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WO2018163375A1 - Dispositif de commande, système de commande et serveur - Google Patents

Dispositif de commande, système de commande et serveur Download PDF

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Publication number
WO2018163375A1
WO2018163375A1 PCT/JP2017/009572 JP2017009572W WO2018163375A1 WO 2018163375 A1 WO2018163375 A1 WO 2018163375A1 JP 2017009572 W JP2017009572 W JP 2017009572W WO 2018163375 A1 WO2018163375 A1 WO 2018163375A1
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Prior art keywords
normal operation
control
operation range
terminals
unit
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PCT/JP2017/009572
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English (en)
Japanese (ja)
Inventor
中川　慎二
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株式会社日立製作所
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Priority to PCT/JP2017/009572 priority Critical patent/WO2018163375A1/fr
Publication of WO2018163375A1 publication Critical patent/WO2018163375A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring

Definitions

  • the present invention relates to a control device, a control system, and a server.
  • Patent Document 1 JP 2014-186631
  • This document describes a diagnosis processing system in which a terminal device mounted on a self-propelled machine and a server installed in a management center are connected by a wireless communication line, and the terminal device is connected to the self-propelled machine.
  • a data receiving unit that receives data from a sensor provided; and a first diagnosis unit that diagnoses an abnormality of the self-propelled machine, wherein the server diagnoses an abnormality of the self-propelled machine.
  • One of the first diagnosis unit and the second diagnosis unit performs a primary diagnosis of the abnormality of the self-propelled machine based on the data received by the data receiving unit and the primary diagnosis.
  • the diagnostic processing system is characterized in that the result of the above is transmitted to the other and the other receiving the result of the primary diagnosis performs the secondary diagnosis based on the result of the primary diagnosis. " (Refer to [Claim 1]).
  • Patent Document 2 JP-A-2016-128971.
  • the predictive diagnosis system acquires sensor data from multiple sensors installed in mechanical equipment as time-series data, and uses statistical techniques using the time-series data as learning data.
  • a state measure calculation unit that calculates a state measure that is an index indicating a state
  • an approximate expression calculation unit that calculates an approximate expression that approximates a transition of the state measure from the past to the present by a polynomial
  • an approximate expression A state measure estimating unit that estimates a state measure up to a predetermined time in the future, and a reference period setting unit that sets a period of a state measure to be referred to calculate an approximate expression.
  • the first period including the acquisition time of the latest time-series data or the second period including the acquisition time of the latest time-series data shorter than the first period is set. Solution Reference).
  • Patent Document 1 shares diagnosis processing between a terminal and a server
  • Patent Document 2 detects an abnormality of a single mechanical device. Yes, if there are multiple terminals, the abnormal range of each terminal includes an abnormal range common to each terminal and a specific abnormal range caused by individual differences, environmental differences, user characteristics differences, changes over time, etc. It is not a diagnosis method or an abnormality detection method that takes into account certain things.
  • An object of the present invention is to provide a control device or the like that can improve control reliability.
  • the present invention provides a receiving unit that receives a first normal operating range common to a plurality of terminals, a control unit that controls a machine, and an operation state of the control unit is the first operating range.
  • An abnormality detection unit that determines that the control unit is abnormal when it is not in the normal operation range.
  • FIG. 3 is an overall view of a control system in Embodiments 1 to 3. It is the figure which showed the terminal (control apparatus) and controlled object in Embodiment 1.
  • 6 is a system configuration diagram of a terminal (control device) in Embodiments 1 to 5.
  • FIG. 6 is a system configuration diagram of a server in Embodiments 1 to 5.
  • FIG. 6 is a diagram showing processing of a normal operation range learning unit common to terminals in Embodiments 1 to 4.
  • FIG. 6 is a diagram showing processing of data dividing means in the first to fourth embodiments. It is a figure which shows the example of a process result of a data division
  • FIG. 6 is a diagram showing processing of a normal operation range setting unit common to terminals in Embodiments 1 to 4.
  • FIG. 5 is a diagram illustrating processing of an abnormality detection unit in the first to third embodiments. It is the figure which showed the terminal (control apparatus) and control object in Embodiment 2.
  • FIG. It is the figure which showed the terminal (control apparatus) and control object in Embodiment 3.
  • 6 is an overall view of a control system in Embodiments 4 to 5.
  • FIG. 6 is a diagram showing terminals (control devices) and controlled objects in Embodiments 4 to 5.
  • FIG. 10 is a diagram illustrating processing of a normal operation range learning unit unique to a terminal in the fourth embodiment. It is the figure which showed the process of the data division
  • FIG. 10 is a diagram illustrating processing of a normal operation range learning unit unique to a terminal in the fourth embodiment. It is the figure which showed the process of the data division
  • FIG. 10 is a diagram illustrating processing of a normal operation range learning unit unique to a terminal in the fourth embodiment. It is the
  • FIG. 10 is a diagram illustrating processing of a normal operation range setting unit unique to a terminal in the fourth embodiment. It is the figure which showed the process of the abnormality detection means in Embodiment 4.
  • FIG. 10 is a diagram illustrating processing of a normal operation range learning unit common to terminals in the fifth embodiment.
  • FIG. 10 is a diagram illustrating processing of a normal operation range learning unit unique to a terminal in the fifth embodiment. It is the figure which showed the process of the abnormality detection means in Embodiment 5. It is the figure which showed the terminal in the modification 1. It is a figure which shows the table used for the terminal in the modification 2.
  • the terminal controls machines such as a robot, an autonomous driving vehicle, and a drone (aircraft).
  • the same numerals indicate the same parts.
  • a normal operation range learning unit that learns a first normal operation range common to the plurality of terminals;
  • An embodiment including an abnormality determination unit that determines that the operation of one terminal among the plurality of terminals is abnormal when the operation state of the one terminal is not in the first normal operation range.
  • the first normal range learning is performed on the server, and the abnormality detection is performed on each terminal.
  • the first normal range is set based on the result of machine learning based on past data.
  • the control system is a device that controls a plurality of robots.
  • FIG. 1 is a diagram showing the entire control system.
  • the plurality of terminals 1 transmit and receive information via the server 4.
  • the server 4 includes a normal operation range learning unit 5 common to terminals.
  • the normal operation range learning unit 5 common normal operation range learning unit
  • the normal operation range learning unit 5 common normal operation range learning unit
  • the details of the normal operation range learning unit 5 common to the terminals will be described later with reference to FIG.
  • the terminal 1 includes an abnormality detection means 2 and a control means 3.
  • FIG. 2 shows a robot 201 on the production line controlled by the terminal 1.
  • the control unit 3 calculates an operation amount (for example, a target angle, a target speed, a target torque, etc.) for controlling the robot 201.
  • the control means 3 controls the robot 201 (machine).
  • the detailed specification of the control means 3 for controlling the robot 201 has many known techniques and will not be described in detail here.
  • FIG. 3 is a system configuration diagram of the terminal 1.
  • the terminal 1 is provided with an input circuit 16 for processing an external signal.
  • the signal from the outside here refers to, for example, a sensor signal installed in the terminal and a normal range common to the terminal transmitted from the server.
  • These external signals are sent to the input / output port 17 through the input circuit 16 as input signals.
  • Each input information sent to the input / output port is written into the RAM 14 (Random Access Memory) through the data bus 15. Alternatively, it is stored in the storage device 11.
  • RAM 14 Random Access Memory
  • the input circuit 16 receives a normal operating range common to terminals (first normal operating range).
  • the ROM 13 Read Only Memory
  • the storage device 11 stores processing described later and is executed by the CPU 12 (Central Processing Unit).
  • the value written in the RAM 14 or the storage device 11 is used as appropriate for calculation.
  • information (value) to be sent to the outside is sent to the input / output port 17 through the data bus 15 and sent to the output circuit 18 as an output signal.
  • the output signal is output to the outside as a signal from the output circuit 18 to the outside.
  • the signal to the outside here refers to an actuator signal for causing the control target to make a desired movement, and an operation range of each terminal to be transmitted to the server 4.
  • FIG. 4 is a system configuration diagram of the server 4.
  • the server 4 is provided with an input circuit 26 for processing an external signal.
  • the signal from the outside here is operation information from each terminal.
  • An external signal is sent to the input / output port 27 as an input signal through the input circuit 26.
  • the input information sent to the input / output port is written into the RAM 24 through the data bus 25. Alternatively, it is stored in the storage device 21.
  • the input circuit 26 receives operation information (operation state) of each terminal 1 from a plurality of terminals 1 having a control unit 3 (control unit) that controls the robot 201 (machine).
  • the operation state of the terminal 1 includes the sensor output (input value) input to the control performed by the control means 3 (control unit) of each terminal 1, the operation amount (output value) output from the control, and the control parameter. Indicated by at least one of the values.
  • the control parameter value is a parameter of a function that determines an output value (operation amount) from an input value (sensor output).
  • Processing described below is written in the ROM 23 or the storage device 21 and is executed by the CPU 22. At that time, the value written in the RAM 24 or the storage device 21 is used as appropriate for calculation. Of the calculation results, information (value) to be sent to the outside is sent to the input / output port 27 through the data bus 25 and sent to the output circuit 28 as an output signal. The output signal is output to the outside as a signal from the output circuit 28 to the outside.
  • the signal to the outside here is a normal operation range common to terminals, and is sent to a plurality of terminals 1.
  • the output circuit 28 transmits the normal operation range common to the terminals (first normal operation range) to the plurality of terminals 1. Details of each process will be described below.
  • FIG. 5 is a diagram showing the entire normal operation range learning unit 5 common to terminals, and includes the following calculation means (calculation unit).
  • Data dividing means 100 Normal operation range setting means 110
  • the data dividing unit 100 divides the distribution of control parameter values and sensor output values into predetermined chunks (clusters).
  • the normal operation range setting unit 110 defines each divided cluster within a predetermined range.
  • the control parameter value and the sensor output value used here are selected to define a normal operation range common to terminals. For example, it is conceivable to use data obtained by prior verification before operating the system (software).
  • ⁇ Data division means (FIG. 6)>
  • the division information is calculated using the data of the control parameter value and the sensor output value. Specifically, it is shown in FIG.
  • the obtained division information is output by the k-means method.
  • the division information here refers to the following information.
  • FIG. 7 shows an example of the result of clustering a data group consisting of a certain two-dimensional vector with 4 clusters by the k-means method.
  • ⁇ Normal operation range setting means (FIG. 8)>
  • a data range is set for each divided data set using the above-described division information calculated by the data division unit 100, and the result is output as range information. Specifically, it is shown in FIG.
  • the minimum value of each dimension of the data belonging to each cluster is set as the lower limit of each dimension in the range corresponding to each cluster.
  • the maximum value of each dimension of data belonging to each cluster is set as the upper limit of each dimension in the range corresponding to each cluster.
  • the range information here refers to the lower limit value and the upper limit value of each dimension that define the range corresponding to each cluster (center vector).
  • the normal operation range learning unit 5 (common normal operation range learning unit) common to the terminals obtains the normal operation range (first normal operation range) from the past operation states of the plurality of terminals 1 by machine learning. Set. Note that statistical processing may be used instead of machine learning. Thereby, the reliability of the normal operation range common to the terminals is improved.
  • the center vector coordinates and range information are transmitted from the server 4 to each terminal 1.
  • FIG. 9 shows an example of the result of setting the range by the setting means based on the division result shown in FIG.
  • FIG. 10 ⁇ Abnormality detection means (FIG. 10) Detects abnormal control operation. Specifically, it is shown in FIG.
  • the area indicated by the range 1 to 4 is the normal operating range.
  • a control parameter value (vector) to be detected exists in the area corresponding to the specified center vector, it is determined to be normal, and if not, it is determined to be abnormal. For example, vector A in FIG. 10 is determined to be normal, and vector B is determined to be abnormal.
  • the abnormality detection unit 2 indicates that the control unit 3 is abnormal when the operation state of the control unit 3 (control unit) is not in the normal operation range common to the terminals (first normal operation range). Judge that there is. Thereby, the abnormality detection which considered the normal operation
  • the parameter to be detected may be an operation amount corresponding to the output of the control means 3. Also, an internal parameter calculated inside the control means 3 may be used. Moreover, the sensor output installed in the control object used with the control means 3 may be sufficient.
  • FIG. 10 shows a two-dimensional case, but it can be expanded to N dimensions (N: natural number).
  • the normality / abnormality of the parameters related to the control of each terminal is determined using the normal range common to the terminals that respectively control the plurality of robots in the production line. Reliability is improved. That is, the reliability of control can be improved.
  • a normal operation range learning unit that learns a first normal operation range common to the plurality of terminals;
  • An embodiment including an abnormality determination unit that determines that the operation of one terminal among the plurality of terminals is abnormal when the operation state of the one terminal is not in the first normal operation range.
  • control system is a device that controls a plurality of autonomous vehicles.
  • FIG. 1 is a diagram showing the entire control system, but since it is the same as that of the first embodiment, it will not be described in detail.
  • FIG. 11 shows the terminal 1 and the automatic driving vehicle 202 controlled by the terminal 1.
  • the control means 3 calculates an operation amount (for example, a target speed, a target rotation speed, etc.) for controlling the autonomous driving vehicle 202.
  • the detailed specifications of the control means 3 for controlling the autonomous vehicle 202 are not described here because there are many known techniques.
  • FIG. 3 is a system configuration diagram of the terminal 1, but since it is the same as that of the first embodiment, it will not be described in detail.
  • FIG. 4 is a system configuration diagram of the server 4, which is the same as that of the first embodiment and will not be described in detail. Details of each process will be described below.
  • FIG. 5 is a diagram showing the entire normal operation range learning unit 5 common to terminals, but since it is the same as that of the first embodiment, it will not be described in detail.
  • ⁇ Data division means (FIG. 6)>
  • the division information is calculated using the data of the control parameter value and the sensor output value. Specifically, although it is shown in FIG. 6, it is the same as that of the first embodiment, and therefore will not be described in detail.
  • ⁇ Abnormality detection means (FIG. 10) Detects abnormal control operation. Specifically, since it is the same as the first embodiment shown in FIG. 10, it will not be described in detail.
  • the normality / abnormality of the parameters related to the control of each terminal is determined using the normal range common to the terminals that respectively control the autonomous driving vehicle. improves.
  • a normal operation range learning unit that learns a first normal operation range common to the plurality of terminals;
  • An embodiment including an abnormality determination unit that determines that the operation of one terminal among the plurality of terminals is abnormal when the operation state of the one terminal is not in the first normal operation range.
  • control system is a device that controls a plurality of drones.
  • FIG. 1 is a diagram showing the entire control system, but since it is the same as that of the first embodiment, it will not be described in detail.
  • FIG. 12 shows the terminal 1 and the drone 203 controlled by the terminal 1.
  • An operation amount for example, a target rotation speed of each rotor
  • the detailed specification of the control means 3 for controlling the drone 203 is not described in detail here because there are many known techniques.
  • FIG. 3 is a system configuration diagram of the terminal 1, but since it is the same as that of the first embodiment, it will not be described in detail.
  • FIG. 4 is a system configuration diagram of the server 4, which is the same as that of the first embodiment and will not be described in detail. Details of each process will be described below.
  • FIG. 5 is a diagram showing the entire normal operation range learning unit 5 common to terminals, but since it is the same as that of the first embodiment, it will not be described in detail.
  • ⁇ Data division means (FIG. 6)>
  • the division information is calculated using the data of the control parameter value and the sensor output value. Specifically, although it is shown in FIG. 6, it is the same as that of the first embodiment, and therefore will not be described in detail.
  • ⁇ Abnormality detection means (FIG. 10) Detects abnormal control operation. Specifically, since it is the same as the first embodiment shown in FIG. 10, it will not be described in detail.
  • the normality / abnormality of the parameters related to the control of each terminal is determined using the normal range common to the terminals that respectively control the drone, so that the reliability of the entire system is improved.
  • a normal operation range learning unit that learns a first normal operation range common to the plurality of terminals;
  • An embodiment including an abnormality determination unit that determines that the operation of one terminal among the plurality of terminals is abnormal when the operation state of the one terminal is not in the first normal operation range.
  • the present embodiment also includes normal operation range learning means for learning a second normal operation range unique to the terminal due to individual differences, environment, user characteristics, changes with time, and the like.
  • the second normal operation range is learned based on the parameter value relating to the control of each terminal or the sensor information provided in each terminal.
  • the second normal range learning is performed at each terminal.
  • FIG. 13 is a diagram showing the entire control system.
  • the plurality of terminals 1 transmit and receive information via the server 4.
  • the server 4 includes a normal operation range learning unit 5 common to terminals.
  • the terminal 1 includes a normal operation range learning unit 6, an abnormality detection unit 7, and a control unit 3 unique to the terminal.
  • the normal operation range learning unit 6 (specific normal operation range learning unit) unique to the terminal learns the normal operation range (second normal operation range) unique to the control means 3 (control unit). Specifically, the terminal-specific normal operation range learning unit 6 learns a normal operation range (second normal operation range) caused by at least one of individual differences, environment, user characteristics, and changes with time. Details of the normal operation range learning unit 6 unique to the terminal will be described later with reference to FIG.
  • FIG. 14 shows a robot 201 on the production line controlled by the terminal 1.
  • An operation amount for example, a target angle, a target speed, a target torque, etc.
  • the detailed specification of the control means 3 for controlling the robot 201 has many known techniques and will not be described in detail here.
  • FIG. 3 is a system configuration diagram of the terminal 1, but since it is the same as that of the first embodiment, it will not be described in detail.
  • FIG. 4 is a system configuration diagram of the server 4, which is the same as that of the first embodiment and will not be described in detail. Details of each process will be described below.
  • FIG. 5 is a diagram showing the entire normal operation range learning unit 5 common to terminals, but since it is the same as that of the first embodiment, it will not be described in detail.
  • ⁇ Data division means (FIG. 6)>
  • the division information is calculated using the data of the control parameter value and the sensor output value. Specifically, although it is shown in FIG. 6, it is the same as that of the first embodiment, and therefore will not be described in detail.
  • ⁇ Normal operation range setting means (FIG. 8)>
  • a data range is set for each divided data set using the above-described division information calculated by the data division unit 100, and the result is output as range information.
  • FIG. 8 it is the same as that of the first embodiment, and thus will not be described in detail.
  • FIG. 15 is a diagram showing the entire normal operation range learning unit 6 unique to the terminal, and includes the following calculation units.
  • Data dividing means 102 Normal operation range setting means 112
  • the data dividing unit 102 divides the distribution of control parameter values and sensor output values into predetermined chunks (clusters).
  • the normal operation range setting unit 112 defines each divided cluster within a predetermined range.
  • the control parameter value and the sensor output value used here are selected to define a normal operation range unique to the terminal. For example, it is conceivable to use performance data when the system (software) is operating.
  • the obtained division information is output by the k-means method.
  • the division information here refers to the following information.
  • Cluster number to which data divided by k-means method belongs ⁇ Average value of data belonging to each cluster (center vector) The details of the k-means method are described in many literatures and books, and therefore will not be described in detail here.
  • the minimum value of each dimension of the data belonging to each cluster is set as the lower limit of each dimension in the range corresponding to each cluster.
  • the maximum value of each dimension of data belonging to each cluster is set as the upper limit of each dimension in the range corresponding to each cluster.
  • the range information here refers to the lower limit value and the upper limit value of each dimension that define the range corresponding to each cluster (center vector).
  • the normal operation range learning unit 6 (inherent normal operation range learning unit) unique to the terminal obtains the normal operation range (second normal operation range) from the past operation state of each terminal 1 by machine learning. Set. Note that statistical processing may be used instead of machine learning.
  • the coordinates and range information of the center vector are sent from the normal operation range setting means 112 to the abnormality detection means 7.
  • FIG. 18 ⁇ Abnormality detection means
  • the area indicated by the ranges 1a to 4a is the normal operating range common to terminals.
  • control parameter value (vector) to be detected exists within the area corresponding to the specified center vector, it is determined to be normal (the value of the abnormal flag a is 0), and if it does not exist, It is determined that there is an abnormality (the value of the abnormality flag a is 1).
  • the area indicated by the ranges 1b to 4b is the normal operating range unique to the terminal.
  • control parameter value (vector) to be detected exists within the area corresponding to the specified center vector, it is determined as normal (the value of the abnormality flag b is 0), and if it does not exist, It is determined that there is an abnormality (the value of the abnormality flag b is 1).
  • the abnormality detection unit 7 is configured such that the operation state of the control unit 3 (control unit) is a normal operation range common to the terminals (first normal operation range) or a normal operation range unique to the terminals (second In the normal operation range), it is determined that the control means 3 is abnormal. Thereby, the abnormality detection which considered the normal operation range common to a terminal and the normal operation range specific to a terminal can be performed.
  • the parameter to be detected may be an operation amount corresponding to the output of the control means 3. Also, an internal parameter calculated inside the control means 3 may be used. Moreover, the sensor output installed in the control object used with the control means 3 may be sufficient.
  • FIG. 18 shows a two-dimensional case, but it can be expanded to N dimensions (N: natural number).
  • a normal operation range learning unit that learns a first normal operation range common to the plurality of terminals;
  • An embodiment including an abnormality determination unit that determines that the operation of one terminal among the plurality of terminals is abnormal when the operation state of the one terminal is not in the first normal operation range.
  • a normal operation range learning means for learning a second normal operation range unique to the terminal due to individual differences, environment, user characteristics, changes with time, and the like.
  • the second normal operation range is learned based on the parameter value relating to the control of each terminal or the sensor information provided in each terminal.
  • the second normal range learning is performed at each terminal.
  • the first normal range and the second normal operating range are determined.
  • the normal range is set on a rule basis.
  • the first normal range (first normal operation range) and the second normal range (at least one of the second normal operation ranges may be set on a rule basis. Anomaly detection can be performed based on empirically obtained rules.
  • FIG. 13 is a diagram showing the entire control system, but since it is the same as that of the fourth embodiment, it will not be described in detail.
  • FIG. 14 shows the robot 201 on the production line controlled by the terminal 1, but since it is the same as that of the fourth embodiment, it will not be described in detail.
  • the detailed specification of the control means 3 for controlling the robot 201 has many known techniques and will not be described in detail here.
  • FIG. 3 is a system configuration diagram of the terminal 1, but since it is the same as that of the first embodiment, it will not be described in detail.
  • FIG. 4 is a system configuration diagram of the server 4, which is the same as that of the first embodiment and will not be described in detail. Details of each process will be described below.
  • FIG. 19 is a diagram showing a normal operation range learning unit 5 common to terminals, and includes the following calculation units.
  • control parameter 1 The minimum value of control parameter 1 is K1a_L and the maximum value is K1a_H
  • control parameter 2 is K2a_L and the maximum value is K2a_H
  • control parameter n Kna_L and the maximum value is Kna_H
  • control parameter values and sensor output values used here are selected to define the normal operating range common to terminals. For example, it is conceivable to use data obtained by prior verification before operating the system (software).
  • FIG. 20 is a diagram showing the normal operation range learning unit 6 unique to the terminal, and includes the following calculation units.
  • control parameter 1 The minimum value of control parameter 1 is K1b_L and the maximum value is K1b_H
  • control parameter 2 is K2b_L and the maximum value is K2b_H
  • control parameter n The minimum value of the control parameter n is Knb_L and the maximum value is Knb_H
  • control parameter values and sensor output values used here are selected to define the normal operating range specific to the terminal. For example, it is conceivable to use performance data when the system (software) is operating.
  • FIG. 21 ⁇ Abnormality detection means (FIG. 21)> Detects abnormal control operation. Specifically, it is shown in FIG.
  • abnormality detection is performed considering that there is a unique abnormality range, both control performance and reliability of a control system having a plurality of terminals are improved.
  • the abnormality detection is performed on a rule basis in the present embodiment, the explanation at the time of abnormality detection in which the abnormality detection method is explicitly given is also improved.
  • Modification 1 In the first modification, the control means 3 of the terminal 1 shown in FIG. 22 performs predetermined control according to an abnormality flag that is an output of the abnormality detection means 2 (abnormality detection unit).
  • the control means 3 may output a warning to the display device or the like or perform predetermined fail-safe control. Thereby, when abnormality is detected, appropriate control can be performed.
  • Modification 2 In the second modification, the control unit 3 of the terminal 1 performs predetermined control according to the determination result of the abnormality detection unit 2 (abnormality detection unit).
  • FIG. 22 shows a combination of whether or not the operation state of the terminal 1 is in the first normal operation range and whether or not the operation state of the terminal 1 is in the second normal operation range, and the abnormality detection means 2 (abnormality detection unit). It is a figure which shows the table 300 which matches and memorize
  • the table 300 is stored in the storage device 11 of the terminal 1, for example, but may be stored in the storage device 21 of the server 4.
  • the abnormality detection means 2 determines that the control means 3 is normal when the operation state of the terminal 1 is within the first normal operation range and within the second normal operation range.
  • the abnormality detection means 2 determines that the control means 3 is abnormal (abnormal 1) when the operation state of the terminal 1 is outside the first normal operation range and within the second normal operation range.
  • the abnormality detection unit 2 determines that the control unit 3 is abnormal (abnormal 2) when the operation state of the terminal 1 is within the first normal operation range and out of the second normal operation range.
  • the abnormality detecting means 2 determines that the control means 3 is abnormal (abnormal 3) when the operating state of the terminal 1 is outside the first normal operating range and outside the second normal operating range.
  • the control unit 3 (control unit) identifies an identifier (normal control, control 1 to 3) identifier corresponding to the determination result (normal, abnormality 1 to 3) of the abnormality detection unit 2 (abnormality detection unit) from the table 300. ID) is read out, and control corresponding to this identifier is executed.
  • control 1 a warning may be output to a display device or the like, in control 2 fail-safe control may be performed, and in control 3 control may be stopped. Thereby, when abnormality is detected, appropriate control according to abnormality can be performed.
  • Control 1 to control 3 may be the same control (for example, output of warning).
  • the present invention is not limited to the above-described embodiment, and includes various modifications.
  • the above-described embodiment has been described in detail for easy understanding of the present invention, and is not necessarily limited to the one having all the configurations described.
  • a part of the configuration of an embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of an embodiment.
  • each of the above-described configurations, functions (means), etc. may be realized by hardware by designing a part or all of them, for example, by an integrated circuit.
  • Each of the above-described configurations, functions (means), and the like may be realized by software by interpreting and executing a program that realizes each function (means) or the like by a processor (CPU).
  • Information such as programs, tables, and files that realize each function (means) is stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD. be able to.
  • control system including a plurality of terminals each controlling a device and communicating with a server
  • the control system learns a first normal operation range common to the plurality of terminals.
  • an abnormality determination unit that determines that the operation of the one terminal is abnormal when an operation state of one of the plurality of terminals is not in the first normal operation range.
  • a control system comprising a normal operation range learning means for learning a second normal operation range unique to a terminal caused by individual differences, environment, user characteristics, changes with time, and the like.
  • a normal operation range learning means for learning a second normal operation range unique to the terminal due to individual differences, environment, user characteristics, changes with time, etc.
  • the second normal operation range is A control system that learns based on at least a parameter value related to control of each terminal, information from a sensor provided in each terminal, or information obtained by communication at the terminal.
  • At least the first normal range or the second normal range sets a past operation state of the terminal based on a result of processing by statistical processing or machine learning. And control system.
  • control system is a device for controlling a robot.
  • control system is a device for controlling an autonomous driving vehicle.
  • control system is a device for controlling a flying object such as a drone.
  • an abnormality taking into account that there is an abnormal range common to each terminal and a specific abnormal range due to individual differences, environmental differences, user characteristic differences, changes with time, etc. Since detection is performed, the reliability of a control system having a plurality of terminals can be improved.
  • Processing of abnormality detection means (based on normal operating range common to terminals) 52. Processing of abnormality detection means (based on normal operation range unique to the terminal) 61 ... Processing of abnormality detection means (based on normal operating range common to terminals) 62 ... Processing of abnormality detection means (based on normal operation range unique to terminal) 100: Data division means (normal operation range learning unit common to terminals) 101: Processing of data dividing means (normal operation range learning unit common to terminals) 102: Data dividing means (terminal-specific normal operating range learning unit) 103 ...
  • Normal operation range setting means normal operation range learning unit common to terminals
  • Processing of normal operation range setting means normal operation range learning unit common to terminals
  • 112 Processing of normal operation range setting means (normal operation range learning unit common to terminals)
  • Normal operation range setting means terminal-specific normal operation range learning unit
  • 113 Processing of normal operation range setting means (terminal-specific normal operation range learning unit) 121 ...
  • Rule-based threshold learning normal operation range learning unit common to terminals
  • Rule-based threshold learning a condition-based

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

L'invention concerne un dispositif de commande, un système de commande et un serveur capables d'améliorer la fiabilité de la commande. Un terminal (1) (dispositif de commande) est pourvu d'un circuit d'entrée (16) (unité de réception), d'un moyen de commande (3) (unité de commande) et d'un moyen de détection d'anomalie (2) (unité de détection d'anomalie). Le circuit d'entrée (16) (unité de réception) reçoit une plage de fonctionnement normal (première plage de fonctionnement normal) commune à une pluralité de terminaux (1). Le moyen de commande (3) (unité de commande) commande une machine telle qu'un robot (201), une voiture à conduite autonome (202) et un drone (203) (corps volant). Dans le cas où un état de fonctionnement du moyen de commande (3) (unité de commande) se trouve en dehors de la plage de fonctionnement normal (première plage de fonctionnement normal) commune aux terminaux, le moyen de détection d'anomalie (2) (unité de détection d'anomalie) détermine que le moyen de commande (3) est anormal.
PCT/JP2017/009572 2017-03-09 2017-03-09 Dispositif de commande, système de commande et serveur WO2018163375A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2020046964A (ja) * 2018-09-19 2020-03-26 ファナック株式会社 特性判定装置、特性判定方法及び特性判定プログラム
JP2021124886A (ja) * 2020-02-04 2021-08-30 富士電機株式会社 情報処理装置、情報処理方法及び情報処理プログラム
JP2021149315A (ja) * 2020-03-17 2021-09-27 富士電機株式会社 情報処理装置、情報処理方法及び情報処理プログラム

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JPH02173809A (ja) * 1988-12-27 1990-07-05 Toshiba Corp 故障診断装置
JP2014186631A (ja) * 2013-03-25 2014-10-02 Hitachi Constr Mach Co Ltd 診断処理システム、端末装置、およびサーバ
WO2016067852A1 (fr) * 2014-10-29 2016-05-06 株式会社日立製作所 Système et procédé de génération de tâche de diagnostic ainsi que procédé d'affichage de génération de tâche de diagnostic

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Publication number Priority date Publication date Assignee Title
JPH02173809A (ja) * 1988-12-27 1990-07-05 Toshiba Corp 故障診断装置
JP2014186631A (ja) * 2013-03-25 2014-10-02 Hitachi Constr Mach Co Ltd 診断処理システム、端末装置、およびサーバ
WO2016067852A1 (fr) * 2014-10-29 2016-05-06 株式会社日立製作所 Système et procédé de génération de tâche de diagnostic ainsi que procédé d'affichage de génération de tâche de diagnostic

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2020046964A (ja) * 2018-09-19 2020-03-26 ファナック株式会社 特性判定装置、特性判定方法及び特性判定プログラム
JP2021124886A (ja) * 2020-02-04 2021-08-30 富士電機株式会社 情報処理装置、情報処理方法及び情報処理プログラム
JP7500980B2 (ja) 2020-02-04 2024-06-18 富士電機株式会社 情報処理装置、情報処理方法及び情報処理プログラム
JP2021149315A (ja) * 2020-03-17 2021-09-27 富士電機株式会社 情報処理装置、情報処理方法及び情報処理プログラム
JP7484261B2 (ja) 2020-03-17 2024-05-16 富士電機株式会社 情報処理装置、情報処理方法及び情報処理プログラム

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