WO2023218752A1 - Dispositif d'analyse de bruit électromagnétique et procédé associé, et dispositif d'évaluation de risque et dispositif de commande comprenant celui-ci - Google Patents

Dispositif d'analyse de bruit électromagnétique et procédé associé, et dispositif d'évaluation de risque et dispositif de commande comprenant celui-ci Download PDF

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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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English (en)
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

Ce dispositif d'analyse de bruit électromagnétique est configuré de façon à pouvoir effectuer une évaluation d'intensité de bruit par bande de fréquence et une analyse de l'influence du codage de signal, ce qui permet d'évaluer un risque d'interférence électromagnétique tout en tenant compte de l'influence d'un codage tel qu'une correction d'erreur et un traitement entrelacé pendant une communication. Pour atteindre ce qui précède, le dispositif d'analyse de bruit électromagnétique est configuré en comprenant : une unité de calcul d'intensité de bruit électromagnétique qui calcule l'intensité du bruit électromagnétique produit par un système en raison d'une commande basée sur des paramètres permettant de commander le système qui est configuré en comprenant une pluralité d'appareils ; une unité de calcul de fragilité qui, d'après les paramètres de commande, calcule la fragilité de chaque appareil par rapport à un motif de bruit électromagnétique produit par le système ; et une unité de calcul de risque qui, d'après l'intensité du bruit électromagnétique calculée par l'unité de calcul d'intensité du bruit électromagnétique et la fragilité de chaque appareil calculée par l'unité de calcul de fragilité, calcule le risque résultant d'un bruit électromagnétique pour chaque appareil.
PCT/JP2023/009967 2022-05-10 2023-03-15 Dispositif d'analyse de bruit électromagnétique et procédé associé, et dispositif d'évaluation de risque et dispositif de commande comprenant celui-ci WO2023218752A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010096658A (ja) * 2008-10-17 2010-04-30 Honda Motor Co Ltd ノイズ環境評価装置およびノイズ環境評価方法
WO2013132948A1 (fr) * 2012-03-08 2013-09-12 株式会社日立製作所 Procédé et dispositif d'analyse de bruit électromagnétique
JP2018018293A (ja) * 2016-07-28 2018-02-01 株式会社日立製作所 電磁ノイズ解析装置、制御装置および制御方法
WO2018131478A1 (fr) * 2017-01-11 2018-07-19 日本電気株式会社 Dispositif de traitement d'informations, procédé de traitement d'informations et support d'enregistrement dans lequel est stocké un programme de traitement d'informations

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010096658A (ja) * 2008-10-17 2010-04-30 Honda Motor Co Ltd ノイズ環境評価装置およびノイズ環境評価方法
WO2013132948A1 (fr) * 2012-03-08 2013-09-12 株式会社日立製作所 Procédé et dispositif d'analyse de bruit électromagnétique
JP2018018293A (ja) * 2016-07-28 2018-02-01 株式会社日立製作所 電磁ノイズ解析装置、制御装置および制御方法
WO2018131478A1 (fr) * 2017-01-11 2018-07-19 日本電気株式会社 Dispositif de traitement d'informations, procédé de traitement d'informations et support d'enregistrement dans lequel est stocké un programme de traitement d'informations

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