Data collection device and program

Abstract

To allow for visualizing object production-related data collected from a production line and object inspection data in association with each other.SOLUTION: A data collection device is provided, comprising a data collection unit 63 configured to collect data on production work-related data items from a production line 3 producing an object through multiple steps, an acquisition unit configured to acquire an identifier of the object produced in the production line, an association unit 64 configured to associate the identifier and an inspection result of the object with data collected in each step during a period in which production work for the object is conducted in the step, and a visualized data generation unit configured to generate visualized data visually showing collected data of each step associated with the identifier and the inspection result.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present disclosure relates to a data collection device, and more particularly to a device that collects data from a production line of FA (Factory Automation). [Background technology]

[0002] Techniques for monitoring production lines with multiple processes have been proposed. For example, Patent Document 1 (JP 2021-86193 A) discloses an information collection system for industrial equipment. This information collection system includes a collection unit that collects, from each of the multiple processing devices, multiple pieces of processing information related to each of the multiple processing processes performed by the multiple processing devices until a corresponding single product is manufactured from at least one workpiece, and an association unit that associates the multiple pieces of processing information collected by the collection unit with each other by assigning an association identifier that allows association between the multiple processing processes related to a single product. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-86193 Summary of the Invention [Problem to be solved by the invention]

[0004] Products and other objects produced on a production line are inspected before being shipped, and there is a need for users at production sites to visually understand data relating to the production of objects collected from the production line and inspection data for the objects by linking them together. Patent Document 1 does not disclose any technology for visualizing the collected information.

[0005] An object of the present disclosure is to provide a configuration capable of creating data that visualizes data relating to the production of an object collected from a production line and that links inspection data for the object. [Means for solving the problem]

[0006] A data collection device according to one example of the present disclosure is a device that collects data on objects produced through one or more processes provided on a production line, and includes a collection unit that collects data on data items related to production work from each process, an acquisition unit that acquires identifiers of the objects produced on the production line, a linking unit that links, for each process, the collected data collected from the process by the collection unit during the period when production work is performed on the objects at that process with the identifiers of the objects and the inspection results of the objects, and a visualization data creation unit that creates visualization data for each process that visualizes and represents the collected data of the process to which the identifiers and inspection results have been linked by the linking unit.

[0007] According to this disclosure, it is possible to provide a configuration capable of creating data that visualizes data relating to the production of an object collected from a production line and that links inspection data of the object.

[0008] In the above disclosure, the collection unit periodically collects data from each process, and the linking unit links, for each process, the identifier of the object and the test result to partial time series data corresponding to the above-mentioned period among the time series data periodically collected from the process.

[0009] According to this disclosure, partial time series data corresponding to the period during which production work is performed on the object can be obtained from time series data periodically collected from the process as data to which the inspection results of the object are linked.

[0010] In the above disclosure, the visualization data includes data that visualizes partial time-series data, to which identifiers and test results are linked, for each process, arranged in order of priority based on the values ​​of the partial time-series data.

[0011] According to this disclosure, it is possible to obtain data that visualizes partial time-series data linked to inspection results by arranging them in order of priority based on the values ​​of the partial time-series data.

[0012] In the above disclosure, each process is configured so that objects are input one by one to that process, the production line is configured so that objects are input from one process to the next in a predetermined order, and the data collection device is configured to, for each process, obtain a representative value of the partial time series data values ​​of each object constituting each of a plurality of groups made up of objects input to that process, and obtain the degree of deviation between the representative value of each group and the representative values ​​of other groups.

[0013] According to this disclosure, the degree of deviation can be obtained from the representative value of the partial time series data of the objects that make up each group.

[0014] In the above disclosure, the visualization data creation unit determines the order of priority based on the degree of deviation acquired for each process.

[0015] According to this disclosure, partial time series data linked to test results can be visualized by arranging them in order of priority based on the degree of deviation.

[0016] In the above disclosure, the representative value includes, for each group, a statistical value of the values ​​of the partial time-series data of each object constituting the group.

[0017] According to this disclosure, the degree of deviation can be obtained from the statistical values ​​of the partial time series data of the objects that make up each group.

[0018] In the above disclosure, the visualized data includes data that visualizes and represents the above statistical values ​​corresponding to each group.

[0019] According to this disclosure, the above statistical values ​​corresponding to each group can be visualized.

[0020] In the above disclosure, the visualization data creation unit determines the order of priority of each process so that the greater the deviation of the process, the higher the process is ranked.

[0021] According to this disclosure, the greater the degree of deviation, the higher the priority in the above order can be.

[0022] In the above disclosure, the types of test results include normal, abnormal, and untested, which indicates that the subject has not yet been tested.

[0023] According to this disclosure, the degree of discrepancy between different groups can include the degree of discrepancy between a group with abnormal test results and a group with normal test results.

[0024] In the above disclosure, the visualization data includes, for each process, characteristic visualization data that visualizes and represents the characteristics of changes in the values ​​of the partial time-series data for each object input into the process.

[0025] According to this disclosure, it is possible to visualize the change characteristics of values ​​of partial time series data.

[0026] In the above disclosure, the characteristic visualization data includes, for each process, temporal characteristic visualization data that visualizes and represents, on a common time axis, characteristics that indicate temporal changes in the values ​​of partial time series data for each object input into the process.

[0027] According to this disclosure, it is possible to create visualized data that represents the characteristics of temporal changes in partial time-series data between different processes on a common time axis.

[0028] In the above disclosure, the visualization data includes data for visualizing collected data in a manner according to the type of test result linked to the collected data.

[0029] According to this disclosure, the manner in which data collected from each process is visualized can be changed based on the inspection results of the object corresponding to the data.

[0030] In the above disclosure, the data collection device is provided in a control device that controls a production line, thereby providing a control device equipped with the data collection device.

[0031] A program according to one example of the present disclosure is a program for causing a computer to execute a method, the method being a method for collecting data on objects produced through one or more processes provided on a production line, the method including the steps of collecting data on data items related to production work from each process, obtaining identifiers for the objects produced on the production line, linking, for each process, the identifiers of the objects and the inspection results of the objects to the collected data collected from that process during the period in which production work is performed on the objects at that process, and creating visualization data for each process that visualizes and represents the collected data for that process linked to the identifiers and inspection results.

[0032] According to this disclosure, when a program is executed, a configuration can be provided that can create data that visualizes data related to the production of an object collected from a production line and inspection data for the object by linking the data together. [Effects of the Invention]

[0033] According to an example of the present disclosure, it is possible to provide a configuration capable of creating visualization data that links and visualizes data related to the production of an object collected from a production line with inspection data of the object. [Brief explanation of the drawings]

[0034] [Figure 1] FIG. 1 is a diagram schematically illustrating an overview of a PLC 100 according to an embodiment. [Figure 2] 1 is a schematic diagram showing an example of an overall configuration of a network system according to an embodiment of the present invention; [Figure 3] 1 is a schematic diagram illustrating an example of a hardware configuration of a PLC 100 according to the present embodiment. [Figure 4] FIG. 1 is a diagram schematically illustrating a hardware configuration of a support device 500 according to the present embodiment. [Figure 5] 1 is a diagram schematically illustrating a module configuration of a PLC 100 according to the present embodiment. [Figure 6]It is a flowchart showing the processing of the inspection method according to this embodiment. [Figure 7] It is a diagram showing an example of the display of the screen according to this embodiment. [Figure 8] It is a diagram for explaining the table configuration of the management of partial time-series data according to this embodiment. [Figure 9] It is a diagram for explaining the table configuration of the management of partial time-series data according to this embodiment. [Figure 10] It is a diagram showing an example of the timing chart according to this embodiment. [Figure 11] It is a diagram schematically showing the processes constituting the production line according to this embodiment. [Figure 12] It is a diagram showing another example of the timing chart according to this embodiment. [Figure 13] It is a diagram schematically showing the processes constituting the production line according to this embodiment. [Figure 14] It is a diagram showing an example of the information setting screen according to this embodiment. [Figure 15] It is a flowchart showing the usage form of the information based on the visualization data according to this embodiment. [Figure 16] It is a diagram showing an example of the display of the screen based on the visualization data according to this embodiment. [Figure 17] It is a diagram showing an example of the display of the screen based on the visualization data according to this embodiment. [Figure 18] It is a diagram showing an example of the display of the screen based on the visualization data according to this embodiment. [Figure 19] It is a diagram showing an example of the display of the screen based on the visualization data according to this embodiment.

Embodiments for Carrying Out the Invention

[0035] Embodiments of the present invention will be described in detail while referring to the drawings. For the same or corresponding parts in the drawings, the same reference numerals are given and the description thereof will not be repeated.

[0036] <A. Application Example> In this embodiment, a PLC (Programmable Logic Controller) will be described as a typical example of a "control system," but the technical ideas disclosed in this specification are applicable to any control system, without being limited to the name PLC. Also, in this embodiment, the "object" to be produced will be called a product, but the term "product" is used to include not only the finished product, but also the parts or intermediate products that make up (assemble) the finished product.

[0037] In this embodiment, the production line may include multiple processes. In a production line with multiple processes, products input into the production line are automatically transported between the equipment that makes up the multiple processes by an appropriate transport device such as a belt conveyor. The products flow through these multiple processes in the order of the processes by the transport device, that is, production work is performed on the products in each process. The products flowing through each process are input from one process to the next. In this embodiment, the final process of the production line includes an inspection process that inspects whether the product quality meets standards.

[0038] FIG. 1 is a diagram illustrating a schematic overview of a PLC 100 according to an embodiment. In this embodiment, the PLC 100 is applied to a product production line having multiple processes. The multiple processes on the production line include a production process in which field devices 90 are installed to perform production work on products, and an inspection process in which field devices 90 are installed to inspect products that have undergone the production process. The production line is further equipped with an individual ID code reader 88 and a lot code reader 89 for detecting product IDs (identifiers). The field devices 90 in the production process include input / output devices such as actuators that apply some kind of physical action to a field such as the production line in accordance with control commands 93, and sensors that exchange information with the field. These input / output devices observe the status of the field devices 90 and output observed values ​​92.

[0039] In this embodiment, the field equipment 90 provided in the inspection process includes an inspection device. When the PLC 100 detects that a product has been input into the inspection process based on an observation value 92 from the field, the inspection device inspects the product by operating in accordance with a control command 93 from the control program 140, and outputs an inspection result 94 as the observation value 92. The inspection result 94 includes the product ID of the product being inspected.

[0040] The PLC 100 has a control unit 10B that repeatedly executes a process of exchanging data with the devices installed on the production line (hereinafter also referred to as IO refresh) and a control calculation process at predetermined control intervals. The control unit 10B can constitute a control device that controls the production line.

[0041] 1, the PLC 100 includes a control unit 10B and an IoT platform 10A. The control unit 10B constitutes a platform for executing programs related to real-time control of industrial equipment under a real-time OS (Operating System), and the IoT platform 10A constitutes a platform for mainly executing programs for IoT processing under a general-purpose OS.

[0042] The control unit 10B includes a control engine 150, a user program executed by the control engine 150, and a data area 42. The user program includes a control program 140 for control calculations, such as a ladder program, which is executed at each predetermined control cycle, and an IO refresh program 40 for IO refresh. The data area 42 includes an area 41 for storing input data 154 and output data 155, an area for storing individual IDs 43 and lot data 44 representing the IDs of products flowing through the process, and an area for various queues 48.

[0043] The control program 140 is a so-called variable program. More specifically, the data of the observation values ​​92, the data for internal calculations, etc. that the control program 140 references during execution are configured so that they can be used by using input variables corresponding to the input data 154, output variables corresponding to the output data 155, and temporary variables, etc.

[0044] Observation values ​​92 including inspection results 94 from field devices 90 are stored as input data 154 in data area 42 by IO refresh, and control program 140 executes control calculations based on input data 154, and calculated control commands 93 are stored in data area 42 as output data 155. Control commands 93 in output data 155 are read from data area 42 and output to field devices 90.

[0045] In this way, the PLC 100 repeatedly performs the IO refresh and the control calculation process in a control cycle, thereby controlling the field devices 90 in synchronization with one another. As a configuration for realizing such synchronous control, the PLC 100 may employ a configuration in which, for example, a user program including the control program 140 and the IO refresh program 40 is provided for each process, and the user programs for each process are executed in parallel in a common control cycle.

[0046] A code reader 88 is provided at each process step. It optically reads the identifier of a product flowing through the process step and outputs an individual ID 43 indicating the read identifier. In a production line, product identifiers are not limited to individual IDs 43 and may be obtained using lot data 44. More specifically, a lot code reader 89 is provided at the first process step of the production line. When a proximity sensor detects, for example, that a product has been introduced into the production line, the lot code reader 89 optically reads a lot name 45 from the product. For each process step, the control unit 10B sets an in-operation flag (described later) from "OFF" to "ON," counts up each time the in-operation flag is set, and outputs a count value 46. In this manner, the count value 46 for each process indicates the order in which each product introduced into the process flows through the process. In this embodiment, the order in which products flow through each process is consistent between processes. Therefore, the kth (k=1, 2, 3, . . . ) product introduced into a process is consistent with the kth product introduced into the next process. Therefore, the same product in the same lot can be assigned common lot data 44 as an identifier between processes.

[0047] As described above, in this embodiment, the mechanism for acquiring the product identifier can be configured to use the individual ID code reader 88 or the lot code reader 89. In the following description, the product identifiers indicated by the individual ID 43 and the lot data 44 will be collectively referred to as "product ID."

[0048] The IoT platform 10A can constitute a "data collection device" that mainly collects information from the production line. It has an IoT engine 250, an IoT program 260 and a web server program 280 executed by the IoT engine 250, as well as setting information 30 and a data accumulation unit 62. Using these elements, the IoT platform 10A provides an environment for creating visualized data 283 that visualizes and represents data of one or more data items associated with a product ID from data stored in the data area 42 of the control unit 10B.

[0049] The setting information 30 includes, for each process, collection setting information 28 indicating the setting of data items to be collected in order to create visualization data 283, and visualization setting information 29 indicating the setting for creating visualization data 283 from data collected in accordance with the collection setting information 28. In this embodiment, the data items set in the collection setting information 28 are set, for example, by the variable names of the data, but the method for setting the data items is not limited to the method using variable names.

[0050] The IoT program 260 includes a collection program 270 that collects (searches) data of data items set for each process from the data area 42 in accordance with the collection setting information 28 and stores the data in the data accumulation unit 62, and a linking program 271.

[0051] The data area 42 has a buffer area capable of storing (holding) data for a predetermined length of time. When the collection program 270 and the linking program 271 are executed, the PLC 100 collects (searches) the data of the set data items and the corresponding product IDs for each process from the buffer area of ​​the data area 42, and links the collected data to the product IDs. The data linked to the product IDs is then stored in the data storage unit 62. As a result, for each process, data of the data items related to the production work set for that process is read from the buffer area and stored in the data storage unit 62 in chronological order. Such data stored in chronological order will hereinafter also be referred to as time-series data.

[0052] More specifically, each time a product ID (individual ID 43 or lot data 44) corresponding to a process in the data area 42 is updated, the PLC 100 searches for (collects) the updated product ID from the data area 42. The PLC 100 identifies a portion of the collected time-series data for the process that corresponds to the time when the product ID was read (i.e., updated), and links (associates) the read product ID with the identified portion of the time-series data. After performing this linking process, the data linked with the product ID is stored in the data storage unit 62. The portion of the time-series data identified in this manner is hereinafter also referred to as "partial time-series data." Furthermore, in the linking process, the PLC 100 links the product ID linked to the partial time-series data with the inspection results 94 corresponding to the product. In this way, the PLC 100 performs a process for linking the product ID and the inspection results 94 with the partial time-series data collected for each production process during the period when production work is performed on the product in that process.

[0053] The web server program 280 includes a setting program 281 for generating setting information 30 and a visualization program 282 for creating visualized data 283 in accordance with visualization setting information 29. The visualization setting information 29 includes, for each process, the data items of the data to be visualized and the product ID. When the visualization program 282 is executed, the PLC 100 performs the visualization process. In the visualization process, the PLC 100 searches the data storage unit 62 for time series data corresponding to the data items of each process based on the data items set for each process indicated in the visualization setting information 29. The PLC 100 then extracts, for each product ID, partial time series data associated with the product ID from the retrieved time series data for each process. The PLC 100 then creates viewable visualization data 283 by associating the extracted partial time series data with each other, even if the partial time series data is associated with the same product ID. The visualization program 282 includes a priority processing program 284 that includes a statistics calculation program 285, which will be described later, for creating the visualization data 283.

[0054] When the Web server program 280 is executed, the PLC 100 operates as a Web server under the IoT platform 10A. The Web server transfers visualization data 283 including Web data to the support device 500. The Web browser of the support device 500 displays a Web screen based on the Web data of the visualization data 283 received from the Web server. The Web data includes GUI (Graphical User Interface) data, and the Web screen is configured to include a GUI screen. The Web data can be created using, for example, HTML (Hypertext Markup Language) to visualize various objects including the Web screen and images within the screen.

[0055] In this way, by displaying the screen based on the created visualization data 283, the data related to the production operations of the products collected from the production line is visualized as an object in which the product ID and the inspection result 94 are associated.

[0056] Note that in FIG. 1, the data collection device configured under the IoT platform 10A is provided in the PLC 100 of the control device that controls the production line, but the implementation mode is not limited to this. For example, the data collection device may be composed of individual devices capable of exchanging data with the control unit 10B via a wired or wireless network.

[0057] Hereinafter, as a more specific application example of the present disclosure, a more detailed configuration and processing of the PLC 100 according to the present embodiment will be described.

[0058] <B. System Configuration> An example of a network system including a production line 3 provided in the FA according to the present embodiment will be described. FIG. 2 is a schematic diagram showing an example of the overall configuration of the network system according to the present embodiment.

[0059] In Fig. 2, a production line 3 applied to an FA comprises production work steps 3A and 3B, and an inspection step 3C. For example, but not limited to, step 3A indicates a screw tightening step for a product, step 3B indicates a soldering step for the screwed product, and step 3C indicates an inspection step for the product after soldering. When multiple types of work are performed on a product by field devices, these steps correspond to the classification (type) of work.

[0060] An inspection device serving as a field device 90 installed in process 3C inspects the product using the captured image acquired by capturing a product in the field of view. More specifically, when the PLC 100 detects that a product has entered the field of view based on the observation value 92, it outputs an imaging control command 93, and the inspection device captures the product in accordance with the imaging control command 93. The inspection device compares the captured image with a model image stored in storage by pattern matching, compares the similarity indicated by the comparison result with a threshold, and outputs the observation value 92, which is an inspection result 94 based on the comparison result, to the PLC 100. For example, if the similarity is equal to or greater than the threshold, the inspection device sets the inspection result 94 to "OK" (normal quality), and if the similarity is less than the threshold, it sets the inspection result 94 to "NG" (abnormal quality) and outputs it. The inspection device is configured to include a product ID in the inspection result 94, so the PLC 100 can acquire the product ID and inspection result 94 for each product flowing through the production line 3.

[0061] The types and number of production work processes included in the production line 3, or the product inspection methods in the inspection processes, are not limited to these, and may vary depending on the type of product being produced or the product specifications.

[0062] Each of processes 3A, 3B, and 3C is provided with one or more field devices 90, an individual ID code reader 88, and a lot code reader 89 for performing the work of that process, and these devices are connected to a field network 11. In addition to being connected to the PLC 100 via the field network 11, these devices may also be connected directly to the PLC 100 via an input / output unit (not shown) associated with the PLC 100.

[0063] Data exchanged between the PLC 100 and devices via the field network 11 is updated at very short intervals, for example, on the order of several hundred microseconds to several tens of milliseconds. Note that the update process of such exchanged data is realized by IO refresh.

[0064] Furthermore, PLC 100 is connected to network 2 via repeater 1. A server device 300 and an HMI (human machine interface) device 310 are connected to network 2, and an information terminal 321 is also connected via the cloud. Server device 300 receives data transferred from PLC 100, for example, data stored in data accumulation unit 62, and stores the data as log file 320. Furthermore, the storage destination of log file 320 is not limited to a device on network 2 (for example, server device 300), but may include a device on the cloud.

[0065] The network 2 and the field network 11 employ protocols and frameworks that correspond to the differences in required characteristics. For example, the protocol for network 2 may be EtherNet / IP (registered trademark), an industrial open network that implements a control protocol on the general-purpose Ethernet (registered trademark). The protocol for field network 11 may be EtherCAT (registered trademark), an example of a machine control network. The protocols for these networks may be the same or different.

[0066] A support device 500 can be connected to the PLC 100. The support device 500 provides support tools that support the user in operating the production line 3. The support tools include setting tools for preparing the execution environment of the control program 140 or the communication environment with the PLC 100. The support tools further include a support tool that supports the setting of the setting information 30. Such support tools are provided to the user by, for example, a UI (User Interface).

[0067] In production line 3, the support device 500 may be connected to the network 2, or may be built into the PLC 100, or may be provided as a portable terminal. Also, the HMI device 310 or the information terminal 321 may be provided as a fixed terminal or a portable terminal.

[0068] <Configuration of PLC 100> FIG. 3 is a schematic diagram showing a hardware configuration example of the PLC 100 according to the present embodiment. Referring to FIG. 3, the PLC 100 mainly includes a power supply circuit 101 that supplies power PW to each part of the PLC 100, a CPU (Central Processing Unit) 102, a chipset 104, a memory 106 mainly composed of a volatile storage medium, a storage 108 mainly composed of a non-volatile storage medium, a USB (Universal Serial Bus) controller 112, a field network controller 113, a memory card interface 114, a timer 115, and a network controller 120.

[0069] The CPU 102 reads out a user program including a control program 140 stored in the storage 108 or the SD card 116 and expands it in the memory 106. By interpreting and executing the expanded program, the CPU 102 controls each part of the PLC 100 and realizes control arithmetic processing for controlling control targets such as the field device 90. Also, the CPU 102 reads out the IoT program 260 and the Web server program 280 control program 140 stored in the storage 108 or the SD card 116 and expands it in the memory 106. By interpreting and executing the expanded program, the CPU 102 controls each part and realizes data collection and association processing from each process, acquisition processing of the setting information 30, and visualization processing.

[0070] The memory 106 is configured with a volatile storage device such as a dynamic random access memory (DRAM) or a static random access memory (SRAM). The storage 108 is configured with a nonvolatile storage device such as a hard disk drive (HDD), a solid state drive (SSD), or a flash memory, but a portion of the storage may be configured with a volatile storage.

[0071] The chipset 104 mediates data exchange between the CPU 102 and each component, thereby realizing processing of the PLC 100 as a whole.

[0072] The storage 108 stores a system program 1082 having an OS (Operating System) 131 and a scheduler program 132 for implementing the basic functions of the PLC 100, as well as a control program 140 created according to the control target and an IO refresh program 40. The storage 108 also stores an IoT program 260 and a Web server program 280. The storage 108 also has an area for storing visualization data 283, an area 41 for storing data collected regarding control, an internal memory 47 that constitutes the area of ​​a data accumulation unit 62 corresponding to a memory unit, and areas for storing setting information 30, an individual ID 43, and lot data 44.

[0073] In this embodiment, the storage format of the time-series data of each process in the data accumulation unit 62 may include a text data file such as a CSV (comma separated values) file. By adding a file name and creation date and time as a header to such a file, it can be configured to be uniquely identifiable.

[0074] The USB controller 112 is responsible for transferring data to and from any information processing device via a USB connection.

[0075] The field network controller 113 has a connector 116a that connects the field network 11 to which the field devices 90 and the like belong, and controls the exchange of data between the PLC 100 and other devices including the field devices 90 via the field network 11. The field network controller 113 has an internal buffer that stores, as input data 154 and output data 155, observation values ​​92 received from the field network 11 and control commands 93 output from the PLC 100 to the field network 11, respectively.

[0076] The memory card interface 114 is configured to allow the SD card 116 to be detachably attached. Under the control of the CPU 102, the memory card interface 114 writes information to the SD card 116 and reads information from the SD card 116. Information read from and written to the SD card 116 includes application programs including the control program 140 and the IoT program 260, data such as various settings, and a log file 320.

[0077] The timer 115 is configured to include a clock circuit or a counter circuit that measures time, but is not limited to such circuits and may be configured by a software module executed by the CPU 102.

[0078] The network controller 120 has a connector 120 a that connects the PLC 100 to the network 2 , and controls the exchange of data between the PLC 100 and other devices via the network 2 .

[0079] The CPU 102 includes one or more processors. By repeatedly executing the control program 140 and the refresh program 40 at a predetermined period (for example, a control period), the one or more processors perform the control operation processing and the IO refresh described above to periodically control the devices connected to the field network 11. Also, the one or more processors of the CPU 102 execute the IoT program 260 and the web server program 280.

[0080] FIG. 2 shows a configuration example in which functions necessary for the CPU 102 to execute a program are provided. However, some or all of these provided functions may be implemented using a dedicated hardware circuit (for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array)). Alternatively, the main part of the PLC 100 may be realized using hardware (for example, an industrial personal computer based on a general-purpose personal computer) that follows a general-purpose architecture. In this case, multi-core technology may be applied to execute processing in parallel. Or, virtualization technology may be used to execute a plurality of different OSs in parallel and execute necessary applications on each OS.

[0081] <Configuration of the support device 500> FIG. 4 is a diagram schematically showing the hardware configuration of the support device 500 according to the present embodiment. Referring to FIG. 4, the support device 500 includes a CPU 510, a memory 512 composed of a volatile storage device such as a DRAM (Dynamic Random Access Memory), a timer 513, a hard disk 514 composed of a non-volatile storage device such as an HDD, an input interface 518, a display controller 520, a communication interface 524, and a data reader / writer 526. These components are connected via a bus 528 so that they can communicate with each other.

[0082] The input interface 518 mediates data transmission between the CPU 510 and input devices such as the keyboard 523, a mouse (not shown), and a touch panel (not shown). The display controller 520 is connected to the display 522 and displays the results of processing in the CPU 510 and the like. The communication interface 524 communicates with the PLC 100 via USB. The data reader / writer 526 mediates data transmission between the CPU 510 and the memory card 516, which is an external storage medium.

[0083] Note that the removable storage media such as the SD card 116 of the PLC 100 and the memory card 516 in FIG. 4 include volatile storage media or non-volatile storage media, and for example, general-purpose semiconductor storage devices such as CF (Compact Flash), SD (Secure Digital), or magnetic storage media such as a flexible disk (Flexible Disk), or optical storage media such as a CD-ROM (Compact Disk Read Only Memory).

[0084] <E.Module Configuration of PLC 100> FIG. 5 is a diagram schematically showing the module configuration of the PLC 100 according to the present embodiment. In FIG. 5, the module configuration of the PLC 100 is shown in association with the modules of the support device 500. The support device 500 includes a web browser 501 and a UI 502 launched by the web browser 501. The UI 502 corresponds to a tool that supports setting of setting information 30 such as visualization setting information 29 and collection setting information 28. The web browser 501 causes the display 522 to display a screen based on web data including the visualization data 283 from the PLC 100.

[0085] The PLC 100 includes a module configured in the IoT platform 10A and a module configured in the control unit 10B. The PLC 100 includes a web server 60 that is realized by executing a web server program 280 under the IoT platform 10A, and an IoT application 61 that is realized by executing an IoT program 260. The IoT application 61 includes a data collection unit 63 that is realized by executing a collection program 270, and an association unit 64 that is realized by executing an association program 271.

[0086] 5 shows an embodiment in which the visualization setting information 29 and collection setting information 28 set by the user by operating the UI 502 are stored in the support device 500 and are used (referenced) by the modules of the PLC 100 as appropriate, but the manner in which the visualization setting information 29 and collection setting information 28 are used is not limited to this. For example, the visualization setting information 29 and collection setting information 28 set via the UI 502 may be transferred to the PLC 100 and stored in the storage 108, and the modules of the PLC 100 may use (reference) the visualization setting information 29 and collection setting information 28 stored in the storage 108.

[0087] The control unit 10B uses IO refresh to collect product ID data from the production line 3, as well as collect variable data indicating observed values ​​92 including inspection results 94, and stores the collected data in the data area 42. The product ID data collected in this manner includes an individual ID 43 and lot data 44. For the sake of explanation, the data area 42 in FIG. 5 shows a data item (variable A) for process 3A set by the collection setting information 28, a data item (variable B) for process 3B, and a data item (variable C) indicating inspection results 94 for inspection process 3C.

[0088] Based on the collection setting information 28, the data collection unit 63 collects data of the data items (variables) of each process set as a collection target from the data area 42, and the time series data of the collected data items undergoes the above-mentioned linking process by the linking unit 64, and is then stored in the data accumulation unit 62. Through the linking process, in the time series data of each process in the data accumulation unit 62, partial time series data collected during the period when production work is performed on a product in that process is linked with the product ID and inspection result 94 of that product.

[0089] The visualization unit 65 performs visualization processing based on the time-series data of each process in the data accumulation unit 62 and in accordance with the settings of the visualization setting information 29, thereby creating visualization data 283. The web browser 501 obtains the visualization data 283 from the web server 60 and causes the display 522 to display a screen based on the visualization data 283.

[0090] <F.フローチャート> Fig. 6 is a flowchart showing the processing of the inspection method according to this embodiment. The flowchart in Fig. 6 is implemented by CPU 102 executing the programs shown in Fig. 1. In the processing in Fig. 6, for example, production line 3 includes processes 3A and 3B for the production work of products in a lot, and an inspection process 3C. If the lot includes products with product IDs "X1" to "X3", the products are input in the order "X1", "X2", and "X3".

[0091] 6, the CPU 102 monitors an operating flag (not shown) corresponding to each process indicated by the input data 154. The operating flag indicates "ON" while the field device 90 performing production work on the product in the process is operating. In other words, the operating flag changes to "ON" when the field device 90 starts operating, and changes to "OFF" when the operation ends. When the operating flag for each process changes from "OFF" to "ON," the CPU 102 starts collecting data for that process, and then ends the data collection when the operating flag changes from "ON" to "OFF." In this way, the CPU 102 collects data for each process during the period when the operating flag indicates "ON."

[0092] The process in Figure 6 shows that a product with product ID "X1" that has passed through process 3B flows to inspection process 3C, a product with product ID "X2" flows from process 3A to process 3B, and a product with product ID "X3" is input to process 3A.

[0093] 6, CPU 102, as linking unit 64, determines whether the product ID should be acquired from individual ID 43 or virtual individual ID (lot data 44) based on collection setting information 28 (step S1). Here, collection setting information 28 includes a setting as to whether the product ID should be acquired from individual ID 43 or lot data 44.

[0094] If it is determined that the product ID is to be obtained from the lot data 44 (NO in step S1), the CPU 102 obtains a virtual individual ID from the lot data 44 for the product flowing through each process (steps S8 and S9). Then, the process proceeds to step S2.

[0095] On the other hand, if it is determined that the product ID is to be acquired from the individual ID 43 (YES in step S1), the CPU 102 collects data based on the collection setting information 28 while the operation flag is "ON", and performs a linking process (steps S2 and S3) of the product ID (individual ID or virtual individual ID) to the time-series data collected in this way. Also, a linking process is performed (step S4) of the product ID (individual ID or virtual individual ID) to the inspection results 94 of the product.

[0096] 6 illustrates partial time-series data 73 acquired in association with the linking processes of steps S2 and S3. Partial time-series data 73 associated with product ID "X3" is acquired for process 3A, and partial time-series data 73 associated with product ID "X2" is acquired for process 3B. Also illustrated is time-series data 73A consisting of inspection results 94 acquired in inspection order in association with the linking process of step S4. Data 73A includes a set of product IDs and inspection results linked in order of input for each product input into process 3C.

[0097] The CPU 102 stores (accumulates) the partial time-series data 73 of each process acquired in steps S2 and S3 and the data 73 acquired in step S4 in the data accumulation unit 62 (steps S4, S5, and S7). FIG. 6 schematically shows data corresponding to each product accumulated (stored) in the data accumulation unit 62 in association with steps S5, S6, and S7. For process 3A, the data accumulation unit 62 performs a linking process for each of the product IDs "X1," "X2," and "X3," thereby storing three partial time-series data 73. For process 3B, the next process after process 3A, the data accumulation unit 62 performs a linking process for each of the product IDs "X1" and "X2," thereby acquiring two partial time-series data 73. For inspection process 3C, the data accumulation unit 62 performs a linking process for the inspection results of product ID "X1."

[0098] The CPU 102 monitors the operation flag corresponding to each process. When it determines that the operation flag has changed to "ON" (Steps S8, S9), it proceeds to Step S1 and performs the subsequent processing in the same manner as described above. As a result, in each process, the linking process is performed for the next product to be input.

[0099] Such monitoring of the operation flag, data collection, and linking process are repeatedly performed for the products input into each process. Through such repeated processing, for each production process (Processes 3A, 3B), the partial time-series data 73 collected during the period when production work is performed on the product in that process, that is, the period when the operation flag is ON, is linked to the product ID and inspection result 94 of the product and stored in the data storage unit 62.

[0100] In FIG. 6, it is described that the linking processes for Process 3A (Steps S2, S5), Process 3B (Steps S3, S6), and Process 3C (Steps S4, S7) are executed simultaneously. However, in actual processing, since the lengths of the time required for production work or inspection may differ between processes, the execution times of the linking processes may be different. Similarly, in FIG. 6, the timings of the determination of the operation flags for Processes 3A and 3B (Step S8) and the determination of the operation flag for Process 3C (Step S9) are shown as being simultaneous, but in actual processing, since the lengths of the time required for production work or inspection may differ between processes, the two timings may be different.

[0101] <G. Display Example Based on Visualization Data> FIG. 7 is a diagram showing a display example of the screen according to the present embodiment. When the Web server program 280 is started, the PLC 100 provides services as a Web server for the support device 500. The provided services include the provision of visualization data 283.

[0102] 7, for example, display 522 includes window 51A showing product inspection results, windows 52 and 53 for visually displaying partial time series data, window 55 showing the period of the partial time series data to be visualized and the lot to which the product belongs, window 79B showing a histogram of the values ​​of the partial time series data, and window 79A showing a representative value of the values ​​of the partial time series data. In this embodiment, the term "window" is used to distinguish between information display areas on the screen, but the display technical concept disclosed in this specification is not limited to the name of the window and can be applied to any screen.

[0103] In Figure 7, a screen based on visualization data 283 is displayed, which is based on the product ID, process 3A, and process 3B set as the visualization target by the visualization setting information 29, and it is also displayed that the data items set as the collection target by the IoT program 260 based on the collection setting information 28 are "variable A" and "variable B" for process 3A and process 3B, respectively.

[0104] Window 51A displays product ID "X2" and the product's inspection result 94, "normal." Window 52 displays the name of the data item (variable A) to be collected for process 3A and a graph showing the characteristics indicating the temporal change in the value of the partial time-series data for that data item. Window 53 displays the name of the data item (variable B) to be collected for process 3B and a graph showing the characteristics indicating the temporal change in the value of the partial time-series data for that data item. In each of windows 52 and 53, the vertical axis of the graph represents the data value, and the horizontal axis represents the elapsed time T. Elapsed time T represents a common time axis for the partial time-series data of each product, and corresponds to the period from the start to the end of production work for that product, for example, the period during which the operation flag is "ON."

[0105] 7, the length of elapsed time T is the same for process 3A and process 3B, but because the production work performed in each process is different, the length of elapsed time T actually differs between processes. Based on such differences, the visualization data 283 includes data that visualizes the partial time-series data of each process by expanding or contracting it along the time axis so that the period of each process matches the common length of elapsed time T.

[0106] In window 52 of step 3A in FIG. 7, a graph of partial time series data corresponding to each product corresponding to a data item (variable A) is displayed, and in window 52 of step 3B, a graph of partial time series data corresponding to each product corresponding to a data item (variable B) is displayed.

[0107] The visualization data 283 is configured to display the partial time-series data of a product in each process as a blue graph 54C when the product's inspection result 94 indicates normal, a red graph 54A when the product indicates abnormal, and a gray graph 54B indicating that the product has not yet been inspected in the inspection process. The visualization program 282 generates the visualization data 283 periodically or whenever the screen is updated by the web browser. The screen of the display 522 in FIG. 7 is updated to a screen based on the visualization data 283 each time the visualization data 283 is generated. Therefore, when the corresponding product is inspected in the inspection process, the gray graph 54B in the window 52 or 53 changes to a blue graph 54C or a red graph 54A based on the inspection result 94. Note that when the visualization is performed in a manner corresponding to the type of inspection result (normal, abnormal, or uninspected), the manner is not limited to display colors and may be set by the visualization setting information 29.

[0108] Window 79A displays statistics 79 (maximum, minimum, average, and standard deviation), which are examples of representative values ​​calculated by data collection unit 63 for the values ​​of partial time-series data for multiple products in a target lot that flowed through process 3A during the period shown in window 55, for a group of products determined to be normal, a group of products determined to be abnormal, and a group of uninspected products. For example, the maximum value of statistics 79 for a certain product group indicates the representative value (maximum, minimum, average, and standard deviation) for the maximum values ​​indicated by the partial time-series data for each product that makes up that group. Statistics 79 are derived similarly for other types of groups.

[0109] In this way, window 79A quantitatively displays statistics of, for example, the product and partial time-series data of product with product ID "X2" and the partial time-series data of products with other product IDs. A module for calculating such statistics and generating visualization data 283 based on the statistics includes, for example, a visualization program 282 including a statistics calculation program 285.

[0110] Window 79B displays a histogram associated with the waveform graph of the partial time-series data of process 3A. In this histogram, the horizontal axis shows multiple classes of values ​​indicated by the waveform in window 52, ​​and the vertical axis shows the frequency of values ​​belonging to each class. Like the waveform graph in window 52, ​​the visualized data that makes up the histogram is data that visualizes and represents the characteristics of changes in the values ​​of the partial time-series data for each product input to process 3A. In window 79B, histograms based on the partial time-series data for the group of products with normal quality, the group of products with abnormal quality, and the group of uninspected products are displayed in blue, red, and gray, respectively.

[0111] 7, visualization data 283 is configured to display statistics 79 and histograms in windows 79A and 79B in association with window 52 of step 3A, but is not limited to this. Similar to window 52, ​​visualization data 283 may be configured to display a window presenting statistics and histograms corresponding to the partial time-series data graphed in window 53 in association with window 53 of step 3B.

[0112] The visualization data 283 includes data for visualizing partial time-series data associated with product IDs and inspection results 94 for each production process, arranged on the same screen in a priority order based on the values ​​of the partial time-series data. The priority order of such arrangement can be determined based on the degree of deviation acquired for each process. More specifically, by executing the priority processing program 284, the CPU 102 acquires, for each process, a representative value of the partial time-series data of each product constituting each of a plurality of groups (groups classified based on inspection results 94) made up of products input into the process, and acquires the degree of deviation between the representative value of each group and the representative values ​​of other groups. The priority order is determined based on the degree of deviation acquired in this manner.

[0113] For example, the degree of deviation between normal and abnormal values ​​of statistics 79 for a certain group is compared with the degree of deviation between normal and abnormal values ​​of statistics 79 for another group, and the visualization data 283 is configured so that the graph of the partial time-series data of the group with the larger degree of deviation is displayed higher on the screen than the graphs of the partial time-series data of the other group. In this way, the two groups to be used for calculating the deviation may have different types of test results, and typically, the deviation is calculated between an abnormal group and a normal group.

[0114] 7, since the deviation degree is greater for process 3A than for process 3B, window 52 for process 3A is displayed at the top of the screen, and window 53 for process 3B is displayed next (below). The order may be from right to left on the screen. A module that generates visualization data 283 that enables the screen display of partial time-series data in accordance with a priority order based on statistics in this way includes, for example, a visualization program 282 that includes a priority processing program 284.

[0115] The screen of FIG. 7 may be provided as a GUI. The screen of FIG. 7 has objects (partial images) such as graphs and numerical values ​​that visualize characteristics showing temporal changes in partial time-series data for each product for each process. The GUI data of the visualization data 283 may include the objects that make up the screen of FIG. 7 and commands that are executed in response to receiving a click operation or the like on the object. In FIG. 7, for example, when an object of waveform graph 54C in window 52 is clicked, a command is executed, and product ID "X2" and inspection results 94 corresponding to graph 54C are displayed in window 51A. Such a command may change the display mode of the graph (such as the line type, line width, or color of the graph).

[0116] The visualization setting information 29 may include settings for switching the display mode of the graphs described above. For example, the visualization setting information 29 may include settings regarding the color intensity of the graphs in window 52 or 53, such that graphs of partial time-series data acquired more recently within the target period shown in window 55 are displayed in darker colors, and graphs of partial time-series data from earlier periods are displayed in lighter colors.

[0117] Also, by user operation, in windows 52 and 53, multiple waveforms may be selected simultaneously. Also, in FIG. 7, the waveform is displayed for one lot specified in window 55, but graphs (waveforms) for each lot may be displayed for multiple lots. Also, visualization setting information 29 may be used to set the display period and lot for the display target in window 55.

[0118] Note that the display destination of the screen based on the visualization data 283 is not limited to the support device 500, and may be the HMI device 310, the information terminal 321, or the PLC 100 if the PLC 100 has a display device. In windows 52 and 53 of FIG. 7, waveforms of representative values (e.g., averages) of partial time-series data acquired during the target period (target lot) for the corresponding process may be displayed, or waveforms of partial time-series data of all products acquired during the target period (target lot) may be displayed.

[0119] The screen of FIG. 7 can be provided as information to support the determination of which process causes the product abnormality. For example, the user can estimate from the screen of FIG. 7 that process 3A with a large degree of the above deviation is the process causing the product quality abnormality, and based on the estimation, can estimate that the behavior of the field device 90 where the observed value 92 of the data item "variable A" collected in process 3A is abnormal.

[0120] <H. Example of Data Collection> FIGS. 8 and 9 are diagrams for explaining the table configuration of the management of partial time-series data according to the present embodiment. In the present embodiment, the data collection unit 63 may manage the partial time-series data 73 in the data storage unit 62 by focusing on each process as shown in FIG. 8, or may manage it by focusing on each product as shown in FIG. 9.

[0121] In the management table 62A of FIG. 8, for example, for process 3A, the data collection unit 63 manages partial time series data 73 by associating lot name 77, collection start / end times 78, statistics 79, and inspection results 94 for each product ID, for example, in a single column. For other processes, partial time series data is managed in the same way as for process 3A. The collection start / end times 78 indicate the time from the start to the end of collection of data for the data item in the process. This time corresponds to the time from when the operating flag changes to "ON" to when it changes to "OFF."

[0122] In the management table 62A of FIG. 9, for example, for a product with product ID “X1”, the data collection unit 63 associates partial time-series data 73 with a lot name 77, collection start / end times 78, statistics 79, and inspection results 94 for each process, and manages the data in, for example, one column.

[0123] In the configuration of Fig. 8, data is held as one management table 62A for each process (variable), but if a user wishes to store data for traceability purposes, the data in management table 62A of Fig. 9 can also be held in the format of one CSV file for each product ID. In this embodiment, either Fig. 8 or Fig. 9 may be set as the data accumulation (collection) mode in collection setting information 28. Data collection unit 63 collects partial time series data in the mode of Fig. 8 or Fig. 9 in accordance with the settings of collection setting information 28.

[0124] 6, partial time series data 73 is acquired and stored in parallel with the flow of each product in the lot on the production line 3. However, partial time series data 73 may also be acquired as follows. That is, time series data for each process is constantly collected, and only indexes (such as the time at which the in-operation flag is determined to be ON, product ID, etc.) are accumulated as process data. After all products in the lot have flowed through all processes, a linking process is performed to acquire (extract) partial time series data 73 for each product based on the index from the time series data for each process, and the acquired partial time series data 73 may be stored in the data accumulation unit 62 in the manner shown in FIG. 8 or 9.

[0125] <I.タイミングチャート> FIG. 10 is a diagram showing an example of a timing chart according to this embodiment. FIG. 11 is a diagram showing a schematic diagram of the processes constituting a production line according to this embodiment. In FIG. 11, for example, the production line includes process 3A, where products of a lot are input, and the next process 3B. If the lot includes products with product IDs "X1" to "X6," the products are input in the order of "X1," "X2," "X3," ... "X6." In FIG. 11, the product with product ID "X1" that has passed through process 3C, which is an inspection process, is judged to be "quality OK," i.e., normal. Furthermore, the product with product ID "X2" that has passed through process 3B flows to the next process 3C, the product with product ID "X3" that has passed through process 3A flows to process 3B, and the product with product ID "X4" is input to process 3A. FIG. 10 shows a timing chart for the state of the processes shown in FIG. 11.

[0126] In FIG. 10 , in process 3A, data collection is performed for variable 121 during period 130 when in-operation flag 125 is “ON,” and individual ID 43 is linked to the collected partial time-series data as a product ID. Similarly, in process 3B, data collection is performed for variable 122 during period 130 when in-operation flag 127 is “ON,” and individual ID 43 is linked to the collected partial time-series data. Furthermore, in processes 3A, 3B, and 3C, scans 124, 126, and 141 of individual ID 43 are performed when a product is detected being input to the process. The control unit 10B sets flag 142 from OFF to ON each time inspection result 94 is set in data area 42 from process 3C. The data collection unit 63 acquires inspection result 94 from data area 42 from process 3C, triggered by the setting (ON) of flag 142. In FIG. 10 , lot name 45 of a product input to production line 3 is detected (acquired) each time a lot is changed. In the production line 3, in order to manage products in units of a group of multiple products, for each unit, the products belonging to that unit are assigned a common lot name 45 (for example, lot number) and are classified and managed.

[0127] (I1. Another example of a timing chart) A case will be described in which a virtual individual ID is acquired as a product ID. FIG. 12 is a diagram showing another example of a timing chart according to this embodiment. FIG. 13 is a diagram showing a schematic diagram of processes constituting a production line according to this embodiment. FIG. 13 shows a case in which, during the production of one product of lot "X," a product of a different lot "Y" is input to production line 3. In FIG. 13, a product of product ID "X1" that has undergone process 3C, which is an inspection process, is judged to be "quality OK," i.e., normal. Also, a product of product ID "X2" that has undergone process 3B flows to the next process 3C, a product of product ID "X3" that has undergone process 3A flows to process 3B, and a product of product ID "Y1" of lot Y is input to process 3A. FIG. 12 shows a timing chart for the process state of FIG. 13.

[0128] In FIG. 12, for each lot, when the product of the lot is input into Process 3A, the lot name acquisition 123 is performed and the lot name 45 is set. When the lot is input to the production line and the lot name 45 is acquired, thereafter, each time the in-operation flag for the lot changes to "ON", the count value 46 is set in the lot data 44, and the virtual individual ID is indicated by the combination of the lot name 45 and the count value 46 in the lot data 44. The CPU 102 stores the virtual individual ID acquired in a certain process, for example, in a queue configured in the data area 42. When the in-operation flag in the next process changes from "OFF" to "ON", the CPU 102 reads out the virtual individual ID from the queue and performs an association process of associating the read virtual individual ID with the partial time-series data acquired in the subsequent period 130. In FIG. 12, the virtual individual ID 144 of the previous process (Process 3A) is stored in the queue 145, and when the in-operation flag 127 of the next process 3B changes to "ON", the virtual individual ID 144 read from the queue 145 is used as the virtual individual ID 147 of the next process 3B. Similarly, the virtual individual ID 147 of the previous process (Process 3B) is stored in the queue 146, and the virtual individual ID 144 read from the queue 146 is used as the virtual individual ID 148 of the next process 3C. Thus, when the production line 3 is configured to produce products in lots, the virtual individual ID can be configured to include a lot name that is at least partially a common identifier among multiple processes.

[0129] In the present embodiment, the in-operation flags 125 and 127 are set by executing instructions such as function blocks included in the control program 140. Such instructions may include arithmetic instructions or timer instructions for setting the in-operation flag.

[0130] Thus, the method by which each process acquires the virtual individual ID is not limited to the method of using the unique lot data 44 for each process as shown in FIG. 1, and as shown in FIG. 12, a method of sharing the virtual individual ID stored in the queue 145 among processes may also be used.

[0131] <Example of J. Information Setting Screen> Fig. 14 is a diagram showing an example of an information setting screen according to the present embodiment. In Fig. 14, for example, UI 502 displays a screen on display 522, accepts a user operation on the screen, and acquires collection setting information 28 in accordance with the accepted user operation.

[0132] 14 shows a screen for setting the collection setting information 28 in table format. The items set in the table include, for each process, a process sequence 160 indicating the order in which the product flows on the production line 3, a variable name 161 indicating the data item to be collected at that process, a variable name 162 in which an in-operation flag corresponding to that process is set, an individual ID designation 163, a lot variable name 164, a comment 165, and a priority calculation formula 166. In the process sequence 160, an inspection process (process 3C) is set as the final process on the production line 3. The individual ID designation 163 specifies that the individual ID 43 is to be used as the product ID. The lot variable name 164 indicates the name of the variable to which the lot name 45 is set. The comment 165 indicates a user comment such as an explanation of the data to be collected for that process.

[0133] The priority calculation formula 166 indicates an arithmetic formula used by the priority processing program 284 to calculate the degree of dissociation for determining the priority of graph display on the screen between processes.

[0134] The process sequence 160 is used to exchange virtual individual IDs between processes via a queue. Comments 165 may be displayed along with the graph of partial time-series data shown in FIG. 7. One or more variable names can be set in the collection variable name 161. Such variable names include the variable names of the observation values ​​92 or the variable names of the control commands 93. The variable names of the observation values ​​92 can include, for example, three types of variable names: torque, speed, and Z-axis position, contained in the observation values ​​92 of the field device 90 for screw tightening. Based on the collection setting information 28 in which three types of variable names are set, the data collection unit 63 can simultaneously collect data of three types of data items from the screw tightening process. Furthermore, the control commands 93 indicated by the variable names to be collected may include, for example, commands for the field device 90, such as a servo motor for screw tightening, such as numerical values ​​representing position, speed, acceleration, jerk (jerk), angle, angular acceleration, and angular jerk. The collection variable name 161 for the inspection process is set to the variable name of the inspection result 94.

[0135] If the individual ID designation 163 is set, the linking unit 64 links the individual ID 43 to the partial time series data, and if not set, obtains a virtual individual ID and links it to the partial time series data. In step S1 of Figure 6, a determination is made based on the individual ID designation 163 of the collection setting information 28.

[0136] The rate of change of statistic 79 is calculated for each process by using the statistic 79 of that process and following the reference formula of "degree of deviation of statistic" = "statistic at the time of abnormality" / "statistic at the time of normality". When the user sets "maximum value" as the priority calculation formula 166, the calculation formula of "degree of deviation of statistic" = "maximum value at the time of abnormality" / "maximum value at the time of normality" is set. It is not limited to the maximum value of statistic 79, and it may be the minimum value, average value, standard deviation, etc., or a combination of two or more of these. Also, the degree of deviation of the statistic may be calculated using a value weighted on the degree of deviation of the statistic calculated by the reference formula. For example, "degree of deviation of statistic" = 0.8 × "degree of deviation of average value" + 0.2 × "degree of deviation of standard deviation" may be used. Thus, the degree of deviation of statistic 79 is calculated for each process, and the graph of the partial time-series data of the process with a large degree of deviation is preferentially displayed on the screen compared to the graphs of the partial time-series data of other processes. Through the screen displayed in this way, support information can be provided to the user to identify the process that causes a large degree of deviation, that is, the "abnormality" of the inspection result 94, among the multiple processes of the production line 3.

[0137] <K. Usage form of information based on visualization data> FIG. 15 is a flowchart showing a usage form of information based on visualization data according to this embodiment. FIGS. 16 to 19 are diagrams showing display examples of screens based on visualization data according to this embodiment.

[0138] Referring to FIG. 15, a scene where a user, such as a process manager or a quality manager, uses the information provided by the screen based on the visualization data 283 will be described. The user determines whether a quality abnormality of the product is detected based on the inspection result 94 from the inspection process (step S20). If it is not determined that a quality abnormality is not detected (NO in step S20), the user will repeat the determination in step S20.

[0139] If it is determined that a quality abnormality has been detected (YES in step S20), the user determines which process's waveform graph of the partial time-series data shows the abnormality from the screen based on the visualized data 283 displayed on the display 522 (step S21). On this screen, as described above, the graph of a process with a large degree of deviation is displayed at the top of the list, preferentially over the graphs of other processes. For example, on the screen of FIG. 16, the graph of process 3A is displayed at the top. From the screen of FIG. 16, the user can infer that process 3A is the process that is causing the quality "abnormality."

[0140] The user temporarily stops the production line 3 and performs maintenance on the field devices 90 in the identified target process 3A (step S22). For example, if variable A is a data item to be collected for process 3A, the user can narrow down the field devices 90 in which the value of variable A is observed to be the maintenance target.

[0141] Thereafter, the user operates the production line 3 that was temporarily stopped to resume production (step S23). On the screen of visualized data 283 based on the data collected after the resumption of production, a graph of the waveform of the partial time-series data of each process after maintenance is displayed.

[0142] The user checks the waveforms based on the partial time-series data of each process from a screen based on the visualization data 283 created after the production is resumed (step S24). The user checks from the screen whether the abnormality in the waveform graph has been resolved (step S25). That is, the user checks whether there is any process having a graph with a large rate of change (deviation). If the user confirms that the abnormality has not been resolved (NO in step S25), the process returns to step S22, and the user performs maintenance on the field device 90.

[0143] For example, when the screen of FIG. 19 is displayed in step S24, in the screen of FIG. 19, the waveform of the original uninspected gray graph 54B (FIG. 16) in process 3A has changed to approximate the waveform of the abnormal red graph 54A. From the screen presenting such a waveform change, the user confirms that although the field device 90 in process 3A has been maintained, the cause of the quality abnormality has not been eliminated.

[0144] On the other hand, when it is confirmed that there is no process having a graph with a large degree of deviation, that is, when it is confirmed that the abnormality has been eliminated (YES in step S25), the production of the product continues. For example, when the screen of FIG. 18 is displayed in step S24, in the screen of FIG. 18, in the graph of process 3A, the waveform of the gray graph 54B of the original uninspected product has changed to approximate the waveform of the normal blue graph 54C. From the screen representing such a waveform change, the user can confirm that the quality abnormality has been eliminated by maintaining the field device 90 in process 3A.

[0145] Also, the user monitors the screen based on the visualization data 283 displayed on the display 522 while the production line 3 is operating. For example, the screen of FIG. 17 is displayed. From the screen of FIG. 17, the user confirms that in the graph of process 3A, the waveform of the gray graph 54B of the uninspected product approximates the waveform of the abnormal red graph 54A (step S27). From the information of such a screen, the user can estimate that there is a high possibility that products in which quality abnormalities are detected will continue to be produced due to the behavior of the field device 90 in process 3A. Thereafter, the user proceeds to step S22 and performs maintenance.

[0146] <L. Program and Recording Medium> The IoT program 260 and the web server program 280 according to this embodiment can also be provided as a program product by being recorded on a computer-readable recording medium including a recording medium such as a flexible disk, a CD-ROM, a ROM (Read Only Memory), a RAM (Random Access Memory), or a HDD (Hard Disc Drive) attached to the PLC 100. The recording medium is a medium that accumulates information such as programs by an electrical, magnetic, optical, mechanical, or chemical action so that a computer or other device, machine, etc. can read the information such as the programs recorded thereon.

[0147] Also, the above program can be provided to the PLC 100 by downloading via the network controller 120 from the network 2.

[0148] <M. Supplementary Note> This embodiment includes the following technical ideas. [Configuration 1] An apparatus for collecting data of an object produced through one or more processes provided in a production line (3), a collection unit (63) that collects data of data items related to production work from each of the one or more processes (3A, 3B, 3C), an acquisition unit (88, 89) that acquires an identifier of an object produced in the production line, for each of the processes, an association unit (64) that associates the identifier of the object and the inspection result (94) of the object with the collected data (73) collected from the process by the collection unit during the period in which the production work is performed on the object in the process, a data collection apparatus comprising a visualization data creation unit (65) that creates visualization data (283) for visualizing and representing the collected data of the process in which the identifier and the inspection result are associated by the association unit for each of the processes. [Configuration 2] The collection unit periodically collects the data from each of the processes, The association unit is 2. The data collection device according to claim 1, wherein for each process, an identifier of the object and the inspection result are linked to partial time series data corresponding to the period among the time series data periodically collected from the process. [Configuration 3] 3. The data collection device according to configuration 2, wherein the visualization data includes data visualizing the partial time series data, to which the identifiers and the test results are linked, for each of the processes, by arranging them in order of priority based on values ​​of the partial time series data. [Configuration 4] Each of the steps is configured so that the objects are input one by one into the corresponding step; the production line is configured so that the objects are input from one process to the next process in a predetermined order; The data collection device The data collection device according to configuration 3, configured to acquire, for each of the processes, a representative value of the partial time series data of each of the objects constituting each of a plurality of groups made up of objects input into the process, and acquire a degree of deviation between the representative value of each of the plurality of groups and the representative value of another group. [Configuration 5] The visualization data creation unit 5. The data collection device according to configuration 4, wherein the priority order is determined based on the degree of deviation acquired for each of the processes. [Configuration 6] 6. The data collection device according to configuration 4 or 5, wherein the representative value includes, for each of the plurality of groups, a statistical value (79) of the partial time series data values ​​of each object constituting the group. [Configuration 7] 7. The data collection device according to configuration 6, wherein the visualization data includes data visually representing the statistical values ​​corresponding to the groups. [Configuration 8] The visualization data creation unit 8. The data collection device according to any one of configurations 4 to 7, wherein the order of priority of each of the processes is determined so that the greater the deviation of the process, the higher the process is ranked. [Configuration 9] 9. A data collection device according to any one of configurations 4 to 8, wherein for each group, the type of test results of the objects constituting that group is different from the type of test results of the objects constituting the other groups. [Configuration 10] 10. The data collection device of claim 9, wherein the types of test results include normal, abnormal, and untested, which indicates that the object has not yet been tested. [Configuration 11] The data collection device according to any one of configurations 2 to 10, wherein the visualization data includes characteristic visualization data (54A, 54B, 54C) that visualizes and represents, for each of the processes, the characteristics of changes in the values ​​of the partial time series data for each object input into the process. [Configuration 12] 12. The data collection device according to claim 11, wherein the characteristic visualization data includes temporal characteristic visualization data that visualizes, for each process, characteristics indicating temporal changes in values ​​of the partial time series data for each object input into the process on a common time axis. [Configuration 13] 13. The data collection device according to any one of configurations 1 to 12, wherein the visualization data includes data for visualizing the collected data in a manner corresponding to the type of the test result linked to the collected data. [Configuration 14] 14. The data collection device according to any one of configurations 1 to 13, which is provided in a control device (100) that controls the production line. [Configuration 15] A program for causing a computer (102) to execute the method, The method is a method for collecting data on an object produced through one or more processes provided on a production line (3), A step of collecting data on data items related to production work from each of the one or more processes (3A, 3B, 3C); obtaining an identifier of an object produced on the production line; For each of the processes, linking collected data collected from the process during a period in which the production work is performed on the object in the process with an identifier of the object and an inspection result of the object; The program includes a step of creating, for each of the processes, visualization data that visualizes and represents the collected data of the process to which the identifier and the test result are linked.

[0149] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the above description, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0150] 1 Repeater, 3 Production line, 3A, 3B, 3C Process, 10A IoT platform, 10B Control unit, 11 Field network, 28 Collection setting information, 29 Visualization setting information, 30 Setting information, 40 IO refresh program, 41 Area, 42 Data area, 44 Lot data, 45, 77 Lot name, 46 Count value, 47 Internal memory, 48 Various queues, 51A, 52, 53, 55, 79A, 79B Window, 54A, 54B, 54C Graph, 60 Web server, 61 IoT application, 62 Data storage unit, 62A Management table, 63 Data collection unit, 64 Linking unit, 65 Visualization unit, 73 Partial time series data, 79 Statistics, 88 Code reader, 89 Lot code reader, 90 Field device, 92 Observation value, 93 Control command, 94 Inspection result, 101 Power supply circuit, 104 Chipset, 108 Storage, 112 USB controller, 113 Field network controller, 114 Memory card interface, 115, 513 Timer, 116 SD card, 120 Network controller, 125, 127 Operation flag, 130 Period, 132 Scheduler program, 140 Control program, 142 Flag, 150 Control engine, 154 Input data, 155 Output data, 160 Process sequence, 161, 162 Variable name, 164 Lot variable name, 165 Comment, 166 Priority calculation formula, 250 IoT engine, 260 IoT server program, 270 Collection program, 271 Linking program, 280 Web server program, 281 Setting program, 282 Visualization program, 283 Visualization data, 284 Priority processing program, 285 Statistical calculation program, 500 Support device, 501 Web browser, 522 display.

Claims

1. An apparatus for collecting data on an object produced through a plurality of processes provided on a production line, a collection unit that collects data on data items related to production work from each of the plurality of processes; an acquisition unit that acquires identifiers of objects produced on the production line; a linking unit that links, for each of the processes, collected data collected from the process by the collecting unit during a period in which the production work is performed on the object in the process with an identifier of the object and an inspection result of the object; a visualization data creation unit that creates, for each of the processes, visualization data that visualizes and represents the collected data of the process to which the identifier and the test result are linked by the linking unit; the collection unit periodically collects the data from each of the processes; The linking unit is for each of the processes, linking the identifier of the object and the inspection result to partial time series data corresponding to the period among the time series data periodically collected from the process; The visualization data includes data that visualizes the partial time series data, to which the identifiers and the test results are linked, for each of the processes, by arranging them in order of priority based on the values ​​of the partial time series data.

2. Each of the steps is configured so that the objects are input one by one into the corresponding step; the production line is configured so that the objects are input from one process to the next process in a predetermined order; The data collection device 2. The data collection device according to claim 1, configured to, for each of the processes, acquire a representative value of the partial time series data of each of the objects constituting each of a plurality of groups made up of objects input into the process, and acquire a degree of deviation between the representative value of each of the plurality of groups and the representative value of another group.

3. The visualization data creation unit The data collection device according to claim 2 , wherein the priority order is determined based on the degree of deviation acquired for each of the processes.

4. The data collection device according to claim 2 , wherein the representative value includes, for each of the plurality of groups, a statistical value of the partial time-series data values ​​of each object constituting the group.

5. The data collection device according to claim 4 , wherein the visualization data includes data visually representing the statistical values ​​corresponding to the groups.

6. The visualization data creation unit 4. The data collection device according to claim 2, wherein the order of priority of each of the processes is determined such that the greater the degree of deviation of the process, the higher the process is ranked.

7. 4. The data collection device according to claim 2, wherein for each group, the types of test results of the objects constituting that group are different from the types of test results of the objects constituting the other groups.

8. The data collection device according to claim 7 , wherein the types of the test results include normal, abnormal, and untested, which indicates that the object has not yet been tested.

9. 4. The data collection device according to claim 1, wherein the visualization data includes characteristic visualization data that visualizes and represents, for each of the processes, characteristics of changes in values ​​of the partial time-series data for each object input into the process.

10. 10. The data collection device according to claim 9, wherein the characteristic visualization data includes temporal characteristic visualization data that visualizes, for each of the processes, characteristics indicating temporal changes in values ​​of the partial time-series data for each object input into the process on a common time axis.

11. The data collection device according to any one of claims 1 to 3, wherein the visualization data includes data for visualizing the collected data in a manner corresponding to the type of the test result linked to the collected data.

12. 4. The data collection device according to claim 1, which is provided in a control device that controls the production line.

13. A program for causing a computer to execute a method, The method is a method for collecting data on an object that is produced through a plurality of processes provided on a production line, collecting data items related to production work from each of the plurality of processes; obtaining an identifier of an object produced on the production line; For each of the processes, linking collected data collected from the process during a period in which the production work is performed on the object in the process with an identifier of the object and an inspection result of the object; creating, for each of the processes, visualization data that visualizes and represents the collected data of the process to which the identifiers and the test results are linked; In the collecting step, the data is collected periodically from each of the processes; In the linking step, For each of the processes, the identifier of the object and the inspection result are linked to partial time series data corresponding to the period among the time series data periodically collected from the process; The visualization data includes data for visualizing the partial time series data, to which the identifiers and the test results are linked, for each of the processes, in order of priority based on the values ​​of the partial time series data.

14. Each of the steps is configured so that the objects are input one by one into the corresponding step; the production line is configured so that the objects are input from one process to the next process in a predetermined order; The method of collecting data may further include:

14. The program according to claim 13, further comprising a step of acquiring, for each of a plurality of groups made up of objects input into the process, a representative value of the partial time series data of each of the objects making up the group, and acquiring a degree of deviation between the representative value of each of the plurality of groups and the representative value of another group.

15. The step of creating the visualization data comprises: The program according to claim 13 , further comprising a step of determining the priority order based on the degree of deviation obtained for each of the processes.

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