WO2023218752A1 - Electromagnetic noise analysis device and method therefor, and risk assessment device and control device provided with same - Google Patents

Electromagnetic noise analysis device and method therefor, and risk assessment device and control device provided with same Download PDF

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Publication number
WO2023218752A1
WO2023218752A1 PCT/JP2023/009967 JP2023009967W WO2023218752A1 WO 2023218752 A1 WO2023218752 A1 WO 2023218752A1 JP 2023009967 W JP2023009967 W JP 2023009967W WO 2023218752 A1 WO2023218752 A1 WO 2023218752A1
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noise
vulnerability
electromagnetic noise
unit
calculation unit
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PCT/JP2023/009967
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French (fr)
Japanese (ja)
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斉 谷口
彩 大前
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株式会社日立製作所
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G01M17/007—Wheeled or endless-tracked vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G01M17/08—Railway vehicles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/10—Noise analysis or noise optimisation

Definitions

  • the present invention relates to an electromagnetic noise analysis device and method, and a risk determination device and control device equipped with the same.
  • Patent Document 1 As background technology related to the present invention, there is a technology as described in Patent Document 1.
  • vehicle as a means of solving the problem of providing an electromagnetic noise analysis device, a control device, and a control method that take into account continuous changes in the running conditions of vehicles and railways, vehicle a vehicle running control section that outputs a vehicle drive parameter that is a drive state; a signal conversion section that converts the vehicle drive parameter into a noise parameter that is an electrical parameter; and electromagnetic noise that propagates through the vehicle based on the noise parameter.
  • a control device is described that includes an electromagnetic noise analysis unit that calculates the amount of noise.
  • Patent Document 1 describes a method for analyzing noise at the system level of vehicles and railways by creating and connecting electromagnetic noise models of each component and casing that make up the system to realize noise analysis.
  • the method disclosed in Patent Document 1 only determines the noise intensity for each frequency band, and cannot analyze the influence of communication coding in digital communication. There was a problem in that it was not possible to determine the risk of electromagnetic interference by considering the effects of
  • the present invention solves the problems of the prior art described above, enables noise intensity determination for each frequency band in digital communication and analysis of the influence of communication coding, and improves error correction, interleaving processing, etc. during communication.
  • the present invention provides an electromagnetic noise analysis device and method that make it possible to determine electromagnetic interference risk considering the influence of encoding, and a risk determination device and control device equipped with the same.
  • the present invention calculates the intensity of electromagnetic noise generated from the system by driving an electromagnetic noise analysis device from drive parameters that drive a system configured with a plurality of devices.
  • the present invention provides a method for analyzing electromagnetic noise using an electromagnetic noise analysis device including an electromagnetic noise intensity calculation section, a vulnerability calculation section, and a risk calculation section.
  • the driving parameters for driving a system configured with equipment are input into the electromagnetic noise intensity calculation section to calculate the intensity of electromagnetic noise generated from the system by driving the system, and the driving parameters are input into the vulnerability calculation section.
  • the vulnerability of each device of multiple devices to the electromagnetic noise pattern generated by the system is determined, and the electromagnetic noise intensity information obtained by the electromagnetic noise intensity calculation section and the vulnerability information of each device obtained by the vulnerability calculation section are calculated. Input it into the risk calculation section to calculate the risk of each device caused by electromagnetic noise.
  • the present invention it is possible to determine the electromagnetic interference risk by taking into account the influence of coding such as error correction and interleaving processing during communication in digital communication.
  • FIG. 1 is a block diagram showing the configuration of an electromagnetic noise analysis device according to Example 1 of the present invention.
  • FIG. 2 is a block diagram showing a detailed configuration of a victim equipment vulnerability calculation unit of the electromagnetic noise analysis device according to Example 1 of the present invention.
  • FIG. 2 is a flowchart showing the processing flow of the electromagnetic noise analysis method according to the first embodiment of the present invention.
  • FIG. 2 is a flowchart showing the processing flow of victim equipment vulnerability calculation in the electromagnetic noise analysis method according to the first embodiment of the present invention.
  • FIG. 3 is a diagram showing a detailed processing flow of a noise waveform calculation step in the electromagnetic noise analysis method according to the first embodiment of the present invention.
  • FIG. 1 is a block diagram showing the configuration of an electromagnetic noise analysis device according to Example 1 of the present invention.
  • FIG. 2 is a block diagram showing a detailed configuration of a victim equipment vulnerability calculation unit of the electromagnetic noise analysis device according to Example 1 of the present invention.
  • FIG. 2 is a flowchart showing the processing flow of the electromagnetic
  • FIG. 2 is a flowchart illustrating a detailed process flow of a transmission signal creation step in the electromagnetic noise analysis method according to Example 1 of the present invention.
  • FIG. 3 is a diagram illustrating the concept of data corresponding to each step of creating a transmission signal in the electromagnetic noise analysis method according to the first embodiment of the present invention.
  • FIG. 2 is a block diagram showing the configuration of an electromagnetic noise analysis device according to a second embodiment of the present invention.
  • FIG. 3 is a flowchart showing the process flow of an electromagnetic noise analysis method according to Example 2 of the present invention.
  • FIG. 3 is a block diagram showing a detailed configuration of a victim device vulnerability calculation unit of an electromagnetic noise analysis device according to a third embodiment of the present invention.
  • FIG. 7 is a flowchart showing the processing flow of victim equipment vulnerability calculation of the electromagnetic noise analysis method according to the third embodiment of the present invention.
  • FIG. 7 is a block diagram showing a detailed configuration of a victim device vulnerability calculation unit of an electromagnetic noise analysis device according to a fourth embodiment of the present invention.
  • FIG. 7 is a flowchart showing the processing flow of an electromagnetic noise analysis method according to Example 4 of the present invention.
  • FIG. 7 is a block diagram showing the flow of machine learning data in the electromagnetic noise analysis method according to the fourth embodiment of the present invention.
  • FIG. 7 is a block diagram showing the configuration of a device equipped with an electromagnetic noise analysis device according to a fifth embodiment of the present invention.
  • FIG. 7 is a block diagram showing the configuration of a device equipped with an electromagnetic noise analysis device according to a sixth embodiment of the present invention.
  • FIG. 2 is a diagram showing the hardware configuration of an information processing device (computer).
  • a known method for analyzing noise in system-level digital communications for vehicles and railways is to create and connect electromagnetic noise models of each component and casing that make up the system to perform noise analysis.
  • the present invention solves the problem of the above-mentioned method, which only determines the noise intensity for each frequency band and cannot analyze the influence of communication coding in digital communication.
  • the present invention is an electromagnetic noise intensity calculation that calculates the electromagnetic noise intensity generated in each component of the system that may be damaged by electromagnetic noise (hereinafter referred to as "damaged equipment") from the drive parameters that drive the system. Equipped with a vulnerability calculation unit that calculates vulnerability to noise patterns (periodicity, etc.) from the drive parameters that drive the system, and a risk (error rate) calculation unit due to electromagnetic noise from the electromagnetic noise intensity and vulnerability. This makes it possible to determine the risk of electromagnetic interference by taking into account the effects of coding such as error correction and interleaving processing during communication in digital communications, thereby achieving both low cost and low risk. .
  • the electromagnetic noise analysis device includes: a drive parameter input section that inputs the drive state of the noise source; a first signal conversion section that converts the drive parameters input to the drive parameter input section into electrical noise parameters; an electromagnetic noise analysis section that calculates the amount of electromagnetic noise propagating based on the noise parameter converted by the first signal conversion section; a second signal conversion section that converts the drive parameter input into the parameter input section into a noise parameter; a vulnerability calculation unit that calculates vulnerability in the noise pattern converted by the second signal conversion unit based on the noise parameters converted by the signal conversion unit and/or the amount of electromagnetic noise calculated by the electromagnetic noise analysis unit; and an electromagnetic noise analysis unit
  • the system includes a risk determination section that calculates the electromagnetic noise risk from the amount of electromagnetic noise determined by the amount of electromagnetic noise and the vulnerability determined by the vulnerability calculation section.
  • FIG. 1 shows the configuration of an electromagnetic noise analysis device 100 according to the first embodiment.
  • the electromagnetic noise analysis device 100 includes a drive parameter input section 101, a first signal conversion section 102, a noise intensity calculation section 103, a second signal conversion section 104, a victim equipment vulnerability calculation section 105, a risk determination section ( It includes an error rate calculation section) 106 and a result display section 107, and performs electromagnetic noise analysis and risk determination by exchanging data between these functional sections (functional blocks).
  • the electromagnetic noise analysis device 100 is configured to store information including a processor (CPU) 1401, a memory (RAM) 1402, a storage device 1403, an input device 1404, an output device 1405, a communication device 1406, and a bus 1407 as shown in FIG.
  • the processor 1401 functions as a functional unit (functional block) that provides predetermined functions by executing processing according to a program loaded into the memory 1402.
  • the storage device 1403 stores programs that function as functional units as well as data used by the functional units.
  • a nonvolatile storage medium such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive) is used.
  • the input device 1404 is a keyboard, pointing device, etc.
  • the output device 1405 is a display, etc.
  • the input device 1404 and the output device 1405 may be integrated using a touch panel.
  • a communication device 1406 enables communication with other information processing devices via a network. These are communicably connected to each other by a bus 1407.
  • the electromagnetic noise analysis device 100 does not need to be implemented with one information processing device, and may be implemented with multiple information processing devices. Further, some or all of the functions of the electromagnetic noise analysis device 100 may be realized as an application on the cloud.
  • the drive parameter input unit 101 inputs drive parameters such as motor rotation speed, voltage, current, and output torque when driving the target device (system).
  • the first signal converter 102 converts the signal representing the operating state of the target device input into the drive parameter input unit 101 into a noise parameter that is an electrical parameter related to electromagnetic noise (for example, AC current, voltage, transfer function, etc.). Convert.
  • the noise intensity calculation unit 103 uses the noise parameters converted by the first signal conversion unit 102 and the carrier frequency and voltage command value of the input signal calculated by the second signal conversion unit 104 to determine the target of electromagnetic noise analysis.
  • the noise current, noise voltage, etc. of each victim device are calculated as information on the intensity of electromagnetic noise generated in the victim device that may be affected by electromagnetic noise among the components that make up the system.
  • signals such as the motor rotation speed and output torque when driving the target device that are input to the drive parameter input unit 101 are also input to the second signal conversion unit 104.
  • the carrier frequency, voltage command value, etc. are calculated from the received signal. These are sent to the victim device vulnerability calculation unit 105 and a vulnerability coefficient is calculated. The detailed configuration of the victim device vulnerability calculation unit 105 will be described later.
  • the risk determination unit 106 determines the risk of each victim device using the noise intensity information calculated by the noise intensity calculation unit 103 and the vulnerability coefficient information calculated by the victim device vulnerability calculation unit 105, and determines the risk of each victim device. is sent to the result display section 107.
  • the result display unit 107 displays the determination result on the output device 1405.
  • a drive parameter input unit 101, a first signal conversion unit 102, and a noise intensity calculation unit 103 calculate the electromagnetic noise intensity generated in the victim equipment that constitutes the system from the drive parameters that drive the system.
  • a vulnerability calculation unit is provided which calculates vulnerability to electromagnetic noise patterns (periodicity, etc.) from drive parameters that drive the system using a drive parameter input unit 101, a second signal conversion unit 104, and a victim equipment vulnerability calculation unit 105.
  • the risk determination unit 106 constitutes a risk calculation unit due to electromagnetic noise based on the electromagnetic noise intensity and vulnerability.
  • FIG. 2 shows the detailed configuration of the victim device vulnerability calculation unit 105.
  • Each functional unit (each sub-functional unit that constitutes the victim equipment vulnerability calculation unit 105) that constitutes the victim equipment vulnerability calculation unit 105 will be explained.
  • the victim device vulnerability calculation unit 105 uses a standard noise creation unit 201 that creates a standard noise waveform, which is a noise signal such as AWGN (Additive White Gaussian Noise), and a transmission signal from random bit string data (message).
  • the first noise applying unit 203 applies the standard noise waveform created by the standard noise creating unit 201 to the transmission signal created by the transmitting unit 202, and the first noise applying unit 203 applies the standard noise waveform.
  • a first receiving section 204 that decodes the received signal, and a standard noise that calculates the error rate when a standard noise waveform is applied by comparing the signal decoded by the first receiving section 204 and the random bit string data created by the transmitting section 202.
  • a waveform error rate calculation section 205 is provided.
  • the victim device vulnerability calculation unit 105 also includes a noise waveform calculation unit 206 that calculates a noise waveform from the carrier frequency of the input signal calculated by the second signal conversion unit 104, a voltage command value, etc., and a transmission generated by the transmission unit 202.
  • a second noise application unit 207 that adds the noise waveform calculated by the noise waveform calculation unit 206 to the signal, a second reception unit 208 that decodes the received signal to which the noise waveform has been applied by the second noise application unit 207, and a second reception unit.
  • An error rate calculation section 209 calculates the error rate when a noise waveform is applied from the signal decoded in step 208, and an error rate and error rate calculation section when the standard noise waveform calculated by the standard noise waveform error rate calculation section 205 is applied.
  • a comparison unit 210 is provided to compare the error rate when the noise waveform calculated in step 209 is applied.
  • the noise waveform calculation section 206 includes a noise waveform generation section 2061 and a noise pattern generation section 2062, which will be described later.
  • drive parameters such as motor rotation speed, current, voltage, and output torque when driving the target device are input into the drive parameter input section 101 (S301).
  • the first signal conversion unit 102 converts the signal representing the operating state of the target device input into the drive parameter input unit 101 into electrical parameters related to electromagnetic noise (for example, AC current, voltage, transfer function, etc.). It is converted into a certain noise parameter (S302).
  • the second signal converter 104 also receives the signal representing the operating state of the target device input to the drive parameter input unit 101 in S301, and the second signal converter 104 converts the signal representing the operating state of the target device into a carrier.
  • the frequency, voltage command value, etc. are determined (S303).
  • the noise intensity calculation unit 103 calculates the damage caused by electromagnetic noise. Noise voltage, noise current, etc. are calculated as the noise intensity of electromagnetic noise occurring in potentially damaged equipment (S304).
  • the victim device vulnerability calculation unit 105 calculates the vulnerability of the victim device using the carrier frequency, voltage command value, etc. obtained by the second signal conversion unit 104 (S305). The detailed steps for calculating the vulnerability of this victim device will be explained using FIG. 4A. Note that the noise intensity information calculated in S304 may also be used in the step of calculating the vulnerability of the victim device in S305.
  • the risk determination unit uses information on noise intensity such as noise voltage and noise current calculated by the noise intensity calculation unit 103 in S304 and information on the vulnerability of the victim device calculated in the victim equipment vulnerability calculation unit 105 in S305.
  • the risk of the damaged device is determined in step 106 (S306), and the result display unit 107 displays the determined result on the output device 1405 (S307).
  • a second conversion signal such as a carrier frequency or a voltage command value of a signal representing the operating state of the target device (for example, AC current for driving a motor, motor rotation speed, etc.) obtained by the second signal conversion unit 104 is converted.
  • a PWM (Pulse Width Modulation) signal generator or the like converts signals representing the operating state of the target device input from the second signal converter 104, such as the carrier frequency, fundamental frequency, and voltage command value at the rotation speed of the motor, to a PWM (Pulse Width Modulation) signal generator. (S4021), and calculates the ON/OFF timing of the power module using the PWM signal generator that constitutes the noise pattern generation section 2062 (S4022).
  • the noise waveform generation unit 2061 calculates a noise waveform by analog circuit simulation, and generates a noise waveform 2063 having an intensity peak (noise intensity) 2065 synchronized with the carrier period 2064 determined in S4021 (S4023).
  • FIG. 4B (b) shows an example of a standard noise waveform 2066, which has no time variation and has a uniform amplitude probability, as a noise waveform due to AWGN.
  • the noise waveform 2063 in FIG. 4B(a) is synchronized with the carrier period 2064 corresponding to the operating state of the target device, and the influence of the carrier period etc. on the bit error rate can be calculated. Similarly, the influence of fundamental frequency, voltage command value, etc. can also be calculated.
  • the carrier period 2064, etc. that corresponds to the operating state of the target device has an effect on the bit error rate. cannot be calculated.
  • the influence of fundamental frequency, voltage command value, etc. cannot be calculated.
  • the transmitter 202 creates a transmit signal according to the procedure shown in FIG. 4C (S420). The procedure for creating a transmission signal will be explained.
  • random bit string data 4211 as shown in FIG. 4D is generated (S421), and the generated random bit string data 4211 is converted into a word string 4212 to create communication word string data 4213 (S422). Note that the random bit string data 4211 generated in S421 is used in error rate calculation steps S405 and S409.
  • the created communication word string data 4213 is encoded (error correction code, interleave processing, encryption, etc.) (S423), the encoded communication word string data is modulated (S424), and the transmitted signal 4214 is generated. Output (S425).
  • the transmission signal 4214 output in S425 is used in noise addition steps S403 and S407.
  • the noise waveform calculated by the noise waveform calculation unit 206 in S402 and the transmission signal output from the transmission unit 202 in S420 are input to the second noise application unit 207, and the noise waveform is added to the transmission signal ( S403), a received signal 4215 is created.
  • a decoding process is performed to decode the received signal 4215 created in S403 to convert it into decoded bit string data 4216 (S404), and the random bit string data 4211 generated in S421 and the decoded bit string data 4216 converted in S404 are compared.
  • the error rate is calculated (S405).
  • the standard noise creation unit 201 calculates a standard noise waveform such as AWGN (Additive White Gaussian Noise) (S406).
  • AWGN Additional White Gaussian Noise
  • the transmission signal created by the transmission unit 202 in S420 and the standard noise waveform calculated in S406 are input to the first noise applying unit 203, and the standard noise waveform is added to the transmission signal (S407), thereby converting the received signal into a create.
  • the received signal created in S407 is decoded and converted into decoded bit string data (S408), and the random bit string data 4211 generated in S421 and the decoded bit string data converted in S408 are compared to determine the error rate. is calculated (S409).
  • the vulnerability of the victim device is calculated using the data of the error rate obtained in S405 and the error rate obtained in S409 (S410), and the result is sent to the risk determination step of S306.
  • the processing flow described in FIG. 3 and FIGS. 4A to 4C is performed for each component that constitutes the system targeted for electromagnetic noise analysis, and is performed for each damaged device that may be damaged by electromagnetic noise.
  • FIG. 1 The configuration of an electromagnetic noise analysis device 500 according to a second embodiment of the present invention is shown in FIG.
  • the electromagnetic noise analysis device 500 is also realized by an information processing device (computer) 1400 as shown in FIG. Note that among the functional units (functional blocks) constituting the electromagnetic noise analysis device 500 in this embodiment, the same functional units as those constituting the electromagnetic noise analysis device 100 explained in the first embodiment are given the same numbers. , a detailed explanation thereof will be omitted.
  • the electromagnetic noise analysis device 500 in this embodiment replaces the victim device vulnerability calculation section 105 of the electromagnetic noise analysis device 100 described in the first embodiment with the configuration described in FIG. The difference is that the configuration includes a transmitting section 202, a noise applying section 501, a receiving section 502, and an error rate calculating section 209.
  • the electromagnetic noise analysis device 500 includes a drive parameter input section 101, a first signal conversion section 102, a noise intensity calculation section 103, a second signal conversion section 104, a noise waveform calculation section 206, a transmission section 202, a noise application section 501, and a reception section. 502, an error rate calculation unit 209, a risk determination unit 503, and a result display unit 504, and performs electromagnetic noise analysis and risk determination by exchanging data between these functional units (functional blocks).
  • the noise applying section 501 and the receiving section 502 correspond to the second noise applying section 207 and the second receiving section 208 in the first embodiment, respectively.
  • the victim device vulnerability calculation section 105 in the first embodiment is configured by a noise waveform calculation section 206, a transmission section 202, a noise application section 501, a reception section 502, and an error rate calculation section 209. do.
  • a drive parameter input unit 101, a first signal conversion unit 102, and a noise intensity calculation unit 103 calculate the electromagnetic noise intensity generated in the victim equipment that constitutes the system from the drive parameters that drive the system. It constitutes the calculation section.
  • a vulnerability calculation unit is configured to calculate vulnerability to electromagnetic noise patterns (periodicity, etc.) from drive parameters. Furthermore, if necessary, the risk determination unit 503 configures a risk calculation unit due to electromagnetic noise based on the electromagnetic noise intensity and vulnerability.
  • the flowchart shown in FIG. 6 corresponds to a combination of the flowcharts shown in FIGS. 3 and 4A in the first embodiment, with the steps S406 to S409 removed.
  • drive parameters such as motor rotation speed, current, voltage, and output torque when driving the target device are input into the drive parameter input section 101 (S601).
  • the first signal conversion unit 102 converts the signal representing the operating state of the target device input into the drive parameter input unit 101 into electrical parameters related to electromagnetic noise (for example, AC current, voltage, transfer function, etc.). It is converted into a certain noise parameter (S602).
  • the noise intensity calculation unit 103 calculates noise voltage, noise current, etc. as the noise intensity of electromagnetic noise occurring in a victim device that may be damaged by electromagnetic noise (S603 ).
  • the above processing is the same as steps S301, S302, and S304 in the first embodiment.
  • the second signal conversion unit 104 also receives the signal representing the operating state of the target device (for example, the AC current for driving the motor, the rotation speed of the motor, etc.) input to the drive parameter input unit 101 in S601, and converts the signal into a second signal.
  • the conversion unit 104 obtains the carrier frequency, voltage command value, etc. of the signal representing the operating state of the target device (S604).
  • the noise waveform 2063 is calculated in the noise waveform calculation unit 206, as explained using FIG. 4B (a) in S402 in Example 1 (S605).
  • the noise intensity 2065 may be determined using the information on the noise intensity calculated in S603.
  • FIG. 4B shows an example in which a peak value is used as the noise intensity 2065, the present invention is not limited to this, and an average value, an effective value, or the like may be used.
  • a transmission signal is created in the same procedure as S420 described using FIG. 4C in Example 1 (S606).
  • the noise waveform calculated by the noise waveform calculation unit 206 and the transmission signal created in the transmission unit 202 in S606 are input to the noise application unit 501, the noise waveform is added to the transmission signal (S607), and the transmission signal is received. A signal (corresponding to 4215 in FIG. 4D) is created.
  • the receiving unit 502 performs a decoding process to decode this received signal (S608), and converts it into decoded bit string data (corresponding to 4216 in FIG. 4D).
  • the error rate calculation unit 209 calculates the error rate in the decoded bit string data using the random bit string data (corresponding to 4211 in FIG. 4D) (S609).
  • the error rate calculation unit 209 calculates vulnerability (S610) and/or the risk determination unit 503 performs risk determination (S611).
  • Vulnerability calculation (S610) and/or risk determination (S611) are not necessarily necessary steps and may be omitted in some cases.
  • the result display unit 107 displays the error rate and/or vulnerability and/or risk determination results on the output device 1405 (S612).
  • the electromagnetic interference risk is determined for each component (damaged device) that makes up the system, taking into account the effects of encoding such as error correction and interleaving processing during communication. can do.
  • FIGS. 7 and 8 A third embodiment of the present invention will be described using FIGS. 7 and 8.
  • the noise waveform is compared with the vulnerability noise pattern stored in the storage unit to determine the vulnerability.
  • the configuration of the device vulnerability calculation section 105 is replaced with a victim device vulnerability calculation section 105-1 as shown in FIG.
  • the other configuration is the same as the configuration of the electromagnetic noise analysis apparatus 100 described in FIG. 1 in the first embodiment.
  • the electromagnetic noise analysis device 500 shown in FIG. It also corresponds to the one that replaced the victim equipment vulnerability calculation unit 105-1 shown in 7.
  • the relationship between the noise waveform pattern and the bit error rate determined in advance is stored in the vulnerability noise pattern model storage unit 702. I'll keep it.
  • the noise waveform calculation unit 701 a noise waveform is calculated from the carrier frequency of the input signal, the voltage command value, etc. obtained by the second signal conversion unit 104 shown in FIG. A vulnerability noise pattern that has a high degree of matching with the calculated noise waveform is extracted.
  • the vulnerability calculation unit 703 calculates the error rate and/or vulnerability of the victim device from the extracted vulnerability noise pattern information.
  • the processing flow according to this embodiment differs from the processing flow explained using FIG. 3 and FIG. 4A in the first embodiment, except that the step of calculating the vulnerability of the victim device in S305 explained with FIG. 4A is different.
  • the steps are the same as those described in FIG.
  • step S305-1 of vulnerability calculation of the victim device corresponding to S305 of the first embodiment will be explained using FIG. 8.
  • a second conversion signal such as the carrier frequency and voltage command value of the signal representing the operating state of the target device obtained by the second signal conversion unit 104 in S303 of FIG. 3 is input (S801), and this second conversion signal is
  • the noise waveform calculation unit 206 calculates a noise waveform from the signal and, if necessary, the noise intensity of the target device calculated in S304 (S802).
  • this calculated noise waveform is compared with the vulnerability noise pattern stored in the vulnerability noise pattern model storage unit 702, and the vulnerability noise that has a high degree of coincidence with the calculated noise waveform is A pattern is extracted (S803).
  • the vulnerability calculation unit 703 extracts information regarding the error rate and/or vulnerability of the victim device from the error rate information stored in the vulnerability noise pattern model storage unit 702 in association with the extracted vulnerability noise pattern (S804). , the risk determination step S306 described in FIG. 3 is executed using this information.
  • process from S801 to S804 in FIG. 8 can be applied to the second embodiment by replacing the process from S605 to S611 in FIG. 6 described in the second embodiment.
  • FIG. 9 shows the configuration of the vulnerability noise pattern model storage unit 702-1 according to this embodiment.
  • the vulnerability noise pattern model storage unit 702-1 includes a drive parameter input unit 901, a signal conversion unit 902, a noise waveform calculation unit 903, a machine learning model generation unit 904, a victim device vulnerability calculation unit 905, a vulnerability labeling unit 906,
  • a machine learning model storage unit 907 is provided, and the processing described below is performed by exchanging data between these functional units (functional blocks).
  • the drive parameter input unit 901 inputs drive parameters such as motor rotation speed, voltage, current, and output torque when driving the target device (system). Enter parameters.
  • the signal conversion unit 902 converts the signal representing the operating state of the target device input into the drive parameter input unit 101 into a carrier frequency, voltage command value, etc.
  • a noise waveform calculation unit 903 calculates a noise waveform from the carrier frequency, voltage command value, etc. converted by the signal conversion unit 902, and data 1101 of the calculated noise waveform is generated by the machine learning model generation unit 904.
  • the signal is input to an input layer 1102 of a neural network 1100 as shown in FIG.
  • the victim device vulnerability calculation unit 905 includes the victim device vulnerability calculation unit 105 described in the first embodiment, or the noise waveform calculation unit 206, the transmission unit 202, the noise application unit 501, the reception unit 502, and the noise waveform calculation unit 206 described in the second embodiment. It is composed of an error rate calculation section 209.
  • the vulnerability labeling unit 906 labels each error rate of the noise waveform calculated by the victim equipment vulnerability calculation unit 905 and generates a machine learning model as data 1103 by associating it with the noise waveform data input to the input layer. 904 is input to the output layer 1104 side of the neural network 1100 as shown in FIG.
  • the machine learning model storage unit 907 stores the machine learning model generated by the machine learning model generation unit 904 in the storage device 1403. In addition, the machine learning model storage unit 907 compares the noise waveform calculated by the noise waveform calculation unit 701 in FIG. A vulnerability noise pattern with a high matching degree is extracted from the machine learning model, and information on the extracted vulnerability noise pattern is sent to the vulnerability calculation unit 703 to calculate the vulnerability of the victim device.
  • the noise signal detected by the noise sensor may be directly input to the noise waveform calculation section 903.
  • the drive parameter input section 901 and the signal conversion section 902 may be deleted, and the noise signal detected by the noise sensor may be directly input to the noise waveform calculation section 903.
  • FIG. 10 shows the flow of processing for generating a machine learning model according to this embodiment.
  • drive parameters such as the motor rotation speed, current, voltage, and output torque when driving the target device are input into the drive parameter input section 901 (S1001).
  • the signal conversion unit 902 performs signal conversion processing to calculate the carrier frequency, voltage command value, etc. from the signal representing the operating state of the target device input to the drive parameter input unit 901 (S1002).
  • the noise waveform calculation unit 903 calculates a noise waveform from the converted signal subjected to the signal conversion process in S1002 (S1003).
  • the noise waveform data obtained in S1003 is input to the input layer of the machine learning model generation unit 904 (S1004).
  • the transmitting unit 202 creates a transmitting signal as described in the first embodiment (S1005), and the noise waveform calculating unit 903 adds it to the noise waveform data obtained in S1003 (S1006), and the received signal (Fig. (equivalent to 4215 in 4D).
  • the second receiving unit 208 or the receiving unit 502 performs decoding processing to decode the received signal created in S1006 and converts it into decoded bit string data (corresponding to 4216 in FIG. 4D) (S1007), and the error rate calculation unit 209 calculates an error rate by comparing the random bit string data corresponding to the random bit string data generated in S421 in FIG. 4C described in Example 1 and the decoded bit string data converted in S1007 (S1008).
  • the error rate information obtained in S1008 is sent to the vulnerability labeling unit 906 to determine the vulnerability of the victim device (S1009), and labeling corresponding to the vulnerability data is performed (S1010).
  • the labeled vulnerability data is input to the output layer of the machine learning model generation unit 904 (S1011).
  • the machine learning model generation unit 904 generates a machine learning model by associating the noise waveform data input to the input layer in S1004 with the labeled vulnerability data input to the output layer in S1011 (S1012),
  • the machine learning model storage unit 907 stores it in the storage device 1403 (S1013).
  • a risk determination device 1200 equipped with the electromagnetic noise analysis device 100 or 500 described in the first to fourth embodiments will be described using FIG. 12.
  • the risk determination device 1200 is also realized by an information processing device (computer) 1400 as shown in FIG.
  • the risk determination device 1200 includes a receiving section 1201 that receives drive parameters from a target device 1210, an electromagnetic noise analysis section 1202 that performs electromagnetic noise analysis based on the signal received by the receiving section 1201, and an electromagnetic noise analysis section. It includes a functional unit (functional block) such as a display unit 1203 that outputs and displays the results analyzed in step 1202 on a screen. The results of the analysis by the electromagnetic noise analysis unit 1202 are sent to the control unit 1211 that controls the target device 1210.
  • the electromagnetic noise analysis section 1202 corresponds to the electromagnetic noise analysis device 100 or 500 described in Examples 1 to 4, and the display section 1203 is shared with the result display section 107 in FIG. 1 or the result display section 504 in FIG. It's okay.
  • the target device 1210 can be By determining the risk caused by electromagnetic noise with respect to damaged equipment that may be damaged by electromagnetic noise generated in 1210 and sending the result to the control unit 1211, the control unit 1211 controls the target equipment 1210. By suppressing the generation of electromagnetic noise, it is possible to prevent damage caused by electromagnetic noise in the victim equipment.
  • the electromagnetic noise analysis unit 1202 can be used to perform damage equipment vulnerability calculation as shown in FIG.
  • the vulnerability noise pattern model storage unit 702-1 as explained in the fourth embodiment is applied to the target device 1210 or the victim device vulnerability calculation unit 105-1, The risk caused by electromagnetic noise of the affected equipment can be determined in real time while driving.
  • control unit 1211 controls the target equipment 1210, thereby suppressing the generation of electromagnetic noise in real time and causing damage to the victim equipment due to electromagnetic noise. can be prevented from occurring.
  • the electromagnetic noise analysis device 100 or 500 equipped with the victim device vulnerability calculation unit 105-1 described in the third embodiment, or the vulnerability noise pattern model storage unit described in the fourth embodiment The configuration of a control device 1300 equipped with an electromagnetic noise analysis section 1302 corresponding to the electromagnetic noise analysis device 100 or 500 equipped with the electromagnetic noise analysis device 702-1 will be described using FIG.
  • the control device 1300 is also realized by an information processing device (computer) 1400 as shown in FIG. 14, but includes not only the functions according to this embodiment but also the function of controlling the target device 1310.
  • the control device 1300 is capable of suppressing the generation of electromagnetic noise from the target device 1210 in real time while driving the target device 1210 (for example, a car) to prevent damage caused by electromagnetic noise in the victim device. It is composed of
  • the control device 1300 includes a receiving section 1301 that receives drive parameters from a target device 1310, an electromagnetic noise analysis section 1302 that performs electromagnetic noise analysis based on the signal received by the receiving section 1301, and an electromagnetic noise analysis section 1302. It includes functional units (functional blocks) such as a control unit 1303 that controls the target device 1310 based on the results of the analysis.
  • the drive parameters of the target device 1310 received by the receiving unit 1301 are considered to be caused by electromagnetic noise generated from the power unit that drives the motor.
  • the target device 1310 when the target device 1310 is a car, there are multiple damaged devices that may be damaged by electromagnetic noise generated in the power section. In order to prevent damage caused by electromagnetic noise while driving a car, it is necessary to evaluate the risk caused by electromagnetic noise in real time and control the power unit.
  • the electromagnetic noise analysis unit 1302 is the electromagnetic noise analysis device 100 or 500 described in Embodiment 3, which is equipped with a victim equipment vulnerability calculation unit 105-1 equipped with a vulnerability noise pattern model storage unit 702; Or, the victim device vulnerability calculation unit 105-1 is formed with a configuration in which the vulnerability noise pattern model storage unit 702-1 having the machine learning model storage unit 907 as described in the fourth embodiment is applied. This makes it possible to evaluate the risks caused by electromagnetic noise in real time and control the power section.
  • the present invention made by the present inventor has been specifically explained based on Examples, but it goes without saying that the present invention is not limited to the Examples and can be modified in various ways without departing from the gist thereof. stomach.
  • the embodiments described above are described in detail to explain the present invention in an easy-to-understand manner, and the present invention is not necessarily limited to having all the configurations described. Further, it is possible to add, delete, or replace a part of the configuration of each embodiment with other configurations.
  • Electromagnetic noise analysis device 101, 901... Drive parameter input section, 102... First signal conversion section, 103... Noise intensity calculation section, 104... Second signal conversion section, 105, 105-1, 905... Damage Equipment vulnerability calculation section, 106, 503... Risk determination section, 107, 504... Result display section, 201... Standard noise creation section, 202... Transmission section, 203... First noise applying section, 204... First receiving section, 205 ... Standard noise waveform error rate calculation section, 206, 701 ... Noise waveform calculation section, 207 ... Second noise application section, 208 ... Second reception section, 209 ... Error rate calculation section, 210 ... Comparison section, 501 ...
  • Noise application section 502, 1201, 1301...Receiving section, 702, 702-1...Vulnerability noise pattern model storage section, 703...Vulnerability calculation section, 902...Signal conversion section, 903...Noise waveform calculation section, 904...Machine learning model generation Section, 906... Vulnerability labeling section, 907... Machine learning model storage section, 1200... Risk determination device, 1202, 1302... Electromagnetic noise analysis section, 1203... Display section, 1210, 1310... Target device, 1211, 1303... Control section , 1300...control device

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Abstract

This electromagnetic noise analysis device is configured so as to be able to perform noise intensity assessment by frequency band and analysis of the influence of signal encoding, thereby enabling assessment of electromagnetic interference risk while taking into account the influence of encoding such as error correction and interleaved processing during communication. To achieve the foregoing, the electromagnetic noise analysis device is configured by comprising: an electromagnetic noise intensity calculation unit that calculates the intensity of electromagnetic noise produced by a system due to driving on the basis of drive parameters for driving the system which is configured by comprising a plurality of apparatuses; a fragility calculation unit that, on the basis of the drive parameters, calculates the fragility of each apparatus with respect to an electromagnetic noise pattern produced by the system; and a risk calculation unit that, on the basis of the electromagnetic noise intensity calculated by the electromagnetic noise intensity calculation unit and the fragility of each apparatus calculated by the fragility calculation unit, calculates the risk resulting from electromagnetic noise to each apparatus.

Description

電磁ノイズ解析装置及びその方法並びにそれを備えたリスク判定装置及び制御装置Electromagnetic noise analysis device and method, and risk determination device and control device equipped with the same
　本発明は、電磁ノイズ解析装置及びその方法並びにそれを備えたリスク判定装置及び制御装置に関する。 The present invention relates to an electromagnetic noise analysis device and method, and a risk determination device and control device equipped with the same.
　本発明に関する背景技術として、特許文献１に記載されたような技術が有る。この特許文献１には、車両や鉄道の走行状態の連続的な変化を考慮した電磁ノイズ解析装置、制御装置および制御方法を提供するという課題の解決手段として、車両の運転情報に基づいて車両の駆動状態である車両駆動パラメータを出力する車両走行制御部と、前記車両駆動パラメータを電気的なパラメータであるノイズパラメータに変換する信号変換部と、前記ノイズパラメータに基づいて前記車両を伝搬する電磁ノイズ量を算出する電磁ノイズ解析部と、を備える制御装置が記載されている。 As background technology related to the present invention, there is a technology as described in Patent Document 1. In Patent Document 1, as a means of solving the problem of providing an electromagnetic noise analysis device, a control device, and a control method that take into account continuous changes in the running conditions of vehicles and railways, vehicle a vehicle running control section that outputs a vehicle drive parameter that is a drive state; a signal conversion section that converts the vehicle drive parameter into a noise parameter that is an electrical parameter; and electromagnetic noise that propagates through the vehicle based on the noise parameter. A control device is described that includes an electromagnetic noise analysis unit that calculates the amount of noise.
特開２０１８－１８２９３号公報JP 2018-18293 Publication
　特許文献１には、車両や鉄道のシステムレベルのノイズを解析する手法として、システムを構成する各コンポーネントと筐体の電磁ノイズモデルを作成・接続しノイズ解析を実現する方法が記載されている。しかし、特許文献１に開示されている手法では、周波数帯域毎のノイズ強度判定のみであり、デジタル通信における通信符号化の影響の解析ができず、通信時のエラー訂正・インターリーブ処理等の符号化の影響を考慮した電磁干渉リスクを判定することができないという課題があった。 Patent Document 1 describes a method for analyzing noise at the system level of vehicles and railways by creating and connecting electromagnetic noise models of each component and casing that make up the system to realize noise analysis. However, the method disclosed in Patent Document 1 only determines the noise intensity for each frequency band, and cannot analyze the influence of communication coding in digital communication. There was a problem in that it was not possible to determine the risk of electromagnetic interference by considering the effects of
　本発明は、上記した従来技術の課題を解決して、デジタル通信における周波数帯域毎のノイズ強度判定と通信符号化の影響の解析とを行えるようにして、通信時のエラー訂正・インターリーブ処理等の符号化の影響を考慮した電磁干渉リスクを判定することを可能にする電磁ノイズ解析装置及びその方法並びにそれを備えたリスク判定装置及び制御装置を提供するものである。 The present invention solves the problems of the prior art described above, enables noise intensity determination for each frequency band in digital communication and analysis of the influence of communication coding, and improves error correction, interleaving processing, etc. during communication. The present invention provides an electromagnetic noise analysis device and method that make it possible to determine electromagnetic interference risk considering the influence of encoding, and a risk determination device and control device equipped with the same.
　上記した課題を解決するために、本発明では、電磁ノイズ解析装置を、複数の機器を備えて構成されるシステムを駆動する駆動パラメータから駆動することによりシステムから発生する電磁ノイズの強度を計算する電磁ノイズ強度計算部と、駆動パラメータからシステムが発生する電磁ノイズパターンに対する機器ごとの脆弱性を計算する脆弱性計算部と、電磁ノイズ強度計算部で計算した電磁ノイズ強度と脆弱性計算部で計算した機器ごとの脆弱性から電磁ノイズに起因する機器ごとのリスクを計算するリスク計算部とを備えて構成する。 In order to solve the above-mentioned problems, the present invention calculates the intensity of electromagnetic noise generated from the system by driving an electromagnetic noise analysis device from drive parameters that drive a system configured with a plurality of devices. The electromagnetic noise intensity calculation section, the vulnerability calculation section that calculates the vulnerability of each device to the electromagnetic noise pattern generated by the system from the drive parameters, and the electromagnetic noise intensity calculated by the electromagnetic noise intensity calculation section and the vulnerability calculation section. and a risk calculation section that calculates the risk of each device due to electromagnetic noise from the vulnerability of each device.
　また、上記した課題を解決するために、本発明では、電磁ノイズ強度計算部と脆弱性計算部とリスク計算部とを備えた電磁ノイズ解析装置を用いて電磁ノイズを解析する方法において、複数の機器を備えて構成されるシステムを駆動する駆動パラメータを電磁ノイズ強度計算部に入力してシステムを駆動することによりシステムから発生する電磁ノイズの強度を求め、駆動パラメータを脆弱性計算部に入力してシステムが発生する電磁ノイズパターンに対する複数の機器の機器ごとの脆弱性を求め、電磁ノイズ強度計算部において求めた電磁ノイズ強度の情報と脆弱性計算部で求めた機器ごとの脆弱性の情報をリスク計算部に入力して電磁ノイズに起因する機器ごとのリスクを求めるようにする。 Further, in order to solve the above-mentioned problems, the present invention provides a method for analyzing electromagnetic noise using an electromagnetic noise analysis device including an electromagnetic noise intensity calculation section, a vulnerability calculation section, and a risk calculation section. The driving parameters for driving a system configured with equipment are input into the electromagnetic noise intensity calculation section to calculate the intensity of electromagnetic noise generated from the system by driving the system, and the driving parameters are input into the vulnerability calculation section. The vulnerability of each device of multiple devices to the electromagnetic noise pattern generated by the system is determined, and the electromagnetic noise intensity information obtained by the electromagnetic noise intensity calculation section and the vulnerability information of each device obtained by the vulnerability calculation section are calculated. Input it into the risk calculation section to calculate the risk of each device caused by electromagnetic noise.
　本発明によれば、デジタル通信における通信時のエラー訂正・インターリーブ処理等の符号化の影響を考慮して、電磁干渉リスクを判定することができる。 According to the present invention, it is possible to determine the electromagnetic interference risk by taking into account the influence of coding such as error correction and interleaving processing during communication in digital communication.
　また、本発明によれば、電磁ノイズ解析装置の低コスト化と低リスク化を両立させることが可能になる。 Furthermore, according to the present invention, it is possible to reduce the cost and risk of an electromagnetic noise analysis device at the same time.
本発明の実施例１に係る電磁ノイズ解析装置の構成を示すブロック図である。FIG. 1 is a block diagram showing the configuration of an electromagnetic noise analysis device according to Example 1 of the present invention. 本発明の実施例１に係る電磁ノイズ解析装置の被害機器脆弱性計算部の詳細な構成を示すブロック図である。FIG. 2 is a block diagram showing a detailed configuration of a victim equipment vulnerability calculation unit of the electromagnetic noise analysis device according to Example 1 of the present invention. 本発明の実施例１に係る電磁ノイズ解析方法の処理の流れを示すフロー図である。FIG. 2 is a flowchart showing the processing flow of the electromagnetic noise analysis method according to the first embodiment of the present invention. 本発明の実施例１に係る電磁ノイズ解析方法の被害機器脆弱性計算の処理の流れを示すフロー図である。FIG. 2 is a flowchart showing the processing flow of victim equipment vulnerability calculation in the electromagnetic noise analysis method according to the first embodiment of the present invention. 本発明の実施例１に係る電磁ノイズ解析方法におけるノイズ波形計算のステップの詳細な処理の流れを示す図である。FIG. 3 is a diagram showing a detailed processing flow of a noise waveform calculation step in the electromagnetic noise analysis method according to the first embodiment of the present invention. 本発明の実施例１に係る電磁ノイズ解析方法における送信信号作成工程の詳細な処理の流れを説明するフロー図である。FIG. 2 is a flowchart illustrating a detailed process flow of a transmission signal creation step in the electromagnetic noise analysis method according to Example 1 of the present invention. 本発明の実施例１に係る電磁ノイズ解析方法における送信信号作成の各工程に対応するデータの概念を説明する図である。FIG. 3 is a diagram illustrating the concept of data corresponding to each step of creating a transmission signal in the electromagnetic noise analysis method according to the first embodiment of the present invention. 本発明の実施例２に係る電磁ノイズ解析装置の構成を示すブロック図である。FIG. 2 is a block diagram showing the configuration of an electromagnetic noise analysis device according to a second embodiment of the present invention. 本発明の実施例２に係る電磁ノイズ解析方法の処理の流れを示すフロー図である。FIG. 3 is a flowchart showing the process flow of an electromagnetic noise analysis method according to Example 2 of the present invention. 本発明の実施例３に係る電磁ノイズ解析装置の被害機器脆弱性計算部の詳細な構成を示すブロック図である。FIG. 3 is a block diagram showing a detailed configuration of a victim device vulnerability calculation unit of an electromagnetic noise analysis device according to a third embodiment of the present invention. 本発明の実施例３に係る電磁ノイズ解析方法の被害機器脆弱性計算の処理の流れを示すフロー図である。FIG. 7 is a flowchart showing the processing flow of victim equipment vulnerability calculation of the electromagnetic noise analysis method according to the third embodiment of the present invention. 本発明の実施例４に係る電磁ノイズ解析装置の被害機器脆弱性計算部の詳細な構成を示すブロック図である。FIG. 7 is a block diagram showing a detailed configuration of a victim device vulnerability calculation unit of an electromagnetic noise analysis device according to a fourth embodiment of the present invention. 本発明の実施例４に係る電磁ノイズ解析方法の処理の流れを示すフロー図である。FIG. 7 is a flowchart showing the processing flow of an electromagnetic noise analysis method according to Example 4 of the present invention. 本発明の実施例４に係る電磁ノイズ解析方法における機械学習のデータの流れを示すブロック図である。FIG. 7 is a block diagram showing the flow of machine learning data in the electromagnetic noise analysis method according to the fourth embodiment of the present invention. 本発明の実施例５に係る電磁ノイズ解析装置を搭載した装置の構成を示すブロック図である。FIG. 7 is a block diagram showing the configuration of a device equipped with an electromagnetic noise analysis device according to a fifth embodiment of the present invention. 本発明の実施例６に係る電磁ノイズ解析装置を搭載した装置の構成を示すブロック図である。FIG. 7 is a block diagram showing the configuration of a device equipped with an electromagnetic noise analysis device according to a sixth embodiment of the present invention. 情報処理装置（計算機）のハードウェア構成を示す図である。FIG. 2 is a diagram showing the hardware configuration of an information processing device (computer).
　車両や鉄道のシステムレベルのデジタル通信におけるノイズを解析する手法として、システムを構成する各コンポーネントと筐体の電磁ノイズモデルを作成・接続しノイズ解析を実現する方法が知られている。本発明は、周波数帯域毎のノイズ強度判定のみであり、デジタル通信における通信符号化の影響の解析ができない、という前記手法の課題を解決するものである。本発明は、システムを駆動する駆動パラメータからシステムを構成する各コンポーネントである電磁ノイズの被害を受ける可能性のある機器（以下、被害機器と記す）に生じる電磁ノイズ強度を計算する電磁ノイズ強度計算部とシステムを駆動する駆動パラメータからからノイズパターン（周期性等）に対する脆弱性を計算する脆弱性計算部と、電磁ノイズ強度と脆弱性から電磁ノイズに起因するリスク（エラーレート）計算部を備えることにより、デジタル通信における通信時のエラー訂正・インターリーブ処理等の符号化の影響を考慮して、電磁干渉リスクを判定できるようにし、低コスト化と低リスク化を両立させるようにしたものである。 A known method for analyzing noise in system-level digital communications for vehicles and railways is to create and connect electromagnetic noise models of each component and casing that make up the system to perform noise analysis. The present invention solves the problem of the above-mentioned method, which only determines the noise intensity for each frequency band and cannot analyze the influence of communication coding in digital communication. The present invention is an electromagnetic noise intensity calculation that calculates the electromagnetic noise intensity generated in each component of the system that may be damaged by electromagnetic noise (hereinafter referred to as "damaged equipment") from the drive parameters that drive the system. Equipped with a vulnerability calculation unit that calculates vulnerability to noise patterns (periodicity, etc.) from the drive parameters that drive the system, and a risk (error rate) calculation unit due to electromagnetic noise from the electromagnetic noise intensity and vulnerability. This makes it possible to determine the risk of electromagnetic interference by taking into account the effects of coding such as error correction and interleaving processing during communication in digital communications, thereby achieving both low cost and low risk. .
　本発明では、電磁ノイズ解析装置を、ノイズ源の駆動状態を入力する駆動パラメータ入力部と、この駆動パラメータ入力部に入力した駆動パラメータから電気的なノイズパラメータに変換する第１信号変換部と、第１信号変換部で変換したノイズパラメータに基づいて伝搬する電磁ノイズ量を算出する電磁ノイズ解析部と、パラメータ入力部に入力した駆動パラメータからノイズパラメータに変換する第２信号変換部と、第２信号変換部で変換したノイズパラメータ及び/または電磁ノイズ解析部で算出した電磁ノイズ量に基づいて第２信号変換部で変換したノイズパターンにおける脆弱性を計算する脆弱性計算部と、電磁ノイズ解析部で求めた電磁ノイズ量と脆弱性計算部で求めた脆弱性から電磁ノイズリスクを計算するリスク判定部を備えて構成する。 In the present invention, the electromagnetic noise analysis device includes: a drive parameter input section that inputs the drive state of the noise source; a first signal conversion section that converts the drive parameters input to the drive parameter input section into electrical noise parameters; an electromagnetic noise analysis section that calculates the amount of electromagnetic noise propagating based on the noise parameter converted by the first signal conversion section; a second signal conversion section that converts the drive parameter input into the parameter input section into a noise parameter; a vulnerability calculation unit that calculates vulnerability in the noise pattern converted by the second signal conversion unit based on the noise parameters converted by the signal conversion unit and/or the amount of electromagnetic noise calculated by the electromagnetic noise analysis unit; and an electromagnetic noise analysis unit The system includes a risk determination section that calculates the electromagnetic noise risk from the amount of electromagnetic noise determined by the amount of electromagnetic noise and the vulnerability determined by the vulnerability calculation section.
　以下に、本発明の実施の形態を図面に基づいて詳細に説明する。本実施の形態を説明するための全図において同一機能を有するものは同一の符号を付すようにし、その繰り返しの説明は原則として省略する。 Embodiments of the present invention will be described in detail below based on the drawings. In all the figures for explaining this embodiment, parts having the same functions are given the same reference numerals, and repeated explanations thereof will be omitted in principle.
　ただし、本発明は以下に示す実施の形態の記載内容に限定して解釈されるものではない。本発明の思想ないし趣旨から逸脱しない範囲で、その具体的構成を変更し得ることは当業者であれば容易に理解される。 However, the present invention should not be construed as being limited to the contents described in the embodiments shown below. Those skilled in the art will readily understand that the specific configuration can be changed without departing from the spirit or spirit of the present invention.
　図１に、第１の実施例に係る電磁ノイズ解析装置１００の構成を示す。 FIG. 1 shows the configuration of an electromagnetic noise analysis device 100 according to the first embodiment.
　本実施例に係る電磁ノイズ解析装置１００は、駆動パラメータ入力部１０１、第１信号変換部１０２、ノイズ強度計算部１０３、第２信号変換部１０４、被害機器脆弱性計算部１０５、リスク判定部(エラーレート計算部)１０６、結果表示部１０７を備え、これら機能部（機能ブロック）の間でデータの受け渡しを行うことにより、電磁ノイズ解析及びリスク判定を実行する。 The electromagnetic noise analysis device 100 according to the present embodiment includes a drive parameter input section 101, a first signal conversion section 102, a noise intensity calculation section 103, a second signal conversion section 104, a victim equipment vulnerability calculation section 105, a risk determination section ( It includes an error rate calculation section) 106 and a result display section 107, and performs electromagnetic noise analysis and risk determination by exchanging data between these functional sections (functional blocks).
　電磁ノイズ解析装置１００は、図１４に示すようなプロセッサ（ＣＰＵ）１４０１、メモリ（ＲＡＭ）１４０２、ストレージ装置１４０３、入力装置１４０４、出力装置１４０５、通信装置１４０６、バス１４０７を主要な構成として含む情報処理装置（計算機）１４００により実現される。プロセッサ１４０１は、メモリ１４０２にロードされたプログラムに従って処理を実行することによって、所定の機能を提供する機能部（機能ブロック）として機能する。ストレージ装置１４０３は、機能部として機能させるプログラムの他、機能部で使用するデータを格納する。ストレージ装置１４０３には、例えばＨＤＤ（Hard Disk Drive）やＳＳＤ（Solid State Drive）のような不揮発性記憶媒体が用いられる。入力装置１４０４は、キーボード、ポインティングデバイスなどであり、出力装置１４０５はディスプレイなどである。タッチパネルを用いて入力装置１４０４と出力装置１４０５とを一体化してもよい。通信装置１４０６は、ネットワークを介して他の情報処理装置と通信を可能にする。これらはバス１４０７により互いに通信可能に接続されている。 The electromagnetic noise analysis device 100 is configured to store information including a processor (CPU) 1401, a memory (RAM) 1402, a storage device 1403, an input device 1404, an output device 1405, a communication device 1406, and a bus 1407 as shown in FIG. This is realized by a processing device (computer) 1400. The processor 1401 functions as a functional unit (functional block) that provides predetermined functions by executing processing according to a program loaded into the memory 1402. The storage device 1403 stores programs that function as functional units as well as data used by the functional units. For the storage device 1403, a nonvolatile storage medium such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive) is used. The input device 1404 is a keyboard, pointing device, etc., and the output device 1405 is a display, etc. The input device 1404 and the output device 1405 may be integrated using a touch panel. A communication device 1406 enables communication with other information processing devices via a network. These are communicably connected to each other by a bus 1407.
　なお、電磁ノイズ解析装置１００は、１台の情報処理装置で実現する必要はなく、複数台の情報処理装置で実現してもよい。また、電磁ノイズ解析装置１００の一部、あるいはすべての機能をクラウド上のアプリケーションとして実現してもよい。 Note that the electromagnetic noise analysis device 100 does not need to be implemented with one information processing device, and may be implemented with multiple information processing devices. Further, some or all of the functions of the electromagnetic noise analysis device 100 may be realized as an application on the cloud.
　電磁ノイズ解析装置１００を構成する各機能部について説明する。駆動パラメータ入力部１０１は、対象機器(システム)を駆動するときの例えばモータの回転数や電圧、電流、出力トルクなどの駆動パラメータを入力する。第１信号変換部１０２は、駆動パラメータ入力部１０１に入力された対象機器の動作状態を表す信号から電磁ノイズに関する電気的なパラメータ（例えば、ＡＣ電流、電圧、伝達関数など）であるノイズパラメータに変換する。ノイズ強度計算部１０３は、第１信号変換部１０２で変換されたノイズパラメータや第２信号変換部１０４で計算された入力信号のキャリア周波数や電圧指令値などを用いて、電磁ノイズ解析の対象とするシステムを構成する各コンポーネントのうち電磁ノイズの被害を受ける可能性がある被害機器に生じる電磁ノイズ強度の情報として、被害機器ごとのノイズ電流やノイズ電圧などを算出する。 Each functional unit that makes up the electromagnetic noise analysis device 100 will be explained. The drive parameter input unit 101 inputs drive parameters such as motor rotation speed, voltage, current, and output torque when driving the target device (system). The first signal converter 102 converts the signal representing the operating state of the target device input into the drive parameter input unit 101 into a noise parameter that is an electrical parameter related to electromagnetic noise (for example, AC current, voltage, transfer function, etc.). Convert. The noise intensity calculation unit 103 uses the noise parameters converted by the first signal conversion unit 102 and the carrier frequency and voltage command value of the input signal calculated by the second signal conversion unit 104 to determine the target of electromagnetic noise analysis. The noise current, noise voltage, etc. of each victim device are calculated as information on the intensity of electromagnetic noise generated in the victim device that may be affected by electromagnetic noise among the components that make up the system.
　一方、駆動パラメータ入力部１０１に入力した対象機器を駆動するときの例えばモータの回転数や出力トルクなどの信号は第２信号変換部１０４にも入力され、第２信号変換部１０４は、この入力された信号からキャリア周波数や電圧指令値などを計算する。これらは、被害機器脆弱性計算部１０５に送られて脆弱性係数が算出される。被害機器脆弱性計算部１０５の詳細な構成については後述する。 On the other hand, signals such as the motor rotation speed and output torque when driving the target device that are input to the drive parameter input unit 101 are also input to the second signal conversion unit 104. The carrier frequency, voltage command value, etc. are calculated from the received signal. These are sent to the victim device vulnerability calculation unit 105 and a vulnerability coefficient is calculated. The detailed configuration of the victim device vulnerability calculation unit 105 will be described later.
　リスク判定部１０６は、ノイズ強度計算部１０３で算出したノイズ強度の情報と被害機器脆弱性計算部１０５で算出した脆弱性係数の情報とを用いて被害機器ごとのリスクを判定し、判定した結果を結果表示部１０７に送る。結果表示部１０７は、判定結果を出力装置１４０５上に表示する。 The risk determination unit 106 determines the risk of each victim device using the noise intensity information calculated by the noise intensity calculation unit 103 and the vulnerability coefficient information calculated by the victim device vulnerability calculation unit 105, and determines the risk of each victim device. is sent to the result display section 107. The result display unit 107 displays the determination result on the output device 1405.
　本実施例においては、駆動パラメータ入力部１０１と第１信号変換部１０２とノイズ強度計算部１０３とでシステムを駆動する駆動パラメータからシステムを構成する被害機器に生じる電磁ノイズ強度を計算する電磁ノイズ強度計算部を構成する。また、駆動パラメータ入力部１０１と第２信号変換部１０４と被害機器脆弱性計算部１０５とでシステムを駆動する駆動パラメータから電磁ノイズパターン（周期性等）に対する脆弱性を計算する脆弱性計算部を構成する。また、リスク判定部１０６で電磁ノイズ強度と脆弱性から電磁ノイズに起因するリスク計算部を構成している。 In this embodiment, a drive parameter input unit 101, a first signal conversion unit 102, and a noise intensity calculation unit 103 calculate the electromagnetic noise intensity generated in the victim equipment that constitutes the system from the drive parameters that drive the system. Configure the calculation section. In addition, a vulnerability calculation unit is provided which calculates vulnerability to electromagnetic noise patterns (periodicity, etc.) from drive parameters that drive the system using a drive parameter input unit 101, a second signal conversion unit 104, and a victim equipment vulnerability calculation unit 105. Configure. Further, the risk determination unit 106 constitutes a risk calculation unit due to electromagnetic noise based on the electromagnetic noise intensity and vulnerability.
　図２に、被害機器脆弱性計算部１０５の詳細な構成を示す。被害機器脆弱性計算部１０５を構成する各機能部（被害機器脆弱性計算部１０５を構成する各サブ機能部）について説明する。 FIG. 2 shows the detailed configuration of the victim device vulnerability calculation unit 105. Each functional unit (each sub-functional unit that constitutes the victim equipment vulnerability calculation unit 105) that constitutes the victim equipment vulnerability calculation unit 105 will be explained.
　被害機器脆弱性計算部１０５は、ＡＷＧＮ（Additive White Gaussian Noise:加算性白色雑音）などのノイズ信号である標準ノイズ波形を作成する標準ノイズ作成部２０１、ランダムビット列データ（通信文）から送信信号を作成する送信部２０２、送信部２０２が作成した送信信号に標準ノイズ作成部２０１で作成した標準ノイズ波形を印加する第１ノイズ印加部２０３、第１ノイズ印加部２０３で標準ノイズ波形が印加された受信信号を復号する第１受信部２０４、第１受信部２０４で復号した信号と送信部２０２で作成したランダムビット列データとを比較して標準ノイズ波形を印加した場合のエラー率を計算する標準ノイズ波形エラー率計算部２０５を備えている。 The victim device vulnerability calculation unit 105 uses a standard noise creation unit 201 that creates a standard noise waveform, which is a noise signal such as AWGN (Additive White Gaussian Noise), and a transmission signal from random bit string data (message). The first noise applying unit 203 applies the standard noise waveform created by the standard noise creating unit 201 to the transmission signal created by the transmitting unit 202, and the first noise applying unit 203 applies the standard noise waveform. A first receiving section 204 that decodes the received signal, and a standard noise that calculates the error rate when a standard noise waveform is applied by comparing the signal decoded by the first receiving section 204 and the random bit string data created by the transmitting section 202. A waveform error rate calculation section 205 is provided.
　また、被害機器脆弱性計算部１０５は、第２信号変換部１０４で計算された入力信号のキャリア周波数や電圧指令値などからノイズ波形を計算するノイズ波形計算部２０６、送信部２０２が作成した送信信号にノイズ波形計算部２０６で計算したノイズ波形を加算する第２ノイズ印加部２０７、第２ノイズ印加部２０７でノイズ波形が印加された受信信号を復号する第２受信部２０８、第２受信部２０８で復号した信号からノイズ波形を印加した場合のエラー率を計算するエラー率計算部２０９、標準ノイズ波形エラー率計算部２０５で算出した標準ノイズ波形を印加した場合のエラー率とエラー率計算部２０９で算出したノイズ波形を印加した場合のエラー率とを比較する比較部２１０を備えている。 The victim device vulnerability calculation unit 105 also includes a noise waveform calculation unit 206 that calculates a noise waveform from the carrier frequency of the input signal calculated by the second signal conversion unit 104, a voltage command value, etc., and a transmission generated by the transmission unit 202. A second noise application unit 207 that adds the noise waveform calculated by the noise waveform calculation unit 206 to the signal, a second reception unit 208 that decodes the received signal to which the noise waveform has been applied by the second noise application unit 207, and a second reception unit. An error rate calculation section 209 calculates the error rate when a noise waveform is applied from the signal decoded in step 208, and an error rate and error rate calculation section when the standard noise waveform calculated by the standard noise waveform error rate calculation section 205 is applied. A comparison unit 210 is provided to compare the error rate when the noise waveform calculated in step 209 is applied.
　さらに、ノイズ波形計算部２０６は、後述するノイズ波形生成部２０６１、ノイズパターン生成部２０６２を備えている。 Further, the noise waveform calculation section 206 includes a noise waveform generation section 2061 and a noise pattern generation section 2062, which will be described later.
　次に、図１に示した構成を用いて対象装置に対して電磁ノイズ解析を行う手順について、図３のフロー図を用いて説明する。 Next, a procedure for performing electromagnetic noise analysis on a target device using the configuration shown in FIG. 1 will be described using the flow diagram in FIG. 3.
　まず、対象機器を駆動するときの例えばモータの回転数や電流、電圧、出力トルクなどの駆動パラメータを駆動パラメータ入力部１０１に入力する（Ｓ３０１）。次に、この駆動パラメータ入力部１０１に入力された対象機器の動作状態を表す信号から第１信号変換部１０２において、電磁ノイズに関する電気的なパラメータ（例えば、ＡＣ電流、電圧、伝達関数など）であるノイズパラメータに変換する（Ｓ３０２）。 First, drive parameters such as motor rotation speed, current, voltage, and output torque when driving the target device are input into the drive parameter input section 101 (S301). Next, the first signal conversion unit 102 converts the signal representing the operating state of the target device input into the drive parameter input unit 101 into electrical parameters related to electromagnetic noise (for example, AC current, voltage, transfer function, etc.). It is converted into a certain noise parameter (S302).
　一方、Ｓ３０１で駆動パラメータ入力部１０１に入力された対象機器の動作状態を表す信号を第２信号変換部１０４でも受けて、第２信号変換部１０４において、対象機器の動作状態を表す信号のキャリア周波数や電圧指令値などを求める（Ｓ３０３）。 On the other hand, the second signal converter 104 also receives the signal representing the operating state of the target device input to the drive parameter input unit 101 in S301, and the second signal converter 104 converts the signal representing the operating state of the target device into a carrier. The frequency, voltage command value, etc. are determined (S303).
　次に、Ｓ３０２で変換されたノイズパラメータとＳ３０３で求めた第２信号変換部１０４で計算された入力信号のキャリア周波数や電圧指令値などから、ノイズ強度計算部１０３において、電磁ノイズの被害を受ける可能性がある被害機器に生じる電磁ノイズのノイズ強度として、ノイズ電圧やノイズ電流などを計算する（Ｓ３０４）。 Next, based on the noise parameters converted in S302 and the carrier frequency and voltage command value of the input signal calculated in the second signal conversion unit 104 obtained in S303, the noise intensity calculation unit 103 calculates the damage caused by electromagnetic noise. Noise voltage, noise current, etc. are calculated as the noise intensity of electromagnetic noise occurring in potentially damaged equipment (S304).
　次に、第２信号変換部１０４で求めたキャリア周波数や電圧指令値などを用いて、被害機器脆弱性計算部１０５において被害機器の脆弱性を計算する（Ｓ３０５）。この被害機器の脆弱性を計算する詳細なステップについては、図４Ａを用いて説明する。なお、Ｓ３０５の被害機器の脆弱性を計算する工程において、Ｓ３０４で計算したノイズ強度の情報も用いるようにしてもよい。 Next, the victim device vulnerability calculation unit 105 calculates the vulnerability of the victim device using the carrier frequency, voltage command value, etc. obtained by the second signal conversion unit 104 (S305). The detailed steps for calculating the vulnerability of this victim device will be explained using FIG. 4A. Note that the noise intensity information calculated in S304 may also be used in the step of calculating the vulnerability of the victim device in S305.
　Ｓ３０４においてノイズ強度計算部１０３で計算されたノイズ電圧やノイズ電流などのノイズ強度の情報と、Ｓ３０５において被害機器脆弱性計算部１０５で計算した被害機器の脆弱性の情報とに基づいてリスク判定部１０６で被害機器のリスクを判定し（Ｓ３０６）、この判定した結果を結果表示部１０７は出力装置１４０５上に表示する（Ｓ３０７）。 The risk determination unit uses information on noise intensity such as noise voltage and noise current calculated by the noise intensity calculation unit 103 in S304 and information on the vulnerability of the victim device calculated in the victim equipment vulnerability calculation unit 105 in S305. The risk of the damaged device is determined in step 106 (S306), and the result display unit 107 displays the determined result on the output device 1405 (S307).
　次に、Ｓ３０５において被害機器脆弱性計算部１０５で被害機器の脆弱性を計算する詳細なステップを、図４Ａを用いて説明する。 Next, detailed steps for calculating the vulnerability of the victim device by the victim device vulnerability calculation unit 105 in S305 will be described using FIG. 4A.
　まず、Ｓ３０３において第２信号変換部１０４で求めた対象機器の動作状態を表す信号（例えばモータを駆動するＡＣ電流、モータの回転数など）のキャリア周波数や電圧指令値などの第２の変換信号をノイズ波形計算部２０６に入力し（Ｓ４０１）、この第２の変換信号からノイズ波形計算部２０６においてノイズ波形を計算する（Ｓ４０２）。 First, in S303, a second conversion signal such as a carrier frequency or a voltage command value of a signal representing the operating state of the target device (for example, AC current for driving a motor, motor rotation speed, etc.) obtained by the second signal conversion unit 104 is converted. is input to the noise waveform calculation unit 206 (S401), and the noise waveform calculation unit 206 calculates a noise waveform from this second converted signal (S402).
　Ｓ４０２においてノイズ波形計算部２０６でノイズ波形を計算する手順について、図４Ｂの（a）を用いて説明する。 The procedure for calculating the noise waveform by the noise waveform calculation unit 206 in S402 will be explained using (a) of FIG. 4B.
　まず、第２信号変換部１０４から入力した対象機器の動作状態を表す信号であるＡＣ電流やモータの回転数におけるキャリア周波数・基本周波数・電圧指令値などをＰＷＭ（Pulse Width Modulation）信号生成器などで構成されるノイズパターン生成部２０６２に入力して（Ｓ４０２１）、ノイズパターン生成部２０６２を構成するＰＷＭ信号発生器を用いてパワーモジュールのON/OFFのタイミングを計算する（Ｓ４０２２）。 First, a PWM (Pulse Width Modulation) signal generator or the like converts signals representing the operating state of the target device input from the second signal converter 104, such as the carrier frequency, fundamental frequency, and voltage command value at the rotation speed of the motor, to a PWM (Pulse Width Modulation) signal generator. (S4021), and calculates the ON/OFF timing of the power module using the PWM signal generator that constitutes the noise pattern generation section 2062 (S4022).
　次に、ノイズ波形生成部２０６１において、アナログ回路シミュレーションでノイズ波形を計算し、Ｓ４０２１で求めたキャリア周期２０６４に同期した強度ピーク（ノイズ強度）２０６５を有するノイズ波形２０６３を生成する（Ｓ４０２３）。 Next, the noise waveform generation unit 2061 calculates a noise waveform by analog circuit simulation, and generates a noise waveform 2063 having an intensity peak (noise intensity) 2065 synchronized with the carrier period 2064 determined in S4021 (S4023).
　図４Ｂの（ｂ）には、ＡＷＧＮによるノイズ波形として、時間変動が無く、振幅確率が均一な標準ノイズ波形２０６６の例を示す。 FIG. 4B (b) shows an example of a standard noise waveform 2066, which has no time variation and has a uniform amplitude probability, as a noise waveform due to AWGN.
　図４Ｂ（a）のノイズ波形２０６３は、対象機器の動作状態に対応するキャリア周期２０６４に同期しており、キャリア周期等がビットエラーレートに与える影響を計算できる。同様に、基本周波数・電圧指令値等の影響も計算できる。 The noise waveform 2063 in FIG. 4B(a) is synchronized with the carrier period 2064 corresponding to the operating state of the target device, and the influence of the carrier period etc. on the bit error rate can be calculated. Similarly, the influence of fundamental frequency, voltage command value, etc. can also be calculated.
　これに対して、図４Ｂ（ｂ）に示すような、時間変動が無く、振幅確率が均一な標準ノイズ波形２０６６では、対象機器の動作状態に対応するキャリア周期２０６４等がビットエラーレートに与える影響を計算出来ない。同様に、基本周波数・電圧指令値等の影響も計算出来ない。 On the other hand, in a standard noise waveform 2066 with no time variation and uniform amplitude probability, as shown in FIG. 4B(b), the carrier period 2064, etc. that corresponds to the operating state of the target device has an effect on the bit error rate. cannot be calculated. Similarly, the influence of fundamental frequency, voltage command value, etc. cannot be calculated.
　一方、送信部２０２において、図４Ｃに示すような手順で、送信信号を作成する(Ｓ４２０)。送信信号を作成する手順について説明する。 On the other hand, the transmitter 202 creates a transmit signal according to the procedure shown in FIG. 4C (S420). The procedure for creating a transmission signal will be explained.
　まず、図４Ｄに示すようなランダムビット列データ４２１１を生成し（Ｓ４２１）、この生成したランダムビット列データ４２１１をワード列４２１２に変換して通信ワード列データ４２１３を作成する（Ｓ４２２）。なお、Ｓ４２１で生成したランダムビット列データ４２１１は、エラー率計算ステップＳ４０５及びＳ４０９にて使用する。 First, random bit string data 4211 as shown in FIG. 4D is generated (S421), and the generated random bit string data 4211 is converted into a word string 4212 to create communication word string data 4213 (S422). Note that the random bit string data 4211 generated in S421 is used in error rate calculation steps S405 and S409.
　次に、この作成した通信ワード列データ４２１３を符号化（誤り訂正符号、インターリーブ処理、暗号化等）し（Ｓ４２３）、符号化した通信ワード列データを変調して（Ｓ４２４）、送信信号４２１４として出力する（Ｓ４２５）。Ｓ４２５で出力した送信信号４２１４は、ノイズ加算ステップＳ４０３及びＳ４０７にて使用する。 Next, the created communication word string data 4213 is encoded (error correction code, interleave processing, encryption, etc.) (S423), the encoded communication word string data is modulated (S424), and the transmitted signal 4214 is generated. Output (S425). The transmission signal 4214 output in S425 is used in noise addition steps S403 and S407.
　次に、Ｓ４０２においてノイズ波形計算部２０６が計算したノイズ波形とＳ４２０で送信部２０２から出力された送信信号とを第２ノイズ印加部２０７に入力して、送信信号にノイズ波形を加算して（Ｓ４０３）、受信信号４２１５を作成する。 Next, the noise waveform calculated by the noise waveform calculation unit 206 in S402 and the transmission signal output from the transmission unit 202 in S420 are input to the second noise application unit 207, and the noise waveform is added to the transmission signal ( S403), a received signal 4215 is created.
　次に、Ｓ４０３で作成した受信信号４２１５を復号する復号処理を行って復号ビット列データ４２１６に変換し（Ｓ４０４）、Ｓ４２１で生成したランダムビット列データ４２１１とＳ４０４で変換した復号ビット列データ４２１６とを比較してエラー率を計算する（Ｓ４０５）。 Next, a decoding process is performed to decode the received signal 4215 created in S403 to convert it into decoded bit string data 4216 (S404), and the random bit string data 4211 generated in S421 and the decoded bit string data 4216 converted in S404 are compared. The error rate is calculated (S405).
　同様に、Ｓ４０１において入力した第２変換信号に基づいて、標準ノイズ作成部２０１においてＡＷＧＮ（Additive White Gaussian Noise:加算性白色雑音）などの標準ノイズ波形を計算する（Ｓ４０６）。次に、Ｓ４２０において送信部２０２で作成した送信信号とＳ４０６で計算した標準ノイズ波形とを第１ノイズ印加部２０３に入力して送信信号に標準ノイズ波形を加算して（Ｓ４０７）、受信信号を作成する。 Similarly, based on the second converted signal input in S401, the standard noise creation unit 201 calculates a standard noise waveform such as AWGN (Additive White Gaussian Noise) (S406). Next, the transmission signal created by the transmission unit 202 in S420 and the standard noise waveform calculated in S406 are input to the first noise applying unit 203, and the standard noise waveform is added to the transmission signal (S407), thereby converting the received signal into a create.
　次に、Ｓ４０７で作成した受信信号を復号する復号処理を行って復号ビット列データに変換し（Ｓ４０８）、Ｓ４２１で生成したランダムビット列データ４２１１とＳ４０８で変換した復号ビット列データとを比較してエラー率を計算する（Ｓ４０９）。 Next, the received signal created in S407 is decoded and converted into decoded bit string data (S408), and the random bit string data 4211 generated in S421 and the decoded bit string data converted in S408 are compared to determine the error rate. is calculated (S409).
　このように、送信部２０２と第１受信部２０４，第２受信部２０８とで通信時の符号化・エラー訂正・インターリーブ処理等の影響を考慮することでそれらを考慮した電磁干渉リスクを判定することが可能になる。 In this way, by considering the effects of encoding, error correction, interleaving processing, etc. during communication between the transmitting unit 202, the first receiving unit 204, and the second receiving unit 208, the risk of electromagnetic interference is determined by taking these into consideration. becomes possible.
　次に、Ｓ４０５で求めたエラー率とＳ４０９で求めたエラー率のデータを用いて、被害機器の脆弱性を計算し（Ｓ４１０）、その結果をＳ３０６のリスク判定工程へ送る。 Next, the vulnerability of the victim device is calculated using the data of the error rate obtained in S405 and the error rate obtained in S409 (S410), and the result is sent to the risk determination step of S306.
　この図３と図４Ａ乃至４Ｃで説明した処理フローは、電磁ノイズ解析の対象とするシステムを構成する各コンポーネントであって、電磁ノイズの被害を受ける可能性がある被害機器ごとに行う。 The processing flow described in FIG. 3 and FIGS. 4A to 4C is performed for each component that constitutes the system targeted for electromagnetic noise analysis, and is performed for each damaged device that may be damaged by electromagnetic noise.
　本実施例によれば、通信時のエラー訂正・インターリーブ処理等の符号化の影響を考慮して、システムを構成するコンポーネント(被害機器)毎の電磁干渉リスクを判定することができる。 According to this embodiment, it is possible to determine the electromagnetic interference risk for each component (damaged device) that makes up the system, taking into account the effects of coding such as error correction and interleaving processing during communication.
　本発明の第２の実施例に係る電磁ノイズ解析装置５００の構成を、図５に示す。電磁ノイズ解析装置５００も、図１４に示すような情報処理装置（計算機）１４００により実現される。なお、本実施例における電磁ノイズ解析装置５００を構成する機能部（機能ブロック）のうち、実施例１で説明した電磁ノイズ解析装置１００を構成する機能部と同じものについては同じ番号を付して、その詳細な説明を省略するものとする。 The configuration of an electromagnetic noise analysis device 500 according to a second embodiment of the present invention is shown in FIG. The electromagnetic noise analysis device 500 is also realized by an information processing device (computer) 1400 as shown in FIG. Note that among the functional units (functional blocks) constituting the electromagnetic noise analysis device 500 in this embodiment, the same functional units as those constituting the electromagnetic noise analysis device 100 explained in the first embodiment are given the same numbers. , a detailed explanation thereof will be omitted.
　本実施例における電磁ノイズ解析装置５００は、実施例１で説明した電磁ノイズ解析装置１００の被害機器脆弱性計算部１０５を、図２で説明したような構成に替えて、ノイズ波形計算部２０６、送信部２０２、ノイズ印加部５０１、受信部５０２、エラー率計算部２０９で構成した点が異なる。 The electromagnetic noise analysis device 500 in this embodiment replaces the victim device vulnerability calculation section 105 of the electromagnetic noise analysis device 100 described in the first embodiment with the configuration described in FIG. The difference is that the configuration includes a transmitting section 202, a noise applying section 501, a receiving section 502, and an error rate calculating section 209.
　電磁ノイズ解析装置５００は、駆動パラメータ入力部１０１、第１信号変換部１０２、ノイズ強度計算部１０３、第２信号変換部１０４、ノイズ波形計算部２０６、送信部２０２、ノイズ印加部５０１、受信部５０２、エラー率計算部２０９、リスク判定部５０３、結果表示部５０４を備え、これら機能部（機能ブロック）の間でデータの受け渡しを行うことにより、電磁ノイズ解析及びリスク判定を実行する。 The electromagnetic noise analysis device 500 includes a drive parameter input section 101, a first signal conversion section 102, a noise intensity calculation section 103, a second signal conversion section 104, a noise waveform calculation section 206, a transmission section 202, a noise application section 501, and a reception section. 502, an error rate calculation unit 209, a risk determination unit 503, and a result display unit 504, and performs electromagnetic noise analysis and risk determination by exchanging data between these functional units (functional blocks).
　ノイズ印加部５０１、受信部５０２は、それぞれ実施例１における第２ノイズ印加部２０７、第２受信部２０８に相当する。すなわち、本実施例では、実施例１における被害機器脆弱性計算部１０５を、ノイズ波形計算部２０６、送信部２０２、ノイズ印加部５０１、受信部５０２及びエラー率計算部２０９で構成したものに相当する。 The noise applying section 501 and the receiving section 502 correspond to the second noise applying section 207 and the second receiving section 208 in the first embodiment, respectively. In other words, in this embodiment, the victim device vulnerability calculation section 105 in the first embodiment is configured by a noise waveform calculation section 206, a transmission section 202, a noise application section 501, a reception section 502, and an error rate calculation section 209. do.
　本実施例においては、駆動パラメータ入力部１０１と第１信号変換部１０２とノイズ強度計算部１０３とでシステムを駆動する駆動パラメータからシステムを構成する被害機器に生じる電磁ノイズ強度を計算する電磁ノイズ強度計算部を構成している。 In this embodiment, a drive parameter input unit 101, a first signal conversion unit 102, and a noise intensity calculation unit 103 calculate the electromagnetic noise intensity generated in the victim equipment that constitutes the system from the drive parameters that drive the system. It constitutes the calculation section.
　また、駆動パラメータ入力部１０１と第２信号変換部１０４とノイズ波形計算部２０６と送信部２０２とノイズ印加部５０１と受信部５０２とエラー率計算部２０９とで、必要に応じて、システムを駆動する駆動パラメータから電磁ノイズパターン（周期性等）に対する脆弱性を計算する脆弱性計算部を構成する。さらに必要に応じて、リスク判定部５０３で電磁ノイズ強度と脆弱性から電磁ノイズに起因するリスク計算部を構成する。 Further, the drive parameter input section 101, second signal conversion section 104, noise waveform calculation section 206, transmission section 202, noise application section 501, reception section 502, and error rate calculation section 209 drive the system as necessary. A vulnerability calculation unit is configured to calculate vulnerability to electromagnetic noise patterns (periodicity, etc.) from drive parameters. Furthermore, if necessary, the risk determination unit 503 configures a risk calculation unit due to electromagnetic noise based on the electromagnetic noise intensity and vulnerability.
　次に、図５に示した構成を用いて対象装置に対して電磁ノイズ解析を行う手順について、図６のフロー図を用いて説明する。図６に示したフロー図は、実施例１で図３と図４Ａに示したフロー図を組み合わせたものから、Ｓ４０６乃至Ｓ４０９の工程を削除したものに相当する。 Next, the procedure for performing electromagnetic noise analysis on a target device using the configuration shown in FIG. 5 will be described using the flow diagram in FIG. 6. The flowchart shown in FIG. 6 corresponds to a combination of the flowcharts shown in FIGS. 3 and 4A in the first embodiment, with the steps S406 to S409 removed.
　まず、対象機器を駆動するときの例えばモータの回転数や電流、電圧、出力トルクなどの駆動パラメータを駆動パラメータ入力部１０１に入力する（Ｓ６０１）。次に、この駆動パラメータ入力部１０１に入力された対象機器の動作状態を表す信号から第１信号変換部１０２において、電磁ノイズに関する電気的なパラメータ（例えば、ＡＣ電流、電圧、伝達関数など）であるノイズパラメータに変換する（Ｓ６０２）。次に、この変換されたノイズパラメータから、ノイズ強度計算部１０３において、電磁ノイズの被害を受ける可能性がある被害機器に生じる電磁ノイズのノイズ強度として、ノイズ電圧やノイズ電流などを計算する（Ｓ６０３）。以上の処理は、実施例１におけるＳ３０１、Ｓ３０２、Ｓ３０４のステップと同じである。 First, drive parameters such as motor rotation speed, current, voltage, and output torque when driving the target device are input into the drive parameter input section 101 (S601). Next, the first signal conversion unit 102 converts the signal representing the operating state of the target device input into the drive parameter input unit 101 into electrical parameters related to electromagnetic noise (for example, AC current, voltage, transfer function, etc.). It is converted into a certain noise parameter (S602). Next, from the converted noise parameters, the noise intensity calculation unit 103 calculates noise voltage, noise current, etc. as the noise intensity of electromagnetic noise occurring in a victim device that may be damaged by electromagnetic noise (S603 ). The above processing is the same as steps S301, S302, and S304 in the first embodiment.
　一方、Ｓ６０１で駆動パラメータ入力部１０１に入力された対象機器の動作状態を表す信号（例えばモータを駆動するＡＣ電流、モータの回転数など）を第２信号変換部１０４でも受けて、第２信号変換部１０４において、対象機器の動作状態を表す信号のキャリア周波数や電圧指令値などを求める（Ｓ６０４）。次に、この求めたキャリア周波数や電圧指令値などから、実施例１においてＳ４０２で図４Ｂの（a）を用いて説明したように、ノイズ波形計算部２０６においてノイズ波形２０６３を計算する（Ｓ６０５）。ここで、このノイズ波形２０６３を計算するに際して、Ｓ６０３で計算したノイズ強度の情報を用いてノイズ強度２０６５を決定してもよい。ノイズ強度２０６５として、図４Ｂではピーク値を用いる例を示したが、これに限定されず、平均値や実効値などを用いても良い。 On the other hand, the second signal conversion unit 104 also receives the signal representing the operating state of the target device (for example, the AC current for driving the motor, the rotation speed of the motor, etc.) input to the drive parameter input unit 101 in S601, and converts the signal into a second signal. The conversion unit 104 obtains the carrier frequency, voltage command value, etc. of the signal representing the operating state of the target device (S604). Next, from the obtained carrier frequency, voltage command value, etc., the noise waveform 2063 is calculated in the noise waveform calculation unit 206, as explained using FIG. 4B (a) in S402 in Example 1 (S605). . Here, when calculating this noise waveform 2063, the noise intensity 2065 may be determined using the information on the noise intensity calculated in S603. Although FIG. 4B shows an example in which a peak value is used as the noise intensity 2065, the present invention is not limited to this, and an average value, an effective value, or the like may be used.
　一方、送信部２０２において、実施例１において図４Ｃを用いて説明したＳ４２０と同様な手順で送信信号を作成する（Ｓ６０６）。 On the other hand, in the transmitting unit 202, a transmission signal is created in the same procedure as S420 described using FIG. 4C in Example 1 (S606).
　次に、ノイズ波形計算部２０６で計算したノイズ波形とＳ６０６で送信部２０２において作成された送信信号とをノイズ印加部５０１に入力して、送信信号にノイズ波形を加算して（Ｓ６０７）、受信信号（図４Ｄの４２１５に相当）を作成する。次に、受信部５０２は、この受信信号を復号する復号処理を行って（Ｓ６０８）、復号ビット列データ（図４Ｄの４２１６に相当）に変換する。エラー率計算部２０９は、ランダムビット列データ（図４Ｄの４２１１に相当）を用いて復号ビット列データにおけるエラー率を計算する（Ｓ６０９）。必要に応じて、エラー率計算部２０９は脆弱性の計算（Ｓ６１０）及び／又はリスク判定部５０３はリスク判定（Ｓ６１１）を行う。脆弱性の計算（Ｓ６１０）及び／又はリスク判定（Ｓ６１１）は、必ずしも必要なステップではなく、場合によっては省略してもよい。 Next, the noise waveform calculated by the noise waveform calculation unit 206 and the transmission signal created in the transmission unit 202 in S606 are input to the noise application unit 501, the noise waveform is added to the transmission signal (S607), and the transmission signal is received. A signal (corresponding to 4215 in FIG. 4D) is created. Next, the receiving unit 502 performs a decoding process to decode this received signal (S608), and converts it into decoded bit string data (corresponding to 4216 in FIG. 4D). The error rate calculation unit 209 calculates the error rate in the decoded bit string data using the random bit string data (corresponding to 4211 in FIG. 4D) (S609). If necessary, the error rate calculation unit 209 calculates vulnerability (S610) and/or the risk determination unit 503 performs risk determination (S611). Vulnerability calculation (S610) and/or risk determination (S611) are not necessarily necessary steps and may be omitted in some cases.
　その後、エラー率及び又は脆弱性及び又はリスク判定結果を結果表示部１０７は出力装置１４０５上に表示する（Ｓ６１２）。 Thereafter, the result display unit 107 displays the error rate and/or vulnerability and/or risk determination results on the output device 1405 (S612).
　本実施例においても、実施例１の場合と同様に、通信時のエラー訂正・インターリーブ処理等の符号化の影響を考慮して、システムを構成するコンポーネント（被害機器）毎の電磁干渉リスクを判定することができる。 In this example, as in Example 1, the electromagnetic interference risk is determined for each component (damaged device) that makes up the system, taking into account the effects of encoding such as error correction and interleaving processing during communication. can do.
　本発明の第３の実施例について、図７と図８を用いて説明する。本実施例においては、ノイズ波形を格納部に格納された脆弱性ノイズパターンと比較して脆弱性を求めるように構成したもので、実施例１における図２で説明した電磁ノイズ解析装置１００の被害機器脆弱性計算部１０５の構成を、図７に示したような被害機器脆弱性計算部１０５－１と置き換えたものである。そのほかの構成は、実施例１において図１で説明した電磁ノイズ解析装置１００の構成と同じである。 A third embodiment of the present invention will be described using FIGS. 7 and 8. In this embodiment, the noise waveform is compared with the vulnerability noise pattern stored in the storage unit to determine the vulnerability. The configuration of the device vulnerability calculation section 105 is replaced with a victim device vulnerability calculation section 105-1 as shown in FIG. The other configuration is the same as the configuration of the electromagnetic noise analysis apparatus 100 described in FIG. 1 in the first embodiment.
　また、本実施例は、実施例２で図５に示した電磁ノイズ解析装置５００において、ノイズ波形計算部２０６、送信部２０２、ノイズ印加部５０１、受信部５０２、エラー率計算部２０９を、図７に示した被害機器脆弱性計算部１０５－１と置き換えたものにも相当する。 Further, in this embodiment, in the electromagnetic noise analysis device 500 shown in FIG. It also corresponds to the one that replaced the victim equipment vulnerability calculation unit 105-1 shown in 7.
　すなわち、本実施例においては、被害機器のエラー率及び又は脆弱性を算出するのに、予め求めておいたノイズ波形パターンとビットエラー率との関係を脆弱性ノイズパターンモデル格納部７０２に格納しておく。ノイズ波形計算部７０１において、図１に示した第２信号変換部１０４で求めた入力信号のキャリア周波数や電圧指令値などからノイズ波形を計算し、脆弱性ノイズパターンモデル格納部７０２に格納されている脆弱性ノイズパターンと照合して、計算して求めたノイズ波形との一致度が高い脆弱性ノイズパターンを抽出する。脆弱性計算部７０３では、抽出した脆弱性ノイズパターンの情報から被害機器のエラー率及び又は脆弱性を算出する。 That is, in this embodiment, in order to calculate the error rate and/or vulnerability of the victim device, the relationship between the noise waveform pattern and the bit error rate determined in advance is stored in the vulnerability noise pattern model storage unit 702. I'll keep it. In the noise waveform calculation unit 701, a noise waveform is calculated from the carrier frequency of the input signal, the voltage command value, etc. obtained by the second signal conversion unit 104 shown in FIG. A vulnerability noise pattern that has a high degree of matching with the calculated noise waveform is extracted. The vulnerability calculation unit 703 calculates the error rate and/or vulnerability of the victim device from the extracted vulnerability noise pattern information.
　本実施例に係る処理の流れは、実施例１で図３と図４Ａを用いて説明した処理の流れにおいて、図４Ａで説明したＳ３０５の被害機器の脆弱性計算のステップが異なるだけで、他のステップは、図３で説明したものと同じである。 The processing flow according to this embodiment differs from the processing flow explained using FIG. 3 and FIG. 4A in the first embodiment, except that the step of calculating the vulnerability of the victim device in S305 explained with FIG. 4A is different. The steps are the same as those described in FIG.
　図８を用いて、実施例１のＳ３０５に対応する被害機器の脆弱性計算のステップＳ３０５－１の処理の流れを説明する。 The process flow of step S305-1 of vulnerability calculation of the victim device corresponding to S305 of the first embodiment will be explained using FIG. 8.
　まず、図３のＳ３０３において第２信号変換部１０４で求めた対象機器の動作状態を表す信号のキャリア周波数や電圧指令値などの第２の変換信号を入力し（Ｓ８０１）、この第２の変換信号と必要に応じてＳ３０４において計算した対象機器のノイズ強度からノイズ波形計算部２０６においてノイズ波形を計算する（Ｓ８０２）。 First, a second conversion signal such as the carrier frequency and voltage command value of the signal representing the operating state of the target device obtained by the second signal conversion unit 104 in S303 of FIG. 3 is input (S801), and this second conversion signal is The noise waveform calculation unit 206 calculates a noise waveform from the signal and, if necessary, the noise intensity of the target device calculated in S304 (S802).
　次に、この計算して求めたノイズ波形を脆弱性ノイズパターンモデル格納部７０２に格納されている脆弱性ノイズパターンと照合して、計算して求めたノイズ波形との一致度が高い脆弱性ノイズパターンを抽出する（Ｓ８０３）。抽出した脆弱性ノイズパターンと関連付けて脆弱性ノイズパターンモデル格納部７０２に格納されているエラー率の情報から脆弱性計算部７０３で被害機器のエラー率及び又は脆弱性に関する情報を抽出し（Ｓ８０４）、これらの情報を用いて図３で説明したリスク判定工程Ｓ３０６を実行する。 Next, this calculated noise waveform is compared with the vulnerability noise pattern stored in the vulnerability noise pattern model storage unit 702, and the vulnerability noise that has a high degree of coincidence with the calculated noise waveform is A pattern is extracted (S803). The vulnerability calculation unit 703 extracts information regarding the error rate and/or vulnerability of the victim device from the error rate information stored in the vulnerability noise pattern model storage unit 702 in association with the extracted vulnerability noise pattern (S804). , the risk determination step S306 described in FIG. 3 is executed using this information.
　また、図８のＳ８０１からＳ８０４の処理を実施例２で説明した図６のＳ６０５からＳ６１１までの工程と置き換えることにより、実施例２にも適用することができる。 Furthermore, the process from S801 to S804 in FIG. 8 can be applied to the second embodiment by replacing the process from S605 to S611 in FIG. 6 described in the second embodiment.
　本実施例によれば、実施例１及び２で説明した効果に加えて、電磁干渉リスクを判定する場合に送信信号にノイズ波形を加算したり、加算したデータからビット列データを復号させたりする工程が不要になり、ノイズ波形から被害機器の脆弱性に関する情報を抽出することができるので、被害機器の電磁干渉リスクを高速に、リアルタイムで判定することができる。 According to this embodiment, in addition to the effects described in Examples 1 and 2, there is a step of adding a noise waveform to a transmitted signal and decoding bit string data from the added data when determining electromagnetic interference risk. Since this eliminates the need for information on the vulnerability of the victim equipment from the noise waveform, it is possible to quickly determine the electromagnetic interference risk of the victim equipment in real time.
　本発明の第４の実施例として、実施例３で説明した脆弱性ノイズパターンモデル格納部７０２に格納するデータを機械学習により作成する構成について、図９乃至図１１を用いて説明する。 As a fourth embodiment of the present invention, a configuration in which data to be stored in the vulnerability noise pattern model storage unit 702 described in the third embodiment is created by machine learning will be described with reference to FIGS. 9 to 11.
　図９に、本実施例に係る脆弱性ノイズパターンモデル格納部７０２－１の構成を示す。脆弱性ノイズパターンモデル格納部７０２－１は、駆動パラメータ入力部９０１、信号変換部９０２、ノイズ波形計算部９０３、機械学習モデル生成部９０４、被害機器脆弱性計算部９０５、脆弱性ラベリング部９０６、機械学習モデル保存部９０７を備え、これら機能部（機能ブロック）の間でデータの受け渡しを行うことにより、以下に説明する処理を行う。 FIG. 9 shows the configuration of the vulnerability noise pattern model storage unit 702-1 according to this embodiment. The vulnerability noise pattern model storage unit 702-1 includes a drive parameter input unit 901, a signal conversion unit 902, a noise waveform calculation unit 903, a machine learning model generation unit 904, a victim device vulnerability calculation unit 905, a vulnerability labeling unit 906, A machine learning model storage unit 907 is provided, and the processing described below is performed by exchanging data between these functional units (functional blocks).
　駆動パラメータ入力部９０１は、実施例１の図１で説明した駆動パラメータ入力部１０１と同様に、対象機器(システム)を駆動するときの例えばモータの回転数や電圧、電流、出力トルクなどの駆動パラメータを入力する。信号変換部９０２では、この駆動パラメータ入力部１０１に入力された対象機器の動作状態を表す信号からキャリア周波数や電圧指令値などに変換する。 Similar to the drive parameter input unit 101 described in FIG. 1 of the first embodiment, the drive parameter input unit 901 inputs drive parameters such as motor rotation speed, voltage, current, and output torque when driving the target device (system). Enter parameters. The signal conversion unit 902 converts the signal representing the operating state of the target device input into the drive parameter input unit 101 into a carrier frequency, voltage command value, etc.
　ノイズ波形計算部９０３は、信号変換部９０２で変換されたキャリア周波数や電圧指令値などからノイズ波形を計算し、計算して求められたノイズ波形のデータ１１０１は、機械学習モデル生成部９０４の図１１に示すようなニューラルネットワーク１１００の入力層１１０２に入力される。 A noise waveform calculation unit 903 calculates a noise waveform from the carrier frequency, voltage command value, etc. converted by the signal conversion unit 902, and data 1101 of the calculated noise waveform is generated by the machine learning model generation unit 904. The signal is input to an input layer 1102 of a neural network 1100 as shown in FIG.
　被害機器脆弱性計算部９０５は、実施例１で説明した被害機器脆弱性計算部１０５、または、実施例２で説明したノイズ波形計算部２０６、送信部２０２、ノイズ印加部５０１、受信部５０２及びエラー率計算部２０９で構成される。 The victim device vulnerability calculation unit 905 includes the victim device vulnerability calculation unit 105 described in the first embodiment, or the noise waveform calculation unit 206, the transmission unit 202, the noise application unit 501, the reception unit 502, and the noise waveform calculation unit 206 described in the second embodiment. It is composed of an error rate calculation section 209.
　脆弱性ラベリング部９０６は、被害機器脆弱性計算部９０５で計算されたノイズ波形のエラー率毎にラベリングをして、入力層に入力されたノイズ波形のデータと関連付けてデータ１１０３として機械学習モデル生成部９０４の図１１に示すようなニューラルネットワーク１１００の出力層１１０４の側に入力する。 The vulnerability labeling unit 906 labels each error rate of the noise waveform calculated by the victim equipment vulnerability calculation unit 905 and generates a machine learning model as data 1103 by associating it with the noise waveform data input to the input layer. 904 is input to the output layer 1104 side of the neural network 1100 as shown in FIG.
　機械学習モデル保存部９０７は、機械学習モデル生成部９０４で生成された機械学習モデルをストレージ装置１４０３に保存する。また、機械学習モデル保存部９０７は、図７のノイズ波形計算部７０１において計算して求めたノイズ波形をストレージ装置１４０３に記憶した機械学習モデルと照合して、計算して求めたノイズ波形との一致度が高い脆弱性ノイズパターンを機械学習モデルの中から抽出し、この抽出した脆弱性ノイズパターンの情報を脆弱性計算部７０３に送って被害機器の脆弱性を算出する。 The machine learning model storage unit 907 stores the machine learning model generated by the machine learning model generation unit 904 in the storage device 1403. In addition, the machine learning model storage unit 907 compares the noise waveform calculated by the noise waveform calculation unit 701 in FIG. A vulnerability noise pattern with a high matching degree is extracted from the machine learning model, and information on the extracted vulnerability noise pattern is sent to the vulnerability calculation unit 703 to calculate the vulnerability of the victim device.
　なお、図９に示した脆弱性ノイズパターンモデル格納部７０２－１の構成において、ノイズセンサで検出したノイズ信号を直接ノイズ波形計算部９０３に入力してもよい。また、駆動パラメータ入力部９０１と信号変換部９０２を削除して、ノイズセンサで検出したノイズ信号を直接ノイズ波形計算部９０３に入力する構成としてもよい。 Note that in the configuration of the vulnerable noise pattern model storage section 702-1 shown in FIG. 9, the noise signal detected by the noise sensor may be directly input to the noise waveform calculation section 903. Alternatively, the drive parameter input section 901 and the signal conversion section 902 may be deleted, and the noise signal detected by the noise sensor may be directly input to the noise waveform calculation section 903.
　図１０に、本実施例に係る機械学習モデルを生成する処理の流れを示す。 FIG. 10 shows the flow of processing for generating a machine learning model according to this embodiment.
　まず、対象機器を駆動するときの例えばモータの回転数や電流、電圧、出力トルクなどの駆動パラメータを駆動パラメータ入力部９０１に入力する（Ｓ１００１）。次に、信号変換部９０２において、この駆動パラメータ入力部９０１に入力された対象機器の動作状態を表す信号からキャリア周波数や電圧指令値などを計算する信号変換処理を行う（Ｓ１００２）。次に、Ｓ１００２において信号変換処理した変換信号からノイズ波形計算部９０３においてノイズ波形を計算する（Ｓ１００３）。Ｓ１００３で得られたノイズ波形のデータは機械学習モデル生成部９０４の入力層に入力される（Ｓ１００４）。 First, drive parameters such as the motor rotation speed, current, voltage, and output torque when driving the target device are input into the drive parameter input section 901 (S1001). Next, the signal conversion unit 902 performs signal conversion processing to calculate the carrier frequency, voltage command value, etc. from the signal representing the operating state of the target device input to the drive parameter input unit 901 (S1002). Next, the noise waveform calculation unit 903 calculates a noise waveform from the converted signal subjected to the signal conversion process in S1002 (S1003). The noise waveform data obtained in S1003 is input to the input layer of the machine learning model generation unit 904 (S1004).
　一方、送信部２０２において実施例１で説明したような送信信号が作成され（Ｓ１００５）、ノイズ波形計算部９０３においてＳ１００３で得られたノイズ波形のデータと加算して（Ｓ１００６）、受信信号（図４Ｄの４２１５に相当）を作成する。 On the other hand, the transmitting unit 202 creates a transmitting signal as described in the first embodiment (S1005), and the noise waveform calculating unit 903 adds it to the noise waveform data obtained in S1003 (S1006), and the received signal (Fig. (equivalent to 4215 in 4D).
　次に、第２受信部２０８または受信部５０２において、Ｓ１００６で作成した受信信号を復号する復号処理を行って復号ビット列データ（図４Ｄの４２１６に相当）に変換し（Ｓ１００７）、エラー率計算部２０９は、実施例１で説明した図４ＣにおけるＳ４２１で生成したランダムビット列データに相当するランダムビット列データとＳ１００７で変換した復号ビット列データとを比較してエラー率を計算する（Ｓ１００８）。 Next, the second receiving unit 208 or the receiving unit 502 performs decoding processing to decode the received signal created in S1006 and converts it into decoded bit string data (corresponding to 4216 in FIG. 4D) (S1007), and the error rate calculation unit 209 calculates an error rate by comparing the random bit string data corresponding to the random bit string data generated in S421 in FIG. 4C described in Example 1 and the decoded bit string data converted in S1007 (S1008).
　Ｓ１００８で求めたエラー率の情報は脆弱性ラベリング部９０６へ送られて被害機器の脆弱性が求められ（Ｓ１００９）、脆弱性のデータに対応したラベリングが行われる（Ｓ１０１０）。ラベリングされた脆弱性のデータは、機械学習モデル生成部９０４の出力層に入力される（Ｓ１０１１）。 The error rate information obtained in S1008 is sent to the vulnerability labeling unit 906 to determine the vulnerability of the victim device (S1009), and labeling corresponding to the vulnerability data is performed (S1010). The labeled vulnerability data is input to the output layer of the machine learning model generation unit 904 (S1011).
　機械学習モデル生成部９０４において、Ｓ１００４で入力層に入力されたノイズ波形データと、Ｓ１０１１で出力層に入力されたラベリングされた脆弱性のデータとを関連付けて機械学習モデルを生成し（Ｓ１０１２）、機械学習モデル保存部９０７により、ストレージ装置１４０３に保存される（Ｓ１０１３）。 The machine learning model generation unit 904 generates a machine learning model by associating the noise waveform data input to the input layer in S1004 with the labeled vulnerability data input to the output layer in S1011 (S1012), The machine learning model storage unit 907 stores it in the storage device 1403 (S1013).
　本実施例によれば、実施例１及び２で説明した効果に加えて、機械学習モデルを用いて被害機器ごとの脆弱性を判定でき、実施例１及び２で説明したような送信信号にノイズ波形を加算したり、加算したデータからビット列データを復号させたりする工程が不要になり、被害機器ごとの電磁干渉リスクを高速に、リアルタイムで判定することができる。 According to this embodiment, in addition to the effects explained in embodiments 1 and 2, it is possible to determine the vulnerability of each victim device using a machine learning model, and noise in the transmitted signal as explained in embodiments 1 and 2 can be detected. This eliminates the need for adding waveforms and decoding bit string data from the added data, making it possible to quickly determine the electromagnetic interference risk of each victim device in real time.
　本発明の第５の実施例として、実施例１乃至４で説明した電磁ノイズ解析装置１００又は５００を搭載したリスク判定装置１２００の構成を、図１２を用いて説明する。リスク判定装置１２００も、図１４に示すような情報処理装置（計算機）１４００により実現される。 As a fifth embodiment of the present invention, the configuration of a risk determination device 1200 equipped with the electromagnetic noise analysis device 100 or 500 described in the first to fourth embodiments will be described using FIG. 12. The risk determination device 1200 is also realized by an information processing device (computer) 1400 as shown in FIG.
　本実施例に係るリスク判定装置１２００は、対象機器１２１０からの駆動パラメータを受信する受信部１２０１、受信部１２０１で受信した信号に基づいて電磁ノイズ解析を行う電磁ノイズ解析部１２０２、電磁ノイズ解析部１２０２で解析した結果を画面上に出力して表示する表示部１２０３といった機能部（機能ブロック）を備えている。電磁ノイズ解析部１２０２が解析した結果は、対象機器１２１０を制御する制御部１２１１に送られる。 The risk determination device 1200 according to this embodiment includes a receiving section 1201 that receives drive parameters from a target device 1210, an electromagnetic noise analysis section 1202 that performs electromagnetic noise analysis based on the signal received by the receiving section 1201, and an electromagnetic noise analysis section. It includes a functional unit (functional block) such as a display unit 1203 that outputs and displays the results analyzed in step 1202 on a screen. The results of the analysis by the electromagnetic noise analysis unit 1202 are sent to the control unit 1211 that controls the target device 1210.
　ここで、電磁ノイズ解析部１２０２は実施例１乃至４で説明した電磁ノイズ解析装置１００又は５００に相当し、表示部１２０３は図１の結果表示部１０７又は図５の結果表示部５０４と共有してもよい。 Here, the electromagnetic noise analysis section 1202 corresponds to the electromagnetic noise analysis device 100 or 500 described in Examples 1 to 4, and the display section 1203 is shared with the result display section 107 in FIG. 1 or the result display section 504 in FIG. It's okay.
　本実施例においては、電磁ノイズ解析部１２０２を実施例１で説明した電磁ノイズ解析装置１００又は実施例２で説明した電磁ノイズ解析装置５００で構成することにより、対象機器１２１０を運転中に対象機器１２１０で発生する電磁ノイズの被害を受ける可能性がある被害機器について、電磁ノイズに起因するリスクを判定してその結果を制御部１２１１に送り、制御部１２１１で対象機器１２１０を制御することにより、電磁ノイズの発生を抑えて被害機器において電磁ノイズによる被害の発生を防止することができる。 In this example, by configuring the electromagnetic noise analysis unit 1202 with the electromagnetic noise analysis device 100 described in Example 1 or the electromagnetic noise analysis device 500 described in Example 2, the target device 1210 can be By determining the risk caused by electromagnetic noise with respect to damaged equipment that may be damaged by electromagnetic noise generated in 1210 and sending the result to the control unit 1211, the control unit 1211 controls the target equipment 1210. By suppressing the generation of electromagnetic noise, it is possible to prevent damage caused by electromagnetic noise in the victim equipment.
　また、電磁ノイズ解析部１２０２を実施例１で説明した電磁ノイズ解析装置１００又は実施例２で説明した電磁ノイズ解析装置５００に実施例３で説明した図７に示したような被害機器脆弱性計算部１０５－１、又はこの被害機器脆弱性計算部１０５－１に実施例４で説明したような脆弱性ノイズパターンモデル格納部７０２－１を適用したような構成を採用することにより、対象機器１２１０を運転しながら被害機器の電磁ノイズに起因するリスクをリアルタイムで判定することができる。 In addition, the electromagnetic noise analysis unit 1202 can be used to perform damage equipment vulnerability calculation as shown in FIG. By adopting a configuration in which the vulnerability noise pattern model storage unit 702-1 as explained in the fourth embodiment is applied to the target device 1210 or the victim device vulnerability calculation unit 105-1, The risk caused by electromagnetic noise of the affected equipment can be determined in real time while driving.
　これにより、被害機器の電磁ノイズに起因するリスクの情報を制御部１２１１に送り、制御部１２１１で対象機器１２１０を制御することにより、電磁ノイズの発生をリアルタイムで抑えて被害機器において電磁ノイズによる被害の発生を防止することができる。 As a result, information on risks caused by electromagnetic noise of the victim equipment is sent to the control unit 1211, and the control unit 1211 controls the target equipment 1210, thereby suppressing the generation of electromagnetic noise in real time and causing damage to the victim equipment due to electromagnetic noise. can be prevented from occurring.
　本発明の第６の実施例として、実施例３で説明した被害機器脆弱性計算部１０５－１を備えた電磁ノイズ解析装置１００又は５００、又は実施例４で説明した脆弱性ノイズパターンモデル格納部７０２－１を備えた電磁ノイズ解析装置１００又は５００に相当する電磁ノイズ解析部１３０２を備えた制御装置１３００の構成を、図１３を用いて説明する。制御装置１３００も、図１４に示すような情報処理装置（計算機）１４００により実現され、ただし、本実施例に係る機能のみならず、対象機器１３１０を制御する機能を備えている。 As a sixth embodiment of the present invention, the electromagnetic noise analysis device 100 or 500 equipped with the victim device vulnerability calculation unit 105-1 described in the third embodiment, or the vulnerability noise pattern model storage unit described in the fourth embodiment The configuration of a control device 1300 equipped with an electromagnetic noise analysis section 1302 corresponding to the electromagnetic noise analysis device 100 or 500 equipped with the electromagnetic noise analysis device 702-1 will be described using FIG. The control device 1300 is also realized by an information processing device (computer) 1400 as shown in FIG. 14, but includes not only the functions according to this embodiment but also the function of controlling the target device 1310.
　本実施例に係る制御装置１３００は、対象機器１２１０（例えば自動車）を運転しながら、対象機器１２１０からの電磁ノイズの発生をリアルタイムで抑えて被害機器において電磁ノイズによる被害の発生を防止できるように構成したものである。 The control device 1300 according to the present embodiment is capable of suppressing the generation of electromagnetic noise from the target device 1210 in real time while driving the target device 1210 (for example, a car) to prevent damage caused by electromagnetic noise in the victim device. It is composed of
　本実施例に係る制御装置１３００は、対象機器１３１０からの駆動パラメータを受信する受信部１３０１、受信部１３０１で受信した信号に基づいて電磁ノイズ解析を行う電磁ノイズ解析部１３０２、電磁ノイズ解析部１３０２で解析した結果に基づいて対象機器１３１０を制御する制御部１３０３といった機能部（機能ブロック）を備えている。 The control device 1300 according to the present embodiment includes a receiving section 1301 that receives drive parameters from a target device 1310, an electromagnetic noise analysis section 1302 that performs electromagnetic noise analysis based on the signal received by the receiving section 1301, and an electromagnetic noise analysis section 1302. It includes functional units (functional blocks) such as a control unit 1303 that controls the target device 1310 based on the results of the analysis.
　対象機器１３１０を例えば自動車としたとき、受信部１３０１で受信する対象機器１３１０の駆動パラメータは、モータを駆動する動力部から発生する電磁ノイズに起因するものが考えられる。 When the target device 1310 is a car, for example, the drive parameters of the target device 1310 received by the receiving unit 1301 are considered to be caused by electromagnetic noise generated from the power unit that drives the motor.
　対象機器１３１０を例えば自動車としたとき、動力部で発生する電磁ノイズの被害を受ける可能性がある被害機器は複数ある。自動車を運転中にこの電磁ノイズの被害の発生を抑止するためには、電磁ノイズによるリスクをリアルタイムで評価して動力部を制御しなければならない。 For example, when the target device 1310 is a car, there are multiple damaged devices that may be damaged by electromagnetic noise generated in the power section. In order to prevent damage caused by electromagnetic noise while driving a car, it is necessary to evaluate the risk caused by electromagnetic noise in real time and control the power unit.
　本実施例では、電磁ノイズ解析部１３０２を実施例３で説明した電磁ノイズ解析装置１００又は５００に脆弱性ノイズパターンモデル格納部７０２を備えた被害機器脆弱性計算部１０５－１を搭載したもの、又はこの被害機器脆弱性計算部１０５－１に実施例４で説明したような機械学習モデル保存部９０７を有する脆弱性ノイズパターンモデル格納部７０２－１を適用したような構成を備えて形成したことにより、電磁ノイズによるリスクをリアルタイムで評価して動力部を制御することを可能にする。 In this embodiment, the electromagnetic noise analysis unit 1302 is the electromagnetic noise analysis device 100 or 500 described in Embodiment 3, which is equipped with a victim equipment vulnerability calculation unit 105-1 equipped with a vulnerability noise pattern model storage unit 702; Or, the victim device vulnerability calculation unit 105-1 is formed with a configuration in which the vulnerability noise pattern model storage unit 702-1 having the machine learning model storage unit 907 as described in the fourth embodiment is applied. This makes it possible to evaluate the risks caused by electromagnetic noise in real time and control the power section.
　このような構成とすることにより、被害機器の電磁ノイズに起因するリスクの情報を制御部１３０３に送り、制御部１３０３で対象機器１３１０を制御することにより、電磁ノイズの発生をリアルタイムで抑えて被害機器において電磁ノイズによる被害の発生を防止することができる。 With this configuration, information on risks caused by electromagnetic noise of the victim equipment is sent to the control unit 1303, and the control unit 1303 controls the target equipment 1310, thereby suppressing the generation of electromagnetic noise in real time and preventing damage. It is possible to prevent damage caused by electromagnetic noise in equipment.
　以上、本発明者によってなされた発明を実施例に基づき具体的に説明したが、本発明は前記実施例に限定されるものではなく、その要旨を逸脱しない範囲で種々変更可能であることは言うまでもない。例えば、上記した実施例は本発明を分かりやすく説明するために詳細に説明したものであり、必ずしも説明した全ての構成を備えるものに限定されるものではない。また、各実施例の構成の一部について、他の構成の追加・削除・置換をすることが可能である。 Above, the invention made by the present inventor has been specifically explained based on Examples, but it goes without saying that the present invention is not limited to the Examples and can be modified in various ways without departing from the gist thereof. stomach. For example, the embodiments described above are described in detail to explain the present invention in an easy-to-understand manner, and the present invention is not necessarily limited to having all the configurations described. Further, it is possible to add, delete, or replace a part of the configuration of each embodiment with other configurations.
１００、５００…電磁ノイズ解析装置、１０１、９０１…駆動パラメータ入力部、１０２…第１信号変換部、１０３…ノイズ強度計算部、１０４…第２信号変換部、１０５、１０５－１、９０５…被害機器脆弱性計算部、１０６、５０３…リスク判定部、１０７、５０４…結果表示部、２０１…標準ノイズ作成部、２０２…送信部、２０３…第１ノイズ印加部、２０４…第１受信部、２０５…標準ノイズ波形エラー率計算部、２０６、７０１…ノイズ波形計算部、２０７…第２ノイズ印加部、２０８…第２受信部、２０９…エラー率計算部、２１０…比較部、５０１…ノイズ印加部、５０２、１２０１，１３０１…受信部、７０２、７０２－１…脆弱性ノイズパターンモデル格納部、７０３…脆弱性計算部、９０２…信号変換部、９０３…ノイズ波形計算部、９０４…機械学習モデル生成部、９０６…脆弱性ラベリング部、９０７…機械学習モデル保存部、１２００…リスク判定装置、１２０２、１３０２…電磁ノイズ解析部、１２０３…表示部、１２１０、１３１０…対象機器、１２１１、１３０３…制御部、１３００…制御装置 100, 500... Electromagnetic noise analysis device, 101, 901... Drive parameter input section, 102... First signal conversion section, 103... Noise intensity calculation section, 104... Second signal conversion section, 105, 105-1, 905... Damage Equipment vulnerability calculation section, 106, 503... Risk determination section, 107, 504... Result display section, 201... Standard noise creation section, 202... Transmission section, 203... First noise applying section, 204... First receiving section, 205 ... Standard noise waveform error rate calculation section, 206, 701 ... Noise waveform calculation section, 207 ... Second noise application section, 208 ... Second reception section, 209 ... Error rate calculation section, 210 ... Comparison section, 501 ... Noise application section , 502, 1201, 1301...Receiving section, 702, 702-1...Vulnerability noise pattern model storage section, 703...Vulnerability calculation section, 902...Signal conversion section, 903...Noise waveform calculation section, 904...Machine learning model generation Section, 906... Vulnerability labeling section, 907... Machine learning model storage section, 1200... Risk determination device, 1202, 1302... Electromagnetic noise analysis section, 1203... Display section, 1210, 1310... Target device, 1211, 1303... Control section , 1300...control device

Claims (12)

  1. 　複数の機器を備えて構成されるシステムを駆動する駆動パラメータから前記駆動することにより前記システムから発生する電磁ノイズの強度を計算する電磁ノイズ強度計算部と、
    　前記駆動パラメータから前記システムが発生する電磁ノイズパターンに対する前記複数の機器の機器ごとの脆弱性を計算する被害機器脆弱性計算部と、
    　前記電磁ノイズ強度計算部で計算した電磁ノイズ強度と前記被害機器脆弱性計算部で計算した前記機器ごとの脆弱性から前記電磁ノイズに起因する前記機器ごとのリスクを計算するリスク計算部と
    を備えることを特徴とする電磁ノイズ解析装置。     an electromagnetic noise intensity calculation unit that calculates the intensity of electromagnetic noise generated from the system by driving the system from drive parameters for driving the system including a plurality of devices;
    a victim device vulnerability calculation unit that calculates the vulnerability of each of the plurality of devices to the electromagnetic noise pattern generated by the system from the drive parameters;
    A risk calculation unit that calculates a risk for each of the devices caused by the electromagnetic noise from the electromagnetic noise intensity calculated by the electromagnetic noise intensity calculation unit and the vulnerability of each device calculated by the damaged device vulnerability calculation unit. An electromagnetic noise analysis device characterized by:
  2. 　請求項１記載の電磁ノイズ解析装置であって、
    　前記被害機器脆弱性計算部は、
    　前記電磁ノイズの波形を計算するノイズ波形計算部と、
    　ビット列データを用いて送信信号を作成し前記作成した送信信号を送信する送信部と、　前記送信部から送信された前記送信信号と前記ノイズ波形計算部で計算して求めたノイズ波形とを加算するノイズ印加部と、
    　前記ノイズ印加部で前記加算した信号を復号して復号ビット列データに変換する受信部と、
    　前記受信部で復号して生成した前記ビット列データと前記送信部で用いた前記ビット列データとを用いて前記機器ごとのエラー率を計算するエラー率計算部と
    を備えることを特徴とする電磁ノイズ解析装置。     The electromagnetic noise analysis device according to claim 1,
    The victim device vulnerability calculation unit is
    a noise waveform calculation unit that calculates the waveform of the electromagnetic noise;
    a transmitter that creates a transmit signal using bit string data and transmits the created transmit signal; and adds the transmit signal transmitted from the transmitter and the noise waveform calculated by the noise waveform calculator. a noise applying section;
    a receiving unit that decodes the added signal in the noise applying unit and converts it into decoded bit string data;
    An electromagnetic noise analysis characterized by comprising: an error rate calculation unit that calculates an error rate for each device using the bit string data decoded and generated by the receiving unit and the bit string data used by the transmitting unit. Device.
  3. 　請求項１記載の電磁ノイズ解析装置であって、
    　前記被害機器脆弱性計算部は、
    　前記電磁ノイズの波形を計算するノイズ波形計算部と、
    　前記機器ごとの脆弱性ノイズパターンモデルをエラー率と対応させて格納する脆弱性ノイズパターンモデル格納部と、
    　前記ノイズ波形計算部で計算した前記電磁ノイズの波形と前記脆弱性ノイズパターンモデル格納部に格納された前記機器ごとの前記脆弱性ノイズパターンモデルとを照合して前記エラー率に基づいて前記機器ごとの脆弱性を計算する脆弱性計算部と
    を備えることを特徴とする電磁ノイズ解析装置。     The electromagnetic noise analysis device according to claim 1,
    The victim device vulnerability calculation unit is
    a noise waveform calculation unit that calculates the waveform of the electromagnetic noise;
    a vulnerability noise pattern model storage unit that stores vulnerability noise pattern models for each device in correspondence with error rates;
    The waveform of the electromagnetic noise calculated by the noise waveform calculation unit is compared with the vulnerability noise pattern model for each device stored in the vulnerability noise pattern model storage unit, and the noise pattern is calculated for each device based on the error rate. An electromagnetic noise analysis device comprising: a vulnerability calculation unit that calculates the vulnerability of an electromagnetic noise analyzer.
  4. 　請求項３記載の電磁ノイズ解析装置であって、
    　前記脆弱性ノイズパターンモデル格納部は、前記機器ごとの前記脆弱性ノイズパターンモデルを機械学習により作成して保存することを特徴とする電磁ノイズ解析装置。     The electromagnetic noise analysis device according to claim 3,
    The electromagnetic noise analysis device is characterized in that the vulnerable noise pattern model storage unit creates and stores the vulnerable noise pattern model for each device by machine learning.
  5. 　請求項４記載の電磁ノイズ解析装置であって、
    　前記脆弱性ノイズパターンモデル格納部は、
    　前記複数の機器を備えて構成される前記システムを駆動する前記駆動パラメータを入力する駆動パラメータ入力部と、
    　前記駆動パラメータ入力部に入力した前記駆動パラメータを変換する信号変換部と、
    　前記信号変換部で変換された信号からノイズ波形を計算する第２ノイズ波形計算部と、　前記第２ノイズ波形計算部で求めた前記ノイズ波形を用いて前記複数の機器の機器ごとの脆弱性を計算する第２の被害機器脆弱性計算部と、
    　前記第２の被害機器脆弱性計算部で計算した前記機器ごとの脆弱性をラベリングする脆弱性ラベリング部と、
    　前記第２ノイズ波形計算部で計算した前記ノイズ波形を入力側に入力して前記脆弱性ラベリング部で前記機器ごとの脆弱性をラベリングしたデータを出力側に入力して機械学習データを作成する機械学習データ作成部と、
    　前記機械学習データ作成部で作成した前記機械学習データを保存する機械学習データ保存部と
    を備えることを特徴とする電磁ノイズ解析装置。     The electromagnetic noise analysis device according to claim 4,
    The vulnerability noise pattern model storage unit includes:
    a drive parameter input unit that inputs the drive parameters for driving the system including the plurality of devices;
    a signal conversion unit that converts the drive parameter input to the drive parameter input unit;
    a second noise waveform calculation unit that calculates a noise waveform from the signal converted by the signal conversion unit; and a second noise waveform calculation unit that calculates the vulnerability of each of the plurality of devices using the noise waveform obtained by the second noise waveform calculation unit. a second victim device vulnerability calculation unit that calculates;
    a vulnerability labeling unit that labels the vulnerability of each device calculated by the second victim device vulnerability calculation unit;
    A machine that creates machine learning data by inputting the noise waveform calculated by the second noise waveform calculation unit to the input side and inputting data labeled with the vulnerability of each device by the vulnerability labeling unit to the output side. Learning data creation department,
    An electromagnetic noise analysis device comprising: a machine learning data storage unit that stores the machine learning data created by the machine learning data creation unit.
  6. 　電磁ノイズ強度計算部と被害機器脆弱性計算部とリスク計算部とを備えた電磁ノイズ解析装置を用いて電磁ノイズを解析する方法であって、
    　複数の機器を備えて構成されるシステムを駆動する駆動パラメータを前記電磁ノイズ強度計算部に入力して前記システムを駆動することにより前記システムから発生する電磁ノイズの強度を求め、
    　前記駆動パラメータを前記被害機器脆弱性計算部に入力して前記システムが発生する電磁ノイズパターンに対する前記複数の機器の機器ごとの脆弱性を求め、
    　前記電磁ノイズ強度計算部において求めた前記電磁ノイズの強度の情報と前記被害機器脆弱性計算部で求めた前記機器ごとの脆弱性の情報を前記リスク計算部に入力して前記電磁ノイズに起因する前記機器ごとのリスクを求める
    ことを特徴とする電磁ノイズ解析方法。     A method for analyzing electromagnetic noise using an electromagnetic noise analysis device comprising an electromagnetic noise intensity calculation section, a victim equipment vulnerability calculation section, and a risk calculation section, the method comprising:
    determining the intensity of electromagnetic noise generated from the system by inputting drive parameters for driving a system comprising a plurality of devices into the electromagnetic noise intensity calculation unit and driving the system;
    inputting the driving parameters into the victim device vulnerability calculation unit to determine the vulnerability of each of the plurality of devices to the electromagnetic noise pattern generated by the system;
    The information on the intensity of the electromagnetic noise obtained by the electromagnetic noise intensity calculation section and the vulnerability information for each of the devices obtained by the victim equipment vulnerability calculation section are input into the risk calculation section to determine whether the electromagnetic noise is caused by the electromagnetic noise. An electromagnetic noise analysis method characterized by determining the risk for each device.
  7. 　請求項６記載の電磁ノイズ解析方法であって、
    　前記被害機器脆弱性計算部は、ノイズ波形計算部と送信部とノイズ印加部と受信部とノイズ波形エラー率計算部とを備え
    　前記ノイズ波形計算部で前記電磁ノイズの波形を計算し、
    　前記送信部でビット列データを用いて送信信号を作成し前記作成した送信信号を送信し、
    　前記ノイズ印加部で前記送信部から送信された前記送信信号と前記ノイズ波形計算部で計算して求めたノイズ波形とを加算し、
    　前記受信部で前記ノイズ印加部において前記加算したデータを復号して復号ビット列データに変換し、
    　前記ノイズ波形エラー率計算部で前記受信部において前記変換した前記復号ビット列データと前記送信部で用いた前記ビット列データとを用いて前記機器ごとのノイズ波形エラー率を計算する
    ことを特徴とする電磁ノイズ解析方法。     The electromagnetic noise analysis method according to claim 6,
    The victim equipment vulnerability calculation unit includes a noise waveform calculation unit, a transmission unit, a noise application unit, a reception unit, and a noise waveform error rate calculation unit, and the noise waveform calculation unit calculates the waveform of the electromagnetic noise,
    Creating a transmission signal using the bit string data in the transmission unit and transmitting the created transmission signal,
    The noise applying unit adds the transmission signal transmitted from the transmitting unit and the noise waveform calculated by the noise waveform calculating unit,
    decoding the added data in the noise applying unit in the receiving unit and converting it into decoded bit string data;
    The electromagnetic device is characterized in that the noise waveform error rate calculating section calculates the noise waveform error rate for each of the devices using the decoded bit string data converted in the receiving section and the bit string data used in the transmitting section. Noise analysis method.
  8. 　請求項６記載の電磁ノイズ解析方法であって、
    　前記被害機器脆弱性計算部は、ノイズ波形計算部と脆弱性ノイズパターンモデル格納部と脆弱性計算部とを備え、
    　前記ノイズ波形計算部で前記電磁ノイズの波形を計算し、
    　前記脆弱性ノイズパターンモデル格納部に前記機器ごとの脆弱性ノイズパターンモデルをエラー率と対応させて格納し、
    　前記被害機器脆弱性計算部で前記ノイズ波形計算部において計算した前記電磁ノイズの波形と前記脆弱性ノイズパターンモデル格納部に格納された前記機器ごとの前記脆弱性ノイズパターンモデルとを照合して前記エラー率に基づいて前記機器ごとの脆弱性を計算する
    ことを特徴とする電磁ノイズ解析方法。     The electromagnetic noise analysis method according to claim 6,
    The victim equipment vulnerability calculation unit includes a noise waveform calculation unit, a vulnerability noise pattern model storage unit, and a vulnerability calculation unit,
    Calculating the waveform of the electromagnetic noise in the noise waveform calculation unit,
    storing a vulnerability noise pattern model for each device in the vulnerability noise pattern model storage unit in correspondence with an error rate;
    The victim equipment vulnerability calculation unit compares the waveform of the electromagnetic noise calculated by the noise waveform calculation unit with the vulnerability noise pattern model for each of the devices stored in the vulnerability noise pattern model storage unit to perform the An electromagnetic noise analysis method, characterized in that the vulnerability of each device is calculated based on an error rate.
  9. 　請求項８記載の電磁ノイズ解析方法であって、
    　前記脆弱性ノイズパターンモデル格納部は、機械学習により作成した前記機器ごとの前記脆弱性ノイズパターンモデルを保存し、前記被害機器脆弱性計算部で前記ノイズ波形計算部において計算した前記電磁ノイズの波形と前記脆弱性ノイズパターンモデル格納部に格納された前記機械学習により作成した前記機器ごとの前記脆弱性ノイズパターンモデルとを照合して前記エラー率に基づいて前記機器ごとの脆弱性を計算することを特徴とする電磁ノイズ解析方法。     9. The electromagnetic noise analysis method according to claim 8,
    The vulnerability noise pattern model storage unit stores the vulnerability noise pattern model for each device created by machine learning, and stores the waveform of the electromagnetic noise calculated by the noise waveform calculation unit in the victim device vulnerability calculation unit. and the vulnerability noise pattern model for each device created by the machine learning stored in the vulnerability noise pattern model storage unit, and calculating the vulnerability for each device based on the error rate. An electromagnetic noise analysis method characterized by:
  10. 　請求項９記載の電磁ノイズ解析方法であって、
    　前記脆弱性ノイズパターンモデル格納部は、
    　前記複数の機器を備えて構成される前記システムを駆動する前記駆動パラメータを信号変換し、
    　前記信号変換した信号からノイズ波形を計算し、
    　前記計算して求めた前記ノイズ波形を用いて前記システムを構成する前記複数の機器の機器ごとの脆弱性を計算し、
    　前記計算した前記機器ごとの脆弱性をラベリングし、
    　前記計算して求めた前記ノイズ波形をニューラルネットワークの入力側に入力して前記機器ごとの脆弱性をラベリングしたデータを前記ニューラルネットワークの出力側に入力して機械学習データを作成する
    ことにより前記機器ごとの前記脆弱性ノイズパターンモデルを前記機械学習により作成する
    ことを特徴とする電磁ノイズ解析方法。     The electromagnetic noise analysis method according to claim 9,
    The vulnerability noise pattern model storage unit includes:
    Converting the driving parameters for driving the system including the plurality of devices into signals;
    Calculating a noise waveform from the signal converted,
    Calculating the vulnerability of each device of the plurality of devices constituting the system using the noise waveform obtained by the calculation,
    Labeling the calculated vulnerability of each device,
    The calculated noise waveform is input to the input side of a neural network, and the data labeled with vulnerabilities for each device is input to the output side of the neural network to create machine learning data. An electromagnetic noise analysis method characterized in that the vulnerability noise pattern model for each is created by the machine learning.
  11. 　複数の機器と前記複数の機器を制御する制御部とを備えて構成されるシステムを駆動することにより前記システムから発生する電磁ノイズによる前記複数の機器の機器ごとのリスクを判定するリスク判定装置であって、
    　前記システムを駆動する駆動パラメータを入力する入力部と、
    　前記入力部に入力した前記駆動パラメータを用いて電磁ノイズ解析を行う電磁ノイズ解析部と、
    　前記電磁ノイズ解析部で解析した結果を表示する表示部と
    を備え、
    　前記電磁ノイズ解析部は、
    　前記駆動パラメータから前記駆動することにより前記システムから発生する電磁ノイズの強度を計算する電磁ノイズ強度計算部と、
    　前記駆動パラメータから前記システムが発生する電磁ノイズパターンに対する前記機器ごとの脆弱性を計算する被害機器脆弱性計算部と、
    　前記電磁ノイズ強度計算部で計算した電磁ノイズ強度と前記被害機器脆弱性計算部で計算した前記機器ごとの脆弱性から前記電磁ノイズに起因する前記機器ごとのリスクを計算するリスク計算部とを備え
    　前記電磁ノイズ解析部で解析した結果を前記システムの前記制御部へフィードバックすることを特徴とするリスク判定装置。     A risk determination device that determines the risk of each of the plurality of devices due to electromagnetic noise generated from the system by driving a system including a plurality of devices and a control unit that controls the plurality of devices. There it is,
    an input unit for inputting driving parameters for driving the system;
    an electromagnetic noise analysis unit that performs electromagnetic noise analysis using the drive parameters input to the input unit;
    and a display unit that displays the results of the analysis by the electromagnetic noise analysis unit,
    The electromagnetic noise analysis section includes:
    an electromagnetic noise intensity calculation unit that calculates the intensity of electromagnetic noise generated from the system by the driving from the driving parameters;
    a victim device vulnerability calculation unit that calculates the vulnerability of each device to an electromagnetic noise pattern generated by the system from the drive parameters;
    A risk calculation unit that calculates the risk of each device caused by the electromagnetic noise from the electromagnetic noise intensity calculated by the electromagnetic noise intensity calculation unit and the vulnerability of each device calculated by the damaged device vulnerability calculation unit. A risk determination device characterized in that a result of analysis by the electromagnetic noise analysis section is fed back to the control section of the system.
  12. 　複数の機器を備えて構成されるシステムを駆動することにより前記システムから発生する電磁ノイズによる前記複数の機器の機器ごとのリスクを判定して前記システムを制御する制御装置であって、
    　前記システムを駆動する駆動パラメータを入力する入力部と、
    　前記入力部に入力した前記駆動パラメータを用いて電磁ノイズ解析を行う電磁ノイズ解析部と、
    　前記システムの前記複数の機器を制御する制御部と
    を備え、
    　前記電磁ノイズ解析部は、
    　前記駆動パラメータから前記駆動することにより前記システムから発生する電磁ノイズの強度を計算する電磁ノイズ強度計算部と、
    　前記駆動パラメータから前記システムが発生する電磁ノイズパターンに対する前記複数の機器の機器ごとの脆弱性を計算する被害機器脆弱性計算部と、
    　前記電磁ノイズ強度計算部で計算した電磁ノイズ強度と前記被害機器脆弱性計算部で計算した前記機器ごとの脆弱性から前記電磁ノイズに起因する前記機器ごとのリスクを計算するリスク計算部とを備え
    　前記制御部は前記電磁ノイズ解析部で解析した結果に基づいて前記複数の機器を制御することを特徴とする制御装置。     A control device that controls the system by determining the risk of each of the plurality of devices due to electromagnetic noise generated from the system by driving a system configured with a plurality of devices, the control device comprising:
    an input unit for inputting driving parameters for driving the system;
    an electromagnetic noise analysis unit that performs electromagnetic noise analysis using the drive parameters input to the input unit;
    a control unit that controls the plurality of devices of the system,
    The electromagnetic noise analysis section includes:
    an electromagnetic noise intensity calculation unit that calculates the intensity of electromagnetic noise generated from the system by the driving from the driving parameters;
    a victim device vulnerability calculation unit that calculates the vulnerability of each of the plurality of devices to the electromagnetic noise pattern generated by the system from the drive parameters;
    A risk calculation unit that calculates the risk of each device caused by the electromagnetic noise from the electromagnetic noise intensity calculated by the electromagnetic noise intensity calculation unit and the vulnerability of each device calculated by the damaged device vulnerability calculation unit. The control device is characterized in that the control unit controls the plurality of devices based on the results analyzed by the electromagnetic noise analysis unit.
PCT/JP2023/009967 2022-05-10 2023-03-15 Electromagnetic noise analysis device and method therefor, and risk assessment device and control device provided with same WO2023218752A1 (en)

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Citations (4)

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Publication number Priority date Publication date Assignee Title
JP2010096658A (en) * 2008-10-17 2010-04-30 Honda Motor Co Ltd Device and method for evaluating noise environment
WO2013132948A1 (en) * 2012-03-08 2013-09-12 株式会社日立製作所 Electromagnetic noise analysis method and device
JP2018018293A (en) * 2016-07-28 2018-02-01 株式会社日立製作所 Electromagnetic noise analyzer, control device, and control method
WO2018131478A1 (en) * 2017-01-11 2018-07-19 日本電気株式会社 Information processing device, information processing method and recording medium with information processing program recorded therein

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010096658A (en) * 2008-10-17 2010-04-30 Honda Motor Co Ltd Device and method for evaluating noise environment
WO2013132948A1 (en) * 2012-03-08 2013-09-12 株式会社日立製作所 Electromagnetic noise analysis method and device
JP2018018293A (en) * 2016-07-28 2018-02-01 株式会社日立製作所 Electromagnetic noise analyzer, control device, and control method
WO2018131478A1 (en) * 2017-01-11 2018-07-19 日本電気株式会社 Information processing device, information processing method and recording medium with information processing program recorded therein

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