Factory automation equipment, factory automation system, normal model generation method and program

Abstract

The factory automation equipment (10) according to the present disclosure comprises a housing electrically connected to a frame ground (102), a signal circuit provided inside the housing and having a signal ground (202), a power extraction unit (11) electrically connected to the frame ground (102) and the signal ground (202) to extract power from common mode noise generated between the frame ground (102) and the signal ground (202), and an output unit (12) that outputs information relating to the power extracted from the power extraction unit (11) for display. (Representative diagram) Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to factory automation equipment and the like. [Background technology]

[0002] Noise that factory automation equipment is subjected to can be broadly divided into normal mode noise and common mode noise. Common mode noise has a greater impact on equipment than normal mode noise, making it important to take measures against common mode noise. As a measure against common mode noise, Patent Document 1 discloses technology that can detect the power of common mode noise applied to signal lines. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-290668 Summary of the Invention [Problem to be solved by the invention]

[0004] Factory automation equipment is often used together with many other machines and devices in production sites, and is exposed to a noisy environment caused by the surrounding machines and devices, making it more susceptible to common mode noise. Therefore, it is important to take measures against the common mode noise that factory automation equipment is subjected to. However, the technology in Patent Document 1 is not applied to factory automation equipment, and therefore there is a problem in that it is not easy to grasp the common mode noise that factory automation equipment is subjected to.

[0005] The present disclosure aims to provide factory automation equipment that can easily grasp the common mode noise that the equipment is subjected to. [Means for solving the problem]

[0006] The factory automation equipment according to the present disclosure includes a housing electrically connected to a frame ground, a signal circuit provided inside the housing and having a signal ground, and a power extraction unit electrically connected to the frame ground and the signal ground to extract power from common mode noise generated between the frame ground and the signal ground; a model generation unit that generates a normal model for inferring whether or not an abnormality due to common mode noise has occurred from time-series data of information about power extracted from the power extraction unit, the information being about power during stable operation of the factory automation equipment; and The present invention is characterized by comprising: [Effects of the Invention]

[0007] The factory automation equipment of the present disclosure can easily grasp the common mode noise to which it is subjected. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing a functional configuration of a factory automation device in a factory automation system according to a first embodiment of the present disclosure. [Figure 2] FIG. 1 is a diagram showing the relationship between a frame ground and a signal ground of a factory automation device according to a first embodiment of the present disclosure. [Figure 3] FIG. 1 is a diagram illustrating the relationship between time-series data of information about power consumption during stable operation of the factory automation device according to the first embodiment of the present disclosure and the stable operation lower limit and the stable operation upper limit. [Figure 4] FIG. 1 is a diagram illustrating the relationship between time-series data of information related to the FA device power supply during stable operation of the factory automation device according to the first embodiment of the present disclosure and the stable operation lower limit and the stable operation upper limit. [Figure 5] FIG. 1 is a diagram illustrating an example of a hardware configuration of a factory automation device according to a first embodiment of the present disclosure. [Figure 6] FIG. 10 is a diagram illustrating an example of an operation related to a display process of the factory automation device according to the first embodiment of the present disclosure. [Figure 7]FIG. 1 is a diagram illustrating an example of an operation related to a learning process of the factory automation device according to the first embodiment of the present disclosure. [Figure 8] FIG. 1 is a diagram illustrating an example of an operation related to an inference process for an abnormality occurrence in a factory automation device according to the first embodiment of the present disclosure. [Figure 9] FIG. 10 is a diagram showing an example of an operation relating to a process for comparing the occurrence status of common mode noise between different installation environments of the FA device 10. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, a factory automation device (hereinafter also referred to as FA device) according to an embodiment of the present disclosure will be described with reference to the drawings, in which the same or corresponding parts are designated by the same reference numerals.

[0010] Embodiment 1 The FA device according to the embodiment of the present disclosure is a control device such as a PLC (Programmable Logic Controller) used in the field of factory automation.

[0011] 1 and 2, the factory automation system 1 includes an FA device 10, a display 20, and an FA device power converter 30. The FA device 10 includes a housing 101 and a signal circuit 201.

[0012] The housing 101 is electrically connected to a frame ground 102. Here, the frame ground is the reference potential of the housing of the device, and the frame ground 102, which is an example of the frame ground, is the reference potential of the housing 101 of the FA device 10. Furthermore, the housing 101 is an example of a housing electrically connected to the frame ground.

[0013] Signal circuit 201 is provided inside housing 101 and has signal ground 202. Here, the signal ground is the reference potential of a signal line, and signal ground 202, which is an example of a signal ground, is the reference potential of signal circuit 201. Furthermore, signal circuit 201 is provided inside a housing and is an example of a signal circuit having a signal ground.

[0014] As shown in Figure 1, in a factory automation system 1 in which a display 20 and an FA equipment power converter 30 are connected to an FA equipment 10, the FA equipment 10 includes a power extraction unit 11, an output unit 12, an FA equipment power information acquisition unit 13, a model generation unit 14, a learning data setting unit 15, an inference unit 16, a memory unit 17, and a communication unit 18.

[0015] The power extraction unit 11 is electrically connected to the frame ground 102 and the signal ground 202. The power extraction unit 11 extracts power from common-mode noise generated between the frame ground 102 and the signal ground 202. Common-mode noise is a type of noise current that leaks through stray capacitance and returns to the power line via the ground. Common-mode noise is characterized by the fact that the direction of the noise current flowing on the positive and negative sides of the power supply is the same. Since the power extracted by the power extraction unit 11 is extracted from common-mode noise, the greater the extracted power, the greater the common-mode noise at the source. Therefore, the power extracted by the power extraction unit 11 and the magnitude of the common-mode noise at the source are proportional to each other. Therefore, information related to power may be rephrased as information related to common-mode noise. The power extraction unit 11 is an example of a power extraction unit that is electrically connected to the frame ground and the signal ground and extracts power from common-mode noise generated between the frame ground and the signal ground.

[0016] A specific example of the power extraction unit 11 is an energy harvesting module. The energy harvesting module is a module that performs energy harvesting by converting electromagnetic wave energy into electric power. That is, for example, when an energy harvesting module is employed as the power extraction unit 11 of the FA device 10 according to an embodiment of the present disclosure, the energy harvesting module is electrically connected to the frame ground 102 and the signal ground 202, and converts common mode noise, which is electromagnetic wave energy generated between the frame ground 102 and the signal ground 202, into electric power and extracts the electric power.

[0017] The output unit 12 outputs information about the power extracted from the power extraction unit 11 for display. More specifically, the output unit 12 extracts the information about the power from the power extraction unit 11 and outputs it to a display 20 connected to the FA device 10 via the communication unit 18, thereby displaying the information about the power output from the output unit 12 on the display 20. Here, the information about the power refers to information that includes at least the value of the power extracted from the power extraction unit 11 (power value). The information about the power displayed on the display 20 may be time-series data. For example, as shown in FIG. 3, the display 20 may display time-series data of the power value and may further display a stable operation lower limit and a stable operation upper limit. For example, by checking the range from the minimum to the maximum power value at each time displayed on the display 20, the user can visually grasp the value of the power extracted from the power extraction unit 11, i.e., the magnitude of the common-mode noise received by the FA device 10. Furthermore, by checking whether the value of the power extracted from the power extraction unit 11 falls below the stable operation lower limit or exceeds the stable operation upper limit, it becomes possible to visually grasp the timing at which common mode noise received by the FA device 10 occurs. The stable operation lower limit and stable operation upper limit will be described later in relation to the model generation unit 14. Here, the display unit 20 is an external device, such as a computer such as a personal computer or a programmable display. Note that the FA device 10 may have a display unit (not shown) that displays the output from the output unit 12. The output unit 12 is an example of an output unit that outputs information related to the power extracted from the power extraction unit for display.

[0018] The FA equipment power supply information acquisition unit 13 acquires time-series data of information related to the FA equipment power supply. Here, the FA equipment power supply refers to a DC power supply that supplies the DC voltage used by the FA equipment 10. In the FA equipment 10, AC voltage supplied from an external power supply (not shown) is converted to DC voltage by an FA equipment power converter 30 externally attached to the FA equipment 10, and the converted DC voltage is supplied from the FA equipment power converter 30 to the FA equipment 10. Therefore, the information related to the FA equipment power supply is information related to the DC power supplied from the FA equipment power converter 30 connected to the FA equipment 10, including, for example, the voltage value of the DC power supplied from the FA equipment power converter 30 connected to the FA equipment 10. While the FA equipment power converter 30 has been described as a separate device externally attached to the FA equipment 10, it may also be built into the FA equipment 10. Alternatively, the FA equipment 10 may be one of the constituent units, such as a power supply unit that supplies power to the FA equipment 10 when the FA equipment 10 is a building-block type PLC. Furthermore, although it has been explained that the FA equipment power supply converter 30 converts AC voltage to DC voltage, this is not limiting, and the converter may convert the DC voltage to a smaller voltage without changing it, such as converting a DC voltage of 24 V to a DC voltage of 5 V. The FA equipment power supply information acquiring unit 13 is an example of an FA equipment power supply information acquiring unit that acquires time-series data of information relating to the FA equipment power supply.

[0019] The model generation unit 14 generates a trained model for inferring the presence or absence of an abnormality due to common mode noise from time-series data on information related to power. In particular, the model generation unit 14 generates a normal model as a trained model for inferring the presence or absence of an abnormality due to common mode noise, using training data including time-series data on information related to power during stable operation of the FA device 10. Hereinafter, the model generation unit 14 will be described as generating the normal model as a trained model. Note that, to generate the trained model, data from the time-series data on information related to power during stable operation of the FA device 10, acquired over a certain acquisition period set by the user, is used as training data from the time-series data on information related to power. The acquisition period for the training data will be described later in connection with the training data setting unit 15. The model generation unit 14 is an example of a model generation unit that generates a trained model for inferring the presence or absence of an abnormality due to common mode noise from time-series data on information related to power.

[0020] The learning algorithm used by the model generation unit 14 can be a known algorithm such as supervised learning or unsupervised learning. As an example, a case where k-means (clustering), which is unsupervised learning, is applied will be described.

[0021] The model generating unit 14 learns time-series data of information relating to power when no abnormality due to common mode noise occurs, for example, by so-called unsupervised learning in accordance with a grouping method using the k-means method.

[0022] The k-means method is a non-hierarchical clustering algorithm that uses the cluster means to classify a given number of clusters into k.

[0023] Specifically, the k-means algorithm works as follows: First, each data item xi is randomly assigned to a cluster. Next, the center Vj of each cluster is calculated based on the assigned data. Next, the distance between each xi and each Vj is calculated, and xi is reassigned to the closest central cluster. If the cluster assignment for all xi remains unchanged through the above process, or if the amount of change falls below a preset threshold, it is determined that convergence has occurred and the process ends.

[0024] In the first embodiment of the present disclosure, time series data of information related to power when no abnormality due to common mode noise occurs is learned by so-called unsupervised learning in accordance with learning data created based on time series data of information related to power.

[0025] In the first embodiment of the present disclosure, a specific method in which the model generation unit 14 generates a normal model as a trained model will be described with reference to Fig. 3. As shown in Fig. 3, time-series data of information related to power is shown by a solid line, with the vertical axis representing power and the horizontal axis representing time. This time-series data of information related to power is learning data when the FA device 1 is operating stably, and Fig. 3 illustrates data for a portion (a single cycle) of the acquisition period of the periodic processing.

[0026] In the first embodiment of the present disclosure, the trained model is a model for classifying data into clusters that indicate stable operation, for example. As shown in FIG. 3, the stable operation lower limit and the stable operation upper limit are indicated by dotted lines, and the area surrounded by the stable operation lower limit and the stable operation upper limit corresponds to the cluster that indicates stable operation. In other words, if the time-series data regarding power does not belong to the cluster that indicates stable operation, it is inferred that an abnormality has occurred. In this way, in the first embodiment of the present disclosure, the time-series data regarding power is input to the trained model, and the obtained time-series data regarding power is classified into a cluster that indicates stable operation, thereby inferring whether an abnormality due to common mode noise has occurred.

[0027] The model generation unit 14, for example, repeatedly executes periodic processing during stable operation and aligns the start and end points of each cycle to generate clusters representing stable operation in which power-related information (power values) is distributed. The model generation unit 14 then calculates the smallest value among the values ​​belonging to the cluster representing stable operation at each time as the stable operation lower limit, and calculates the largest value among the values ​​belonging to the cluster representing stable operation at each time as the stable operation upper limit. Instead of inferring whether an abnormality has occurred based on which cluster the time-series data of power-related information belongs to, as described above, the model generation unit 14 may infer whether an abnormality has occurred based on whether the time-series data of power-related information falls outside a threshold value, such as the stable operation lower limit or the stable operation upper limit. That is, it may be inferred that an abnormality has occurred when the time-series data of power-related information falls below the stable operation lower limit or when the time-series data of power-related information exceeds the stable operation upper limit.

[0028] As shown in FIG. 4, the time-series data of the information about the FA device power supply is shown by a solid line, with the vertical axis representing voltage and the horizontal axis representing time. Similarly to FIG. 3, the stable operation lower limit and the stable operation upper limit are also shown by dotted lines. The model generation unit 14 may acquire the time-series data of the information about the FA device power supply during stable operation of the FA device 10 shown in FIG. 4 multiple times when repeatedly executing periodic processing during stable operation, in addition to the time-series data of the information about the power supply during stable operation of the FA device 10 shown in FIG. 3. The model generation unit 14 may align the start and end points of the time width of each period, thereby generating a normal model by using a cluster representing stable operation in which the information about the FA device power supply (voltage values) is distributed as a trained model. That is, the model generation unit 14 may generate the normal model using the time-series data of the information about the FA device power supply during stable operation of the FA device 10 shown in FIG. 4 as the trained model, in addition to the time-series data of the information about the power supply during stable operation of the FA device 10 shown in FIG. 3. A normal model using both time series data on the power information during stable operation of the FA device 10 and time series data on the FA device power supply information during stable operation of the FA device 10 will be a three-dimensional cluster (for example, a cluster with time on the X axis, voltage on the Y axis, and power on the Z axis) that combines Figures 3 and 4.

[0029] The learning data setting unit 15 sets the acquisition period of the learning data. The learning data setting unit 15 may set the acquisition period of the learning data to, for example, a period determined by the user as being in a stable operating state of the FA device 10, or may set the acquisition period of the learning data to a predetermined period starting from a certain time elapsed until stable operation of the FA device 10 is achieved after startup, when it is clear that the FA device 10 is in a stable operating state, such as when it is clear that the installation environment of the FA device 10 is not a noisy environment.

[0030] The inference unit 16 infers whether an abnormality has occurred using the time-series data of information related to power and the trained model. More specifically, the inference unit 16 inputs the time-series data of information related to power acquired by the power extraction unit 11 into the trained model, and outputs whether an abnormality has occurred inferred from the time-series data of information related to power. Here, a normal model is used as the trained model, and the inference unit 16 inputs the time-series data of information related to power into the trained model to infer whether the FA device 10 is in a stable operating state from the time-series data of information related to power. Here, if the inference unit 16 infers that the FA device 10 is in a stable operating state, it means that no abnormality has occurred. If the inference unit 16 infers that the FA device 10 is not in a stable operating state, it means that an abnormality has occurred. In other words, the inference unit 16 inputs the time-series data of information related to power into the trained model, and thereby infers whether the FA device 10 is in a stable operating state from the time-series data of information related to power, thereby inferring whether an abnormality has occurred. More specifically, as described above, the inference unit 16 inputs time-series data about power information into the trained model, and infers whether or not an abnormality has occurred due to common mode noise by classifying the obtained time-series data about power information into clusters that indicate stable operation. The inference unit 16 is an example of an inference unit that infers whether or not an abnormality has occurred using the time-series data about power information and the trained model.

[0031] Here, the inference unit 16 may infer whether or not an abnormality has occurred using time-series data on information related to power, time-series data on information related to the FA equipment power supply, and the trained model. That is, the data used by the inference unit 16 may include time-series data on information related to the FA equipment power supply in addition to time-series data on information related to power. Hereinafter, the time-series data on information related to power and the time-series data on information related to the FA equipment power supply may collectively be referred to simply as time-series data.

[0032] When the inference unit 16 infers that an abnormality has occurred, the storage unit 17 stores the time when the abnormality occurred, information about the power at the time when the abnormality occurred, and information about the FA equipment power supply at the time when the abnormality occurred. That is, when the inference unit 16 infers that an abnormality has occurred, the storage unit 17 stores the power value and the voltage value at the time when the abnormality occurred. Note that the time when the abnormality occurred may be the time when the inference unit 16 infers that an abnormality has occurred, or the time when the time-series data that the inference unit 16 infers that an abnormality has occurred is acquired. The storage unit 17 is an example of a storage unit that, when the inference unit infers that an abnormality has occurred, stores the time when the abnormality occurred and at least one of information about the power at the time when the abnormality occurred and information about the FA equipment power supply at the time when the abnormality occurred.

[0033] Furthermore, the storage unit 17 stores the trained model generated by the model generation unit 14. Note that the trained model stored in the storage unit 17 does not have to be one generated by the model generation unit 14, and for example, the storage unit 17 may store a trained model trained by a learning device other than the FA device 10. Furthermore, the inference unit 16 uses the trained model by referring to the storage unit 17.

[0034] The communication unit 18 communicates with the display device 20 connected to the FA device 10. For example, the FA device 10 can connect to the display device 20 via the communication unit 18 and exchange data such as time series data with the display device 20, thereby displaying the time series data on the display device 20.

[0035] An example of the hardware configuration of the FA device 10 will be described with reference to Fig. 5. The FA device 10 includes a non-volatile memory 1001, a volatile memory 1002, a communication interface 1003, and a processor 1004, which are connected to each other via a bus 1000.

[0036] The nonvolatile memory 1001 includes, for example, a read only memory (ROM), and stores programs executed by the processor 1004.

[0037] The volatile memory 1002 includes, for example, a RAM (Random Access Memory). The volatile memory 1002 stores a program that the processor 1004 has loaded from the non-volatile memory 1001. The volatile memory 1002 also functions as a working memory when the processor 1004 executes the program.

[0038] The non-volatile memory 1001 and the volatile memory 1002 function as the storage unit 17 .

[0039] The communication interface 1003 includes, for example, an I / O (Input / Output) interface, a USB (Universal Serial Bus) port, and a serial port. The FA device 10 communicates with the display device 20 via the communication interface 1003, and exchanges data such as time-series data with the display device 20. The communication interface 1003 functions as a communication unit 18.

[0040] The processor 1004 includes, for example, a CPU and an MPU (Micro Processing Unit). The processor 1004 stores a program from the non-volatile memory 1001 to the volatile memory 1002 and executes the program stored in the volatile memory 1002, thereby realizing an output unit 12, an FA equipment power supply information acquisition unit 13, a model generation unit 14, a learning data setting unit 15, and an inference unit 16, and performing each operation. Each operation will be described later in the description of the operation of the FA equipment 10. The program executed by the processor 1004 may also be realized by a computer program product, which is a recording medium on which the program is stored. An example of the recording medium in this case is a non-transitory computer-readable medium on which the program is stored.

[0041] The non-volatile memory 1001, the volatile memory 1002, and the processor 1004 are an example of a signal circuit having a signal ground.

[0042] Next, the operation of the FA device 10 configured as above will be described. The FA device 10 performs operations related to the display processing of the FA device 10, operations related to the learning processing of the FA device 10, operations related to the inference processing of the occurrence of an abnormality in the FA device 10, and operations related to the comparison processing of the occurrence status of common mode noise between different installation environments of the FA device 10, which will be described later. An example of the operation related to the display processing of the FA device 10 will be described with reference to Fig. 6. Here, Fig. 6 is a flowchart showing an example of the operation related to the display processing of the FA device 10, and the display processing of the FA device 10 when displaying on a display unit 20 connected to the FA device 10 will be described.

[0043] As shown in FIG. 6, the power extraction unit 11 extracts power from common mode noise generated between the frame ground 102 and the signal ground 202 (step S11).

[0044] After the power extraction unit 11 extracts power from the common mode noise generated between the frame ground 102 and the signal ground 202 in step S11, the output unit 12 outputs information about the power extracted from the power extraction unit 11 to the display unit 20 connected to the FA device 10 (step S12). After step S12, the information about the power extracted from the power extraction unit 11 is displayed on the display unit 20 connected to the FA device 10 as shown in Fig. 3, and the FA device 10 ends its operation related to the display processing of the FA device 10. Note that information about the FA device power supply when the FA device 10 is operating stably may also be displayed as shown in Fig. 4.

[0045] Next, an example of the operation of the FA device 10 configured as described above regarding the learning process will be described with reference to Fig. 7. Fig. 7 is a flowchart showing an example of the operation of the FA device 10 regarding the learning process.

[0046] As shown in FIG. 7, the learning data setting unit 15 sets a period for acquiring learning data (step S21).

[0047] After the learning data setting unit 15 sets the learning data acquisition period in step S21, the learning data setting unit 15 acquires learning data during the set acquisition period (step S22).

[0048] After acquiring the learning data for the acquisition period set by the learning data setting unit 15 in step S22, the model generation unit 14 learns the time series data when no abnormality due to common mode noise occurs by so-called unsupervised learning according to the learning data acquired from the learning data setting unit 15, and generates a learned model (step S23).

[0049] In step S23, the model generation unit 14 uses the learning data acquired from the learning data setting unit 15 to generate a trained model for inferring whether or not an abnormality due to common mode noise has occurred, and then the model generation unit 14 stores the generated trained model in the storage unit 17 (step S24). After step S24, the FA device 10 ends the operation related to the learning process of the FA device 10.

[0050] Next, an example of the operation of the FA device 10 configured as described above regarding the inference processing will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of the operation of the FA device 10 regarding the inference processing.

[0051] As shown in FIG. 8, the power extraction unit 11 and the FA equipment power supply information acquisition unit 13 acquire time-series data (step S31).

[0052] After the power extraction unit 11 and the FA equipment power supply information acquisition unit 13 acquire time series data in step S31, the inference unit 16 inputs the time series data acquired by the power extraction unit 11 and the FA equipment power supply information acquisition unit 13 into the trained model generated by the model generation unit 14 (step S32).

[0053] In step S32, the inference unit 16 inputs the time series data acquired by the power extraction unit 11 and the FA equipment power supply information acquisition unit 13 into the trained model generated by the model generation unit 14. Then, the inference unit 16 acquires the trained model generated by the model generation unit 14 from the memory unit 17, inputs the time series data acquired by the power extraction unit 11 and the FA equipment power supply information acquisition unit 13 into the acquired trained model, and infers whether or not an abnormality has occurred (step S33).

[0054] If the inference unit 16 infers that no abnormality has occurred (step S33: No), the process returns to step S31, where the power extraction unit 11 and the FA equipment power supply information acquisition unit 13 acquire new time-series data again, and thereafter the FA equipment 10 repeats the operations from step S32.

[0055] If the inference unit 16 infers that an abnormality has occurred (step S33: Yes), the storage unit 17 stores the power value and voltage value at the time the abnormality occurred (step S34). After step S34, the FA device 10 ends the operation related to the inference process of the FA device 10.

[0056] Next, an example of the operation relating to the process of comparing the occurrence status of common mode noise between different installation environments of the FA device 10 configured as described above will be described with reference to Fig. 9. Here, Fig. 9 is a flowchart showing an example of the operation relating to the process of comparing the occurrence status of common mode noise between different installation environments (installation environment A, installation environment B) of the FA device 10. Specifically, normal models are created between the different installation environments of the FA device 10 (installation environment A, installation environment B), and feature quantities calculated from each normal model are compared.

[0057] 9, the model generation unit 14 and the learning data setting unit 15 of the FA device 10 perform a learning process in the installation environment A (step S41). That is, the FA device 10 performs the learning process from step S21 to step S24 shown in FIG. 7 in the installation environment A, and generates a normal model in the installation environment A as a learned model in the installation environment A.

[0058] After the model generation unit 14 and the learning data setting unit 15 of the FA device 10 perform learning processing for the installation environment A in step S41, the inference unit 16 of the FA device 10 calculates the amount of power in the installation environment A as a feature of the normal model for the installation environment A (step S42). In order to understand the occurrence status of common mode noise in the installation environment A, it is necessary to calculate the total amount of power generated in the installation environment A, i.e., the amount of power in the installation environment A. Therefore, using the generated normal model for the installation environment A, the FA device 10 calculates the "area surrounded by the stable operation lower limit value and the stable operation upper limit value (i.e., the area surrounded by the stable operation lower limit value and the stable operation upper limit value and the start point and end point of the time width in the installation environment A in FIG. 3)" in the normal model for the installation environment A as a feature of the normal model for the installation environment A.

[0059] After the inference unit 16 of the FA device 1 calculates the feature quantities of the normal model in the installation environment A in step S42, the model generation unit 14 and the learning data setting unit 15 of the FA device 10 perform learning processing in the installation environment B (step S43).

[0060] After the model generation unit 14 and the learning data setting unit 15 of the FA device 10 perform the learning process for the installation environment B in step S43, the inference unit 16 of the FA device 10 calculates the amount of power in the installation environment B as a feature of the normal model for the installation environment B (step S44). That is, using the generated normal model for the installation environment B, the inference unit 16 of the FA device 10 calculates the "area surrounded by the stable operation lower limit value and the stable operation upper limit value (i.e., the area surrounded by the stable operation lower limit value and the stable operation upper limit value and the start point and end point of the time width in the installation environment B for the installation environment B)" in the normal model for the installation environment B as a feature of the normal model for the installation environment B.

[0061] After the inference unit 16 of the FA device 10 calculates the feature quantities of the normal model in installation environment B in step S44, the inference unit 16 of the FA device 10 compares the feature quantities of the normal models for installation environments A and B by calculating the change in the feature quantities of the normal model due to the change in the installation environment (step S45). More specifically, the inference unit 16 of the FA device 10 calculates the difference between the amount of power consumed in installation environment B and the amount of power consumed in installation environment A as the amount of power consumed due to the change in the installation environment. Therefore, if the amount of power consumed due to the change in the installation environment is a positive value, it indicates that the amount of power consumed, i.e., the common-mode noise, increased due to the change in the installation environment (the change in the installation environment did not have any effect on the noise countermeasures, but rather worsened). If it is 0, it indicates that the common-mode noise did not change even after the change in the installation environment (the change in the installation environment did not have any effect on the noise countermeasures). If it is a negative value, it indicates that the common-mode noise decreased due to the change in the installation environment (the change in the installation environment had an effect on the noise countermeasures).

[0062] After the inference unit 16 of the FA device 10 calculates the amount of change in the feature quantities of the normal model due to the change in the installation environment in step S45, the output unit 12 outputs the amount of change for display as a comparison result of the feature quantities of the normal model before and after the change in the installation environment in step S46. Then, the inference unit 16 of the FA device 10 ends the operation related to the process of comparing the occurrence status of common mode noise between different installation environments of the FA device 10.

[0063] In the above example, the change in the amount of power consumption before and after the change in the installation environment is calculated as the feature amount of the normal model before and after the change in the installation environment, but as a modified example, the rate of change in the amount of power consumption before and after the change in the installation environment may be calculated and compared. Specifically, the rate of change in the amount of power consumption may be calculated by subtracting the amount of power consumption in installation environment A from the amount of power consumption in installation environment B, and the rate of change may be output for display.

[0064] As a variation of the above example, the upper and lower ranges of the stable operation lower and upper limits of power before and after the change in the installation environment may be calculated as feature quantities of the normal model and compared before and after the change in the installation environment. Specifically, the difference between the upper and lower ranges of the stable operation lower and upper limits at a specific time in installation environment B and the upper and lower ranges of the stable operation lower and upper limits at a specific time in installation environment A is calculated as the change in the range. A positive change in the range indicates an increase in common-mode noise due to the change in the installation environment. A zero change in the range indicates no change in common-mode noise due to the change in the installation environment. A negative change in the range indicates a decrease in common-mode noise due to the change in the installation environment. Note that a specific time refers to the same time within one cycle, and the upper and lower ranges of the stable operation lower and upper limits at the same time within one cycle in installation environments A and B are compared.

[0065] Furthermore, as a variation of the above example, when generating a three-dimensional normal cluster that takes into consideration information about the FA equipment power supply before and after the change in the installation environment in addition to information about power before and after the change in the installation environment, the volume of the part surrounded by the stable operation lower limit value and stable operation upper limit value of the information about power (power) and the information about the FA equipment power supply (voltage) in installation environments A and B and the start and end points of the time width may be calculated and compared as feature quantities of the normal model before and after the change in the installation environment. Specifically, the inference unit 16 of the FA equipment 10 calculates the difference obtained by subtracting the volume in installation environment A from the volume in installation environment B as the amount of change in common mode noise due to the change in the installation environment. Therefore, if the amount of change in volume due to a change in the installation environment is a positive value, it indicates that the common mode noise increased as a result of the change in the installation environment (the change in the installation environment had no effect on noise countermeasures, and in fact worsened), if it is 0, it indicates that the common mode noise did not change even after the change in the installation environment (the change in the installation environment had no effect on noise countermeasures), and if it is a negative value, it indicates that the common mode noise decreased as a result of the change in the installation environment (the change in the installation environment had an effect on noise countermeasures).In addition, instead of the amount of change in volume before and after a change in the installation environment, the rate of change in volume before and after a change in the installation environment may be calculated.

[0066] In this way, the FA device 10 according to this embodiment extracts power from the common mode noise generated between the frame ground 102 and the signal ground 202, and outputs information about the extracted power from the power extraction unit 11 for display. If it becomes possible to output information about power, it becomes possible to display the information about power, for example, as shown in Fig. 3, at the output destination to which the information about power is output. This makes it possible to display information about power, i.e., information about the common mode noise received by the FA device 10, and provides the effect of making it possible to easily grasp the common mode noise received by the FA device 10 itself.

[0067] Furthermore, since it is now possible to display information about the common mode noise received by the FA device 10, for example, if a noise countermeasure is implemented by changing the installation environment of the FA device 10, it is possible to visually grasp the magnitude of the common mode noise received by the FA device 10 in each installation environment by simply displaying information about the common mode noise received by the FA device 10 in the installation environment of each FA device 10 and comparing the range of the minimum to maximum power values ​​at each time in each of the displayed installation environments, and to verify the noise countermeasure. This eliminates the need for verification work of the noise countermeasure and the preparations required for the verification work, thereby achieving the effect of reducing the effort and time required for verifying the noise countermeasure.

[0068] Furthermore, the FA device 10 according to the present embodiment generates a trained model for inferring the presence or absence of an abnormality due to common mode noise from time-series data on information related to power. It is possible to generate a trained model that can infer the presence or absence of an abnormality due to common mode noise by learning the common mode noise pattern based on the time-series data on information related to common mode noise. This provides the advantage of being able to obtain an index for detecting an abnormal state due to common mode noise in the installation environment of the FA device. In particular, the FA device 10 according to the present disclosure generates a normal model as a trained model from time-series data on information related to power during stable operation of the FA device 10. This provides the advantage of being able to obtain an index for detecting an abnormal state due to common mode noise based on time-series data on information related to common mode noise during stable operation of the FA device 10.

[0069] Furthermore, when the installation environment is changed, a normal model is generated for each installation environment, and the generated normal models are used to calculate and compare the feature quantities of the normal models before and after the change in the installation environment, thereby making it possible to compare the occurrence status of common mode noise between each installation environment. Therefore, it is possible to grasp the effect of noise countermeasures due to the change in the installation environment from the change in the occurrence status of common mode noise due to the change in the installation environment, and it is also possible to obtain an index for evaluating the effect of improvement measures for noise countermeasures.

[0070] Furthermore, in the FA device 10 according to this embodiment, a normal model is generated as a trained model from time series data on information related to the FA device power supply during stable operation of the FA device 10, in addition to time series data on information related to power during stable operation of the FA device 10. In this way, by acquiring time series data on information related to the FA device power supply as training data in addition to time series data on information related to power and generating a normal model, the number of parameters when generating the normal model increases, which has the effect of enabling the accuracy of the normal model to be improved.

[0071] Furthermore, the FA device 10 according to this embodiment uses time-series data of information related to power and a trained model to infer whether an abnormality has occurred. Therefore, when acquiring time-series data of information related to power, i.e., time-series data of information related to common mode noise received by the FA device 10, it is possible to automatically detect the occurrence of an abnormality in common mode noise.

[0072] Furthermore, the FA device 10 according to this embodiment infers whether an abnormality has occurred using time-series data on information related to power, time-series data on information related to the FA device power supply, and the trained model. In this way, by inputting the time-series data on information related to the FA device power supply in addition to the time-series data on information related to power into the trained model, the number of parameters used by the inference unit 16 when making an inference increases, improving the accuracy of the inference and achieving the effect of enabling improved accuracy in detecting the occurrence of an abnormality.

[0073] Furthermore, in the FA device 10 according to this embodiment, when the inference unit 16 infers that an abnormality has occurred, the storage unit 17 stores the time when the abnormality occurred and at least one of information relating to the power at the time when the abnormality occurred and information relating to the FA device power supply at the time when the abnormality occurred. In this way, by recording detailed information when an abnormality occurs, it is possible to quickly identify the cause and take measures even when an abnormality occurs.

[0074] Here, the output unit 12 has been described as outputting information related to the power extracted from the power extraction unit 11 for display. However, it may also output information related to the FA equipment power supply acquired by the FA equipment power supply information acquisition unit 13 for display. More specifically, the output unit 12 may acquire information related to the FA equipment power supply from the FA equipment power supply information acquisition unit 13 and output the information to a display 20 connected to the FA equipment 10 via the communication unit 18, thereby displaying the information related to the FA equipment power supply output from the output unit 12 on the display 20. The information related to the FA equipment power supply displayed on the display 20 may be time-series data. For example, the display 20 may display time-series data of the FA equipment power supply value as shown in FIG. 4 in addition to time-series data related to power as shown in FIG. 3 , and may further display a stable operation lower limit and a stable operation upper limit. Even in this case, for example, a user can visually grasp the value of the voltage extracted from the FA equipment power converter 30 by checking the range from the minimum to maximum voltage value or the range from the stable operation lower limit to the stable operation upper limit at each time displayed on the display 20. In addition, since it is now possible to display time series data on information related to FA equipment power supplies in addition to time series data on information related to electricity, it becomes easier to understand the relationship between time series data on electricity and time series data on information related to FA equipment power supplies, which also has the effect of making it easier to troubleshoot when an abnormality occurs.

[0075] The present disclosure allows various embodiments and modifications without departing from the broad spirit and scope of the present disclosure. Furthermore, the above-described embodiments are intended to explain the present disclosure and do not limit the scope of the present disclosure. In other words, the scope of the present disclosure is defined by the claims, not the embodiments. Various modifications made within the scope of the claims and the meaning of equivalent disclosures are considered to be within the scope of the present disclosure. [Explanation of symbols]

[0076] 1. Factory automation system, 10 Factory automation equipment, 11 Power extraction section, 12 Output section, 13 FA equipment power supply information acquisition unit, 14 model generation unit, 15 learning data setting unit, 16 inference unit, 17 memory unit, 18 communication unit, 20 display unit, 30 FA equipment power converter, 101 housing, 102 frame ground, 201 signal circuit, 202 Signal Ground, 1000 Bus, 1001 Non-volatile Memory, 1002 volatile memory, 1003 communication interface, 1004 processor

Claims

1. a housing electrically connected to a frame ground; a signal circuit provided inside the housing and having a signal ground; a power extraction unit electrically connected to the frame ground and the signal ground, extracting power from common mode noise generated between the frame ground and the signal ground; A factory automation device comprising: a model generation unit that generates a normal model for inferring whether or not an abnormality due to the common mode noise has occurred from time-series data of information about the power extracted from the power extraction unit, the information being about the power when the factory automation equipment is operating stably; and 1. A factory automation device comprising:

2. an inference unit that infers whether or not the abnormality has occurred using time-series data of information related to the power and the normal model; 10. The factory automation equipment of claim 1, further comprising:

3. an FA equipment power supply information acquisition unit that acquires time-series data of information related to the FA equipment power supply; 2. The factory automation device according to claim 1, wherein the model generation unit generates the normal model from time series data of information related to the FA device power supply during the stable operation in addition to time series data of information related to power during the stable operation.

4. an FA equipment power supply information acquisition unit that acquires time-series data of information related to the FA equipment power supply; the model generation unit generates the normal model from time series data of information about the FA equipment power supply during the stable operation in addition to time series data of information about the power during the stable operation, 3. The factory automation device according to claim 2, wherein the inference unit infers whether or not the abnormality has occurred by using time-series data of the information related to the power, time-series data of the information related to the FA device power supply, and the normal model.

5. a storage unit configured to store, when the inference unit infers that an abnormality has occurred, a time when the abnormality occurred and at least one of information about the power at the time when the abnormality occurred and information about the FA equipment power supply at the time when the abnormality occurred; 5. The factory automation equipment of claim 4, further comprising:

6. an output unit that outputs information about the power extracted from the power extraction unit for display; 6. The factory automation device according to claim 1, further comprising:

7. An output unit that outputs information relating to the power extracted from the power extraction unit and information relating to the FA equipment power supply acquired from the FA equipment power supply information acquisition unit to display the information; 6. The factory automation device according to claim 3, further comprising:

8. an output unit that outputs information about the power extracted from the power extraction unit for display; Furthermore, the model generation unit generates the normal model before an installation environment change and the normal model after the installation environment change; the inference unit calculates feature amounts of the normal model before the change in the installation environment and after the change in the installation environment, and compares the feature amounts of the normal model before the change in the installation environment and after the change in the installation environment; the output unit outputs for display a comparison result of the feature quantity of the normal model before the change in the installation environment and after the change in the installation environment by the inference unit.

6. The factory automation device according to claim 2, claim 4, or claim 5.

9. The factory automation equipment of claim 6; a display that displays information about the power output from the output unit; and A factory automation system comprising:

10. A first step of extracting power from common mode noise generated between a frame ground of a housing and a signal ground of a signal circuit in a factory automation device; a second step of generating a normal model for inferring whether or not an abnormality has occurred due to the common mode noise from time series data of information about power during stable operation of the factory automation equipment, among the extracted information about power; A normal model generation method comprising:

11. On the computer, A first step of extracting power from common mode noise generated between a frame ground of a housing and a signal ground of a signal circuit in a factory automation device; a second step of generating a normal model for inferring whether or not an abnormality has occurred due to the common mode noise from time series data of information about power during stable operation of the factory automation equipment, among the extracted information about power; A program characterized by executing the following.

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