Patent Application
20230266742
The present invention relates to a display system, a display method, and a display program.
Patent Document 1 proposes a method for monitoring the state of a facility by dividing operating states into modes based on event signals, creating a normal model for each mode, and performing abnormality determination based on the created normal models. According to this method, the sufficiency of the learning data used to create the normal models is checked, and the thresholds to be used to determine abnormality are set based on the results of the check, so that incorrect notifications, in which a normal state is determined to be an abnormal state, can be prevented from being provided.
Patent Document 2 proposes a method for detecting the occurrence of an abnormality in products produced in a production facility. Specifically, Patent Document 2 proposes a method of classifying pieces of data collected from a production system into normal and abnormal cases of the products, identifying feature values that produce a significant difference between the normal case and the abnormal case, and diagnosing products regarding whether or not the products are normal, based on the identified feature values.
If an abnormality occurs in a production facility, such an abnormality must be immediately resolved. However, it is common that the user needs to investigate the cause of the abnormality with reference to a manual or the like before taking an action to resolve the abnormality. However, looking up the manual every time an abnormality occurs takes time and may delay the action. The present invention has been made to solve this problem, and aims to provide a display system, a display method, and a display program that make it possible to easily check components related to abnormalities that may occur in a production facility.
A display system according to the present invention is a display system to be provided in a production facility that produces products and includes, as components, at least one driving means for driving the production facility and at least one monitoring means for monitoring the production, the display system including: a control unit; a display unit; and a storage unit. The storage unit is configured to store a causal relationship model representing a causal relationship between two or more components of the plurality of components and the abnormality that may occur in the production facility, and the control unit is configured to: display, on the display unit, a model diagram that has nodes and one or more edges connecting the nodes to each other, based on the causal relationship model; and in response to a mode selection, such as a requestfrom a user, displaying the model diagram in either a first mode in which all of the nodes corresponding to the components are displayed as the nodes in the model diagram, or displaying the model diagram in a second mode in which the nodes corresponding to the components are allocated among a predetermined number of groups based on a predetermined criterion and the groups are displayed as the nodes in the model diagram.
With this configuration, when a causal relationship model is to be displayed, the user can select a mode in which all the nodes constituting the causal relationship model are displayed or a mode in which some nodes are allocated among groups and the groups are displayed as nodes. Therefore, the causal relationship model can be displayed in a mode that suits the user’s purpose. For example, when the user wishes to check the detailed causal relationship between the components, the nodes can be displayed in the first mode, and when the user wishes to check the overall picture of the causal relationship model, the nodes can be displayed in the second mode.
In the above-described display system, it is possible to employ a configuration in which the control unit in the second mode for the model diagram is configured to display the nodes in a plurality of tiers that have different display modes, by further dividing the plurality of groups into a predetermined number of groups stepwise from a lower tier to a higher tier, and display the groups.
In the above-described display system, the control unit may be configured to, when an abnormality occurs in the production facility: if the model diagram is in the first mode, change the display mode of the node corresponding to the component related to the abnormality; and if the model diagram is in the second mode, change the display mode of the group that includes the node corresponding to the component related to the abnormality.
As a result, it is easier for the user to visually check the component related to the abnormality. In particular, when the causal relationship model is displayed in the second mode, the user can swiftly visually check where the component of interest is located.
In the above-described display system, the control unit may be configured to, if the model diagram is in the second mode, when a group is selected from the groups, display the nodes included in the group.
With this configuration, it is possible to display the node corresponding to the detailed component only regarding the group to be checked. Therefore, it is possible to prevent the overall picture from being complex, which improves visibility.
A display method according to the present invention is a display method for displaying, on a display unit, a causal relationship between components of a production facility related to an abnormality that may occur in the production facility, the production facility producing products and including, as the components, at least one driving means for driving the production facility and at least one monitoring means for monitoring the production, the display method including: a step of storing a causal relationship model representing a causal relationship between two or more components of the plurality of components and the abnormality that may occur in the production facility; and a step of displaying, on the display unit, a model diagram comprising nodes and one or more edges connecting the nodes to each other, based on the causal relationship model. In response to a mode selection, such as a request from a user, the model diagram is displayed in a first mode in which all of the nodes corresponding to the components are displayed as the nodes in the model diagram, or the model diagram is displayed in a second mode in which the nodes corresponding to the components are allocated among a predetermined number of groups based on a predetermined criterion and the groups are displayed.
A display program according to the present invention is a display program for displaying, on a display unit, a causal relationship between components of a production facility related to an abnormality that may occur in the production facility, the production facility producing products and including, as the components, at least one driving means for driving the production facility and at least one monitoring means for monitoring the production, the display program enabling a computer to carry out: a step of storing a causal relationship model representing a relationship between two or more components of the plurality of components and the abnormality that may occur in the production facility; and a step of displaying, on the display unit, a model diagram comprising nodes and one or more edges connecting the nodes to each other, based on the causal relationship model. In response to a mode selection, such as a request from a user, the model diagram is displayed in a first mode in which all of the nodes corresponding to the components are displayed as the nodes in the model diagram, or the model diagram is displayed in a second mode in which the nodes corresponding to the components are allocated among a predetermined number of groups based on a predetermined criterion and the groups are displayed as the nodes in the model diagram.
According to the present invention, it is possible to easily check components related to abnormalities that may occur in a production facility.
Hereinafter, an embodiment according to one aspect of the present invention (hereinafter also referred to as the “present embodiment”) will be described with reference to the drawings. However, the embodiment described below is merely an example of the present invention in all aspects. It goes without saying that various modifications and variations can be made without departing from the scope of the present invention. That is to say, when carrying out the present invention, specific configurations depending on the embodiment may be adopted as appropriate. The data that appears in the present embodiment is described using a natural language. However, more specifically, the data is specified using, for example, a computer-recognizable pseudo-language, commands, parameters, or a machine language.
First, an example of a scenario to which the present invention is applied will be described with reference to
The analysis device 1 generates a causal relationship model representing relationships between components regarding an abnormality that may occur in the packaging machine 3, and displays the causal relationship model on a screen 21 of the display device 2. The example in
The example in
For example, the example in
In one example in the present embodiment, the causal relationship model can be displayed in any of the modes shown in the first to third tiers. As a result, when the detailed causal relationship model regarding the relationship between the components is to be checked, the third tier can be displayed, and when the causal relationship model regarding the relationship between the devices or the facilities, the first or second tier can be displayed.
In the above description, the packaging machine 3, the feeder, the P & P robot, and the inspection facility are shown as examples of production facilities, but any production facility may be employed as long as some kind of product can be produced, and the type thereof need not be particularly limited. The type of its components need not be particularly limited and may be selected as appropriate depending on the embodiment. Examples of the components include a conveyor, a robot arm, a servomotor, a cylinder (such as a molding machine), a suction pad, a cutter device, and a sealing device. Instead of the above-described packaging machine 3, the production facility may be, for example, a multifunction apparatus including a printer, a mounting machine, a reflow oven, a board inspection device, and so on. Furthermore, instead of a device that involves some physical actions as described above, the production facility may include, for example, a device that performs internal processing such as a device that detects some information with various sensors, a device that acquires data from various sensors, a device that detects some information from the acquired data, a device that performs information processing on the acquired data, and so on. One production facility may be constituted by one or more devices, or constituted by a portion of a device. When the same device performs a plurality of kinds of processes, the device may be regarded as a different component for each kind of process. For example, if the same device performs a first process and a second process, the device may be regarded as a first component when performing the first process, and may be regarded as a second component when performing the second process.
Next, an example of a hardware configuration of the production system according to the present embodiment will be described.
First, an example of a hardware configuration of the analysis device 1 according to the present embodiment will be described with reference to
The control unit 11 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), and so on, and controls each component to perform information processing. The storage unit 12 is, for example, an auxiliary storage device such as a hard disk drive or a solid state drive, and stores a program 121 that is to be executed by the control unit 11, schematic diagram data 122, causal relationship model data 123, operation state data 124, and so on.
The program 121 is a program for generating a causal relationship model representing a relationship between an abnormality occurring in the packaging machine 3 and a component, displaying the model on the display device 2, and so on. The schematic diagram data 122 is data that represents a schematic diagram showing the target production facility, and is data that represents a schematic diagram showing the packaging machine 3 in the present embodiment. The schematic diagram need only be a schematic diagram of the entire packaging machine, showing at least the locations of the components indicated by the causal model, and does not necessarily have to be a detailed diagram. Alternatively, the diagram may be an enlarged diagram showing only a portion of the packaging machine 3.
The causal relationship model data 123 is data that represents a causal relationship model regarding the occurrence of an abnormality built using the feature values of the components extracted from the packaging machine 3. That is to say, the causal relationship model data 123 is data that represents the causal relationship between the components when an abnormality occurs. In this analysis device 1, as will be described later, causal relationship model data is generated from the feature values or the like extracted from the packaging machine 3. However, the analysis device 1 may also store causal relationship model that has been generated in advance in an external device.
The operation state data 124 is data indicating the operation state of the packaging machine 3. Although details will be described later, for example, the operation state data 124 may be data that can be generated through the driving of the components described above, such as measurement data regarding torque, speed, acceleration, temperature, and pressure, for example. When the component is a sensor, the operation state data 124 may be the result of detection, for example, detection data indicating “on” or “off”, which represents whether or not an item WA is present.
The communication interface 13 is, for example, a wired LAN (Local Area Network) module, a wireless LAN module, or the like, and is an interface for performing wired or wireless communication. That is to say, the communication interface 13 is an example of a communication unit configured to perform communication with another device. The analysis device 1 according to the present embodiment is connected to the packaging machine 3 via the communication interface 13.
The external interface 14 is an interface for connecting to an external device, and is configured as appropriate for the external device to be connected. In the present embodiment, the external interface 14 is connected to the display device 2. Note that the display device 2 may be a well-known liquid crystal display, touch panel display, or the like.
The input device 15 is a device for inputting, such as a mouse or a keyboard.
The drive 16 is, for example, a CD (Compact Disk) drive, a DVD (Digital Versatile Disk) drive, or the like, and is a device for reading a program stored on a storage medium 17. The type of the drive 16 may be appropriately selected according to the type of the storage medium 17. Note that at least some of the various kinds of data 122 to 124 including the programs 121 stored in the storage unit may be stored on this storage medium 17.
The storage medium 17 is a medium that stores information such as programs through electrical, magnetic, optical, mechanical, or chemical actions so that a computer, another device, a machine, and so on can read the information such as the programs.
Regarding the specific hardware configuration of the analysis device 1, it is possible to omit, replace, or add components as appropriate depending on the embodiment. For example, the control unit 11 may also include a plurality of processors. The analysis device 1 may also be constituted by a plurality of information processing device. An information processing device designed exclusively for the service to be provided may also be used as the analysis device 1, or a general-purpose server device or the like may be used instead.
Next, an example of a hardware configuration of the packaging machine 3 according to the present embodiment will be described with reference to
The packaging film can be, for example, a resin film such as a polyethylene film. The film roll 30 is provided with a winding core, and the packaging film is wound around the winding core. The winding core is supported so as to be rotatable about an axis, which enables the film roll 30 to feed out the packaging film while rotating.
The film transport unit 31 includes a drive roller that is driven by a servomotor 311 (the servo 1), a passive roller 312 to which a rotational force is applied from the drive roller, and a plurality of pulleys 313 that guide the packaging film while applying a tension thereto. Thus, the film transport unit 31 is configured to feed out the packaging film from the film roll 30 and transport the fed-out packaging film to the bag-making unit 33 without relaxing it.
The item transport unit 32 includes a conveyor 321 that conveys the item WA to be packaged, and a servo motor 322 (the servo 2) that drives the conveyor 321. As illustrated in
The bag-making unit 33 includes a conveyor 331, a servo motor 332 (a servo 3) that drives the conveyor 331, a center sealing unit 333 that seals the packaging film in the transport direction, and an end sealing unit 334 that cuts the packaging film at both ends in the transport direction and seals the packaging film at each end.
The conveyor 331 transports the item WA transported from the item transport unit 32 and the packaging film supplied from the film transport unit 31. The packaging film supplied from the film transport unit 31 is supplied to the center sealing unit 333 while being appropriately folded so that both side edge portions in the width direction thereof overlap each other. The center sealing unit 333 is constituted by, for example, a pair of left and right heating rollers (heaters 1 and 2), and heats and seals both side edge portions of the folded packaging film in the transport direction. As a result, the packaging film is formed into a tubular shape. The item WA is put into this tubular packaging film. A fiber sensor 336 (a sensor 3) for detecting the position of the item WA is provided upstream of the end sealing unit 334, above the conveyor 331.
On the other hand, the end sealing unit 334 includes, for example, a roller that is driven by a servomotor 335, a pair of cutters that are opened and closed by the rotation of the roller, and heaters (heaters 3) that are provided on both sides of each cutter. With these components, the end sealing unit 334 is configured to cut the tubular packaging film in the direction orthogonal to the transport direction and to heat and seal the cut portion. After passing through the end sealing unit 334, the leading end of the tubular packaging film is sealed on both sides in the transport direction and is separated from the following portion to form a package WB that encloses the item WA.
The packaging machine 3 described above can package the item WA through the following process. That is to say, the film transport unit 31 feeds out the packaging film from the film roll 30. Also, the item transport unit 32 transports the item WA to be packaged. Next, the center sealing unit 333 of the bag-making unit 33 forms the fed-out packaging film into a tubular shape. After the item WA is put into the formed tubular packaging film, the tubular packaging film is cut in the direction orthogonal to the transport direction by the end sealing unit 334, and both sides of the cut portion in the transport direction are heated and sealed. Thus, a horizontal pillow type package WB that encloses the item WA is formed. That is to say, packaging of the item WA is complete.
Note that the drive control of the packaging machine 3 can also be performed using a PLC or the like provided separately from the packaging machine 3. If this is the case, the operation state data 124 described above can be acquired from the PLC. In addition, for example, ten components are set in the packaging machine 3 with the above-described configuration, in order to construct causal relationships regarding an abnormality (see
Next, the functional configuration (software configuration) of the analysis device 1 will be described.
The feature value acquisition unit 111 acquires a plurality of types of feature values calculated from operation state data 124 that indicates the operation states of the packaging machine 3, for both the normal case in which the packaging machine 3 has normally formed the package WB and the abnormal case in which an abnormality has occurred in the formed package WB. Using the plurality of types of feature values acquired for the normal case and the abnormal case, the model building unit 112 selects feature values that are useful for prediction of an abnormality from the plurality of types of feature values acquired, based on a predetermined algorithm for deriving the degree of association between an abnormality occurring in the formed package WB and each type of feature value. Thereafter, using the selected feature values, the model building unit 112 builds a causal relationship model 123 indicating a causal relationship between the components when an abnormality occurs.
The display control unit 113 has the function of displaying a schematic diagram of the above-described packaging machine 3, a causal relationship model (which may be the above-described grouping display), various types of feature values, and so on, on the screen 21 of the display device 2. In addition, the display control unit 113 performs control to display various kinds of information on the screen 21 of the display device 2.
Each function of the analysis device 1 will be described in detail in the operation example below. Note that the present embodiment describes an example in which all the above functions are realized using a general-purpose CPU. However, some or all of the above functions may be realized using one or more dedicated processors. In addition, regarding the functional configuration of the analysis device 1, the functions may be omitted, replaced, or added as appropriate depending on the embodiment.
Next, an operation example of the production system with the above-described configuration will be described.
First, with reference to
In the first step S101, the control unit 11 of the analysis device 1 functions as the feature value acquisition unit 111, and acquires a plurality of types of feature values calculated from operation state data 124 that indicates the operation states of the packaging machine 3, for both the normal case in which the packaging machine 3 has normally formed the package WB and the abnormal case in which an abnormality has occurred in the formed package WB.
Specifically, first, the control unit 11 collects operation state data 124 classified into data for the normal case and data for the abnormal case. The type of operating state data 124 to be collected is not particularly limited as long as it indicates the state of the packaging machine 3. In the present embodiment, the operating state data 124 may be data that can be generated through the driving of the components described above, and examples of which include measured data such as torque, speed, acceleration, temperature, and pressure.
When the component is a sensor, the operating state data 124 can be measured data such as an ON time, an OFF time, a turn ON time, and a turn OFF time. The ON time and the OFF time are respectively the total time during which the control signal is ON and the total time during which the control signal is OFF within the target frame, as shown in
Next, the control unit 11 divides the collected operation state data 124 into frames to define a processing range for calculating features values. For example, the control unit 11 may divide the operation state data 124 into frames of a fixed time length. However, the packaging machine 3 does not necessarily operate at regular time intervals. Therefore, if the operation state data 124 is divided into frames of a fixed time length, the operations of the packaging machine 3 reflected in the frames may be shifted with respect to each other.
Therefore, in the present embodiment, the control unit 11 divides the operation state data 124 into frames of a takt time. The takt time is the time required to produce a predetermined number of products, i.e., the time required to form a predetermined number of packages WB. This takt time can be specified based on a signal that controls the packaging machine 3, which is, for example, a control signal that controls the operation of each servomotor or the like of the packaging machine 3.
The relationship between the control signal and the takt time will be described with reference to
For example, in the control signal shown in
Note that the type of the control signal is not particularly limited as long as the signal can be used to control the packaging machine 3. For example, if the packaging machine 3 includes a sensor for detecting a mark provided on a packaging film, and a signal output from this sensor is used to adjust the feeding amount of the packaging film, this output signal from the sensor may be used as a control signal.
Next, the control unit 11 calculates feature values from each frame of the operation state data 124. The type of feature values is not particularly limited as long as each feature value indicates a feature of the production facility.
For example, if the operation state data 124 is quantitative data such as the above measured data (the physical quantity data in
Alternatively, for example, if the operation state data 124 is qualitative data such as the above detection data (the pulse data in
Furthermore, the feature value may be derived not only from a single type of operation state data 124, but from a plurality of types of operation state data 124. For example, the control unit 11 may calculate a cross-correlation coefficient, a ratio, a difference, a synchronization deviation amount, a distance, and so on between frames corresponding to two types of operation state data 124 as feature values.
The control unit 11 calculates a plurality of types of feature values as described above, from the operation state data 124. As a result, the control unit 11 can acquire a plurality of types of feature values calculated from the operation state data 124 for both the normal case and the abnormal case. Note that the processing from the collection of the operating state data 124 to the calculation of feature values may be performed by the analysis device 1 or various devices that control the packaging machine 3, instead of the analysis device 1. In addition, the control unit 11 may discretize the various types of feature values. For example, values higher than a threshold may be regarded as “1” or “high”, and values lower than the threshold may be regarded as “0” or “low”.
In the next step S102, the control unit 11 functions as the model building unit 112 to select a feature value that is effective for predicting an abnormality, from the plurality of types of feature values in the normal case and the abnormal case acquired in step S101, based on a predetermined algorithm that identifies the degree of association between the abnormalities that may occur in the formed package WB and the plurality of types of feature values.
The predetermined algorithm may be formed using, for example, a Bayesian network. A Bayesian network is one of graphical modeling methods for expressing the causal relationship between a plurality of random variables in the form of a directed acyclic graph structure, and expresses the causal relationship between random variables using a conditional probability.
The control unit 11 uses the acquired feature values and the state of the package WB as random variables, i.e., sets the acquired feature values and the state of the package WB to the nodes, to build a Bayesian network, thereby being able to derive the causal relationship between the acquired feature values and the state of the package WB. A known method may be used to build the Bayesian network. For example, the Greedy Search algorithm, the Stingy Search algorithm, or a structure learning algorithm such as the exhaustive search method may be used to build the Bayesian network. The AIC (Akaike’s Information Criterion), C4.5, the CHM (Cooper Herskovits Measure), the MDL (Minimum Description Length), the ML (Maximum Likelihood), or the like may be used as an evaluation criterion for the built Bayesian network. Also, a pairwise method, a listwise method, or the like may be used as a processing method for the case in which missing values are included in the learning data (operation state data 124) used to build the Bayesian network.
For example,
Note that the method of using the acquired feature values and the state of the packaged WB as random variables can be set as appropriate depending on the embodiment. For example, the state of the packaged WB can be regarded as a random variable by defining the event that the packaged WB is normal as “0” and defining the event that an abnormality occurs in the packaged WB as “1”, and associating each event with a probability. Alternatively, for example, the states of each feature value can be regarded as a random variable by defining the event in which the feature value is no greater than a threshold value as “0” and defining the event in which the feature value is greater than the threshold value as “1”, and associating each event with a probability. Note that the number of states to be set for each feature value is not limited to two and may also be three or more.
Next, the display of the causal relationship model built as described above will be described. In this case, the control unit 11 of the analysis device 1 functions as a display control unit 113. The display control unit 113 controls the display of the screen 21 described below. First, the display control unit 113 overlays a schematic diagram 122 read out from the storage unit 12 and the above-described causal relationship model 123 on the screen 21 of the display device 2.
A model diagram 212 in which a schematic diagram showing the packaging machine and a causal relationship model are overlaid with each other is displayed below the selection box 211. In the example in
Furthermore, a graph 214 is displayed on the right side of the list 213, indicating changes over time in the selected feature. In this example, “SERVO 1 -TORQUE AVERAGE VALUE” is selected, and accordingly a line graph 214 indicating changes over time in the value is displayed.
The above-described operations of the screen 21 can be summarized as follows. First, the user selects an anormal event that is to be checked from the selection box 211, using the input device 15. As a result, the display control unit 113 displays the model diagram 212 and the list 213 corresponding to the selected abnormal event, on the screen. Thereafter, when one of the features values is selected from the list 213, the node corresponding thereto is highlighted in the model diagram 212, and the graph 214 indicating changes over time in the selected feature value is displayed. Therefore, the user can visually check the causal relationship regarding the abnormal event while viewing this screen 21. Note that the period corresponding to the changes over time in the feature value to be displayed in the graph 214 can be set by the user as desired.
Also, in the present embodiment, the components can be displayed in groups as described above. For example, when a grouping button 215 at the top of the screen 21 in
Next, the screen display when an abnormality occurs will be described with reference to
Note that if an abnormality occurs when the components are grouped and the screen in
(1) According to the present embodiment, when a causal relationship model is to be displayed, the user can select a mode in which all the nodes constituting the causal relationship model are displayed, or a mode in which some nodes are allocated among groups and the groups are displayed as nodes. Therefore, the causal relationship model can be displayed in a mode that suits the user’s purpose. For example, when the user wishes to check the detailed causal relationship between the components, the nodes can be displayed as shown in
(2) A causal relationship model related to an abnormality that may occur in the packaging machine 3 is displayed, and when an abnormality occurs in the packaging machine 3, a node corresponding to the component related to the abnormality is highlighted. Therefore, it is easy for the user to visually check the component related to the abnormality, and to swiftly proceed with taking care of the abnormality. The same applies to when the components are grouped, and a group displayed as a node can be highlighted.
(3) Changes over time in a feature value of each component are shown in a graph, and therefore the user can visually check changes over time in the feature value of the relevant component when an abnormality occurs. As a result, for example, it is possible to perform an a posteriori check regarding how the feature value has changed to cause the abnormality. Alternatively, by visually checking changes in the feature value, it is possible to detect a sign of the occurrence of an abnormality.
Although an embodiment of the present invention has been described in detail above, the above description is merely an example of the present invention in every respect. It goes without saying that various modifications and variations can be made without departing from the scope of the present invention. For example, the following modifications may be made. Note that, in the following, the same symbols are used for the same components as in the above embodiment, and descriptions of the same features as in the above embodiment are omitted as appropriate. The following modifications can be combined with each other as appropriate.
In the above embodiment, the selection box 211 for an abnormal event, the model diagram 212, the list 213, and the graph 214 are displayed on the screen 21. However, the screen 21 does not necessarily have to display these elements as long as at least the model diagram 212 is displayed. For example, depending on the target production facility, there may be only one abnormal event, and the selection box 211 is also unnecessary in such a case. In addition, it is not necessary to display all the elements 211 to 214 on the screen 21, and these elements may also be displayed separately on a plurality of screens so that the user can switch between them.
The display mode in which the nodes are displayed when an abnormality occurs, e.g. the display attributes, is not particularly limited. Nodes related to an abnormality need only be displayed in a display mode different from nodes corresponding to the components not related to the abnormality. For example, various display modes such as display modes related to color, shape, or animation may be employed. The display mode may also change over time. For example, by changing the display mode between immediately after the occurrence of an abnormality and after the elapse of a predetermined period of time therefrom, the user can visually recognize an approximate time lapse from the occurrence of the abnormality. Moreover, not only display mode of the node but also the display mode of the edges connected to the node can be changed. That is to say, the display mode of at least either the node related to the abnormality and/or its edges may be changed. Furthermore, in addition to changing the display mode of the node and the edges, it is also possible to notify concerned parties of the abnormality by emitting warning sounds or using means such as e-mails. In addition, the display mode of the nodes can be changed not only when an abnormality occurs, but also when a sign of an impending abnormality is detected.
The node grouping shown in
The criteria for grouping are not particularly limited, and the components may be grouped based on various criteria, such as by facility, by device, by measurement item, by mechanism, or by process. Also, when there are many components, as shown in
In addition, it is possible to employ a configuration with which, when a group including nodes is selected in the model diagram, the nodes included in the group are displayed. For example, in the screen in
The method of building a causal relationship model described in the above embodiment is just an example, and other methods may be used. In addition, schematic diagram data 122 or causal relationship model data 123 that have been built using another device may be sequentially stored in the storage unit 12.
The present invention is also applicable to production facilities other than the packaging machine 3. In such a case, the components used to build causal relationship models may also be selected as appropriate depending on the production facility. Schematic diagram data related to a plurality of production facilities may be stored in the storage unit 12 and displayed on the display device 2 for each production facility. However, the schematic diagrams of production facilities are not essential, and it is also possible to display only the causal relationship models.
A display system according to the present invention can be constituted by the analysis device 1 and the display device 2 in the above-described production system. Therefore, the display device 2 in the above-described embodiment corresponds to the display unit according to the present invention, and the control unit 11 and the storage unit 12 of the analysis device 1 correspond to the control unit and the storage unit according to the present invention. For example, the control unit, the storage unit, and the display unit according to the present invention may be realized using a tablet PC or the like.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2020-133640 | Aug 2020 | JP | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2021/029041 | 8/5/2021 | WO |
