Control signal monitoring device, control signal monitoring method, and control signal monitoring program

Abstract

The model generation unit (17) generates a change interval model that models the change interval data that represents the interval between a start signal that causes the device to operate and a completion signal that appears as a result of the device's operation, based on the change interval data obtained from the log data stored in the log data storage unit (12). The abnormality degree calculation unit (18) calculates the abnormality degree of the device's operation from an actual measurement value that is the interval between the start signal and the completion signal obtained from the device and a predicted value that is the interval predicted using the change interval model generated by the model generation unit (17).

Description

[Technical field]

[0001] The present disclosure relates to techniques for monitoring control signals that control the operation of devices. [Background technology]

[0002] PLC exchanges control signals with various devices in the manufacturing facility. PLC stands for Programmable Logic Controller. Examples of devices include robots, conveyors, and cylinders. Control signals are characterized by being composed mainly of binary digital signals that have ON and OFF states. The control signals input and output to the PLC include signals whose state changes when each device starts to operate (hereafter referred to as start signals) and signals whose state changes when the device completes its operation (hereafter referred to as completion signals). By appropriately monitoring the start and completion signals, it is possible to monitor the operation of the devices.

[0003] Patent Document 1 describes a method for detecting abnormal operation of a device by monitoring the time difference between the rising edge of a certain PLC signal and the falling edge of another PLC signal. In Patent Document 1, an allowable time width is set in the reference operation pattern, and a timing deviation within the normal range is not detected as abnormal operation. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 09-22308 Summary of the Invention [Problem to be solved by the invention]

[0005] In the technology described in Patent Document 1, the time difference between changes in signals is compared with a tolerance to determine whether the device is operating abnormally. Therefore, normal operational fluctuations that occur over the long term due to temperature changes may be detected as abnormal. Also, in a device that has two different operating patterns, it may be necessary to set a tolerance that overlooks an abnormality in one of the operations. An object of the present disclosure is to make it possible to appropriately determine an abnormality in an apparatus using a start signal and a completion signal. [Means for solving the problem]

[0006] A control signal monitoring device according to the present disclosure comprises: a model generating unit configured to generate a change interval model that models change interval data representing an interval between a start signal that causes an operation of the device and a completion signal that appears as a result of the operation of the device; an anomaly degree calculation unit that calculates an anomaly degree of the operation of the device by using the change interval model generated by the model generation unit; Equipped with. Effect of the Invention

[0007] In the present disclosure, an abnormality in the device is monitored using a change interval model that models change interval data that represents the interval between a start signal and a completion signal. By using the change interval model, an abnormality in the device can be appropriately determined from the start signal and the completion signal. [Brief description of the drawings]

[0008] [Figure 1] 1 is a configuration diagram of a control signal monitoring system 100 according to a first embodiment. [Diagram 2] 1 is a functional configuration diagram of a control signal monitoring device 10 according to a first embodiment. [Diagram 3] 1 is a hardware configuration diagram of a control signal monitoring device 10 according to a first embodiment. [Figure 4] 4 is a flowchart showing a process flow of the control signal monitoring device 10 according to the first embodiment. [Diagram 5] FIG. 4 is an explanatory diagram of reduction in the size of stored data according to the first embodiment. [Figure 6] FIG. 4 is an explanatory diagram of a method for generating change interval data according to the first embodiment. [Figure 7] FIG. 4 is an explanatory diagram of a learning method for generating a change interval model in which change interval data is modeled according to the first embodiment. [Figure 8] FIG. 4 is a diagram showing an example of displaying an abnormality degree according to the first embodiment. [Figure 9] 5A to 5C are explanatory diagrams of abnormalities in the device having two operation patterns according to the first embodiment. [Figure 10] 10 is a flowchart showing the flow of processing of a control signal monitoring device 10 according to the second embodiment. [Figure 11] FIG. 11 is an explanatory diagram of a method for predicting timing at which an abnormality will occur according to the second embodiment. [Figure 12] FIG. 11 is a diagram showing an example of a display of timing at which an abnormality occurs according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Embodiment 1 ***Configuration Description***

[0010] The configuration of a control signal monitoring system 100 according to the first embodiment will be described with reference to FIG. The control signal monitoring system 100 includes a control signal monitoring device 10, a PLC 20, and a manufacturing facility 30. The manufacturing facility 30 includes one or more devices 31. The control signal monitoring device 10 and the PLC 20 are connected via a transmission path 41. Furthermore, the PLC 20 and each of the one or more devices 31 included in the manufacturing facility 30 are connected via a transmission path 42. The PLC 20 exchanges control signals with each device 31 via a transmission line 42. The control signal monitoring device 10 acquires control signals or logs of control signals from the PLC 20 via a transmission line 41.

[0011] The functional configuration of the control signal monitoring device 10 according to the first embodiment will be described with reference to FIG. The control signal monitoring device 10 includes, as functional components, an acquisition unit 11, a log data storage unit 12, an input unit 13, a signal set storage unit 14, a change interval calculation unit 15, a change interval storage unit 16, a model generation unit 17, an anomaly degree calculation unit 18, and a display control unit 19.

[0012] The hardware configuration of the control signal monitoring device 10 according to the first embodiment will be described with reference to FIG. The control signal monitor 10 is a computer. The control signal monitoring device 10 includes the following hardware components: a processor 101, a memory 102, a storage 103, and a communication interface 104. The processor 101 is connected to other hardware components via signal lines and controls the other hardware components.

[0013] The processor 101 is an IC that performs processing. IC is an abbreviation for Integrated Circuit. Specific examples of the processor 101 include a CPU, a DSP, and a GPU. CPU is an abbreviation for Central Processing Unit. DSP is an abbreviation for Digital Signal Processor. GPU is an abbreviation for Graphics Processing Unit.

[0014] The memory 102 is a storage device that temporarily stores data. Specific examples of the memory 102 include SRAM and DRAM. SRAM is an abbreviation for Static Random Access Memory. DRAM is an abbreviation for Dynamic Random Access Memory.

[0015] The storage 103 is a storage device that stores data. A specific example of the storage 103 is an SSD. SSD is an abbreviation for Solid State Drive. The storage 103 may also be a portable recording medium such as an SD (registered trademark) memory card, CompactFlash (registered trademark), NAND flash, a flexible disk, an optical disk, a compact disk, a Blu-ray (registered trademark) disk, or a DVD. SD is an abbreviation for Secure Digital. DVD is an abbreviation for Digital Versatile Disk.

[0016] The communication interface 104 is an interface for communicating with an external device. Specific examples of the communication interface 104 include Ethernet (registered trademark), USB, and HDMI (registered trademark) ports. USB is an abbreviation for Universal Serial Bus. HDMI is an abbreviation for High-Definition Multimedia Interface.

[0017] The functions of the acquisition unit 11, input unit 13, change interval calculation unit 15, model generation unit 17, anomaly degree calculation unit 18, and display control unit 19 of the control signal monitoring device 10 are realized by software. A program for realizing these functions is stored in the storage 103. This program is loaded into the memory 102 by the processor 101 and executed by the processor 101. In this way, the functions of each functional component of the control signal monitoring device 10 are realized.

[0018] The functions of the log data storage unit 12, the signal set storage unit 14, and the change interval storage unit 16 of the control signal monitoring device 10 are realized by a storage device such as the memory 102 or the storage 103. These functions may be realized by a storage device external to the control signal monitoring device 10.

[0019] ***Explanation of Operation*** The operation of the control signal monitoring device 10 according to the first embodiment will be described with reference to FIGS. An operation procedure of the control signal monitoring device 10 according to the embodiment 1 corresponds to a control signal monitoring method according to the embodiment 1. Moreover, a program for realizing the operation of the control signal monitoring device 10 according to the embodiment 1 corresponds to a control signal monitoring program according to the embodiment 1.

[0020] The flow of processing performed by the control signal monitoring device 10 according to the first embodiment will be described with reference to FIG. (Step S101: Control signal collection process) The PLC 20 communicates a plurality of control signals with each device 31 included in the manufacturing facility 30. The acquisition unit 11 acquires the plurality of control signals communicating with each device 31 from the PLC 20. Here, the acquisition unit 11 is described as acquiring the plurality of control signals themselves, but it may also acquire a log of the plurality of control signals. In this case, the acquisition unit 11 acquires the plurality of control signals for at least a period of time during which a series of operations of each device occurs two or more times. The multiple control signals include a start signal and a completion signal. The start signal is a signal that causes the device to operate, and is a signal whose state changes when the device operation starts. The completion signal is a signal that appears as a result of the device operation, and is a signal whose state changes when the device operation is completed. All of the multiple control signals are binary digital signals that represent ON and OFF.

[0021] The acquiring unit 11 stores the acquired control signal as log data in the log data storage unit 12. At this time, the acquiring unit 11 does not store the control signal as log data as it is as a binary signal value, but replaces it with change time data indicating the time when the value changed between ON and OFF, and stores it as log data. This is expected to reduce the size of the data to be stored.

[0022] The reduction in the size of stored data will now be described with reference to FIG. The control signal is a binary digital signal whose state changes over time. The signal sequence d100 indicates the state of a single signal observed at a fixed interval. If the signal sequence d100 were stored as log data as is, then binary values ​​(d101 to d10n) for each time would be stored. However, each control signal changes only once every few seconds. Therefore, storing the log data as time information, such as the signal rise time d201 and fall time d202, often results in less storage capacity being used. In addition, storing the log data as time information is expected to reduce the calculation time required to calculate the time difference between changes in signals.

[0023] (Step S102: Signal set information input process) The input unit 13 inputs signal set information for a device to be monitored. When there are multiple devices to be monitored, the input unit 13 inputs signal set information for each of the multiple devices to be monitored. The input unit 13 may input multiple pieces of signal set information for one device. The input unit 13 stores the input signal set information in the signal set storage unit 14. The signal set information includes at least the signal name of the start signal, the change trigger of the start signal, the signal name of the completion signal, and the change trigger of the completion signal for each monitoring target. The signal name is a name that allows the control signal to be identified. The change trigger is information indicating whether to look at the rising or falling edge of the signal. The signal set information also includes supplementary information for strictly defining the correspondence between signals when a signal changes multiple times within one operation cycle. The supplementary information indicates, for example, which change to look at within the cycle. There are various methods for creating the signal set information, such as manual setting by a person, extraction from design information of the device, automatic extraction from data, etc. Any method may be used here.

[0024] (Step S103: Change interval calculation process) The change interval calculation unit 15 generates change interval data from the signal log data stored in step S101 and the signal set information stored in step S102. The change interval calculation unit 15 stores the change interval data in the change interval storage unit 16.

[0025] A method for generating change interval data will be described with reference to Fig. 6. The change interval calculation unit 15 identifies the start signal d311 and the completion signal d321 specified in the signal set information d400 from among the signals included in the signal log data d300. The change interval calculation unit 15 identifies the time of the change d312 specified by the change trigger in the identified start signal. The change interval calculation unit 15 also identifies the time of the change d322 specified by the change trigger in the identified completion signal, which occurred immediately after the change d312. Then, the change interval calculation unit 15 generates the difference between the time of the change d312 and the time of the change d322 as the change interval data d301, d302. At this time, if there is a number of times designation d423 or the like due to the supplementary information in the signal set information, the change interval calculation unit 15 adjusts the calculation target according to the number of times designation d423, etc. In this case, since the number of times designation of the completion signal is 2, adjustment is made to look at the second change in the cycle (see d323).

[0026] (Step S104: Model generation process) The model generating unit 17 uses a regression model to generate a change interval model by modeling the change interval data stored in step S103. The regression model to be used may be an autoregressive model, a neural network model, or the like.

[0027] A learning method for generating a change interval model that models change interval data will be described with reference to FIG. The change interval data d500 is time-series data having an occurrence order. The change interval model uses past data as input data d511 and predicts data 512 ahead of the input data d511. At this time, multiple pieces of data d501 may be input instead of one piece of data. The model generation unit 17 performs learning so as to minimize the difference d513 between the future data d512 predicted by the change interval model and the actual data. This enables the change interval model to calculate the expected value of the future change interval based on the past change interval.

[0028] (Step S105: Abnormality degree calculation process) The abnormality degree calculation unit 18 calculates the abnormality degree of the operation of the device to be monitored, using the change interval model generated in step S104. Specifically, the abnormality degree calculation unit 18 compares an actual measurement value, which is the interval between a start signal and a completion signal obtained from the device, with a predicted value, which is the interval predicted using a change interval model, to calculate the abnormality degree of the device. n and the expected value P n Using the above, the abnormality level = (|P n -L n |) / P n There are methods such as: The actual measurement value here is the latest interval between the start signal and the completion signal specified in the signal set information input in step S102.

[0029] (Step S106: Display control process) The display control unit 19 displays the degree of anomaly etc. calculated in step S105 on a display device connected via the communication interface 104. Specifically, the display control unit 19 may display the degree of anomaly, expected value, and actual measurement value for the monitored device as the monitoring result.

[0030] An example of displaying the abnormality degree will be described with reference to FIG. The display control unit 19 displays an abnormality level display screen d610 that indicates the abnormality level for each device. This allows the user to monitor the abnormality level of each device. At this time, the display control unit 19 may highlight devices whose abnormality level exceeds a threshold by changing the color, for example. When the user wishes to check the degree of abnormality for a specific device in detail, the user selects the device to be checked. The display control unit 19 then displays a change interval transition graph d600. The change interval transition graph d600 shows the transition of an expected value d601 of the change interval calculated using a change interval model, an actual measurement value d602 of the change interval, and an abnormality degree d603 calculated by comparing these. By referring to the change interval transition graph d600, the user can visually check the details of the abnormality, such as the operation of the selected device being delayed from the original schedule or fluctuating irregularly.

[0031] The process of FIG. 4 is executed periodically. 4 is executed for the second or subsequent time, the processes from step S102 to step S104 may be skipped. The process of step S102 may be executed only when the control signal to be monitored is changed. Moreover, the processes of steps S103 and S104 may be executed only when the change interval model is regenerated.

[0032] ***Advantages of the First Embodiment*** As described above, the control signal monitoring device 10 according to the first embodiment monitors an abnormality in the device by using a change interval model that models the change interval data that represents the interval between the start signal and the completion signal. By using the change interval model, it is possible to appropriately determine an abnormality in the device.

[0033] By using the change interval model, it is possible to prevent normal operation fluctuations that occur over the long term due to temperature changes such as air temperature from being detected as an abnormality, as in the technology described in Patent Document 1. In addition, by using the change interval model, it is possible to avoid having to set a tolerance that overlooks an abnormality in one of the operations in a device that has two different operation patterns, as in the technology described in Patent Document 1.

[0034] An abnormality in a device having two operation patterns will be specifically described with reference to FIG. Assume that the robot 50 has two motion patterns, one for moving an object from point A to point C, and the other for moving an object from point B to point C. When the motion time from point A is longer than the motion time from point B, using the method of Patent Document 1 requires that the allowable time width be set longer than the motion time width from point A. As a result, even if an abnormality occurs during movement from point B and the motion becomes slow, it will fall within the allowable time width, and there is a possibility that the abnormality will be overlooked. However, the control signal monitoring device 10 according to the first embodiment calculates the degree of abnormality of the change interval of a specified signal using a change interval model. Therefore, it is possible to calculate the degree of abnormality using different change interval models for operation from point A and operation from point B. This makes it possible to appropriately judge abnormality in a device having two operation patterns.

[0035] Furthermore, the control signal monitoring device 10 according to the first embodiment calculates the degree of abnormality using the interval between the start signal and the completion signal specified in the signal set information, and therefore does not need to perform calculations using the interval between unnecessary signals. The technology described in Patent Document 1 does not have a method for narrowing down the signals connected to the PLC to be monitored. Therefore, there may be a large amount of data to be monitored. If the number of signals connected to the PLC is n, the maximum number of combinations of ON and OFF signals with order is P 2n,2 = 2n × (2n-1) monitoring targets are possible. In contrast, in the control signal monitoring device 10 according to the first embodiment, only the necessary number of monitoring targets are monitored, so that the processing load can be reduced.

[0036] ***Other configurations*** <Variation 1> In the first embodiment, each functional component is realized by software. However, as a first modification, each functional component may be realized by hardware. The following describes the first modification in terms of differences from the first embodiment.

[0037] When each functional component is realized by hardware, the control signal monitoring device 10 includes an electronic circuit instead of the processor 101, the memory 102, and the storage 103. The electronic circuit is a dedicated circuit for realizing the functions of each functional component, the memory 102, and the storage 103.

[0038] The electronic circuits include single circuits, composite circuits, programmed processors, parallel programmed processors, logic ICs, GAs, ASICs, and FPGAs. GA stands for Gate Array. ASIC stands for Application Specific Integrated Circuit. FPGA stands for Field-Programmable Gate Array. Each functional component may be realized by one electronic circuit, or each functional component may be realized by distributing it among a plurality of electronic circuits.

[0039] <Variation 2> As a second modification, some of the functional components may be realized by hardware, and other functional components may be realized by software.

[0040] The processor 101, the memory 102, the storage 103, and the electronic circuitry are collectively referred to as a processing circuit. In other words, the functions of the respective functional components are realized by the processing circuit.

[0041] Furthermore, the term "part" in the above description may be read as a "circuit," "step," "procedure," "processing," or "processing circuit."

[0042] Embodiment 2 The second embodiment differs from the first embodiment in that it predicts the timing at which an abnormality occurs. In the second embodiment, this difference will be explained, and explanations of the same points will be omitted.

[0043] ***Explanation of Operation*** The flow of processing by the control signal monitoring device 10 according to the second embodiment will be described with reference to FIG. The processing from step S201 to step S203 is the same as the processing from step S101 to step S103 in FIG.

[0044] (Step S204: Model generation process) The model generating unit 17 uses a regression model to generate a change interval model that models the change interval data stored in step S203. The regression model used in the second embodiment is capable of trend analysis like an autoregressive model. The regression model breaks down the variation factors of the change interval data, such as trend variation, seasonal variation, and irregular variation, and stores them as parameters that enable reproduction and prediction of data.

[0045] (Step S205: Anomaly Prediction Processing) The abnormality degree calculation unit 18 predicts the timing at which an abnormality will occur, using the change interval model generated in step S204. Specifically, the anomaly degree calculation unit 18 predicts the timing at which an anomaly will occur based on the timing at which a predicted value, which is an interval predicted using a change interval model, reaches a threshold value used when determining whether or not there is an anomaly in the operation of the device. The threshold value may be set manually, or may be set by multiplying an average value for a certain period of time in the past by a coefficient.

[0046] A method for predicting the timing at which an abnormality will occur will be described with reference to FIG. A predicted value d702 for each time is obtained using a change interval model obtained from the change interval d701 of the device. An intersection point between the predicted value d702 and a threshold d703, which is determined to be an abnormality when exceeded, is identified. The time of the identified intersection point becomes the predicted time d704 at which the abnormality will occur. If there is no intersection point or if there is no threshold, there is no prediction that the abnormality will occur.

[0047] (Step S206: Display control process) The display control unit 19 displays the timing at which the abnormality predicted in step S205 will occur, etc., on a display device connected via the communication interface 104. Specifically, as shown in Fig. 12, the display control unit 19 displays the time at which the abnormality is predicted to occur for each device.

[0048] ***Effects of the second embodiment*** As described above, the control signal monitoring device 10 according to the second embodiment identifies the timing at which the predicted value, which is predicted using the change interval model, reaches the threshold value as the timing at which an abnormality will occur. This enables the user to plan repair parts or maintenance of the device in advance, thereby improving the availability of the facility.

[0049] The above describes the embodiments and modifications of the present disclosure. Some of these embodiments and modifications may be combined and implemented. Also, one or some of them may be partially implemented. Note that the present disclosure is not limited to the above embodiments and modifications, and various modifications are possible as necessary. [Explanation of symbols]

[0050] 100 control signal monitoring system, 10 control signal monitoring device, 20 PLC, 30 manufacturing equipment, 31 device, 41 transmission path, 42 transmission path, 11 acquisition unit, 12 log data storage unit, 13 input unit, 14 signal set storage unit, 15 change interval calculation unit, 16 change interval storage unit, 17 model generation unit, 18 anomaly calculation unit, 19 display control unit, 101 processor, 102 memory, 103 storage, 104 communication interface.

Claims

1. A model generation unit that generates a change interval model that models change interval data that represents the interval between a start signal output from a PLC, the start signal whose state changes when an operation of a device starts, and a completion signal whose state changes when the operation of the device ends, the completion signal that is input to the PLC; an abnormality degree calculation unit that calculates an abnormality degree of the operation of the device using the change interval model generated by the model generation unit; A control signal monitoring device comprising:

2. The abnormality degree calculation unit calculates the abnormality degree of the operation of the device by comparing an actual measurement value, which is the interval between the start signal and the completion signal obtained from the device, with a predicted value, which is the interval predicted using the change interval model.

2. The control signal monitoring device according to claim 1.

3. The control signal monitoring device further an acquisition unit that acquires the start signal and stores it in a log data storage unit as log data of the start signal, and acquires the completion signal and stores it in the log data storage unit as log data of the completion signal; Equipped with The model generation unit generates the change interval model based on the log data stored in the log data storage unit by the acquisition unit.

2. The control signal monitoring device according to claim 1.

4. the start signal and the completion signal are each a binary signal, The acquisition unit replaces the start signal with the time when the value of the start signal changed, and stores the log data of the start signal, and replaces the completion signal with the time when the value of the completion signal changed, and stores the log data of the completion signal.

4. The control signal monitoring device according to claim 3.

5. The control signal monitoring device further a change interval calculation unit that identifies the start signal and the completion signal designated by the signal set information from the log data and generates the change interval data; Equipped with The model generation unit generates the change interval model by modeling the change interval data generated by the change interval calculation unit.

4. The control signal monitoring device according to claim 3.

6. the log data storage unit stores log data for each of the one or more start signals and the one or more completion signals; The signal set information includes a signal name that can identify the start signal and a signal name that can identify the completion signal.

6. The control signal monitoring device according to claim 5.

7. The abnormality degree calculation unit predicts the timing at which an abnormality will occur using a predicted value, which is an interval predicted using the change interval model, and a threshold value used when determining whether or not there is an abnormality in the operation of the device.

2. The control signal monitoring device according to claim 1.

8. a computer generates a change interval model that models change interval data representing an interval between a start signal output from a PLC, the start signal changing its state when an operation of a device starts, and a completion signal input to the PLC, the completion signal changing its state when the operation of the device is completed; A control signal monitoring method, wherein a computer calculates the degree of abnormality in the operation of the device using the change interval model.

9. A model generation process for generating a change interval model that models change interval data representing the interval between a start signal output from a PLC, the start signal changing its state when the device starts operating, and a completion signal, the completion signal changing its state when the device completes operating, and the completion signal input to the PLC; an abnormality degree calculation process for calculating an abnormality degree of the operation of the device using the change interval model generated by the model generation process; A control signal monitoring program that causes a computer to function as a control signal monitoring device that performs the above.

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