates coincidence C between the shape data of winding bodies  204  produced after the maintenance work and facility state diagnosis model M for each maintenance group, and determines whether to perform issuing of an alarm and a notification of a content of the maintenance work, to perform only the notification of the content of the maintenance work, or not to perform the notification on the basis of a magnitude of coincidence C. 
     In the update process, maintenance display apparatus  100  determines whether or not maintenance work is effective on the basis of registered new maintenance result data MD and shape data included in production result data PD of winding bodies  204  produced before and after the maintenance work, generates new facility state diagnosis model M new  by using maintenance result data MD corresponding to the maintenance work determined as being effective and the shape data, and updates facility state diagnosis model M by using new facility state diagnosis model M new . 
     With this configuration, it is possible to appropriately diagnose a state of winding apparatus  200  by using a learned model (facility state diagnosis model M) that is generated on the basis of effective maintenance work (reduced defect ratio) among pieces of actually performed maintenance work. Since the learned model is updated at any time, the accuracy of diagnosis can be improved. In a case where it is diagnosed that an abnormality has occurred in winding apparatus  200 , a user can take an emergency response by issuing an alarm, and, in a case where it is diagnosed that a sign of an abnormality has occurred, the user is notified of a content of the maintenance work by which the abnormality is expected to be improved, and thus the maintenance work can be executed while the occurrence ratio of defective products in winding apparatus  200  is low. 
     In the maintenance display apparatus  100  according to the first exemplary embodiment, it is assumed that take-out chuck  214  or any of plurality of winding cores  206  is a cause of winding body  204  being determined as being defective as an inspection result. In maintenance display apparatus  100  according to the first exemplary embodiment, notification determinator  122  aggregates coincidence C for each maintenance group and determines a content of maintenance work of which a notification is sent to a user on the basis of a magnitude of aggregation value A. Thus, for take-out chuck  214  or any of plurality of winding cores  206 , a notification of maintenance work having the highest probability of defect improvement through maintenance thereof is sent to the user. In a case where there are a plurality of pieces of maintenance work having a high probability, the plurality of pieces of maintenance work are displayed in a ranked state. Consequently, a user performs maintenance work of which a notification has been sent in a descending order of rank, and thus a defect of winding body  204  is suitably improved. 
     The maintenance display apparatus according to the present exemplary embodiment includes a notifier, a maintenance effect determinator, and a facility state diagnosis model generator. For each piece of maintenance work performed in the past, the notifier performs a notification of a content of the maintenance work on the basis of a facility state diagnosis model that is registered in a database in correlation between the content of the maintenance work and production result data before the maintenance work, and input new production result data. The maintenance effect determinator determines whether or not the maintenance work is effective on the basis of production result data before the time at which the maintenance work is performed and production result data after the time at which the maintenance work is performed. The facility state diagnosis model generator generates a new facility state diagnosis model on the basis of the production result data before the time at which the maintenance work determined as being effective is performed and the content of the maintenance work determined as being effective. 
     The maintenance display apparatus according to the present exemplary embodiment further includes a facility state diagnoser that generates a facility state diagnosis index indicating the degree of coincidence between registered new production result data and production result data before the maintenance work included in the facility state diagnosis model. The notifier performs a notification of the content of the maintenance work on the basis of the facility state diagnosis index. 
     In the maintenance display apparatus according to the present exemplary embodiment, the facility state diagnosis model generator generates the facility state diagnosis model through machine learning by using production result data before the time at which maintenance work determined as being effective is performed and maintenance result data regarding the maintenance work. 
     The maintenance display apparatus according to the present exemplary embodiment calculates a defect ratio in which an inspection result indicates defective in production result data for a predetermined time before the time at which maintenance work in input new maintenance result data is performed, on the basis of data regarding the inspection result for a product of a production facility included in production result data in a case where maintenance work not based on a content of maintenance work of which a notification has been performed by the notifier is performed and the new maintenance result data regarding the maintenance work is input. A defect ratio in which an inspection result indicates defective in production result data for a predetermined time after the time at which maintenance work in the input new maintenance result data is performed is calculated. The maintenance effect determinator calculates a difference between the defect ratio before the maintenance work and the defect ratio after the maintenance work, and determines whether or not the maintenance work is effective on the basis of a magnitude of the difference. 
     Second Exemplary Embodiment 
     Hereinafter, a second exemplary embodiment of the present disclosure will be described.  FIG.  21    is a diagram exemplifying a configuration of maintenance display apparatus  100 A according to the second exemplary embodiment. In maintenance display apparatus  100 A according to the second exemplary embodiment, a process performed by maintenance effect determinator  123 A included in controller  120 A of server  10 A is different from the process performed by maintenance effect determinator  123  according to the first exemplary embodiment described above. 
     Hereinafter, differences from the first exemplary embodiment will be described. The same constituent as that in the first exemplary embodiment will be given the same reference numeral as that in the first exemplary embodiment, and a constituent different from that in the first exemplary embodiment will be given the reference numeral added with “A”. 
     In the first exemplary embodiment, it is not supposed that a user of maintenance display apparatus  100  performs maintenance work other than a content of which notification has been performed by maintenance display apparatus  100 . However, actually, in terms of operation of winding apparatus  200 , maintenance work (maintenance work other than a maintenance content of which a notification has been performed by maintenance display apparatus  100 ) may be performed at any time depending on the decisions on the site or the like. In the second exemplary embodiment, a description will be made of maintenance display apparatus  100 A that can cope with a case of performing maintenance work other than a maintenance content of which a notification has been performed by maintenance display apparatus  100 A. 
       FIG.  22    is a flowchart for describing processes executed by maintenance effect determinator  123 A in the second exemplary embodiment. 
     In step S 91  in  FIG.  22   , maintenance effect determinator  123 A determines whether or not new maintenance result data MD new  is registered in maintenance result database  113  of storage  110 . In a case where it is determined that new maintenance result data MD new  is not registered (step S 91 : NO), maintenance effect determinator  123 A repeatedly executes step S 91 . In a case where it is determined that new maintenance result data MD new  is registered (step S 91 : YES), maintenance effect determinator  123 A causes the process to proceed to step S 92 . 
     In step S 92 , maintenance effect determinator  123 A determines whether or not a predetermined time has elapsed from execution of maintenance work corresponding to registered new maintenance result data MD new  on the basis of the maintenance date and time data included in registered new maintenance result data MD new . In the same manner as the predetermined time described in the first exemplary embodiment, the predetermined time is the time required for target winding apparatus  200  to manufacture a certain number or more of winding bodies  204  after execution of maintenance work. 
     In a case where it is determined that the predetermined time has elapsed from the execution of maintenance work (step S 92 : YES), maintenance effect determinator  123 A causes the process to proceed to step S 93 . In a case where it is determined that the predetermined time has not elapsed from the execution of maintenance work (step S 92 : NO), maintenance effect determinator  123 A repeatedly executes the process in step S 92 . 
     In step S 93 , maintenance effect determinator  123 A determines whether or not there is a maintenance plan ID correlated with registered new maintenance result data MD new . As described in the first exemplary embodiment, notification determinator  122  performs a notification of a maintenance work content and a maintenance plan ID correlated with a maintenance group having the maintenance content. A worker performs maintenance work indicated by the maintenance plan ID of which a notification has been performed. The worker inputs maintenance result data MD by correlating the performed maintenance work with the maintenance plan ID of which a notification has been performed. Consequently, maintenance result data MD and the maintenance plan ID triggering the maintenance are correlated with each other. In this step S 93 , it is determined whether or not registered new maintenance result data MD new  is maintenance performed with the notification performed by maintenance display apparatus  100 A as a trigger in the above-described way. 
     In step S 93 , in a case where there is a maintenance plan ID correlated with registered new maintenance result data MD, it is determined that the maintenance work corresponding to maintenance result data MD new  has been performed with a notification of maintenance contents from maintenance display apparatus  100 A as a trigger. In a case where a maintenance plan ID correlated with registered new maintenance result data MD new  is not present, it is determined that the maintenance work corresponding to maintenance result data MD new  has not been performed with a notification of maintenance contents from maintenance display apparatus  100 A as a trigger. 
     In step S 93 , in a case where it is determined that the maintenance plan ID is included in registered new maintenance result data MD new  (step S 93 : YES), maintenance effect determinator  123 A causes the process to proceed to step S 94 . On the other hand, in a case where it is determined that the maintenance plan ID is not included in maintenance result data MD new  (step S 93 : NO), maintenance effect determinator  123 A causes the process to proceed to step S 95 . 
     Step S 94  is a process in a case where the maintenance work corresponding to registered new maintenance result data MD new  has been triggered by the notification of the maintenance content from maintenance display apparatus  100 A. Thus, in step S 94 , maintenance effect determinator  123 A proceeds to a process of determining whether or not there is an effect of the maintenance work triggered by the notification of the maintenance content from maintenance display apparatus  100 A. The maintenance effect determination process for maintenance triggered by the notification of the maintenance content from maintenance display apparatus  100 A is substantially the same as the process described with reference to  FIG.  18    in the above-described first exemplary embodiment, and thus a description thereof will not be repeated. 
     On the other hand, step S 95  is a process in a case where the maintenance work corresponding to maintenance result data MD new  has not been triggered by the notification of the maintenance content from maintenance display apparatus  100 A. Thus, maintenance effect determinator  123 A proceeds to a process of determining whether or not there is an effect of the maintenance work not triggered by maintenance display apparatus  100 A. The maintenance effect determination process for maintenance not triggered by the notification of the maintenance content from maintenance display apparatus  100 A is substantially the same as the process described with reference to  FIG.  12    in the above-described first exemplary embodiment, and thus a description thereof will not be repeated. 
     As described above, according to maintenance display apparatus  100 A related to the second exemplary embodiment, maintenance result data MD new  can be suitably registered even in a case where maintenance work not triggered by a notification of a maintenance content from maintenance display apparatus  100 A has been performed. The process of maintenance effect determinator  123 A described with reference to  FIG.  22    may be executed in either the learning process or the update process described above. 
     The maintenance display apparatus according to the present exemplary embodiment generates a facility state diagnosis index before maintenance work on the basis of production result data for a predetermined time before the time at which the maintenance work in registered new maintenance result data is performed, and a facility state diagnosis model correlated with a content of maintenance work in a notification triggering maintenance work in input new maintenance result data. A facility state diagnosis index after maintenance work is generated on the basis of production result data for a predetermined time after the time at which the maintenance work in input new maintenance result data is performed, and a facility state diagnosis model correlated with a content of maintenance work in a notification triggering maintenance work in input new maintenance result data. The maintenance effect determinator calculates a difference between the facility state diagnosis index before the maintenance work and the facility state diagnosis index after the maintenance work, and determines whether or not the maintenance work is effective on the basis of a magnitude of the difference. 
     Third Exemplary Embodiment 
     Hereinafter, a third exemplary embodiment of the present disclosure will be described.  FIG.  23    is a diagram exemplifying a configuration of maintenance display apparatus  100 B according to the third exemplary embodiment. Maintenance display apparatus  100 B according to the third exemplary embodiment is different from maintenance display apparatus  100  according to the first exemplary embodiment described above in that storage  110 B of server  10 B further includes non-effect facility state diagnosis model database  114 , and controller  120 B includes notification determinator  122 B, maintenance effect determinator  123 B, and facility state diagnosis model generator  124 B. 
     In the first exemplary embodiment described above, facility state diagnosis model generator  124  generates new facility state diagnosis model M new  by using maintenance result data MD determined as being effective (refer to  FIG.  14   ). In the third exemplary embodiment, facility state diagnosis model generator  124 B generates new facility state diagnosis model M new  by also using maintenance result data MD determined as being ineffective. 
       FIG.  24    is a flowchart for describing processes performed by facility state diagnosis model generator  124 B in the third exemplary embodiment. The processes described with reference to  FIG.  24    may be executed in either the learning process or the update process. 
     In step S 101 , facility state diagnosis model generator  124 B reads registered new maintenance result data MD new  from maintenance result database  113 . Here, facility state diagnosis model generator  124 B reads maintenance result data MD new  regardless of an effect determination result by maintenance effect determinator  123 B. 
     In step S 102 , facility state diagnosis model generator  124 B reads production result data list PL before  before maintenance work from production result database  111 . 
     In step S 103 , facility state diagnosis model generator  124 B generates facility state diagnosis model M new  by using read maintenance result data MD new  and production result data PD included in production result data list PL before . 
     In step S 104 , facility state diagnosis model generator  124 B registers a model that is generated on the basis of maintenance result data MD determined as being ineffective among generated new facility state diagnosis models M new , into non-effect facility state diagnosis model database  114 . On the other hand, facility state diagnosis model generator  124 B registers a model that is generated on the basis of maintenance result data MD determined as being effective among generated new facility state diagnosis models M new , into facility state diagnosis model database  112 . 
     In the above-described way, facility state diagnosis model generator  124 B not only generates facility state diagnosis model M using maintenance result data MD related to maintenance determined as being effective but also generates facility state diagnosis model M using maintenance result data MD related to maintenance determined as being ineffective. 
     An identification process is executed by facility state diagnoser  121  and notification determinator  122 B by using facility state diagnosis model M generated in the above-described way. Processes executed by facility state diagnoser  121  is substantially the same as the processes described with reference to  FIG.  15    in the first exemplary embodiment described above, and thus a description thereof will not be repeated. 
     Hereinafter, a description will be made of processes executed by notification determinator  122 B in the identification process of the third exemplary embodiment.  FIG.  25    is a flowchart for describing the processes performed by notification determinator  122 B in the third exemplary embodiment. 
     In step S 111 , notification determinator  122 B aggregates coincidence C for each maintenance group by using coincidence C generated by facility state diagnoser  121 , and thus generates aggregation value A. In the third exemplary embodiment, information (flag) indicating whether or not maintenance work is determined as being effective is correlated with each maintenance group by maintenance effect determinator  123 B. 
     In step S 112 , notification determinator  122 B generates maintenance plan list ML that is a list of maintenance groups arranged in a descending order of aggregation value A. 
     In step S 113 , notification determinator  122 B determines whether or not each maintenance group included in maintenance plan list ML is determined as being effective. As described above, in the third exemplary embodiment, since facility state diagnoser  121  correlates a flag indicating the presence or absence of an effect with each maintenance group, notification determinator  122 B performs the process in step S 113  by referring to the flag. Notification determinator  122 B causes the process to proceed to step S 114  with respect to a maintenance group of maintenance work determined as being effective (step S 113 : YES). On the other hand, notification determinator  122 B causes the process to proceed to step S 117  with respect to a maintenance group of maintenance work determined as being ineffective (step S 113 : NO). 
     In step S 114 , notification determinator  122 B determines whether or not aggregation value A is greater than predetermined sign threshold value Th f  for each maintenance group determined as being effective. In a case where there is at least one maintenance group for which aggregation value A is greater than sign threshold value Th f  (step S 114 : YES), notification determinator  122 B causes the process to proceed to step S 115 . In a case where there is no maintenance group for which aggregation value A is greater than sign threshold value Th f  (step S 114 : NO), notification determinator  122 B finishes the process. 
     In step S 115 , notification determinator  122 B determines whether or not there is a maintenance group for which aggregation value A is greater than predetermined abnormality threshold value Th a  among maintenance groups determined as being effective. In a case where there is a maintenance group for which aggregation value A is greater than abnormality threshold value Th a  (step S 115 : YES), notification determinator  122 B causes the process to proceed to step S 116 . In a case where there is no maintenance group for which aggregation value A is greater than abnormality threshold value Th a  (step S 115 : NO), notification determinator  122 B causes the process to proceed to step S 118 . 
     In step S 116 , notification determinator  122 B performs a notification of a maintenance content corresponding to the maintenance group for which aggregation value A is determined as being greater than sign threshold value Th f  in step S 114 , and also issues an alarm for a notification that an abnormality has occurred in target winding apparatus  200 . 
     In step S 117 , notification determinator  122 B determines whether or not aggregation value A is greater than predetermined non-effect threshold value Th ie  for each maintenance group related to maintenance determined as being ineffective. Non-effect threshold value Th ie  is the minimum value of aggregation values supposed to perform a notification that there is no effect. In a case where there is a maintenance group for which aggregation value A is greater than non-effect threshold value Th ie  (step S 117 : YES), notification determinator  122 B causes the process to proceed to step S 118 . In a case where there is no maintenance group for which aggregation value A is greater than non-effect threshold value Th ie  (step S 117 : NO), notification determinator  122 B finishes the process. 
     In step S 118 , notification determinator  122 B performs a notification of a maintenance content corresponding to the maintenance group for which aggregation value A is determined as being greater than sign threshold value Th f  in step S 114 . Notification determinator  122 B also performs a notification of a maintenance content corresponding to the maintenance group for which aggregation value A is determined as being greater than non-effect threshold value Th ie  in step S 117 . 
     With this configuration, according to maintenance display apparatus  100 B related to the third exemplary embodiment, it is possible to notify a user of not only a maintenance content that is supposed to be able to improve winding apparatus  200  but also a content of maintenance work that was performed in the past but was not effective. Consequently, the user can avoid a situation in which ineffective maintenance work is repeatedly performed, so that the time required for maintenance can be reduced and the labor required for the maintenance can also be reduced. 
     In the maintenance display apparatus according to the present exemplary embodiment, the facility state diagnosis model generator generates a new facility state diagnosis model on the basis of production result data before the time at which maintenance work determined as being ineffective is performed, and maintenance result data regarding the maintenance work. The notifier performs a notification of a content of maintenance work determined as being effective as effective maintenance work, and also performs a notification of a content of maintenance work correlated with a facility state diagnosis model that is generated on the basis of maintenance result data regarding maintenance work determined as being ineffective as ineffective maintenance work. 
     MODIFICATION EXAMPLES 
     Although the exemplary embodiments according to the present disclosure have been described above with reference to the drawings, the present disclosure is not limited to such examples. It is clear that a person skilled in the art can conceive of various changes or modifications within the scope of the claims, and it is understood that they are naturally included in the technical scope of the present disclosure. The respective constituents in the above-described exemplary embodiment may be arbitrarily combined with each other within the scope without departing from the disclosed concept. 
     Modification Example 1 
     In the above-described exemplary embodiment, in the learning process, in the process of maintenance effect determinator  123  determining whether or not maintenance work is effective, it is determined whether or not the maintenance work is effective depending on whether or not a difference between defect ratios before and after the maintenance work is greater than a predetermined threshold value (refer to  FIGS.  13 A and  13 B ). 
     However, maintenance effect determinator  123  may determine whether or not maintenance work is effective by using other methods.  FIGS.  26 A and  26 B  are diagrams for describing a modification example of a method of maintenance effect determinator  123  determining whether or not maintenance work is effective in the learning process. 
     In the examples illustrated in  FIGS.  26 A and  26 B , the presence or absence of an effect is determined on the basis of whether or not post-maintenance defect ratio Nf after  is greater than a predetermined threshold value (for example, 20%) without referring to a pre-maintenance defect ratio. In the example illustrated in  FIG.  26 A , Nf after =0%, which is less than the predetermined threshold value of 20%, and thus it is determined that there is an effect. On the other hand, in the example illustrated in  FIG.  26 B , Nf after =40%, which is greater than the predetermined threshold value of 20%, and thus it is determined that there is no effect. 
     Similarly, in the update process, maintenance effect determinator  123  may determine whether or not the maintenance work is effective by using a method different from that in the above-described exemplary embodiment. 
     In the above-described exemplary embodiment, in the update process, in the process of maintenance effect determinator  123  determining whether or not maintenance work is effective, the presence or absence of an effect is determined depending on whether or not a difference between coincidences before and after the maintenance work is greater than a predetermined threshold value (refer to  FIGS.  19 A and  19 B ). 
       FIGS.  27 A and  27 B  are diagrams for describing a modification example of a method of maintenance effect determinator  123  determining whether or not maintenance work is effective in the update process. 
     In the examples illustrated in  FIGS.  27 A and  27 B , the presence or absence of an effect is determined on the basis of whether or not post-maintenance coincidence C after  is greater than a predetermined threshold value (for example, 0.30) without referring to the pre-maintenance coincidence. In the example illustrated in  FIG.  27 A , C after =0.20, which is less than the predetermined threshold value of 0.30, and thus it is determined that there is an effect. On the other hand, in the example illustrated in  FIG.  27 B , C after =0.90, which is greater than the predetermined threshold value of 0.30, and thus it is determined that there is no effect. 
     Modification Example 2 
     In the above-described exemplary embodiment, facility state diagnosis model generator  124  generates facility state diagnosis model M that is a learned model in which corresponding maintenance work that is effective to a certain defect has been learned, and notification determinator  122  determines whether or not to perform a notification that maintenance work is to be performed by using the model. However, the present disclosure is not limited thereto, and it may be determined whether or not an inspection result indicates defective on the basis of only shape data (refer to  FIG.  5 C ) of produced winding body  204 . In a case where the inspection result of the produced winding body  204  indicates defective, a notification may be performed such that maintenance work is performed on take-out chuck  214  or any of plurality of winding cores  206  which is a cause of the defect. 
     In Modification Example 2, the controller of the maintenance display apparatus performs the following control. In other words, when the controller acquires shape data of a new winding body, the controller determines whether or not continuous positions of both ends of each of the first sheet material and the second sheet material are separated from the reference positions. In a case where it is determined that the continuous positions of both ends of each of the first sheet material and the second sheet material are not separated from the reference positions or are within a predetermined range centering on the reference positions, the controller does not perform a notification that maintenance work is to be performed. In a case where it is determined that the continuous positions of both ends of each of the first sheet material and the second sheet material are separated from the reference positions by a predetermined distance or more, or are not within the predetermined range centering on the reference positions, the controller determines whether only inspection results of a plurality of winding bodies wound on a plurality of winding cores indicate defects, or inspection results of the winding bodies wound on all of the plurality of winding cores indicate defects. 
     In a case where only inspection results of a plurality of winding bodies wound on a plurality of winding cores indicate defects, the controller performs a notification that a winding core on which a winding body determined as being defective as an inspection result is wound is to be maintained. On the other hand, in a case where inspection results of the winding bodies wound on all of the plurality of winding cores indicate defects, the controller performs a notification that take-out chuck  214  is to be maintained. 
     In Modification Example 2, the controller may change a notification method depending on whether a distance of continuous positions of both ends of each of the first sheet material and the second sheet material from the reference positions is less than or equal to a predetermined threshold value or is greater than the threshold value. 
     According to Modification Example 2, it can be determined which any of take-out chuck  214  or plurality of winding cores  206  is to be maintained without generating facility state diagnosis model M and a notification thereof can be performed. However, since the first to third exemplary embodiments have higher accuracy in specifying a cause of a defect than Modification Example 2, the first to third exemplary embodiments are more preferable than Modification Example 2 in order to achieve the object of the present disclosure. 
     Modification Example 3 
     In the above-described exemplary embodiments, in a case where the continuous positions of both ends of each of first sheet material  202  and second sheet material  203  in image I error  indicating the sectional shape of single winding body  204  are separated from the reference positions in image I normal , controller  120  determines that an inspection result of winding body  204  is defective as an inspection result, but the present disclosure is not limited thereto. In a case where a position of winding body  204  held by take-out chuck  214  is deviated relative to the normal position, the position of winding body  204  held by take-out chuck  214  may not be uniformly deviated for each produced winding body  204 . In this case, winding body  204  may cause vertical variations in the positions of both ends of each of first sheet material  202  and second sheet material  203  in the sectional shape. 
     In Modification Example 3, in consideration of such variations, controller  120  may collect, in a time series, information regarding the positions of both ends of each of first sheet material  202  and second sheet material  203  in images generated from plurality of winding bodies  204  produced within a predetermined time after winding body  204  starts to be produced in winding apparatus  200 , and may inspect winding body  204  by using the information. 
     Modification Example 4 
     In the above-described exemplary embodiments, as shape data that causes a winding body to be determined as being defective as an inspection result, image I error  has been exemplified in which the continuous positions of both ends of each of first sheet material  202  and second sheet material  203  are separated from the reference position. However, in the present disclosure, shape data that causes a winding body to be determined as being defective as an inspection result is not limited thereto. For example, in a case where a camera (not illustrated) used by inspection machine  207  to scan winding body  204  is dirty, positions of both ends of each of first sheet material  202  and second sheet material  203  may not be partially detected in a generated image. In this case, an image in which positions of both ends of each of first sheet material  202  and second sheet material  203  are partially lost is generated. Also in a case where such an image is generated, a winding body corresponding to the image is naturally determined as being defective as an inspection result. 
     In Modification Example 4, in consideration of such a case, in a case where an image in which the positions of both ends of each of first sheet material  202  and second sheet material  203  are partially lost is generated, a winding body corresponding to the image may be determined as being defective as an inspection result, and a cause of a defect may be determined as being the camera of inspection machine  207 . 
     Modification Example 5 
     In the above-described exemplary embodiments, for the sake of description, the configuration in which the maintenance display apparatus  100  ( 100 A,  100 B) includes storage  110  ( 110 B), controller  120  ( 120 A,  120 B), and notifier  130  has been described, but the present disclosure is not limited thereto. As described in the exemplary embodiments, the storage and the controller may be configured separately from each other and disposed at distant positions as long as the storage and the controller are configured to be able to communicate with each other. The notifier may be included in a production apparatus or may be installed outside the production apparatus. The notifier may be connected to the storage and the controller via a network, or may be directly connected to the storage and the controller. 
     As described above, in the maintenance display apparatus according to the present disclosure, the storage, the controller, and the notifier may be separate devices that are independent from each other and may operate independently from each other. As long as the storage, the controller, and the notifier can communicate with each other, a place where they are disposed is not particularly limited. The notification device may be disposed in a factory or the like where the production apparatus is disposed, and the storage and the controller may be included in, for example, a so-called cloud server disposed on a cloud. 
     In the above-described exemplary embodiments, controller  120  ( 120 A,  120 B) has performed all the learning process, the update process, and the identification process. The learning process is a process of generating facility state diagnosis model M, and the update process is a process of updating facility state diagnosis model M. The identification process is a process of identifying whether or not an abnormality or a sign of an abnormality has occurred in plurality of produced new winding bodies  204  by using facility state diagnosis model M. In the identification process, controller  120  ( 120 A,  120 B) controls notifier  130  to perform the notification process. However, the present disclosure is not limited thereto. 
     For example, the controller may perform only the learning process or the update process, and the notifier may receive the facility state diagnosis model from the controller and perform the identification process by using the received facility state diagnosis model. With this configuration, it is possible to suppress an increase in an amount of communication between the controller and the notifier, and, even in a case where a plurality of notifiers are connected to the controller, a load of the identification process can be distributed to each notifier. Therefore, it is possible to prevent a processing delay due to processes being concentrated on the controller. 
     According to the present disclosure, it is possible to detect a sign of an abnormality in a facility. 
     According to an aspect of the present disclosure, there is provided an apparatus generating a learned model for maintenance of a winding apparatus including a first supply mechanism that supplies a first electrode sheet, a second supply mechanism that supplies a second electrode sheet, a first bonding roller that is provided on a first electrode sheet side, a second bonding roller that is provided on a second electrode sheet side, and is paired with the first bonding roller to bond the first electrode sheet and the second electrode sheet to each other, a first winding core, a drive mechanism that moves the first winding core to a predetermined winding position and winds the first electrode sheet and the second electrode sheet in an overlapping manner on the first winding core, and a sensor that reads a first end surface of the first electrode sheet and a second end surface of the second electrode sheet along a radial direction of a first winding body in which the first electrode sheet and the second electrode sheet are wound in an overlapping manner by a plurality of turns on the first winding core, the apparatus including an acquirer that acquires, from the sensor, first group data indicating a position of the first end surface read along the radial direction of the first winding body, and second group data indicating a position of the second end surface read along the radial direction of the first winding body; a notification determinator that determines that detection of the first winding body in the sensor is not normally performed in a case where continuous positions of the first end surface and the second end surface indicated by the first group data and the second group data are separated from reference positions by a predetermined distance or more; and a model generator that outputs information indicating that detection of the first winding body in the sensor is not normally performed to the display apparatus, determines whether or not the first group data and the second group data read before the maintenance of the winding apparatus are used for generating the learned model on the basis of a first occurrence degree of a defect of the first winding body before maintenance of the winding apparatus and a second occurrence degree of a defect of the first winding body after the maintenance of the winding apparatus, and, in a case where it is determined that the first group data and the second group data are used, generates a learned model for outputting information indicating that the detection of the first winding body is not normally performed by using the first group data and the second group data read before the maintenance of the winding apparatus. 
     According to another aspect of the present disclosure, there is provided a computer readable recording medium storing a program executed by a computer outputting information for displaying information regarding maintenance of a winding apparatus including a first supply mechanism that supplies a first electrode sheet, a second supply mechanism that supplies a second electrode sheet, a first bonding roller that is provided on a first electrode sheet side, a second bonding roller that is provided on a second electrode sheet side, and is paired with the first bonding roller to bond the first electrode sheet and the second electrode sheet to each other, a first winding core, a drive mechanism that moves the first winding core to a predetermined winding position and winds the first electrode sheet and the second electrode sheet in an overlapping manner on the first winding core, and a sensor that reads a first end surface of the first electrode sheet and a second end surface of the second electrode sheet along a radial direction of a first winding body in which the first electrode sheet and the second electrode sheet are wound in an overlapping manner by a plurality of turns on the first winding core, the program causing the computer to execute a procedure of acquiring, from the sensor, first group data indicating a position of the first end surface read along the radial direction of the first winding body, and second group data indicating a position of the second end surface read along the radial direction of the first winding body; a procedure of determining that detection of the first winding body in the sensor is not normally performed in a case where continuous positions of the first end surface and the second end surface indicated by the first group data and the second group data are separated from reference positions by a predetermined distance or more; and a procedure of outputting information indicating that the detection of the first winding body in the sensor is not normally performed to the display apparatus. 
     According to still another aspect of the present disclosure, there is provided a computer readable recording medium storing a program executed by a computer generating a learned model for maintenance of a winding apparatus including a first supply mechanism that supplies a first electrode sheet, a second supply mechanism that supplies a second electrode sheet, a first bonding roller that is provided on a first electrode sheet side, a second bonding roller that is provided on a second electrode sheet side, and is paired with the first bonding roller to bond the first electrode sheet and the second electrode sheet to each other, a first winding core, a drive mechanism that moves the first winding core to a predetermined winding position and winds the first electrode sheet and the second electrode sheet in an overlapping manner on the first winding core, and a sensor that reads a first end surface of the first electrode sheet and a second end surface of the second electrode sheet along a radial direction of a first winding body in which the first electrode sheet and the second electrode sheet are wound in an overlapping manner by a plurality of turns on the first winding core, the program causing the computer to execute a procedure of acquiring, from the sensor, first group data indicating a position of the first end surface read along the radial direction of the first winding body, and second group data indicating a position of the second end surface read along the radial direction of the first winding body; a procedure of determining that detection of the first winding body in the sensor is not normally performed in a case where continuous positions of the first end surface and the second end surface indicated by the first group data and the second group data are separated from reference positions by a predetermined distance or more; and a procedure of outputting information indicating that detection of the first winding body in the sensor is not normally performed to the display apparatus, determining whether or not the first group data and the second group data read before the maintenance of the winding apparatus are used for generating the learned model on the basis of a first occurrence degree of a defect of the first winding body before maintenance of the winding apparatus and a second occurrence degree of a defect of the first winding body after the maintenance of the winding apparatus, and, in a case where it is determined that the first group data and the second group data are used, generating a learned model for outputting information indicating that the detection of the first winding body is not normally performed by using the first group data and the second group data read before the maintenance of the winding apparatus. 
     The present disclosure is useful for a maintenance display apparatus that displays information regarding maintenance of a production facility.